Analyzing the Impact of Learning and Growth Perspective Metrics on Organizational Performance Indicators and the Moderating Role of Management Support: A Thorough Exploration of Ghana’s Oil and Gas, and Telecommunication Sectors

Analyzing the Impact of Learning and Growth Perspective Metrics on Organizational Performance Indicators and the Moderating Role of Management Support: A Thorough Exploration of Ghana’s Oil and Gas, and Telecommunication Sectors

Suleman Mohammed Yakubu, Kingsley Tornyeva^*

Accra Institute of Technology (AIT) Department of Business Administration Accra-North-Ghana

^*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400400

Received: 15 April 2025; Accepted: 19 April 2025; Published: 19 May 2025

ABSTRACT

This particular study seeks to examine how learning and growth activity affect organizational performance, particularly through management support, in the oil and gas, and telecommunication sectors in Ghana. In using a case study approach, the researchers adopt a mixed-methods explanatory sequential design: data collected through surveys and interviews on 240 respondents within selected key organizations were analysed using both Partial Least Square Structural Equation Modeling (PLS-SEM) and Microsoft Excel. Key findings revealed that the learning and growth measures of employee training, innovation, and knowledge management show a positive and significant impact on performance indicators such as Return on Assets (ROA) and market share. For instance, 1% increases in initiatives corresponded to a 59.6% improvement in ROA and a 64.9% increase in market share. The findings therefore validate the relevance of enhancing employee skills, innovation, and organizational knowledge towards attaining long-term competitiveness. However, contrary to expectations; management support did not significantly moderate the relationship between these learning and growth initiatives and performance outcomes. This may imply that such organizational learning might be more valuable when occurring in individual or team contexts as compared to institutionalized managerial support structures. Investments in employee development and knowledge-sharing systems will contribute significantly towards greater financial performance and market competitiveness in organizations. In this regard, policy initiatives should include tax incentives and support for industry-academic collaborations to get the private sector on board in fostering innovation and continuous learning. Finally, management structures should be reviewed for their alignment with strategic learning goals so as to enhance the overall adaptability and resilience of these critical sectors.

Keywords: Dynamic Capabilities, Balanced Scorecard (BSC), Organizational Performance, Management Support, Learning and Growth Perspective.

INTRODUCTION

The learning and growth perspective of the Balanced Scorecard (BSC) has emerged as a critical driver of sustainable organizational performance, particularly within knowledge-intensive and dynamic sectors such as oil and gas and telecommunications. In Ghana, these sectors have witnessed significant transformation, with firms increasingly leveraging employee training, knowledge management systems, and innovation capabilities as primary metrics for learning and growth (Kaplan & Norton, 2004; Amoako-Gyampah & Salam, 2004). These metrics, grounded in the dynamic capabilities theory (Teece, Pisano, & Shuen, 1997), enable organizations to adapt, integrate, and reconfigure internal competencies in response to external environmental changes, thereby enhancing performance indicators such as financial outcomes, customer satisfaction, and employee engagement (Agyapong, 2020; Boateng et al., 2019).However, the extent to which these learning and growth metrics translate into improved performance is often contingent upon the level of management support. Supportive leadership fosters a culture of continuous learning, allocates necessary resources, and aligns strategic goals with employee development initiatives, thus amplifying the effectiveness of these metrics (Kwarteng et al., 2021). To address this, financial metrics should be supplemented with non-financial indicators. The Balanced Scorecard (BSC) framework suggests that organizations establish four essential perspectives for effective performance measurement: Financial Measures, Customer Satisfaction, Internal Processes, and Learning and Growth. By combining both financial and non-financial metrics, the BSC offers a more holistic and balanced approach to assessing performance (Robbins & Coulter, 2024). This study arises from the challenges and shortcomings of relying solely on quantitative metrics for performance evaluation. It aims is analyzing the impact of learning and growth perspective metrics on organizational performance indicators and the moderating role of management support: a thorough exploration of Ghana’s oil and gas, and telecommunication sectors. These industries are pivotal to the nation’s economy, driving GDP growth, fostering job creation, and enhancing overall economic development. Therefore, a comprehensive evaluation of their performance is vital for ensuring their sustainability and reinforcing their contribution to Ghana’s economic progress (Sasu, 2023). By employing the Balanced Scorecard, this research seeks to establish a robust framework for performance assessment and strategic forecasting, applicable not only to these sectors but also to broader contexts.

Statement of the Problem

The learning and growth perspective of the Balanced Scorecard is increasingly recognized as a vital driver of sustained organizational performance. This perspective prioritizes employee training, development, and knowledge management as essential strategic tools to enhance productivity, employee engagement, and customer satisfaction (Kaplan & Norton, 2004). However, empirical evidence linking learning and growth metrics to organizational performance indicators remains sparse, particularly in developing economies like Ghana. In sectors such as oil and gas and telecommunications, which are marked by rapid technological change and fierce competition, the successful implementation of learning and growth initiatives is crucial for long-term viability. The effectiveness of these initiatives often depends on management support, which can either amplify or diminish their influence (Amah & Nwuche, 2013). Management support plays a pivotal moderating role by enabling resource allocation, fostering a culture of learning, and ensuring alignment between human capital development and organizational objectives. This study aims to explore the relationship between learning and growth metrics and key organizational performance indicators, emphasizing the moderating effect of management support in Ghana’s oil and gas and telecommunications sectors. By doing so, it seeks to enhance understanding of how well-supported learning and growth strategies can lead to improved organizational outcomes, ultimately addressing knowledge gaps and informing policy and strategic decision-making.

General Objectives

The general objective is to examine the impact of learning and growth perspective measures on organizational performance indicators and investigate the moderating effect of management support on the relationship between learning and growth perspective measures and organizational performance indicators.

Theoretical Framework

The study is anchored on Balanced Scorecard (BSC) framework and Dynamic Capabilities Theory (DCT). Dynamic Capabilities Theory (DCT), which asserts that firms attain and maintain competitive advantage through ongoing development, integration, and reconfiguration of internal competencies in reaction to evolving market conditions (Teece, Pisano, & Shuen, 1997). The learning and growth perspective measures significantly influence return on assets (ROA) and market share, underscoring the principles of DCT and emphasizing the importance of organisational learning, employee development, and technological innovation in enhancing firm performance. Teece and Pisano (1994) introduced Dynamic Capabilities Theory as an extension of the firm’s Resource-Based View (RVB) Barney, (1986). According to RVB, organizations in the same sectors perform distinctly because they have a variety of resources and capabilities Barney, (1986), Peteraf, (1993), while RVB is seen as static and inadequate to articulate an organization’s competitive edge in an evolving market environment (Priem & Butler, (2001). Furthermore, the resource-based perspective of the firm considers the firm’s distinctive, scarce, and imitable resources, which provides a competitive edge and promotes organizational growth (Barney, 1986). However, the process of maintaining a competitive edge is unrestricted and unique (Hung, et al, 2010), so, scholars have proposed the proposition that in order for an organization to remain competitive in the market, it must develop specific capabilities and continuously develop (knowledge Zott, 2003), which is from a dynamic capability stand point, particularly relevant to a new or evolving market. (Teece, Pisano, and Shuen 1997) describe dynamic capabilities as a company’s capacity to incorporate, develop, and restructure internal and external knowledge to respond to rapidly evolving situations. DCs are the structural and strategic processes by which companies obtain new resource combinations as market develop, collide, clash, change, and collapse (Eisenhardt & Martin, 2000). (Teece 2007) made significant contribution to DC theory by putting together the micro-foundations for each of the three dimensions: Sensing (identification and evaluation of an opportunity), seizing (mobilization of resources to tackle an opportunity and capture value), and transforming (continued renewal “reconfiguring the firm’s tangible and intangible assets). A number of scholars have made theoretical contributions to DCs, seeking to further understand their essential elements. (Adner and Helfat 2003) proposed the concept of dynamic management capacity to articulate differences in managerial decisions and organizational strategy, arguing that managerial counsel has a significant impact on organizational performance. As a result, management capacities are based on three underlying variables: managerial human capital, managerial social capital, and managing cognition. Adner and Helfat (2003) argue that because managerial decisions are based on a company’s resource and capability, discrepancies in firm resources and capabilities may result in variances in managerial decisions. (Wang and Ahmed 2007) extended the DC strategy by identifying three element variables which define the shared characteristics of DCs across organizations: adaptable competence, absorptive capability, and inventive capability. (Wang and Ahmed 2007) interpret adaptive capability as firm’s capacity to capitalize on market opportunities. Absorptive competence is described as the ability to perceive and use external information for business purposes. Organizations with higher absorptive aptitude are better equipped to learn from their partners and translate that knowledge into capabilities (Wang & Ahmed, 2007). A firm’s innovative potential is its capacity to develop new goods or markets. According to (Wang and Ahmed 2007), these characteristics illustrate the uncertainty over how resources and competencies could be utilized to sustain long-term corporate development. The current literature confirmed the significant association between Dynamic Capability and organizational performance. Dynamic capabilities create the foundation for the firm’s unique resource configuration, influencing its level of competition and success (Galunic & Eisenhardt, 2001). According to (Zollo and Winter 2002), dynamic capabilities enable businesses to continuously adjust their business practices to evolving environmental situations, allowing them to improve their effectiveness competitiveness. Furthermore, dynamic capabilities increase the efficiency, and speed of a firm response to external demands, thus promoting excellent achievements (Chmielewski & Paladino, 2007). Similarly, dynamic capabilities may influence corporate efficacy and productivity by helping companies to discover new and technically significant opportunities, identify changes in suppliers and competitors, and uncover threats in a timely manner (Wilhelm et al., 2015). Ultimately, dynamic capability allows firms to develop new information, restore operational capacities with new knowledge, and achieve superiors operating-routine objectives Helfat et al., (2007). According to Schreyogg and (Kliesch-Eberl 2007), dynamic capabilities can boost the performance of operational procedures by helping companies to seize opportunities and overcome risk faster than rivals. Nevertheless, major criticisms have been leveled at the theory, including the nature of the term itself and the constraints in determining the benefits of the theory’s outcomes (Zahra, Sapienza, & Davidson, 2006), the challenge involved in comprehending the nature of DCs, and the lack of specific models for evaluating these capabilities and their effect corporate performance (Zott, 2003). The concept has also been criticized for being repetitive (Zoll & Winter, 2002) and ineffective in offering a deeper comprehension of DCs and how they function (Schreyogg & Kliesch-Eberl, 2007). Dynamic Capability theory also suffers from a lack of understanding regarding its core concepts (Ambrosini & Bowman, 2009). Another issue is that the theory fails to efficiently address the gap between its theoretical conceptions and practical corporate operations, making it hard to transform into practical guidance for managers. This gap in application raises concerns about its practical relevance, particularly in rapidly evolving sectors where adaptability is essential. Dynamic capabilities theory also lacks consistency concerning its core models, such as the discrepancy between dynamic capabilities and other daily operations. Without such understanding, researchers struggle to differentiate between what actually defines a dynamic capability and what is merely a corporate resource or competency. To overcome these constraints, companies can employ dynamic capabilities to effectively discover, and translate resources and opportunities, thereby placing themselves for success in a rapidly changing environment (Cabral & Van Winden, 2022). Developing the appropriate competencies is crucial for companies which want to withstand the fast technological developments and transitions which the fourth industrial revolution will entail. By exploiting these capabilities, organizations can remain agile and responsive to changes in consumer needs and developing technologies. Dynamic capabilities and strategy, on the other hand, work synergistically to prepare for change, restructure resources, and generate wealth through innovation. This dynamic strategy allows organizations to not just thrive, but prosper in the midst of uncertainty. Consequently, the incorporation of dynamic capabilities into a firm’s strategic plan ensures that it is well-positioned to effect changes and mold its future development (Aghimien et al., 2021). The Dynamic Capabilities Theory is extremely important to this study since it emphasizes the significance of continues learning, adaptability, and innovation as important elements in maintaining competitive edge in evolving sectors. The theory underscores the importance of intangible assets, such as knowledge, skills, and corporate routines, which is consistent with the learning and growth perspective of the balanced scorecard. By assessing how measures of learning and growth affect corporate performance, this research will explore how competencies such as employee training, knowledge management, and innovation contribute to success within the oil and gas and telecommunication sectors in Ghana. These sectors need consistent upgrades in technological skills and the ability to react speedily to regulatory changes and market changes, making dynamic capabilities essential for long-term success. Understanding the effect of these assets on performance allows firms to priorities their growth, while enabling optimal responsiveness to environmental changes. This strategy not only improves existing performance, but it also establishes a solid basis for long-term competitiveness. By incorporating dynamic capabilities with the balanced scorecard, the study provides a comprehensive picture of financial and non-financial performance, enabling firms to prioritize long-term growth over short-term results. The conclusion of this study can help businesses within the oil and gas and telecommunication sectors in Ghana adopt flexible strategies. The Dynamic Capabilities Theory supports the objective of examining the impact of learning and growth perspective measures on organizational performance indicators and investigating the moderating effect of management support on the relationship between learning and growth perspective measures and organizational performance indicators within the oil and gas and telecommunication sectors in Ghana. Dynamic Capabilities Theory offers a model for comprehending how firms change and develop to sustain their competitive edge. It underlines the need of reorganizing resources in reaction to market dynamics, as well as the importance of learning and growth initiatives, such as competence and business knowledge, in promoting performance. By assessing how management moderates the relationship between financial metrics such as profitability and general performance, the theory sheds light on how leadership actions influence financial results. By Balancing and integrating Dynamic Capabilities Theory with financial objectives with internal growth metrics firms are more responsive to market changes, maximizing their resources, and maintaining a long-term success in these sectors.

EMPIRICAL REVIEW

The Learning and Growth perspective highlights the importance of continuous improvement, innovation, and effective knowledge management, particularly in knowledge-driven and rapidly changing industries such as oil and gas and telecommunications. Organizations in these sectors face on-going technological disruptions, regulatory changes, and intense competition, underscoring the necessity of investing in learning initiatives to enhance long-term performance. Despite the theoretical significance of Learning and Growth metrics, their empirical relationship with organizational performance indicators is still insufficiently researched, especially in developing economies like Ghana, where understanding this connection could inform strategies for sustainable growth and competitiveness. Studies by (Lee and Roberts 2024) conducted a quantitative study to assess the use of staff training and development measures in the balanced scorecard of enterprises within the technology industry. The study revealed that capital injection in training greatly enhances both effectiveness and financial performance, underlining the importance of trained staff in promoting business growth. Nonetheless, the study concentrates basically on the short-term effects of training, ignoring long-term benefits such as career progression, staff retention, and sustained performance advancements. A detailed examination of both the short-term and long-term effects of training would yield an in-depth assessment. Incorporating long-term impacts would present important perspectives for tactical human resource planning, aiding companies better comprehend the continued benefits of employee advancement initiatives. (Jackson 2023) performed a qualitative study on the correlation between the Balanced Scorecard and corporate learning across several Australia firms. The study sought to assess how the BSC could increase corporate learning and foster a culture of continuous improvements. The findings demonstrated that implementing the BSC encourages continuous learning and enhancements, aiding businesses to better adapt to changes and drive general outcome. Even though the research presents a significant impact, it relied on data from possibly homogeneous groups in Australia which overlooked the complexities of diverse sectors and cultural contexts. A more in-depth look at various sectors and context would offer a broader comprehension of how the BSC stimulates organizational learning around the world. This strategy has the potential to enhance the results and offer valuable perspectives into their influence in variety of circumstances. (Khaled and Bani-Ahmad 2018) established a positive correlation between organizational learning and financial performance, positing that a learning-oriented culture enables employees to recognize and enact improvements. This, in turn, enhances efficiency, fosters product innovation, and contributes to sustainable financial growth. Furthermore, previous studies underscore the distinct impacts of customer focus, internal processes, and learning and growth on sustainable performance. Research indicates that management commitment serves as a moderating factor in this dynamic, influencing how these elements interact to drive overall organizational success. This perspective focuses on how a company can continually enhance and adapt its capabilities. It emphasizes indicators that reflect the organization’s learning and growth, such as product improvements and advancements in manufacturing techniques. Learning and growth metrics serve as vital indicators of effective operational processes. To develop successful strategies in this area, it is essential to consider human, informational, and organizational capital Raval et al., (2020). Key indicators for assessing growth and learning may include knowledge management practices, efficiency ratios, absenteeism rates, value added per employee, training hours, and overall employee productivity (Niven, and 2014). Additionally, Brown and McDonnell (1995) introduced new dimensions such as environmental and social factors, which can be integrated into the internal operations framework based on the organization’s core elements and strategic objectives. This holistic approach underscores the importance of aligning growth metrics with broader organizational goals to foster sustainable development. (Mio, C., Costantini, A., & Panfilo, S. 2022) posited that the Balanced Scorecard (BSC) offers a comprehensive overview and effective monitoring of a firm’s environment. (Haya 2019) demonstrated that implementing BSC perspectives positively influences employee mind set and fosters the development of a new organizational culture. Pham et al. (2020) established that the BSC contributes to improved firm performance. Similarly, (Tuan 2020) indicated that BSC perspectives significantly enhance the performance of banks, a finding corroborated by (Abueid et al. 2023), who identified strong positive correlations between BSC perspectives and banking performance. (Rafiq et al. 2020) further revealed that the BSC’s various dimensions financial; customer, internal business processes, and learning and growth are positively associated with sustainable development and overall firm performance. (Yawson, R. M., & Paros, A. K., 2023) argued that organizations can leverage BSC perspectives as a holistic framework to optimize capital costs, thereby enhancing operational efficiency and strategic alignment. (Yilmaz and Inel 2018) posited that the Balanced Scorecard (BSC) approach can significantly enhance a firm’s performance by establishing future performance goals and assessing current standings through business performance reports. These goals serve as both a strategic framework and a sustainability factor for organizations. (Rafiq et al. 2020) emphasized that firms can achieve substantial results by employing the BSC, as it encompasses comprehensive strategies across the organization. Furthermore, Abdelraheem and (Hussien 2022) demonstrated that the BSC perspectives play a crucial role in effectively evaluating a firm’s performance. Additionally, (Sands, J. S., et al. 2016) found that companies utilizing the BSC framework tend to experience high success rates and reduced instances of failure. This evidence underscores the BSC’s importance in strategic management and performance evaluation within contemporary business practices. (Carayannis et al. 2022) identified learning and growth as the paramount perspectives within the Balanced Scorecard (BSC) framework. Additionally, (Quesado et al. 2025) demonstrated that the BSC serves as an effective management tool, significantly improving organizational performance and facilitating the achievement of strategic objectives. This underscores the BSC’s role in aligning operational activities with long-term goals, thereby fostering a culture of continuous improvement and strategic alignment in firms.

METHODOLOGY

Research Design, Method and Approach

The research employed a case study design. An explanatory sequential research design has been chosen because it allowed the researcher to administer questionnaire to a quantitative larger sample size and conduct random interviews with a smaller qualitative sample size. For this study, the study used a population size of 500 employees from major sectors in Ghana, particularly the oil and gas, and telecommunication industries, both of which are crucial to the nation’s economy. The sample will encompass private companies such as MTN Ghana, Telecel, Airtel/Tigo, as well as public sectors entities such as Ghana Post Company, Bulk Oil Storage and Transportation Company Limited, National Petroleum Authority, Ghana National Petroleum Corporation, Petroleum Commission, Ghana Gas, and Ghana Oil Company. The sample size was derived from the formula;

Population size (N) =500, Sample size (n): 240, Z-value for 95% confidence level (Z):1.96

Estimated proportion (P):0.5, Margin for error (E):4.56% or 0.0456

n= NxZ2x P x (1-p)

     (N-1) x E2 +Z2x P x (1-p)

Z2=1.962 = 3.8416

P x (1-p) = 0.5 x (1-05) = 0.5×0.5=0.25

N x Z2 x P x (1-p) = 500 x 3.8416 x 0.25

500×0.9604=480.2

(N-1) x E2

(N-1) x E2= (500-1) x 0.04562

(N-1) x E2= 499 x 0.00207936

(N-1) x E2 =1.03889364

(N-1) x E2 +Z2x p x (1-p) = 1.03889364+0.9604=1.99929364

n =   480.2

   = 240

The approximate sample size of 240 is 5% of the target population of 500 which was representative enough of the entire population. For this research, purposive sampling was applied to intentionally target individuals which offers the most important perspectives related to the research objectives. Both closed and open-ended questions were used to allow easy compilation of responses collected in the questionnaires. This is to ask for more information on answers to closed ended questions as to the reasons why that answer so that we can have an in-depth understanding of the topic at hand. The collected data was analyzed using PLS –SEM as well as Microsoft Excel where descriptive statistical analysis were obtained, and results were summarized as graphs and tables for discussion.

Ethical considerations for this study included obtaining informed consent from participants, ensuring anonymity and confidentiality, and minimizing any potential harm to the participants.

RESULTS AND DISCUSSION

This chapter presents the findings of this study. The purpose of this study is to exploring the role of financial perspective measures on organizational performance indicators and the moderating effect of management support: An empirical analysis of the oil and gas, and telecommunication sectors in Ghana. These findings enhance performance management literature by providing insights for decision-makers aiming to reconcile financial imperatives with strategic growth initiatives. The findings are presented in the form of tables and figures.

Table 4.1 Descriptive Statistical Analysis Result -Demographic

Source: Field Data (2025)

Table 4.1 presents a demographic analysis that emphasizes gender distribution among the study participants. The results showed that female respondents formed the majority, with a count of 122, accounting for 51.05% of the overall sample. Conversely, 117 participants (48.95 %) were male. Near-equal gender representation shows a balanced distribution of perspectives, thereby enhancing the generalizability of the findings of the study. The slight variation in participation rates between genders may show wider trends within the research context, possibly shaped by sector composition, organizational culture, or societal influences. Higher female representation may suggest an increasing presence of women in the targeted professional domain, showing shifts in workforce dynamics. Minimal disparity ensures that gender-based biases do not disproportionately influence the study’s insights, thereby reinforcing the robustness of the analysis. This demographic structure shows a robust basis for evaluating gender-related differences in organizational performance metrics. The demographic analysis of participants by age distribution, as shown in Table 4.1, showed that most respondents were in the 20–30-year age group, with a frequency of 75, accounting for 31.38% of the total sample. This shows a relatively young workforce, suggesting a significant presence of early career professionals within the study population. The second-largest age group was 41–50 years, consisting of 66 respondents (27.62%), followed by the 31–40-year age group, which included 61 participants (25.52%). The age group of 51–60 years was underrepresented, making up 35 participants (14.64%), while individuals aged 60 years and above stood for the smallest proportion, with only two respondents (0.84%). This distribution shows a varied age composition, which is crucial for understanding generational perspectives on organizational performance. The limited representation of older participants may show trends in workforce retirement or the predominance of mid-career professionals in the sampled population. The examination of participants’ educational qualifications, as illustrated in Table 4.1, underscores their varied academic backgrounds. The results show that most participants possessed a bachelor’s degree, with a count of 86, accounting for 36% of the overall sample. This indicates that undergraduate education is the main academic qualification among the study population, likely mirroring industry-hiring trends. Subsequently, 71 respondents (29.7%) held a master’s degree, indicating a significant representation of advanced degree holders, potentially influencing strategic decision making and improving competencies within the workforce. Furthermore, 63 participants (26.4%) possessed professional certifications, highlighting the importance of specialized skills and industry-specific knowledge. Ultimately, only 19 participants (7.9%) achieved a doctorate (PhD), reflecting a comparatively low representation of highly specialized academic professionals. This distribution highlights the workforce’s dependence on bachelor’s and master’s degrees while acknowledging the significance of professional certifications in enhancing formal education. The demographic analysis of participants’ age distribution, as shown in Table 4.1, offers essential insights into the workforce composition. The results show that the 20–30-year age group comprised the largest percentage of respondents, with a count of 75, accounting for 31.38% of the overall sample. This shows that young workers are likely to be predisposed to adaptability and innovation. The 41–50-year age group accounted for 27.62% (66 respondents), indicating a significant representation of experienced professionals in mid-to-senior management positions. The 31–40-year age group represented 25.52% (61 respondents), highlighting a notable part of employees in the critical stage of their careers. The 51–60-year age group, including 14.64% (35 respondents), shows a lower representation of late-career professionals. Individuals aged 60 and above stood for the smallest demographic group, making up only 0.84% (two respondents), and showing limited workforce participation among retirees or senior executives exiting active employment. Table 4.1 illustrates the sectorial distribution of participants, revealing that the telecommunications industry made up 60.3% (144 respondents) of the total sample. This signifies a notable representation of a sector characterized by swift technological progress and competitive market dynamics. The oil and gas sector constituted 39.7% (95 respondents) of the sample, showing a significant yet relatively low representation. The distribution shows that the findings of this study are likely more representative of the operational environment in the telecommunications sector, while insights from the oil and gas sector offer a complementary perspective on the dynamics of organizational performance specific to that industry. The evaluation of the participants’ experience levels, as shown in Table 4.1, showed that most respondents had a moderate level of professional experience. Individuals with four to six years of experience stood for the largest cohort, including 31.8% (76 respondents). This shows that a considerable segment of the workforce is in the mid-career stage, likely combining foundational knowledge with industry experience. Participants with 1–3 years of experience made up 21.8% (52 respondents), while those with less than one year of experience accounted for 21.3% (51 respondents), showing a significant representation of early career professionals. Furthermore, 20.5% (49 respondents) had 7–10 years of experience, showing a robust skill set. Only 4.6% (11 respondents) had more than ten years of experience, showing the limited presence of highly experienced professionals. This distribution highlights a workforce that is primarily composed of emerging and mid-level professionals.

Measurement Assessment Model

The first phase of assessing the measurement model, known as outer model assessment, uses Partial Least Squares (PLS) for Confirmatory Factor Analysis (CFA). This phase is essential for confirming the proposed relationships between the latent constructs and their corresponding indicators within the model. The PLS-based CFA assesses the extent to which empirical data align with a theoretically defined measurement framework, confirming that reflective indicators accurately stand for their associated latent constructs. Confirming the validity and reliability of the measurement model is essential to ensure methodological rigor and analytical robustness. The evaluation of internal consistency reliability, commonly assessed using Cronbach’s alpha and composite reliability (CR), confirms that items related to a specific construct reliably reflect the underlying latent variable. High internal consistency reliability shows that the indicators produce stable and coherent results across repeated measurements, thereby enhancing the credibility of the model. The validity of the measurement model was examined through assessments of convergent and discriminant validity in addition to reliability. Convergent validity assesses the degree to which the indicators designed to measure the same construct show significant intercorrelations. This evaluation is commonly conducted using Average Variance Extracted (AVE) and standardized factor loadings. An AVE value exceeding 0.50 proves sufficient convergent validity, showing that the latent construct accounts for a significant portion of the variance in its related indicators. Item loadings, greater than 0.70 show robust relationships between indicators and their corresponding constructs. Discriminant validity ensures that each construct keeps conceptual distinctiveness by showing that it shares greater variance with its own indicators than any other construct in the model. The Fornell-Larcker criterion and cross-loading analysis are typically employed for evaluation, proving that each construct shows stronger relationships with its corresponding indicators compared to other constructs. The validity and reliability parameters were rigorously examined, confirming that the measurement model was methodologically sound and statistically robust, thereby showing a reliable foundation for subsequent structural analysis.

Factor Loading

Factor loadings are essential metrics in factor analysis that measure the extent of association between observed variables and their respective latent constructs or principal components. Pett, Lackey, and Sullivan (2003) showed that factor loadings vary from -1.0 to +1.0, where higher absolute values denote a stronger association between the observed variable and its corresponding factor. Loadings near ±1.0 signify a strong representation of the underlying construct, while values close to 0 show a weak association. In the context of Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA), factor loadings show the degree to which individual items account for variance in latent constructs Hair, Black, Babin, & Anderson, (2019).Factor loadings above 0.70 are typically considered significant, showing that an item effectively stands for the conceptual characteristics of the related construct Fornell & Larcker, (1981). Loadings below 0.40 may show weak representation, suggesting potential issues with the item’s appropriateness within the model and possibly needing its exclusion Tabachnick & Fidell, (2019). Table 4.2 presents the factor loadings, which offer essential insights into the alignment of observed variables with their corresponding latent constructs, thus enabling an empirical assessment of construct validity within the measurement model. Increased loadings strengthen the theoretical framework of the model by confirming the effectiveness of the indicators in being latent constructs, thereby improving the accuracy and dependability of the measurement tool Hair et al., (2019). This empirical alignment confirms the model’s ability to accurately represent underlying theoretical constructs.

Table 4.2 Factor Loading

Source: Field Data (2025)

Indicator multicollinearity

The Variance Inflation Factor (VIF) is a key diagnostic tool used to identify multicollinearity among the predictor variables in regression analyses. Multicollinearity arises when independent variables demonstrate significant intercorrelations, potentially distorting parameter estimates, inflating standard errors, and undermining the statistical significance of predictors (Fornell & Bookstein, 1982). This can result in unreliable coefficient estimations and diminish the explanatory power of the model (Gujarati & Porter, 2009). (Hair, Ringle, and Sarstedt 2016) suggest that VIF values greater than five indicate significant multicollinearity, requiring corrective measures such as variable transformation, removal, or model re-specification. This study indicates that The VIF values presented in Table 3 are significantly lower than the critical threshold, demonstrating no multicollinearity issues. The absence of significant collinearity guarantees that predictor variables independently influence the model, thus reducing estimation bias and improving the accuracy of coefficient interpretation (Kline, 2015). The robustness of the model’s estimations enhances the credibility of the statistical inferences, confirming that the observed relationships among variables are not artificially inflated because of the redundancy among predictors. Thus, the study’s findings were enhanced in validity and reliability, establishing a robust foundation for inferential conclusions.

Figure 4.1 Measurement Model Analyses

Structure Model Assessment

A structural model serves as an essential analytical framework in empirical research, enabling systematic analysis of complex relationships among variables and improving the predictive validity of theoretical constructs. It functions as a methodological instrument for clarifying causal relationships and dynamic interactions between exogenous and endogenous constructs, thus reinforcing the theoretical foundations of a study (Rindskopf, 2023).

This discussion highlights the importance of structural modeling, focusing on its essential components, methodological evaluation criteria, and implications for enhancing predictive accuracy. The evaluation of the structural model involves not only the establishment of convergent validity, but also a thorough examination of its explanatory power and predictive ability in representing the relationships among constructs. In structural models, exogenous constructs serve as independent variables that impact the dependent variables without being influenced by other variables in the model. Endogenous constructs function as dependent variables, with their variation explained by exogenous factors, and may subsequently influence other endogenous variables (Dai & Fang, 2023). Advanced analytical techniques, including path analysis, structural equation modeling (SEM), and multiple regression analysis, were employed to assess the relationships among these constructs and to estimate both direct and indirect effects (Dai & Fang, 2023). The methodological rigor inherent in structural modeling is essential for empirical research, offering a solid framework for unraveling complex interdependencies and producing empirically supported predictions (Shaukat & Wang, 2022). An assessment of the model’s structural integrity improves its theoretical contributions, thereby strengthening the credibility and generalizability of the research findings. The structural model is represented through directional pathways that illustrate causal relationships between exogenous and endogenous constructs, based on theoretical postulations and empirical evidence. A systematic analysis of these pathways is crucial to assess how variations in exogenous constructs influence changes in endogenous variables, thus affecting the model’s predictive outcomes. The structural model provides a detailed understanding of the transmission mechanisms that connect external determinants to internal processes by mapping causal trajectories, thereby enhancing the model’s explanatory and predictive strength. This analytical method guarantees a thorough assessment of the influence of changes in the independent variables within the theoretical framework, thus improving its empirical validity. Structural modeling allows researchers to distinguish between direct and indirect effects, facilitating a more detailed understanding of the mechanisms underlying observed relationships. The ability to outline these complex effects enhances the understanding of how external variables impact intermediary constructs, thus improving the theoretical framework. A well-specified structural model offers insights into the relationship between external drivers and endogenous responses, thus enhancing empirical precision and theoretical development. Analytical rigor improves the validity of the research outcomes, ensuring that the model functions as a strong tool for advancing both theoretical discourse and practical applications.

Table 4.3 Direct Relationship Result (FP)

Source: Field Data (2025)

Table 4.4 Direct Relationship Result (LGP)

Source: Field Data (2025)

To examine the impact of learning and growth perspective measures on organisational performance indicators and investigate the moderating effect of management support on the relationship between learning and growth perspective measures and organizational performance indicators.

a. What is the direct effect of the learning and growth perspective measure on ROA as a measure of organisational performance?

The examination of the connection between learning and growth perspective metrics and return on assets (ROA) yields important insights regarding the influence of human capital, organisational learning, and innovation on financial performance. The findings in Table 4.4 indicate a positive and statistically significant impact of the learning and growth perspective measures on ROA, with a statistical value of (β = 0.596, t = 10.335, p > 0.001). A 1% increase in learning and growth initiatives within an organization results in a 59.6% improvement in ROA, highlighting the significant influence of employee development, knowledge management, and technological innovation on financial performance.  Analysis of this finding indicates that companies investing in learning and growth initiatives improve their internal capabilities, resulting in enhanced efficiency, productivity, and profitability. Learning and growth measures include employee training, leadership development, knowledge-sharing systems, and technology adoption, all of which contribute to the formation of a dynamic and adaptive workforce. Employees who develop new skills and competencies enhance their proficiency in achieving strategic objectives, minimizing operational inefficiencies, and fostering sustainable performance.  The statistically significant impact of the learning and growth perspective measures on ROA underscores the importance of intellectual capital as a competitive advantage.  In knowledge-driven economies, firms that priorities continuous learning, employee engagement, and innovation capabilities generally achieve superior financial sustainability compared to their competitors.  This is consistent with the resource-based view (RBV) of the firm, which asserts that distinctive and non-replicable internal resources, including skilled human capital and innovative capabilities, play a crucial role in achieving long-term competitive advantage.  The findings indicate a significant relationship between learning and organisational adaptability.  Companies in dynamic business environments must consistently enhance their knowledge bases in order to adapt to market changes, technological disruptions, and shifting customer preferences.  Organizations that implement comprehensive learning and development programs foster an adaptable workforce capable of swiftly responding to new challenges, reducing risks, and seizing emerging opportunities, which results in enhanced financial performance, as indicated by the rise in ROA.  The notable positive correlation between learning and growth perspective measures and ROA highlights the necessity of aligning human capital strategies with financial goals.  Organizations that align employee development initiatives with corporate financial objectives ensure that investments in learning yield measurable performance improvements.  A structured approach is necessary to assess the effectiveness of training programs, promote a culture of continuous improvement, and utilize data analytics to monitor skill development and knowledge applications in the workplace.  The findings demonstrate that measures from learning and growth perspectives are critical determinants of financial performance, as shown by their significant influence on ROA.  Organizations that emphasize employee development, innovation, and knowledge management improve their competitive positioning and attain better financial results.  This underscores the necessity for businesses to implement strategic human resource practices that priorities learning agility, technological advancement, and organisational resilience as essential factors for sustainable profitability.

b. How does the learning and growth perspective measure impact market share as a measure of organisational performance?

The empirical analysis of the relationship between learning and growth perspective measures and market share offers essential insights into the role of organisational capabilities in influencing competitive positioning and business expansion. The results shown in Table 4.4 demonstrate that the learning and growth perspective measures exert a statistically significant and positive influence on market share, as evidenced by a coefficient of (β = 0.649, t = 13.823, p >0.001).  This indicates that a 1% rise in learning and growth initiatives leads to a 64.9% increase in market share, underscoring the significant impact of organisational learning, employee development, and technological innovation on market performance.  An analysis of these findings indicates that learning and growth measures underpin the enhancement of an organization’s core competencies, allowing it to surpass competitors and secure a greater market share.  Organizations that invest in employee training, leadership development, and knowledge management cultivate a culture of innovation and adaptability.  Employees with updated skills and expertise are more likely to enhance service delivery, improve customer satisfaction, and foster product and process innovation, thus facilitating market expansion.  The positive and statistically significant relationship between measures of learning and growth perspectives and market share supports the resource-based view (RBV) of the firm, which asserts that firms achieve sustainable competitive advantage by developing valuable, rare, inimitable, and non-substitutable resources.  Human capital, knowledge assets, and technological capabilities are essential intangible resources that allow organizations to distinguish themselves in competitive markets.  Organizations that consistently allocate resources to learning and development foster a workforce that is adept at driving innovation, addressing customer needs, and sustaining operational efficiency, which enhances their market position.  Additionally, measures of learning and growth perspectives directly enhance the customer experience, which is a crucial factor in the expansion of market share.  Employees with advanced training and problem-solving abilities demonstrate greater effectiveness in meeting customer demands, personalizing service offerings, and cultivating robust customer relationships.  This results in enhanced customer retention, favorable word-of-mouth referrals, and heightened brand loyalty, which collectively fortify an organization’s market presence.  The continuous improvement of internal capabilities enables firms to maintain agility and responsiveness to industry trends, consumer preferences, and technological disruptions, which are crucial for sustaining market leadership.  The significant influence of learning and growth perspective measures on market share underscores the necessity for organizations to align learning strategies with their overall business objectives.  Investments in human capital development must align with corporate growth objectives to ensure that learning results in competitive advantage.  A data-driven approach is essential to assess the effectiveness of training programs, knowledge-sharing initiatives, and technological advancements in facilitating market expansion.  The results indicate that measures of learning and growth perspectives are essential for improving an organization’s market share.  Prioritizing employee development, knowledge acquisition, and continuous improvement enables firms to cultivate resilient and innovative workforces that enhance market performance.  The findings highlight the critical role of learning and growth initiatives in developing sustainable competitive advantages and ensuring long-term business success.

c. Does management support moderate the relationship between learning and growth perspective measure and organizational performance indicators?

The empirical assessment of the moderating role of management support in the relationship between learning and growth perspective measures and key organisational performance indicators, namely return on assets (ROA) and market share, yields insightful implications for strategic management and performance enhancement. The findings presented in Table 4.4 indicate that management support has an insignificant direct effect on ROA (β = 0.012, t = 0.171, p = 0.864) and an equally insignificant negative direct effect on market share (β = -0.014, t = 0.194, p = 0.846). Furthermore, when management support was introduced as an interactive variable, its moderating effect on both ROA (β =-0.083, t = 1.087, p = 0.277) and market share (β =-0.063, t = 0.852, p = 0.395) remained statistically insignificant. These results suggest that management support does not significantly moderate the relationship between learning and growth perspective measures and organisational performance.  A critical interpretation of these findings suggests that, while learning and growth perspective measures are essential drivers of organisational performance, the extent to which management support enhances or diminishes their effectiveness is not evident within the context of the study. This implies that organizations may rely more on individual and team-driven learning initiatives rather than structured managerial support to foster growth and performance. In strategic management literature, management support is often considered a key facilitator of organisational learning, resource allocation, and employee development. However, the absence of a statistically significant moderating effect suggests that either the level of management involvement is insufficient to create a measurable impact or that learning or growth processes are sufficiently embedded within the organisational culture, reducing the necessity for active managerial intervention.  The insignificant moderating effect of management support may also be attributed to variations in leadership styles and organisational structures. In decentralized or knowledge-intensive firms, employees may take greater ownership of their professional development, limiting the direct influence of managerial support. Additionally, if management support mechanisms are not effectively aligned with learning and growth objectives, such as mentorship programs, leadership training, and performance-based incentives, their impact on performance outcomes may be diminished.  From a market share perspective, the findings suggest that while learning and growth initiatives can enhance competitive positioning, managerial support does not necessarily strengthen this relationship. This could indicate that market expansion is driven more by external market forces, such as customer demand and competitive strategies, than by internal managerial interventions. Organizations that cultivate a learning culture through institutional mechanisms, such as continuous professional development, knowledge-sharing platforms, and innovation-driven strategies, may not require direct management reinforcement to translate learning and growth into improved market share.  In addition, the findings challenge the conventional assumption that management support universally enhances the effectiveness of organisational learning initiatives. The results indicate that, while learning and growth perspective measures are critical for performance improvement, management support does not significantly moderate their impact. This suggests that organizations should evaluate the effectiveness of their management support strategies to ensure that they meaningfully contribute to learning, innovation, and competitive advantage. Future research could explore alternative moderating variables, such as organisational culture or technological infrastructure, to provide deeper insights into the mechanisms through which learning and growth translate into superior organisational performance.

DISCUSSIONS AND IMPLICATIONS OF THE FINDINGS

The findings of this study demonstrate a significant relationship between Learning and Growth perspective measures and organizational performance, specifically regarding return on assets (ROA) and market share. These findings demonstrate that investments in employee development, knowledge management, and technological innovation significantly enhance financial and market performance. The findings corroborate previous studies indicating that organizations emphasizing continuous learning and capacity-building attain enhanced financial performance and competitive advantage (Goh et al., 2022). The Resource-Based View (RBV) posits that intangible assets, including human capital and organisational learning capabilities, are essential sources of competitive advantage (Barney, 1991), thereby supporting the notion that learning and growth initiatives play a significant role in business success. Numerous studies confirm the beneficial effects of employee development and technological advancement on financial performance. (Kianto et al. 2017) demonstrate that organizations with effective intellectual capital management exhibit higher ROA, thereby reinforcing the idea that knowledge-driven initiatives contribute to increased profitability. (Ardito and Petruzzelli 2017) highlight that firms integrating learning-oriented strategies with technological innovation attain sustained financial growth. The findings of this study align with these perspectives, indicating that a structured approach to learning and growth results in notable enhancements in ROA. The significant influence of these measures on market share indicates that firms that utilize knowledge assets and employee capabilities achieve competitive advantage. This perspective is corroborated by (Chen et al. 2020), who discover that market-oriented learning strategies enhance customer acquisition and retention. However, the results contradict some previous studies that indicate that management support is essential for enhancing the advantages of learning and growth initiatives. Unexpectedly, management support demonstrated an insignificant direct effect on ROA and market share, suggesting that leadership commitment alone may not suffice for achieving financial and market success. This contradicts the findings of (García-Morales et al. 2018), who posited that robust managerial support cultivates an environment that promotes knowledge sharing and innovation, thereby improving organisational performance. This study indicates that management support is significant; however, it does not have a direct impact unless it is enhanced by additional strategic enablers including organisational culture, employee engagement, and sector-specific factors. The minimal moderating effect of management support on the relationship between learning and growth perspective measures and performance outcomes questions the common belief that managerial involvement enhances the influence of human capital and innovation investment. Previous studies have highlighted the importance of leadership in influencing learning organizations (Senge, 2006) and facilitating strategic alignment between growth initiatives and business objectives (Teece, 2018). The current findings indicate that management support does not significantly influence the relationship between learning, growth, and organisational performance. This suggests that elements such as leadership style, organisational structure, and industry dynamics may affect the degree to which managerial interventions impact learning-related outcomes. The findings illustrate the intricate nature of organisational performance determinants, indicating that, although learning and growth metrics are essential for financial and market success, managerial interventions do not consistently produce quantifiable advantages. This supports the assertion of Donate and (Sánchez de Pablo 2015) that the effectiveness of leadership in knowledge management is contingent upon contextual factors, including employee autonomy and organisational agility. Firms should integrate leadership strategies with adaptive business models to promote innovation and continuous improvement. This study confirms the essential role of learning and growth initiatives in improving financial performance and market competitiveness, consistent with previous research that highlights the strategic significance of human capital and innovation. The findings challenge the assumption that management support consistently enhances these relationships, indicating that its role may depend on broader organisational and industry-specific dynamics. Future research should investigate the contextual factors influencing the effectiveness of management support in learning-driven organizations, providing insights into optimizing leadership strategies for sustained competitive advantage. The statistically significant positive effect of learning and growth initiatives on ROA indicates that firms with robust dynamic capabilities can improve their financial performance through the effective utilization of human capital development, knowledge management, and innovation (Eisenhardt & Martin, 2000). These finding highlights that learning-oriented organizations can reorganize their resource base to enhance efficiency, productivity, and profitability. The capacity of firms to cultivate knowledge-based assets via ongoing learning and innovation corresponds to the principles of sensing, seizing, and transforming capabilities, which are essential elements of dynamic capabilities theory (Teece, 2007). Systematic investment in human capital and technology enables organizations to generate superior value, resulting in enhanced profitability and sustained financial stability. The strong relationship between learning, growth measures, and market share highlights the need for businesses to incorporate human capital development initiatives into their competitive positioning strategies. Organizations that priorities employee skill development and technological innovation are more adept at navigating market fluctuations, adjusting to industry changes, and sustaining competitive advantage. Considering the significant impact of these factors on market performance, firms should align learning and growth initiatives with customer-driven innovations and agile business models to enhance their market presence. Nonetheless, the results suggest that management support does not significantly improve the effect of learning and growth initiatives on organisational performance. This indicates that leadership commitment alone is inadequate to convert learning and development initiatives into measurable financial or market benefits. Considering the substantial influence of employee development, knowledge management, and technological innovation on financial and market performance, it is imperative for policymakers to priorities incentives that encourage firms to invest in continuous learning, workforce development, and digital transformation. Governments and regulatory bodies should establish policies to promote corporate investment in employee training and skill development. Tax incentives, subsidies, and grants for firms investing in structured learning programs can improve organisational capabilities and promote long-term financial sustainability and market competitiveness. Moreover, policies that encourage partnerships between industry and academia can enhance knowledge transfer and innovation, thereby reinforcing firms’ learning capabilities. The robust positive correlation between learning initiatives and market share indicates the need for policies that improve firms’ adaptability to evolving market dynamics. Governments should facilitate research and development (R&D) initiatives through the provision of financial and technical support to organizations engaged in technological innovation. Policies that promote digital transformation, including incentives for the adoption of artificial intelligence (AI) and automation, can enhance firm efficiency, broaden market reach, and improve competitiveness. Additionally, although the study indicated that management support does not significantly moderate the relationship between learning and growth initiatives and organisational performance, it is essential for policymakers to recognize the importance of leadership development. Policies that require corporate governance frameworks to priorities leadership accountability in talent development and innovation adoption can facilitate the translation of learning initiatives into performance enhancements. Promoting decentralized decision-making frameworks via regulatory reforms can improve firms’ capacity to adapt to changing market conditions.

CONCLUSIONS

The findings highlight the significant impact of learning and growth initiatives on organisational performance, especially in improving financial returns and market competitiveness. The positive influence of these measures on return on assets (ROA) and market share indicates that firms should priorities investments in employee development, knowledge management, and technological innovation for sustainable growth. Organizations aimed at enhancing financial performance must implement structured learning programs, ongoing skills development, and effective knowledge-sharing systems. Fostering a culture of continuous improvement enables firms to enhance their workforce capabilities, resulting in increased efficiency, innovation, and, ultimately, improved financial outcomes. The strong relationship between learning, growth measures, and market share highlights the need for businesses to incorporate human capital development initiatives into their competitive positioning strategies. Organizations that priorities employee skill development and technological innovation are more adept at navigating market fluctuations, adjusting to industry changes, and sustaining competitive advantage. Considering the significant impact of these factors on market performance, firms should align learning and growth initiatives with customer-driven innovations and agile business models to enhance their market presence. Governments and regulatory bodies should establish policies to promote corporate investment in employee training and skill development. Tax incentives, subsidies, and grants for firms investing in structured learning programs can improve organisational capabilities and promote long-term financial sustainability and market competitiveness. Moreover, policies that encourage partnerships between industry and academia can enhance knowledge transfer and innovation, thereby reinforcing firms’ learning capabilities. The robust positive correlation between learning initiatives and market share indicates the need for policies that improve firms’ adaptability to evolving market dynamics. Governments should facilitate research and development (R&D) initiatives through the provision of financial and technical support to organizations engaged in technological innovation. Policymakers should establish frameworks that facilitate knowledge sharing across industries. The establishment of industry-wide knowledge-sharing platforms or regulatory mechanisms that promote inter-organizational learning can expedite the diffusion of innovation, thereby improving national economic growth and sectorial competitiveness. In addition, policies that foster continuous learning, technological innovation, and collaboration across industries are crucial for optimizing the advantages of learning and growth initiatives on organisational performance. Governments and regulatory bodies should create an enabling environment that fosters corporate learning, thereby supporting sustained financial growth and market expansion.

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