Impact of Organisational performance on internal business process metrics. Moderating the role of Management Support

Impact of Organisational performance on internal business process metrics. 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Moderating the role of Management Support Suleman Mohammed Yakubu, Kingsley Tornyeva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7166545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite the widespread use of the Balanced Scorecard (BSC), the direct impact of internal business process efficiency rooted in learning and growth metrics on financial and market performance remains underexplored in emerging economies. Methods This study employed an explanatory sequential mixed-methods design. Quantitative data from 240 employees across Ghana’s oil, gas, and telecom sectors were analysed using partial least squares structural equation modelling (PLS-SEM). Follow-up qualitative interviews with 15 managers provided the contextual information. Results Internal process efficiency showed strong positive effects on ROA (β = 0.576, t = 11.33, p < .001) and market share (β = 0.492, t = 8.47, p < .001). Management support did not significantly moderate these relationships. Qualitative findings suggest that decentralised processes diminish the need for active managerial intervention. Conclusion Operational excellence independently drives financial and market performance in Ghana’s resource-intensive sector. Process automation, lean management, and empowered teams are critical. Policymakers should incentivise technology adoption and workforce capacity building. Future studies should investigate longitudinal and cross-sector dynamics. Balanced Scorecard internal business processes organisational performance management support return on assets market share Figures Figure 1 Figure 2 Introduction Strategic performance management increasingly leverages the Balanced Scorecard (BSC) to integrate financial and non-financial metrics (Kaplan & Norton, 1996 ). In this context, the learning and growth perspective, which includes developing human capital, building knowledge infrastructure, and increasing innovation capacity, has been suggested to be necessary for maintaining a competitive edge (Teece, 2019 ). However, in Ghana's oil, gas, and telecommunications industries, there is evidence that there is still a gap between investments in organisational learning and measurable performance gains. In 2023, top companies like MTN Ghana and Telecel had average returns on assets (ROA) of less than 6.5%, even though they were working on big digital transformation projects (Bank of Ghana, 2024 ). Concurrently, sectoral analyses estimate that approximately 18% of potential revenue remains unrealised owing to inefficiencies in strategic alignment and learning systems (IMANI Ghana, 2023 ). Such figures indicate substantial financial stakes, underscoring the necessity of understanding how learning and growth metrics affect profitability and market competitiveness. Using dynamic capabilities theory and the balanced scorecard, this study examines how much internal learning and growth metrics affect ROA and market share in Ghana's oil, gas, and telecommunications industries. This study also examines whether management support changes these relationships, giving practitioners and policymakers evidence-based advice specific to the local economy. 1.2 Problem Statement Even though the Balanced Scorecard (BSC) is widely used, there have not been many studies in emerging markets that have looked at how learning and growth metrics affect financial performance (like ROA) and market outcomes (like market share) at the same time, or how management support plays a role in these effects. Companies in Ghana, where the oil, gas, and telecommunications sectors together make up more than 25% of the GDP, are still not doing well. The average ROA is still below 6.5%, and bad learning investments cost the country approximately 18% of its potential revenue (Bank of Ghana 2024 ; IMANI Ghana 2023 ). This study addresses the urgent need to quantify the impact of learning and growth systems on organizational performance and determine whether management support amplifies or attenuates that impact. 1.3 Research Objectives This study was guided by the following objectives: To examine the direct effect of learning and growth metrics operationalised via internal business process indicators on Return on Assets (ROA). Analyse the direct effect of learning and growth metrics on market share. To assess the moderating influence of management support on The relationship between learning and growth metrics and ROA; and The relationship between learning and growth metrics and market share. Theoretical Framework This study is based on resource-based theory (RBT), which emanates from Penrose’s ( 1959 ) theory of firm growth and Ricardo’s ( 1817 ) rent theory. It focuses on the unique business resources that affect firm performance, especially at the meso level of an organisation rather than the macro level. According to Barney (1991), resource-based theory argues that resource combinations unique to firms are valuable, rare, inimitable, and non-substitutable, leading to a sustained competitive advantage. This theory is based on two main concepts: resource heterogeneity and immobility. Resource heterogeneity means that even though companies may compete in the same industry, they have different sets of tangible and intangible resources, such as human capital, intellectual property, and brand reputation, that put them in different competitive positions (Ahmad, 2024 ). Furthermore, resource immobility implies that certain assets, especially intangible ones such as culture and proprietary knowledge, are deeply embedded within organizational routines and histories, making them difficult to replicate (Monteiro, 2023 ). Kaur and Kumar ( 2024 ) agree with this view, saying that different asset configurations make firms more or less competitive. RBT has been used in business settings by several researchers. Aker et al. ( 2016 ) demonstrated that when big data analytics are aligned with resource-based principles, firms improve performance through the integration of technology, human capital, and strategic management. Nambisan et al. ( 2019 ) stressed how important it is to have the ability to innovate, such as specialised tools and data applications, to stay competitive. RBT also helps with marketing innovation strategies, which are ways for companies to create resources that are focused on the market so that they can deal with changes in the environment (Kaur & Kumar, 2024 ). RBT has been criticised, even though it is important. Scholars argue that it is tautological, static, and difficult to validate empirically (Barney, Ketchen Jr., & Wright, 2021 ). Grover and Dresner ( 2022 ) add that it often ignores external factors like regulatory shifts and market volatility. Bloodgood ( 2023 ) questions whether its emphasis on rarity is overstated, whereas Haag ( 2021 ) proposes expanding its scope to include dynamic capabilities and co-specialisation. Dynamic capabilities posit that companies must adapt their resources and learning routines in line with changing market requirements (Robson, Ojiako, & Maguire, 2024 ). Some of these involve training, R&D, and models, such as PESTEL and Porter's Five Forces. Resource-based theory plays a key role in measuring both financial and non-financial performance indicators in Ghana’s oil, gas, and telecommunications industries. The balanced scorecard, in integrating financial with customer, internal process, and learning views, fills the internal focus gap of the RBT. Superior drilling technologies, IT infrastructure, and safety procedures are valuable assets in these industries. Management support was tested as a moderator in the internal process-performance relationship because it is a strategic asset in the deployment of assets. In general, RBT provides sound recommendations on how companies should weigh inner dynamism with strategic imperatives to enhance their power and competitiveness in the future. Empirical Review 2.1 Balanced Scorecard Adoption and Performance Outcomes There is still worldwide evidence that reinforces learning and development measures, including the intensity of training for employees, knowledge management systems, and culture of innovation, can contribute to financial performance. Rahman and Ahsan (2022), through Australian logistics companies PLS-SEM, documented a 15 percent return on assets increase linked to systematic learning activities but did not account for implications on market share. Shanker et al. ( 2023 ) indicated that investments in knowledge‐infrastructures yielded a 12 percent market‐share boost in Indian service businesses, but theirs is an analysis of a single industry and thus non-generalizable. Oyewo, Moses, and Erin ( 2022 ) indicated that learning metrics improved customer retention in Nigeria's manufacturing sector but omitted financial return measures. Together, these works confirm the Balanced Scorecard's learning and growth orientation, but pose three studies that simultaneously discuss both ROA and market share. 2.2 Management Support as a Moderator Empirical studies of the moderating effects of management support are incongruent. The European manufacturing case studies by Van Assen (2018) provided evidence that lean-management benefits were attained only where senior‐leadership sponsorship existed. Conversely, Sousa and Voss (2022) surveyed 200 Canadian service companies and found no significant moderating effect of leadership commitment on process-efficiency performance linkages. Mitchell (2024) employed qualitative interviews in Philippine service companies to propose that managerial empowerment increases decision cycles but provided no quantitative findings. These divergent findings represent methodological diversity and an open question: Under what conditions does management support an increase in learning and growth investment advantages? 2.3 Ghana and Sub-Saharan African Evidence However, empirical research on Ghana is limited. Adomako-Kwakye (2021) analysed public petroleum agency transparency tools and found process development without ROA or market‐share improvement. Osei et al. (2024) surveyed telecommunications subscribers in Ghana and found that process flexibility decreased subscriber churning by 8 percent, without measuring firm‐level improvement. Aker et al. ( 2016 ) analyzed mobile‐money transfer in Niger, with technology facilitation of learning improvement without measuring organizational performance measures. Thus, no PLS‐SEM study in Ghana’s oil, gas, or telecommunications sectors has simultaneously evaluated the effects of learning and growth metrics on both ROA and market share, nor examined management support as a moderator. 2.4 Synthesis and Research Gaps The literature affirms that learning and growth capabilities can drive either financial or market outcomes, but rarely both in a single empirical model. Methodological heterogeneity, ranging from case studies to cross-sectional surveys, impedes cross-study comparisons. Crucially, the absence of comprehensive PLS-SEM analyses in Ghana’s key industries leaves unanswered whether internal process improvements translate into simultaneous enhancements in ROA and market share and whether management support strengthens or weakens these effects. Addressing these gaps, the present study employs a mixed‐methods design to test direct and moderating relationships, thereby advancing both theoretical understanding and managerial practice in emerging‐market contexts. Methods Research Design and Setting An explanatory sequential mixed-methods design was employed to quantify and contextualise the impact of learning and growth metrics on organizational performance in Ghana’s oil, gas, and telecommunications sectors (Ivankova et al., 2006 ). The quantitative phase was conducted from January to April 2025 across major private and public enterprises in Accra, Ghana. Participants and Sampling The target population comprised 500 employees from leading firms, including MTN Ghana, Telecel, AirtelTigo, 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. A sample of 240 respondents was determined using Cochran’s formula for a 95% confidence level and a 4.56% margin of error. Purposive sampling targeted individuals in operations, finance, human resources, and performance management roles to ensure that the perspectives aligned with the research objectives. Data Collection Procedures Data were collected in two phases. First, a structured questionnaire featuring closed- and open-ended items was administered to all 240 participants. The instrument was pilot tested with 20 respondents to assess clarity and reliability (Cronbach’s α > .70). Second, semi-structured interviews were conducted with 15 senior and middle managers to enrich and explain the quantitative findings. Interviews were audio-recorded, transcribed verbatim, and anonymised to protect the participants’ identity. Instrument Validity and Reliability Confirmatory factor analysis using partial least squares structural equation modelling (PLS-SEM) in SmartPLS 3.0 evaluated the measurement model. Composite reliability values exceeded .70, average variance extracted (AVE) values exceeded .50, and item loadings surpassed .70, confirming convergent validity. The Fornell–Larcker criterion establishes discriminant validity. Variance inflation factor (VIF) values below 5 indicate no significant multicollinearity. Data Analysis Quantitative data were analysed in Microsoft Excel for descriptive statistics and in SmartPLS 3.0 for structural model assessment. Bootstrap resampling (5,000 subsamples) generated path coefficients, t-values, and p-values for hypothesis testing. Qualitative interview transcripts were coded thematically using NVivo 12 to identify patterns and contextual factors influencing the moderating role of management support. Ethical Considerations Ethical approval was obtained from the University of Ghana Ethics Committee (UG-ERC-2025-01). All participants provided written informed consent. Anonymity and confidentiality were maintained throughout data collection and analysis. Participants were informed of their right to withdraw at any stage without consequences. Results and Discussion This section presents the results of the analysis and discussion of the findings. Table 4.1 Descriptive Statistics for Sample Demographics (n = 239) Frequency Percent Male 117 48.95 Gender Female 122 51.05 20–30 years 75 31.38 31–40 years 61 25.52 Age 41–50 years 66 27.62 51–60 years 35 14.64 61 years and above 2 0.84 Bachelor’s degree 86 36 Doctorate/PhD degree 19 7.9 Education level Master’s degree 71 29.7 professional certificate 63 26.4 financial and account professionals 27 11.3 Roles Head of department 34 14.2 HR and performance professionals 28 11.7 middle/line manager 60 25.1 senior manager 31 13 supervisor 59 24.7 Sectors oil and gas 95 39.7 Telecommunications 144 60.3 more than 10 years 11 4.6 Experience level 1–3 years 52 21.8 4–6 years 76 31.8 7–10 years 49 20.5 less than 1 year 51 21.3 Source: Survey Data (2025) Table 4.1 shows a balanced gender split, a majority aged 20–40, 60% from telecommunications, and most holding bachelor’s or master’s degrees with 4–6 years’ experience. 4.1 Measurement Model Assessment 4.1.1 Factor Loading All items loaded ≥ 0.75 onto their intended constructs (Table 4.2 ), confirming convergent validity. Table 4.2 Factor Loadings for Measurement Items IBP LGP MANST MARKS RETA 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 RETA1 0.753 RETA2 0.751 RETA3 0.800 RETA4 0.738 RETA5 0.753 RETA6 0.725 Source: Survey Data (2025) 4.1.2 Indicator Multicollinearity (VIF) All VIF values were ≤ 2.98 (Table 4.3 ), indicating negligible collinearity. Table 4.3 Variance Inflation Factors (VIF VIF IBP1 2.291 IBP2 2.844 IBP3 2.489 IBP4 2.220 IBP5 1.798 LGP1 1.938 LGP2 2.097 LGP3 2.306 LGP4 2.697 LGP5 2.104 MANST1 2.160 MANST2 2.982 MANST3 2.646 MANST4 2.052 MARKS1 2.398 MARKS2 2.943 MARKS3 2.317 MARKS4 2.147 MARKS5 2.152 RETA1 2.158 RETA2 2.155 RETA3 2.013 RETA4 2.025 RETA5 2.028 RETA6 1.719 Source: Survey Data (2025) 4.2 Reliability and Validity 4.2.1 Internal Consistency Reliability and Composite Reliability Cronbach’s α = 0.848–0.883; Composite Reliability = 0.887–0.914 (Table 4.4 ), supporting reliability. Table 4.4 Cronbach’s α and Composite Reliability Cronbach's alpha Composite reliability (rho_c) IBP 0.883 0.914 LGP 0.860 0.899 MANST 0.878 0.914 MARKS 0.861 0.900 RETA 0.848 0.887 Source: Survey Data (2025) 4.2.2 Convergent Validity AVE values range from 0.57 to 0.73 (all ≥ 0.50; Table 4.5 ), confirming convergent validity. Table 4.5 Average Variance Extracted (AVE) Average variance extracted (AVE) IBP 0.680 LGP 0.641 MANST 0.726 MARKS 0.642 RETA 0.568 Source: Survey Data (2025) AVE values range from 0.57 to 0.73 (Table 4.5 ), all above the 0.50 benchmark, confirming that constructs capture sufficient indicator variance. 4.2.3 Discriminant Validity (Fornell–Larcker & HTMT) The square roots of AVE exceed inter-construct correlations, and all HTMT ratios are < 0.90; Tables 4.6 –4.7. Table 4.6 Discriminant Validity via Fornell–Larcker Criterion IBP LGP MANST MARKS RETA IBP 0.825 LGP 0.596 0.801 MANST 0.044 0.093 0.852 MARKS 0.501 0.614 0.047 0.801 RETA 0.588 0.662 0.074 0.737 0.754 Source: Survey Data (2025) The square root of each construct’s AVE exceeded its highest correlation with any other construct (Table 4.6 ), evidencing empirical distinctiveness. 4.2.3 Discriminant Validity (Fornell–Larcker & HTMT) All Fornell–Larcker criteria are satisfied (square roots of AVE exceed inter-construct correlations) and HTMT ratios are below 0.90 (Table 4.7 ), confirming discriminant validity. Table 4.7 Discriminant Validity via HTMT Ratio IBP LGP MANST MARKS RETA IBP LGP 0.675 MANST 0.065 0.11 MARKS 0.561 0.708 0.074 RETA 0.66 0.767 0.08 0.878 Source: Survey Data (2025) All HTMT values fall below 0.90 (range = 0.065–0.878; Table 4.7 ), further supporting discriminant validity among constructs. 4.3 Structural Model Evaluation 4.3.1 Model Fit (R²) The model explains 47.7% of the variance in market share and 60.4% in ROA (Table 4.8 ). The explanatory strength of the model, with R² values of 0.604 and 0.477 for ROA and market share, respectively, is illustrated in Fig. 1 . Table 4.8 Coefficients of Determination (R²) for Dependent Variables R-square R-square adjusted MARKS 0.477 0.463 RETA 0.604 0.593 Source: Survey Data (2025) The model explains 47.7% of the variance in market share and 60.4% of the variance in ROA (see Table 4.8 ), indicating substantial explanatory power. Table 4.9. Model Predictive Relevance (Q²) This table reports the Stone-Geisser’s Q² values, which assess how well the model predicts the observed data. It is an essential complement to R², especially in PLS-SEM, where prediction matters more than fit. Construct Q² Value Market Share (MARKS) 0.318 Return on Assets (RETA) 0.415 Source: Survey Data (2025) Table 4.9 confirms that the model demonstrates predictive relevance for both dependent variables, with Q² values exceeding the minimum threshold of 0.00 (Hair et al., 2021 ). Q² values above zero indicate the predictive relevance of the endogenous variables. The values (MARKS = 0.318, RETA = 0.415) suggest moderate to strong predictive power showing that your model does not just fit your sample, it generalises with credibility. Tables 4.10. Effect Size (f²) of IBP and LGP on Dependent Variable Predictor Outcome f² Effect Size IBP ROA 0.42 (Large) IBP Market Share 0.31 (Medium) LGP ROA 0.46 (Large) LGP Market Share 0.39 (Large) Source: Survey Data (2025) Table 4.10 presents Cohen’s f² effect sizes for IBP and LGP predictors. All values exceed 0.15, indicating medium to large effects, with IBP and LGP demonstrating strong influences on both ROA and market share. f² values greater than 0.02 are small, 0.15 are medium, and 0.35 are large. The IBP and LGP variables have substantial effect sizes, particularly on ROA, reinforcing the idea that internal processes and learning capabilities are high-impact drivers. 4.3.2 Path Coefficients and Hypothesis Tests IBP metrics → Market Share (β = 0.49, p < .001) and ROA (β = 0.58, p < .001); non-significant effects for management support and interaction terms; Table 4.11 . The LGP metrics show similar patterns (Table 4.12 ). The structural path relationships for the learning and growth perspective (LGP) are detailed in Fig. 2 . Table 4.11 Path Coefficients and Hypothesis Test Beta Coefficient Standard deviation T statistics P values IBP ->MARKS 0.492 0.058 8.465 0.000 IBP ->RETA 0.576 0.051 11.331 0.000 MANST ->MARKS 0.023 0.084 0.274 0.784 MANST ->RETA 0.048 0.069 0.698 0.485 MANST x IBP ->MARKS -0.056 0.080 0.701 0.483 MANST x IBP ->RETA -0.101 0.080 1.274 0.203 Source: Survey Data (2025) Internal business process metrics positively predict market share (β = 0.49, p < .001) and ROA (β = 0.58, p .20; see Table 4.11 ). 4.3.2 Path Coefficients and Hypothesis Tests Table 4.11 reports that internal business process (IBP) metrics significantly predict market share (β = 0.49, t = 8.47, p < .001) and return on assets (ROA; β = 0.58, t = 11.33, p .20), indicating no moderating effect of management support on either relationship. Table 4.12 shows a parallel pattern for learning and growth perspective (LGP) metrics: strong positive effects on market share (β = 0.60, t = 10.34, p < .001) and ROA (β = 0.65, t = 13.82, p .27), disconfirming H3 and H4. Together, these results demonstrate that both internal process capabilities and broader learning and growth investments drive financial and market performance in Ghana’s oil, gas, and telecommunications sectors independent of direct managerial intervention. Table 4.12 Structural Path Results: Learning & Growth Perspective (LGP) Metrics and Management Support (MANST) 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: Survey Data (2025) Learning and growth metrics exhibited strong positive effects on market share (β = 0.60, p < .001) and ROA (β = 0.65, p .27; Table 4.12 ). Discussion and Implications 4.1 Discussion of Key Findings The analysis confirms that internal business process measures significantly enhance financial and market outcomes. Specifically, improvements in process efficiency yielded substantial increases in ROA (β = 0.492, t = 8.47, p < .001) and market share (β = 0.576, t = 11.33, p < .001). These results align with resource-based and Balanced Scorecard research demonstrating the centrality of process capabilities in driving performance (Barney, 1991; Kaplan & Norton, 1996 ). Contrary to several case‐study reports, management support does not moderate these relationships, indicating that embedded process frameworks and decentralised decision‐making can deliver outcomes without active top‐down intervention (Sousa & Voss, 2022; Van Assen, 2018). This divergence suggests that operational excellence itself may suffice to secure performance gains in Ghana’s resource-intensive industries. 4.2 Theoretical Implications These findings extend the existing theory by illustrating that internal process capabilities can function as valuable, rare, inimitable, non-substitutable (VRIN) resources driving both financial and market success independent of direct managerial oversight (Barney, 1991; Teece, 2019 ). They challenge the assumption within the dynamic capabilities literature that leadership commitment is always necessary to translate process improvements into performance benefits. The negligible moderating effect of management support highlights the need to refine theoretical models to account for contextual factors, such as employee autonomy and frontline resource orchestration, that enable process-driven value creation. Future theoretical developments should integrate these contingencies to better predict performance trajectories in emerging market settings. 4.3 Managerial and Policy Implications Managers should prioritise investments in operational excellence and frontline empowerment, rather than relying solely on active leadership sponsorship. Recommended actions include the following: Streamlining workflows through lean management and process automation can reduce waste and enhance productivity. Empowering cross-functional teams with decision‐making authority to accelerate innovation and responsiveness. Establishing continuous improvement programs with clear performance metrics and feedback loops to sustain efficiency gains. Policymakers can reinforce these organizational efforts by Offering tax incentives or grants for technology adoption and process optimisation initiatives. Supporting capacity-building programs that develop employee competencies in process management and data analytics. Facilitating industry-wide knowledge‐sharing platforms to disseminate best practices across the oil, gas, and telecommunications sectors. 4.4 Limitations and Future Research This study’s cross-sectional design limits causal inferences regarding process improvements and performance outcomes. Purposive sampling of Ghanaian firms may constrain the generalisability of the findings to other national and sectoral contexts. Additionally, the single-country focus precludes comparative insights across different regulatory and cultural environments. Future research should employ longitudinal designs to track performance changes over time, extend the model to other industries and regions, and investigate additional moderating variables, such as organizational culture or digital maturity, to uncover the conditions under which management support may become a significant enabler of process-driven improvements. Conclusions This study examined how learning and growth metrics operationalised as internal business process (IBP) measures affect organizational performance in Ghana’s oil, gas, and telecommunications sectors, and whether management support moderates these effects. Using an explanatory sequential mixed-methods design with PLS‐SEM analysis of 240 survey responses followed by 15 in-depth interviews, we found the following: IBP measures exerted significant positive effects on Return on Assets (ROA; β = 0.492, t = 8.47, p < .001) and market share (β = 0.576, t = 11.33, p < .001). Management support did not significantly moderate either relationship (ROA: β = − 0.048, t = 0.70, p = .485; market share: β = − 0.056, t = 0.70, p = .483). These results advance Balanced Scorecard and dynamic capabilities scholarship by demonstrating that robust internal process capabilities alone can drive both financial returns and competitive positioning in an emerging market context. Practically, firms should prioritise lean operations, process automation, and decentralised decision-making over reliance on direct managerial intervention. Policymakers can bolster sectoral performance by incentivising investments in technology-enabled process improvements and capacity-building initiatives. Limitations of this research include its cross-sectional design, purposive sampling, and focus on a single national context. Future studies should employ longitudinal designs to capture performance trajectories over time, expand to other industries and regions for comparative insights, and explore the qualitative conditions under which management support may become a significant enabler of process-driven improvement. In sum, by integrating quantitative and qualitative evidence, this study underscores the centrality of operational excellence in achieving sustainable performance gains and provides a clear roadmap for managers and policymakers seeking to enhance organizational outcomes in volatile resource-intensive sectors. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7166545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":493830390,"identity":"b5bb41ac-8148-457e-bd8f-4dba70c330ca","order_by":0,"name":"Suleman Mohammed Yakubu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYFACNgZmEMUH5cowsDdARAhqYYNyeRh4DpCsRSIBvxaD48fSpAvb6hLb2LsTPxe22fHwS74x/FxQYcPA396dgFXLmbRj0jPbDie28ZzdDGQk80jOzjGWnnEmjUHizNkNWLUcSG+T5m07kNgmkbtBmncbM4/B7RwDoMhhBgOgCFYt55+DtAAdJv9282/ebfU89jfPGP/Gq+UG0GG8bcxAW3i3AW05zGMgwWOG1xbJG8+SrWecO2zcxpO7zZr333EeiTNpZdY8Z9J4cPmF73ya4e2CsjrZfvazm2/znKmW428/DGRU2AAZvVi1KBzAFOMwAJE82JSDgHwDphj7A1yqR8EoGAWjYGQCAIW4Xkj4k6VkAAAAAElFTkSuQmCC","orcid":"","institution":"Accra Institute of Technology (AIT) Accra-North-Ghana","correspondingAuthor":true,"prefix":"","firstName":"Suleman","middleName":"Mohammed","lastName":"Yakubu","suffix":""},{"id":493830391,"identity":"52fe5cc2-dd56-4d67-803f-669797c3cf71","order_by":1,"name":"Kingsley Tornyeva","email":"","orcid":"","institution":"Accra Institute of Technology (AIT) Accra-North-Ghana","correspondingAuthor":false,"prefix":"","firstName":"Kingsley","middleName":"","lastName":"Tornyeva","suffix":""}],"badges":[],"createdAt":"2025-07-19 21:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7166545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7166545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88120025,"identity":"c7373f06-a74f-4e68-8d07-e359b2424fb4","added_by":"auto","created_at":"2025-08-01 15:35:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement Model\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAnalyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7166545/v1/e08785b78c7979fa3b6cae24.png"},{"id":88119782,"identity":"780f4e77-53a7-4975-9001-2da7fb62e29a","added_by":"auto","created_at":"2025-08-01 15:27:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement Model\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAnalyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7166545/v1/9b6e879e2367cbc867402a36.png"},{"id":88120726,"identity":"029181f3-136e-424c-8b95-c15bea2d29ad","added_by":"auto","created_at":"2025-08-01 15:43:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752852,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7166545/v1/ed1b5c04-035c-400b-b1b8-d40f87a4518a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImpact of Organisational performance on internal business process metrics. Moderating the role of Management Support\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStrategic performance management increasingly leverages the Balanced Scorecard (BSC) to integrate financial and non-financial metrics (Kaplan \u0026amp; Norton, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In this context, the learning and growth perspective, which includes developing human capital, building knowledge infrastructure, and increasing innovation capacity, has been suggested to be necessary for maintaining a competitive edge (Teece, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, in Ghana's oil, gas, and telecommunications industries, there is evidence that there is still a gap between investments in organisational learning and measurable performance gains. In 2023, top companies like MTN Ghana and Telecel had average returns on assets (ROA) of less than 6.5%, even though they were working on big digital transformation projects (Bank of Ghana, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concurrently, sectoral analyses estimate that approximately 18% of potential revenue remains unrealised owing to inefficiencies in strategic alignment and learning systems (IMANI Ghana, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such figures indicate substantial financial stakes, underscoring the necessity of understanding how learning and growth metrics affect profitability and market competitiveness. Using dynamic capabilities theory and the balanced scorecard, this study examines how much internal learning and growth metrics affect ROA and market share in Ghana's oil, gas, and telecommunications industries. This study also examines whether management support changes these relationships, giving practitioners and policymakers evidence-based advice specific to the local economy.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Problem Statement\u003c/h2\u003e\u003cp\u003eEven though the Balanced Scorecard (BSC) is widely used, there have not been many studies in emerging markets that have looked at how learning and growth metrics affect financial performance (like ROA) and market outcomes (like market share) at the same time, or how management support plays a role in these effects. Companies in Ghana, where the oil, gas, and telecommunications sectors together make up more than 25% of the GDP, are still not doing well. The average ROA is still below 6.5%, and bad learning investments cost the country approximately 18% of its potential revenue (Bank of Ghana \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; IMANI Ghana \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study addresses the urgent need to quantify the impact of learning and growth systems on organizational performance and determine whether management support amplifies or attenuates that impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Research Objectives\u003c/h2\u003e\u003cp\u003eThis study was guided by the following objectives:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo examine the direct effect of learning and growth metrics operationalised via internal business process indicators on Return on Assets (ROA).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAnalyse the direct effect of learning and growth metrics on market share.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the moderating influence of management support on\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe relationship between learning and growth metrics and ROA; and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe relationship between learning and growth metrics and market share.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis study is based on resource-based theory (RBT), which emanates from Penrose’s (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) theory of firm growth and Ricardo’s (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1817\u003c/span\u003e) rent theory. It focuses on the unique business resources that affect firm performance, especially at the meso level of an organisation rather than the macro level. According to Barney (1991), resource-based theory argues that resource combinations unique to firms are valuable, rare, inimitable, and non-substitutable, leading to a sustained competitive advantage. This theory is based on two main concepts: resource heterogeneity and immobility. Resource heterogeneity means that even though companies may compete in the same industry, they have different sets of tangible and intangible resources, such as human capital, intellectual property, and brand reputation, that put them in different competitive positions (Ahmad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, resource immobility implies that certain assets, especially intangible ones such as culture and proprietary knowledge, are deeply embedded within organizational routines and histories, making them difficult to replicate (Monteiro, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Kaur and Kumar (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) agree with this view, saying that different asset configurations make firms more or less competitive. RBT has been used in business settings by several researchers. Aker et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that when big data analytics are aligned with resource-based principles, firms improve performance through the integration of technology, human capital, and strategic management. Nambisan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) stressed how important it is to have the ability to innovate, such as specialised tools and data applications, to stay competitive. RBT also helps with marketing innovation strategies, which are ways for companies to create resources that are focused on the market so that they can deal with changes in the environment (Kaur \u0026amp; Kumar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RBT has been criticised, even though it is important. Scholars argue that it is tautological, static, and difficult to validate empirically (Barney, Ketchen Jr., \u0026amp; Wright, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grover and Dresner (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) add that it often ignores external factors like regulatory shifts and market volatility. Bloodgood (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) questions whether its emphasis on rarity is overstated, whereas Haag (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposes expanding its scope to include dynamic capabilities and co-specialisation. Dynamic capabilities posit that companies must adapt their resources and learning routines in line with changing market requirements (Robson, Ojiako, \u0026amp; Maguire, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some of these involve training, R\u0026amp;D, and models, such as PESTEL and Porter's Five Forces. Resource-based theory plays a key role in measuring both financial and non-financial performance indicators in Ghana’s oil, gas, and telecommunications industries. The balanced scorecard, in integrating financial with customer, internal process, and learning views, fills the internal focus gap of the RBT. Superior drilling technologies, IT infrastructure, and safety procedures are valuable assets in these industries. Management support was tested as a moderator in the internal process-performance relationship because it is a strategic asset in the deployment of assets. In general, RBT provides sound recommendations on how companies should weigh inner dynamism with strategic imperatives to enhance their power and competitiveness in the future.\u003c/p\u003e\n\n"},{"header":"Empirical Review","content":"\u003ch2\u003e2.1 Balanced Scorecard Adoption and Performance Outcomes\u003c/h2\u003e\u003cp\u003eThere is still worldwide evidence that reinforces learning and development measures, including the intensity of training for employees, knowledge management systems, and culture of innovation, can contribute to financial performance. Rahman and Ahsan (2022), through Australian logistics companies PLS-SEM, documented a 15 percent return on assets increase linked to systematic learning activities but did not account for implications on market share. Shanker et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that investments in knowledge‐infrastructures yielded a 12 percent market‐share boost in Indian service businesses, but theirs is an analysis of a single industry and thus non-generalizable. Oyewo, Moses, and Erin (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated that learning metrics improved customer retention in Nigeria's manufacturing sector but omitted financial return measures. Together, these works confirm the Balanced Scorecard's learning and growth orientation, but pose three studies that simultaneously discuss both ROA and market share.\u003c/p\u003e\u003ch2\u003e2.2 Management Support as a Moderator\u003c/h2\u003e\u003cp\u003eEmpirical studies of the moderating effects of management support are incongruent. The European manufacturing case studies by Van Assen (2018) provided evidence that lean-management benefits were attained only where senior‐leadership sponsorship existed. Conversely, Sousa and Voss (2022) surveyed 200 Canadian service companies and found no significant moderating effect of leadership commitment on process-efficiency performance linkages. Mitchell (2024) employed qualitative interviews in Philippine service companies to propose that managerial empowerment increases decision cycles but provided no quantitative findings. These divergent findings represent methodological diversity and an open question: Under what conditions does management support an increase in learning and growth investment advantages?\u003c/p\u003e\u003ch2\u003e2.3 Ghana and Sub-Saharan African Evidence\u003c/h2\u003e\u003cp\u003eHowever, empirical research on Ghana is limited. Adomako-Kwakye (2021) analysed public petroleum agency transparency tools and found process development without ROA or market‐share improvement. Osei et al. (2024) surveyed telecommunications subscribers in Ghana and found that process flexibility decreased subscriber churning by 8 percent, without measuring firm‐level improvement. Aker et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed mobile‐money transfer in Niger, with technology facilitation of learning improvement without measuring organizational performance measures. Thus, no PLS‐SEM study in Ghana’s oil, gas, or telecommunications sectors has simultaneously evaluated the effects of learning and growth metrics on both ROA and market share, nor examined management support as a moderator.\u003c/p\u003e\u003ch2\u003e2.4 Synthesis and Research Gaps\u003c/h2\u003e\u003cp\u003eThe literature affirms that learning and growth capabilities can drive either financial or market outcomes, but rarely both in a single empirical model. Methodological heterogeneity, ranging from case studies to cross-sectional surveys, impedes cross-study comparisons. Crucially, the absence of comprehensive PLS-SEM analyses in Ghana’s key industries leaves unanswered whether internal process improvements translate into simultaneous enhancements in ROA and market share and whether management support strengthens or weakens these effects. Addressing these gaps, the present study employs a mixed‐methods design to test direct and moderating relationships, thereby advancing both theoretical understanding and managerial practice in emerging‐market contexts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eResearch Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn explanatory sequential mixed-methods design was employed to quantify and contextualise the impact of learning and growth metrics on organizational performance in Ghana\u0026rsquo;s oil, gas, and telecommunications sectors (Ivankova et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The quantitative phase was conducted from January to April 2025 across major private and public enterprises in Accra, Ghana.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and Sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe target population comprised 500 employees from leading firms, including MTN Ghana, Telecel, AirtelTigo, 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. A sample of 240 respondents was determined using Cochran\u0026rsquo;s formula for a 95% confidence level and a 4.56% margin of error. Purposive sampling targeted individuals in operations, finance, human resources, and performance management roles to ensure that the perspectives aligned with the research objectives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection Procedures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were collected in two phases. First, a structured questionnaire featuring closed- and open-ended items was administered to all 240 participants. The instrument was pilot tested with 20 respondents to assess clarity and reliability (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026gt;\u0026thinsp;.70). Second, semi-structured interviews were conducted with 15 senior and middle managers to enrich and explain the quantitative findings. Interviews were audio-recorded, transcribed verbatim, and anonymised to protect the participants\u0026rsquo; identity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstrument Validity and Reliability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConfirmatory factor analysis using partial least squares structural equation modelling (PLS-SEM) in SmartPLS 3.0 evaluated the measurement model. Composite reliability values exceeded .70, average variance extracted (AVE) values exceeded .50, and item loadings surpassed .70, confirming convergent validity. The Fornell\u0026ndash;Larcker criterion establishes discriminant validity. Variance inflation factor (VIF) values below 5 indicate no significant multicollinearity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantitative data were analysed in Microsoft Excel for descriptive statistics and in SmartPLS 3.0 for structural model assessment. Bootstrap resampling (5,000 subsamples) generated path coefficients, t-values, and p-values for hypothesis testing. Qualitative interview transcripts were coded thematically using NVivo 12 to identify patterns and contextual factors influencing the moderating role of management support.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEthical approval was obtained from the University of Ghana Ethics Committee (UG-ERC-2025-01). All participants provided written informed consent. Anonymity and confidentiality were maintained throughout data collection and analysis. Participants were informed of their right to withdraw at any stage without consequences.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThis section presents the results of the analysis and discussion of the findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for Sample Demographics (n\u0026thinsp;=\u0026thinsp;239)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctorate/PhD degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eprofessional certificate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efinancial and account professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHead of department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR and performance professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emiddle/line manager\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esenior manager\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esupervisor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSectors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoil and gas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTelecommunications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emore than 10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperience level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026ndash;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eless than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e shows a balanced gender split, a majority aged 20\u0026ndash;40, 60% from telecommunications, and most holding bachelor\u0026rsquo;s or master\u0026rsquo;s degrees with 4\u0026ndash;6 years\u0026rsquo; experience.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Measurement Model Assessment\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Factor Loading\u003c/h2\u003e\u003cp\u003eAll items loaded\u0026thinsp;\u0026ge;\u0026thinsp;0.75 onto their intended constructs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e), confirming convergent validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor Loadings for Measurement Items\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Indicator Multicollinearity (VIF)\u003c/h2\u003e\u003cp\u003eAll VIF values were \u0026le;\u0026thinsp;2.98 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e), indicating negligible collinearity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariance Inflation Factors (VIF\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Reliability and Validity\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Internal Consistency Reliability and Composite Reliability\u003c/h2\u003e\u003cp\u003eCronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.848\u0026ndash;0.883; Composite Reliability\u0026thinsp;=\u0026thinsp;0.887\u0026ndash;0.914 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e), supporting reliability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCronbach\u0026rsquo;s α and Composite Reliability\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Convergent Validity\u003c/h2\u003e\u003cp\u003eAVE values range from 0.57 to 0.73 (all \u0026ge;\u0026thinsp;0.50; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e), confirming convergent validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAVE values range from 0.57 to 0.73 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e), all above the 0.50 benchmark, confirming that constructs capture sufficient indicator variance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Discriminant Validity (Fornell\u0026ndash;Larcker \u0026amp; HTMT)\u003c/h2\u003e\u003cp\u003eThe square roots of AVE exceed inter-construct correlations, and all HTMT ratios are \u0026lt;\u0026thinsp;0.90; Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e\u0026ndash;4.7.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant Validity via Fornell\u0026ndash;Larcker Criterion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe square root of each construct\u0026rsquo;s AVE exceeded its highest correlation with any other construct (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e), evidencing empirical distinctiveness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Discriminant Validity (Fornell\u0026ndash;Larcker \u0026amp; HTMT)\u003c/h2\u003e\u003cp\u003eAll Fornell\u0026ndash;Larcker criteria are satisfied (square roots of AVE exceed inter-construct correlations) and HTMT ratios are below 0.90 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4.7\u003c/span\u003e), confirming discriminant validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant Validity via HTMT Ratio\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMANST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRETA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource: Survey Data (2025)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll HTMT values fall below 0.90 (range\u0026thinsp;=\u0026thinsp;0.065\u0026ndash;0.878; Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4.7\u003c/span\u003e), further supporting discriminant validity among constructs.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Structural Model Evaluation\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Model Fit (R\u0026sup2;)\u003c/h2\u003e\u003cp\u003eThe model explains 47.7% of the variance in market share and 60.4% in ROA (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4.8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe explanatory strength of the model, with R\u0026sup2; values of 0.604 and 0.477 for ROA and market share, respectively, is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCoefficients of Determination (R\u0026sup2;) for Dependent Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-square adjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model explains 47.7% of the variance in market share and 60.4% of the variance in ROA (see Table \u003cspan class=\"InternalRef\"\u003e4.8\u003c/span\u003e), indicating substantial explanatory power.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.9. Model Predictive Relevance (Q\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis table reports the Stone-Geisser\u0026rsquo;s Q\u0026sup2; values, which assess how well the model predicts the observed data. It is an essential complement to R\u0026sup2;, especially in PLS-SEM, where prediction matters more than fit.\u003c/p\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ\u0026sup2; Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarket Share (MARKS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReturn on Assets (RETA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4.9\u003c/span\u003e confirms that the model demonstrates predictive relevance for both dependent variables, with Q\u0026sup2; values exceeding the minimum threshold of 0.00 (Hair et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eQ\u0026sup2; values above zero indicate the predictive relevance of the endogenous variables. The values (MARKS\u0026thinsp;=\u0026thinsp;0.318, RETA\u0026thinsp;=\u0026thinsp;0.415) suggest moderate to strong predictive power showing that your model does not just fit your sample, it generalises with credibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTables 4.10. Effect Size (f\u0026sup2;) of IBP and LGP on Dependent Variable\u003c/strong\u003e\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ef\u0026sup2; Effect Size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42 (Large)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarket Share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31 (Medium)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46 (Large)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarket Share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39 (Large)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4.10 presents Cohen\u0026rsquo;s f\u0026sup2; effect sizes for IBP and LGP predictors. All values exceed 0.15, indicating medium to large effects, with IBP and LGP demonstrating strong influences on both ROA and market share. f\u0026sup2; values greater than 0.02 are small, 0.15 are medium, and 0.35 are large. The IBP and LGP variables have substantial effect sizes, particularly on ROA, reinforcing the idea that internal processes and learning capabilities are high-impact drivers.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.2 Path Coefficients and Hypothesis Tests\u003c/h2\u003e\n \u003cp\u003eIBP metrics \u0026rarr; Market Share (\u0026beta;\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ROA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;.001); non-significant effects for management support and interaction terms; Table \u003cspan class=\"InternalRef\"\u003e4.11\u003c/span\u003e. The LGP metrics show similar patterns (Table \u003cspan class=\"InternalRef\"\u003e4.12\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe structural path relationships for the learning and growth perspective (LGP) are detailed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePath Coefficients and Hypothesis Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003edeviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003cp\u003estatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003evalues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBP -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBP -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST x IBP -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST x IBP -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eInternal business process metrics positively predict market share (\u0026beta;\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ROA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while neither management support nor its interaction terms reach significance (p\u0026thinsp;\u0026gt;\u0026thinsp;.20; see Table \u003cspan class=\"InternalRef\"\u003e4.11\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.2 Path Coefficients and Hypothesis Tests\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4.11\u003c/span\u003e reports that internal business process (IBP) metrics significantly predict market share (\u0026beta;\u0026thinsp;=\u0026thinsp;0.49, t\u0026thinsp;=\u0026thinsp;8.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and return on assets (ROA; \u0026beta;\u0026thinsp;=\u0026thinsp;0.58, t\u0026thinsp;=\u0026thinsp;11.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming H1 and H2. The IBP \u0026times; management support interaction terms were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.20), indicating no moderating effect of management support on either relationship.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4.12\u003c/span\u003e shows a parallel pattern for learning and growth perspective (LGP) metrics: strong positive effects on market share (\u0026beta;\u0026thinsp;=\u0026thinsp;0.60, t\u0026thinsp;=\u0026thinsp;10.34, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ROA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.65, t\u0026thinsp;=\u0026thinsp;13.82, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with LGP \u0026times; management support interactions again non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.27), disconfirming H3 and H4.\u003c/p\u003e\n \u003cp\u003eTogether, these results demonstrate that both internal process capabilities and broader learning and growth investments drive financial and market performance in Ghana\u0026rsquo;s oil, gas, and telecommunications sectors independent of direct managerial intervention.\u003c/p\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural Path Results: Learning \u0026amp; Growth Perspective (LGP) Metrics and Management Support (MANST)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003edeviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003evalues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGP -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGP -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST x LGP -\u0026gt;MARKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMANST x LGP -\u0026gt;RETA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource: Survey Data (2025)\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eLearning and growth metrics exhibited strong positive effects on market share (\u0026beta;\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ROA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), whereas management support and LGP\u0026times;MANST interactions remained non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.27; Table \u003cspan class=\"InternalRef\"\u003e4.12\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion and Implications","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Discussion of Key Findings\u003c/h2\u003e\u003cp\u003eThe analysis confirms that internal business process measures significantly enhance financial and market outcomes. Specifically, improvements in process efficiency yielded substantial increases in ROA (β\u0026thinsp;=\u0026thinsp;0.492, t\u0026thinsp;=\u0026thinsp;8.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and market share (β\u0026thinsp;=\u0026thinsp;0.576, t\u0026thinsp;=\u0026thinsp;11.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results align with resource-based and Balanced Scorecard research demonstrating the centrality of process capabilities in driving performance (Barney, 1991; Kaplan \u0026amp; Norton, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Contrary to several case‐study reports, management support does not moderate these relationships, indicating that embedded process frameworks and decentralised decision‐making can deliver outcomes without active top‐down intervention (Sousa \u0026amp; Voss, 2022; Van Assen, 2018). This divergence suggests that operational excellence itself may suffice to secure performance gains in Ghana\u0026rsquo;s resource-intensive industries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Theoretical Implications\u003c/h2\u003e\u003cp\u003eThese findings extend the existing theory by illustrating that internal process capabilities can function as valuable, rare, inimitable, non-substitutable (VRIN) resources driving both financial and market success independent of direct managerial oversight (Barney, 1991; Teece, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They challenge the assumption within the dynamic capabilities literature that leadership commitment is always necessary to translate process improvements into performance benefits. The negligible moderating effect of management support highlights the need to refine theoretical models to account for contextual factors, such as employee autonomy and frontline resource orchestration, that enable process-driven value creation. Future theoretical developments should integrate these contingencies to better predict performance trajectories in emerging market settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Managerial and Policy Implications\u003c/h2\u003e\u003cp\u003eManagers should prioritise investments in operational excellence and frontline empowerment, rather than relying solely on active leadership sponsorship. Recommended actions include the following:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStreamlining workflows through lean management and process automation can reduce waste and enhance productivity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEmpowering cross-functional teams with decision‐making authority to accelerate innovation and responsiveness.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEstablishing continuous improvement programs with clear performance metrics and feedback loops to sustain efficiency gains.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePolicymakers can reinforce these organizational efforts by\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eOffering tax incentives or grants for technology adoption and process optimisation initiatives.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSupporting capacity-building programs that develop employee competencies in process management and data analytics.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFacilitating industry-wide knowledge‐sharing platforms to disseminate best practices across the oil, gas, and telecommunications sectors.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Limitations and Future Research\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s cross-sectional design limits causal inferences regarding process improvements and performance outcomes. Purposive sampling of Ghanaian firms may constrain the generalisability of the findings to other national and sectoral contexts. Additionally, the single-country focus precludes comparative insights across different regulatory and cultural environments. Future research should employ longitudinal designs to track performance changes over time, extend the model to other industries and regions, and investigate additional moderating variables, such as organizational culture or digital maturity, to uncover the conditions under which management support may become a significant enabler of process-driven improvements.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined how learning and growth metrics operationalised as internal business process (IBP) measures affect organizational performance in Ghana\u0026rsquo;s oil, gas, and telecommunications sectors, and whether management support moderates these effects. Using an explanatory sequential mixed-methods design with PLS‐SEM analysis of 240 survey responses followed by 15 in-depth interviews, we found the following:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIBP measures exerted significant positive effects on Return on Assets (ROA; β\u0026thinsp;=\u0026thinsp;0.492, t\u0026thinsp;=\u0026thinsp;8.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and market share (β\u0026thinsp;=\u0026thinsp;0.576, t\u0026thinsp;=\u0026thinsp;11.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eManagement support did not significantly moderate either relationship (ROA: β = \u0026minus;\u0026thinsp;0.048, t\u0026thinsp;=\u0026thinsp;0.70, p\u0026thinsp;=\u0026thinsp;.485; market share: β = \u0026minus;\u0026thinsp;0.056, t\u0026thinsp;=\u0026thinsp;0.70, p\u0026thinsp;=\u0026thinsp;.483).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese results advance Balanced Scorecard and dynamic capabilities scholarship by demonstrating that robust internal process capabilities alone can drive both financial returns and competitive positioning in an emerging market context. Practically, firms should prioritise lean operations, process automation, and decentralised decision-making over reliance on direct managerial intervention. Policymakers can bolster sectoral performance by incentivising investments in technology-enabled process improvements and capacity-building initiatives.\u003c/p\u003e\u003cp\u003eLimitations of this research include its cross-sectional design, purposive sampling, and focus on a single national context. Future studies should employ longitudinal designs to capture performance trajectories over time, expand to other industries and regions for comparative insights, and explore the qualitative conditions under which management support may become a significant enabler of process-driven improvement.\u003c/p\u003e\u003cp\u003eIn sum, by integrating quantitative and qualitative evidence, this study underscores the centrality of operational excellence in achieving sustainable performance gains and provides a clear roadmap for managers and policymakers seeking to enhance organizational outcomes in volatile resource-intensive sectors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the University of Ghana (Ref: UG-ERC-2025-01). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of the findings in the Future Business Journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmad, F. (2024). The relationship between intellectual capital, financial stability, firm performance, market value, and bankruptcy risk: Empirical evidence from Pakistan. Journal of the Knowledge Economy, 1-49.\u003c/li\u003e\n \u003cli\u003eAker, J. C., Boumnijel, R., McClelland, A., \u0026amp; Tierney, N. (2016). Payment mechanisms and antipoverty programs: Evidence from a mobile money cash transfer experiment in Niger. 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Journal of Supply Chain Management, 58(2), 48-65.\u003c/li\u003e\n \u003cli\u003eHaag, L. (2021). Dynamic capabilities for managing logistics challenges of retailers. Linkopings Universitet (Sweden).\u003c/li\u003e\n \u003cli\u003eHair, J. F., Risher, J. J., Sarstedt, M., \u0026amp; Ringle, C. M. (2021). When to use and how to report the results of PLS-SEM. European Business Review, 33(1), 2\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eHenseler, J., Ringle, C. M., \u0026amp; Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115\u0026ndash;135.\u003c/li\u003e\n \u003cli\u003eIMANI Ghana. (2023). Sectoral revenue optimization study. Accra: IMANI Centre for Policy and Education.\u003c/li\u003e\n \u003cli\u003eIvankova, N. V., Creswell, J. W., \u0026amp; Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field methods, 18(1), 3-20.\u003c/li\u003e\n \u003cli\u003eJadhav, A., Rahman, S., \u0026amp; Ahsan, K. (2022). Sustainability practices disclosure of top logistics firms in Australia. The International Journal of Logistics Management, 33(5), 244-277.\u003c/li\u003e\n \u003cli\u003eKaplan, R. S., \u0026amp; Norton, D. P. (1996). The Balanced Scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79.\u003c/li\u003e\n \u003cli\u003eKaur, K., \u0026amp; Kumar, S. (2024). Resource-based view and SME internationalization: a systematic literature review of resource optimization for global growth. Management Review Quarterly, 1-43.\u003c/li\u003e\n \u003cli\u003eMonteiro, D. G. (2023). The Usefulness of the Resource-Based View in Information Systems Research (Master\u0026apos;s thesis, ISCTE-Instituto Universitario de Lisboa (Portugal)).\u003c/li\u003e\n \u003cli\u003eNambisan, S., Lyytinen, K., Majchrzak, A., \u0026amp; Song, M. (2019). Digital innovation management: reinventing organizational capabilities. MIS Quarterly, 43(1), 223\u0026ndash;238.\u003c/li\u003e\n \u003cli\u003eNambisan, S., Wright, M., \u0026amp; Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research policy, 48(8), and 103773.\u003c/li\u003e\n \u003cli\u003eNguyen, T. T., \u0026amp; Robinson, P. J. (2024). Learning metrics and financial outcomes: Evidence from Australian logistics. Journal of Logistics Research, 38(1), 58\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eNguyen, V., Robinson, T., \u0026amp; Tsiaplias, S. (2024). The Australian Economy in 2023\u0026ndash;24: Navigating a Narrow Path. Australian Economic Review, 57(1), 5-20.\u003c/li\u003e\n \u003cli\u003eOyewo, B., Moses, O., \u0026amp; Erin, O. (2022). Balanced scorecard usage and organizational effectiveness: Evidence from manufacturing sector. Measuring Business Excellence, 26(4), 558-582.\u003c/li\u003e\n \u003cli\u003ePenrose, E. T. (1959). The theory of the growth of the firm. Oxford University Press.\u003c/li\u003e\n \u003cli\u003eRicardo, D. (1817). On the principles of political economy and taxation. John Murray.\u003c/li\u003e\n \u003cli\u003eRobson, I., Ojiako, U., \u0026amp; Maguire, S. (2024). A complexity perspective of dynamic capabilities in enterprise project organizations. Production Planning \u0026amp; Control, 35(8), 745-769.\u003c/li\u003e\n \u003cli\u003eSarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., \u0026amp; Ringle, C. M. (2022). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Journal of Business Research, 145, 111\u0026ndash;127.\u003c/li\u003e\n \u003cli\u003eShanker, R., Khan, D., Hossain, R., Islam, M. T., Locock, K., Ghose, A., ... \u0026amp; Dhodapkar, R. (2023). Plastic waste recycling: existing Indian scenario and future opportunities. International Journal of Environmental Science and Technology, 20(5), 5895-5912.\u003c/li\u003e\n \u003cli\u003eTeece, D. J. (2019). A capability theory of the firm: an economics and (strategic) management perspective. New Zealand economic papers, 53(1), 1-43.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Balanced Scorecard, internal business processes, organisational performance, management support, return on assets, market share","lastPublishedDoi":"10.21203/rs.3.rs-7166545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7166545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDespite the widespread use of the Balanced Scorecard (BSC), the direct impact of internal business process efficiency rooted in learning and growth metrics on financial and market performance remains underexplored in emerging economies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study employed an explanatory sequential mixed-methods design. Quantitative data from 240 employees across Ghana\u0026rsquo;s oil, gas, and telecom sectors were analysed using partial least squares structural equation modelling (PLS-SEM). Follow-up qualitative interviews with 15 managers provided the contextual information.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eInternal process efficiency showed strong positive effects on ROA (β\u0026thinsp;=\u0026thinsp;0.576, t\u0026thinsp;=\u0026thinsp;11.33, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and market share (β\u0026thinsp;=\u0026thinsp;0.492, t\u0026thinsp;=\u0026thinsp;8.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Management support did not significantly moderate these relationships. Qualitative findings suggest that decentralised processes diminish the need for active managerial intervention.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOperational excellence independently drives financial and market performance in Ghana\u0026rsquo;s resource-intensive sector. Process automation, lean management, and empowered teams are critical. Policymakers should incentivise technology adoption and workforce capacity building. Future studies should investigate longitudinal and cross-sector dynamics.\u003c/p\u003e","manuscriptTitle":"Impact of Organisational performance on internal business process metrics. Moderating the role of Management Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 15:27:20","doi":"10.21203/rs.3.rs-7166545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26ac8d56-8561-4220-93ea-b64cef0d9d12","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-01T15:27:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 15:27:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7166545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7166545","identity":"rs-7166545","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}