Unleashing Synergies: Interplay between Human Resource Management, Strategic Marketing, and Corporate Financial Performance

,


Introduction
The merger between Human Resource Management (HRM), Strategic Marketing Management, and Corporate Financial Performance has become an increasingly interesting research topic within the business and management world (Farza et al., 2021); (Xia & Wang, 2021).The combination of these three aspects is the result of an in-depth understanding of how key elements in organizations interact and impact each other.Human Resource Management (HRM) refers to the practices and strategies that companies use to manage their human assets, namely employees (Tuan, 2022); (Aguinis et al., 2024).It involves various aspects, from recruitment and selection, training and development, to performance management and compensation.Meanwhile, Strategic Marketing Management leads to a company's efforts in planning, implementing, and overseeing marketing strategies that will lead to business growth and competitive advantage (Kolbe et al., 2022).Both have a crucial role in determining how organizations interact with their markets and customers.Corporate Financial Performance is an objective measure of a company's financial health (Sacer et al., 2016).It includes various metrics such as revenue, net income, profit margin, and stock value.Strong financial performance is the primary goal of almost every company, and good financial performance is often the benchmark for success in business (Fang et al., 2022).
Research on the merging of HRM, Strategic Marketing Management, and Corporate Financial Performance aims to explore how effective HRM practices and smart marketing strategies can impact overall financial performance (Zhang et al., 2020).The research explores the extent to which synergy between these three components can enhance a company's competitiveness and create added value for stakeholders (R. Das Gupta & Deb, 2023).
A range of studies have explored the relationship between human resource management (HRM), marketing strategy, and financial performance.Cetin (2010) found that marketing and manufacturing performance mediate the relationship between HRM and financial performance.(Panayotopoulou et al., 2003) and (e Cunha et al., 2020) both highlighted the importance of HRM being aligned with competitive strategy, with the latter also emphasizing the role of HRM content and process in organizational performance.(M.Goswami & Jena, 2023) and (Olson et al., 2018) both underscored the impact of HRM policies on business and marketing strategy implementation, with the latter emphasizing the importance of alignment between business and marketing strategies.(Mulolli & Boskovska, 2020) and (Akhtar et al., 2008) both found a positive relationship between HRM practices and financial performance, with the latter identifying specific practices that contribute to product/service and financial performance.Lastly, Giannakis & Harker (2014) proposed the role of a Human Capital Chief Enabling Officer in aligning HRM policies with relationship marketing in financial services organizations.The existing literature on the relationship between Human Resource Management (HRM), Strategic Marketing Management (SMM), and firm financial performance reveals a complex and multifaceted relationship.While some studies have found a positive correlation between HRM practices and firm financial performance (Sojka, 2015); Mulolli & Islami, 2020;(Priyadharshini et al., 2015), others have highlighted the need for a more nuanced understanding, suggesting that the impact of HRM on financial performance may be mediated by factors such as competitive strategy, external environment, and organizational size (Panayotopoulou et al., 2003;(Akdag & Zehir, 2016).Furthermore, the role of SMM in this relationship has been explored, with some studies suggesting that a high orientation in HRM models may not necessarily lead to improved financial performance (Panayotopoulou et al., 2003).The gap in the literature lies in the need for more comprehensive and integrated research that considers the combined impact of HRM, SMM, and other relevant factors on firm financial performance.In

Literature Review
The merger between Human Resource Management (HRM), Strategic Marketing Management, and Corporate Financial Performance has gained significant attention in the business and management world.Companies are increasingly realizing the interdependence of these three aspects and the impact they have on each other (Hansen & Løvås, 2004).This literature review aims to delve into existing research and explore the relationships and synergies between HRM, strategic marketing management, and corporate financial performance.Human Resource Management (HRM) plays a vital role in managing a company's most valuable asset -its employees.HRM encompasses various practices and strategies, including recruitment and selection, training and development, performance management, and compensation (Ahammad, 2017).Effective HRM practices not only help attract and retain talented individuals but also ensure their continuous growth and development.Numerous studies have highlighted the positive impact of HRM practices on employee satisfaction, productivity, and overall organizational performance (Ogbonnaya & Aryee, 2022); (Khalid et al., 2016).
Strategic Marketing Management, on the other hand, focuses on creating and implementing marketing strategies to achieve business growth and competitive advantage (Mehra & Coleman, 2016).It involves understanding target markets, analyzing customer behavior, developing marketing campaigns, and evaluating their effectiveness (Rosário & Raimundo, 2021).The alignment between marketing strategies and organizational objectives is crucial for achieving desired financial outcomes.Research has emphasized the importance of marketing capabilities in driving firm performance (Wang et al., 2009;Vorhies et al., 2009).Corporate Financial Performance serves as an objective measure of a company's financial health and success.It encompasses various financial metrics such as revenue, net income, profit margin, and stock value.Strong financial performance not only satisfies shareholders but also allows companies to invest in growth opportunities, innovate, and stay ahead of competition.Numerous studies have established the association between financial performance and organizational success (Rover et al., 2013); (Shaheer et al., 2015); (Taoufik, 2022).
The integration of HRM, strategic marketing management, and corporate financial performance has been explored in the literature due to the potential synergies and value creation opportunities it offers.The effective integration of HRM practices can lead to improved employee skills, motivation, and engagement, ultimately driving better customer satisfaction and loyalty (Kinnie et al., 2017); (M.Gupta, 2022).Moreover, aligning marketing strategies with HRM practices can enhance brand reputation, customer trust, and market share (Malik, 2015); (Bhatnagar & Srivastava, 2008).The impact of HRM and strategic marketing management on corporate financial performance has been extensively examined in separate research streams.However, limited literature explicitly addresses the interplay and combined effect of the three components.Recent studies, however, have started focusing on exploring these relationships.For instance, Huang et al (2023) found that the integration of HRM practices with marketing capabilities positively influences corporate financial performance.Similarly, Kolbe et al (2022) highlighted the role of human capital and marketing capabilities in driving firm performance.Taking into account the complex interdependencies among HRM, strategic marketing management, and corporate financial performance, it is clear that a comprehensive understanding of their interactions is crucial for organizations.By strategically combining HRM practices and marketing strategies, companies can build a competitive advantage that translates into improved financial performance.However, it should be noted that the implementation and effectiveness of these practices and strategies might vary across different industries, organizational sizes, cultures, and contexts (Katou & Budhwar, 2010).
The first study, conducted by Cetin, 2010 in Turkey, showed that human resource management has no statistically significant effect on the financial performance of the company.However, marketing management and production management have a significant positive influence on financial performance (Shanak & Abu-Alhaija, 2023).In addition, this study highlights the role of full mediation by production management and partial mediation by marketing management in the relationship between human resource management and financial performance.Another study, conducted by Panayotopoulou et al (2003) in Greece, found that when human resource management is consistent with a firm's competitive strategy, it has a significant impact on financial performance.This study also identified the effect of flexibility and control of human resource management on marketing performance, depending on the complexity of the external environment.Another study in 2002 by Cunha et al (2002) emphasized that strategic management orientation contributes to a stronger human resource management system.The results showed that the strength of the human resource management system had a positive impact on several aspects of organizational performance.In 2023, research by I. Goswami et al (2023) focused on the effect of human resource management policies on business growth and marketing strategy execution.The results show that human resource management policies can improve business growth and provide better quality of work by understanding internal and external factors.Other research conducted by Lindgreen et al (2018) found that companies that effectively align their business and marketing strategies achieve better performance than those that do not.In addition, there are other studies conducted in various countries such as India, Nigeria, Malaysia, Spain, and Singapore that also highlight the relationship between human resource management and the financial performance of their respective companies.The general conclusion is that human resource management practices and strategies play an important role in achieving better performance in various business contexts.These studies also acknowledged limitations in their methodology and generalizability of their results.Some of the commonly identified limitations are the small sample size, the use of self-report data, the focus on short-term financial performance, and the inability to identify a number of contextual factors that moderate the relationships tested.Overall, these studies confirm the importance of human capital management in supporting corporate financial performance and highlight the various factors that can influence the relationship.The relationship between Human Resource Management (HRM), Strategic Marketing Management (SMM), and Corporate Financial Performance (CFP) has gained significant attention in the field of business and management.This literature review aims to explore the important concepts in this field, identify key factors that play a role in this relationship, and analyze its impact on a company's financial performance.To begin with, HRM is responsible for effectively managing an organization's most valuable asset -its employees.This includes various practices such as recruitment, training, performance management, and internal communication.Numerous studies have emphasized the importance of effective HRM practices in attracting and retaining talent, leading to increased employee satisfaction, commitment, and overall organizational performance.On the other hand, SMM focuses on creating and executing marketing strategies to drive business growth.This involves market analysis, strategy development, campaign execution, innovation, and performance measurement.Alignment of marketing strategies with organizational goals is crucial in achieving desirable financial results.Research has highlighted the significance of marketing capabilities in influencing firm performance.
CFP serves as a metric to assess an organization's financial health, encompassing financial indicators like revenue, profit margin, and stock value.Strong financial performance enables companies to invest in innovation and remain competitive.Several studies have established the relationship between financial performance and overall organizational success.The integration of HRM, SMM, and CFP has gained attention due to its potential for value creation.Effective HRM practices can enhance employee skills and engagement, leading to increased customer satisfaction and loyalty.Aligning marketing strategies with HRM practices can improve brand reputation and increase market share.Although the impact of HRM and SMM on CFP has been explored independently, limited research has addressed their combined effects.However, recent studies suggest that integrating HRM with marketing capabilities has a positive effect on CFP.This literature review aims to provide a comprehensive understanding of the interaction between HRM, SMM, and CFP.It acknowledges that the effectiveness of these practices may vary across industries, organization sizes, cultures, and contexts.Therefore, strategic combination of HRM practices and marketing strategies is crucial for gaining a competitive advantage that positively affects financial performance.To fill the gap in the existing literature, this study will employ empirical analysis and case studies to gain valuable insights into the integration of HRM, SMM, and CFP.This will contribute to the field of business and management by providing a comprehensive understanding of how these dimensions interact to achieve organizational success and financial prosperity.

Study Design
The research adopted a quantitative approach to examine the dynamic relationships between HRM, SMM, and CFP.This approach was chosen to provide a systematic and rigorous investigation of these dimensions within organizations.The SMARTPLS statistical software was utilized to analyse the data and test the proposed hypotheses.

Data Collection
Data collection for this study involved the use of a structured questionnaire.The questionnaire was designed to gather information on HRM practices, SMM strategies, and CFP indicators.To ensure the validity and reliability of the questionnaire, a pilot test was conducted with a sample of 150 respondents to refine the wording of the questions and ensure clarity (See.Table 1).The questionnaire comprised three sections.The first section focused on gathering data related to HRM practices, including recruitment and selection, training and development, performance management, and internal communication.The second section aimed to capture information on SMM strategies, such as market analysis, strategy development, campaign execution, innovation, and performance measurement.The third section addressed CFP indicators, including revenue, profit margin, and stock value.The sample for this study consisted of organizations across various industries and sizes.A multi-stage sampling technique was employed to ensure diversity in the sample.In the first stage, industries were selected based on their representation in the business landscape.In the second stage, organizations within each selected industry were randomly selected.Finally, within each selected organization, a sample of employees responsible for HRM and marketing functions was chosen to respond to the questionnaire.

Data Analysis
The data obtained from the questionnaire were analyzed using the structural equation 216 odelling (SEM) technique with the SMARTPLS software.SEM allowed for the examination of both the direct and indirect relationships between HRM, SMM, and CFP.The analysis aimed to test the proposed hypotheses and assess the significance and strength of these relationships.The analysis consisted of several steps.First, the data were subjected to a series of statistical tests to ensure their suitability for the SEM analysis.These tests included assessing the sample size adequacy, testing for multicollinearity, and assessing the normality of data distribution.Next, the measurement model was evaluated by assessing the reliability and validity of the constructs.Reliability analysis was conducted using Cronbach's alpha coefficient, while validity was assessed through convergent and discriminant validity analysis.Finally, the structural model was tested to examine the relationships between HRM, SMM, and CFP.The analysis involved calculating path coefficients, testing the significance of these coefficients using bootstrapping, and assessing the goodness of fit of the model using various fit indices, such as the chi-square, standardized root means square residual (SRMR), and goodness of fit index (GFI).

Result and Discussion
Data Result The data in this table 2 reveals that the sample population is evenly divided between males and females.The largest age group is between 20 and 30 years old, comprising 39.2% of the sample, while the 30-40 age group appears to have a typographical error in its percentage.In terms of education, most respondents have either a D3 diploma or a bachelor's degree, with smaller proportions having senior high school or master's degrees.

Human Resource Management (HRM)
Human Resource Management (HRM) is a pivotal aspect of organizational strategy that encompasses various dimensions aimed at effectively managing and developing a company's most valuable asset: its human capital.Within the framework of HRM, several indicators are utilized to gauge the effectiveness of different practices and policies.Understanding the outer loadings of these indicators provides valuable insights into their contribution to the overarching construct of HRM.In this context, this study focuses on exploring the outer loadings of indicators within the HRM construct, specifically examining dimensions such as Recruitment and Selection Management, Employee Development and Training, Performance Management, Internal Communication, Employee Satisfaction, Organizational Commitment, and Employee Welfare.Recruitment and Selection Management, as a vital component of HRM, aims to attract and retain top talent within an organization.Indicators within this dimension, such as the understanding of job responsibilities outlined in job descriptions and the clarity of communication during the recruitment process, offer insights into the effectiveness of the organization's talent acquisition strategies.Employee Development and Training focus on enhancing employees' skills, knowledge, and career growth within the organization.Indicators such as the provision of regular training, the presence of a clear career development program, and the perception of support for personal and professional development provide a glimpse into the organization's commitment to nurturing its workforce.
Performance Management encompasses processes for evaluating and improving employee performance.Indicators within this dimension, including the receipt of constructive feedback, regular performance evaluations, and feelings of appreciation and recognition, shed light on the effectiveness of performance management practices in driving employee engagement and productivity.Internal Communication plays a crucial role in facilitating information flow and collaboration within the organization.Indicators such as the effectiveness of communication channels, clarity in disseminating important information, and ease of communication with supervisors and colleagues reflect the organization's communication practices and their impact on employee engagement and cohesion.Employee Satisfaction is essential for maintaining a motivated and committed workforce.Indicators related to working conditions, work-life balance, and responsiveness to employee feedback provide insights into employees' overall satisfaction with their workplace environment.
Organizational Commitment reflects employees' dedication and loyalty to the organization.Indicators such as perceived commitment to the company, intention to stay in the long term, and beliefs about the organization's commitment to employee development gauge the strength of employees' attachment to the organization and their alignment with its goals.Employee Welfare focuses on ensuring the well-being and welfare of employees.Indicators related to access to welfare programs, care for physical and mental well-being, and perceptions of the organization's efforts in employee welfare highlight the organization's commitment to supporting its employees' holistic needs.By examining the outer loadings of these indicators within the HRM construct, this study aims to provide valuable insights into the effectiveness of HRM practices and their impact on organizational performance and employee well-being.Through a comprehensive understanding of these dimensions, organizations can develop tailored strategies to optimize HRM practices and cultivate a positive and productive work environment.Demonstrate of HRM Outer loading shown in table 2. Table 2 shows the outerloading of some variables in the Human Resource Management (HRM) dimension.Outerloading refers to the strength of the relationship between specific indicators and the general construct, HRM in this context.Outerloading analysis is important to understand how well the indicators represent the broader dimension of HRM.From table 2, it can be seen that some indicators have significant outerloading on HRM, while others have less strong or insignificant outerloading.For example, the Employee Development and Training (EDT) indicator shows high outerloading, with coefficients ranging between 0.838 and 0.883.This indicates that the indicators in EDT are strongly related to the overall HRM dimension.Similarly, the indicators of Employee Satisfaction (ES) and Employee Welfare (EW) have significant outerloadings.However, there are also some indicators that have lower or insignificant outerloading.For example, the Recruitment and Selection Management (RS) indicator shows lower outerloading compared to the other indicators in this table.This may indicate that the indicators in the RS dimension do not fully reflect the overall HRM construct or may need to be improved.From this, it can be concluded that there is variation in the strength of the relationship between specific indicators and HRM dimensions.To improve the validity of HRM constructs, it is important to pay attention to indicators that have strong outerloading and possibly adjust or further development on indicators that have lower outerloading.This analysis provides a better understanding of the contribution of each indicator to the overall HRM dimension, which can help in the development and improvement of human resource management strategies within the organization.Table 4 presents the discriminant validity of various constructs related to organizational behavior and human resource management.Discriminant validity assesses the extent to which a concept is truly distinct from other concepts by correlating the scores of the concept with scores of other concepts.According to the Fornell-Larcker criterion, for discriminant validity to be established, the square root of the Average Variance Extracted (AVE) for each construct (which should be represented on the diagonal and appears to be missing here) should be larger than the correlations between the construct and all other constructs in the model.In table 4 , the diagonals, which typically would show the square root of AVE values, are not provided.Instead, we see strong correlations between several constructs, with the highest correlation observed between Employee Development and Training and Employee Satisfaction at 0.865.Such a high correlation suggests that these constructs share a substantial amount of variance.The correlation coefficients between constructs range from moderate to high, indicating varying degrees of overlap.For instance, Employee Welfare has a moderately strong correlation with Employee Satisfaction at 0.633, suggesting that employees who are satisfied may also perceive their welfare as being addressed adequately.Another high correlation is seen between Human Resource Management (HRM) and Performance Management at 0.917, which may imply that effective HRM practices are closely linked to the performance management processes in an organization.Internal Communication shows a significant correlation with HRM at 0.867, which is intuitive given that communication is a pivotal part of human resource practices.Similarly, Recruitment and Selection Management has a strong correlation with HRM at 0.913, indicating a possible overlap in the constructs or the measures used to evaluate these areas.Organizational Commitment is moderately correlated with Internal Communication and HRM, with values of 0.611 and 0.818 respectively, suggesting that communication within the company and HRM practices might influence how committed employees feel toward the organization.Table 5 presents the results of a regression analysis examining the effects of various organizational factors on Human Resource Management (HRM).The table lists the coefficients from the regression analysis, along with their mean values, standard deviations, T statistics, and P values for each relationship.The Original Sample (O) column represents the regression coefficients, which measure the strength and direction of the relationship between the independent variables (such as Employee Development and Training, Employee Satisfaction, etc.) and the dependent variable (HRM).The Sample Mean (M) column shows the average coefficient calculated from multiple samples, indicating the stability of the regression coefficient across different samples.Standard Deviation (STDEV) provides an estimate of the variability in the regression coefficients across the samples.Lower standard deviations indicate that the coefficients are consistent across multiple samples, which adds to the robustness of the model.T Statistics (|O/STDEV|) are used to test the hypothesis that the true value of the regression coefficient is zero (no effect).A higher T statistic indicates a stronger evidence against the null hypothesis.In this table, all T statistics are significantly high, suggesting strong evidence against the null hypothesis.The P Values are used to determine the statistical significance of the results.A P value of 0.000 suggests that the findings are statistically significant, which means that there is a negligible probability that the observed relationships are due to chance.Interpretation of the table suggests the following:

Strategic Marketing Management (SMM)
In the provided study on Strategic Marketing Management (SMM), we delve into the intricacies and impacts of various facets of marketing strategies within a corporate setting.The research unfolds a comprehensive overview of how regular market analyses are pivotal in shaping strategic decisions to meet customer needs and enhance market understanding.We explore the formulation of clear, targeted marketing strategies that align with business objectives, scrutinized through the lens of a company's capacity to draft and execute a robust marketing plan.The implementation of these strategies through adept marketing teams is critically assessed, with a focus on the efficacy of the campaigns and the importance of regular evaluations to measure their outcomes.Innovation, a cornerstone of competitive advantage, is evaluated by observing the frequency and significance of new product and service developments within the company, revealing the company's dedication to innovation in the market.
Customer relationships are another cornerstone, with emphasis on the company's approach to customer feedback and satisfaction, which in turn informs the loyalty and retention of the customer base.The efficacy of marketing endeavors is quantified through regular performance measurements, and the subsequent use of this evaluation in strategic refinement is considered essential.Lastly, the research highlights the significance of long-term strategic planning, reflecting on how a clear and focused vision for the future underpins the company's sustained efforts toward achieving its long-term goals.This holistic analysis provides a vivid depiction of the company's strategic marketing acumen, offering valuable insights into the elements that constitute a successful and dynamic marketing management framework.Based on that, all of explanation describe below:  Outer loadings indicate the strength of the relationship between observed variables (items) and their respective latent variables (constructs).In this context, the observed variables are specific statements or measures related to aspects of SMM, while the latent variables are the broader marketing concepts they represent.The loadings range from 0 to 1, with values closer to 1 indicating a stronger relationship.Typically, a loading of 0.7 or above is considered satisfactory, indicating that the item has a substantial relationship with the construct it is supposed to measure.Interpreting the table, we observe that items related to Customer Relationship have loadings of 0.765, 0.820, and 0.861, all above the 0.7 threshold, signifying a strong relationship with the construct.These items effectively capture the essence of customer relationships within the framework of Strategic Marketing Management.Long-term Strategic Planning items showcase even higher loadings, with values of 0.918, 0.940, and 0.916, which strongly affirm the items' relevance in capturing strategic planning elements within the marketing domain.For Market Analysis, the observed variables have loadings of 0.893, 0.939, and 0.958, indicating that market analysis is robustly represented in the model and is likely a significant component of the SMM construct.Marketing Campaign Implementation items have loadings ranging from 0.816 to 0.890, suggesting that these measures are highly relevant and reflect the campaign implementation aspect of SMM effectively.Marketing Performance Measurement is another construct with high loadings between 0.872 and 0.920, reinforcing the notion that the items used to measure this construct are highly indicative of the company's performance measurement practices.Items related to Marketing Strategy Development have loadings of 0.864, 0.902, and 0.921, which are exceptionally high, pointing to a strong linkage between these items and the strategic development aspect of marketing.Product and Service Innovation items show loadings of 0.831, 0.894, and 0.878, well above the threshold, affirming their strong connection to innovation within the strategic marketing construct.The rightmost column under the Strategic Marketing Management (SMM) header reflects the loadings of each item on the overall SMM construct.These loadings vary more significantly, with some items showing a strong relationship (e.g., 0.821 for X2CR3) while others indicate a weaker relationship (e.g., 0.360 for X2MA2).This variability could suggest that certain aspects of SMM, like Customer Relationship and Long-term Strategic Planning, are more central to the construct in the context of this analysis than others, such as Market Analysis.Cronbach's Alpha values in this table serve as an initial reliability coefficient, indicating internal consistency among the items within each construct.Generally, a Cronbach's Alpha of 0.7 or above is considered acceptable.The values here range from 0.749 for Customer Relationship to 0.950 for Strategic Marketing Management (SMM), which shows that all constructs have satisfactory to excellent internal consistency.The rho_A values, presumably a measure similar to Cronbach's Alpha, confirm the internal consistency shown by Cronbach's Alpha, with all constructs exceeding the 0.7 threshold, reinforcing the reliability of the constructs.Composite Reliability (CR) extends the concept of Cronbach's Alpha by considering the different loadings of items in the construct.CR values here are all above 0.8, with most above 0.9, indicating very high internal consistency and reliability for all constructs.Average Variance Extracted (AVE) measures the level of variance captured by the construct versus the level due to measurement error and is used to assess convergent validity.AVE values above 0.5 are generally considered acceptable, suggesting that the construct captures more than half of the variance of its indicators.In this analysis, all constructs except for Strategic Marketing Management (SMM) exceed this threshold, indicating strong convergent validity.The AVE for SMM is slightly above 0.5, which is the borderline of acceptability, suggesting that while it may capture the variance of its indicators, the degree to which it does so is less robust compared to the other constructs.Table 8 provides a discriminant validity assessment of various constructs within the scope of Strategic Marketing Management (SMM).Discriminant validity tests whether concepts or measurements that are supposed to be unrelated are, in fact, unrelated.In table 8, the diagonal elements represent the square roots of the Average Variance Extracted (AVE) for each construct, which should be compared against the off-diagonal elements in the corresponding rows and columns.According to the Fornell-Larcker criterion, for a construct to have adequate discriminant validity, the square root of its AVE should be greater than its highest correlation with any other construct.Starting with Customer Relationship, the square root of its AVE is 0.816, which is higher than its correlations with all other constructs, satisfying the Fornell-Larcker criterion for discriminant validity.This suggests that Customer Relationship is distinctly captured by its items and does not overly overlap with other constructs.Long-term Strategic Planning shows a square root of AVE at 0.925, which is substantially higher than its correlations with other constructs, again confirming its discriminant validity and indicating a well-differentiated construct in the context of SMM.Market Analysis has a square root of AVE of 0.930, the highest among all constructs, and its correlations with other constructs are significantly lower, providing strong evidence of discriminant validity for this construct.Marketing Campaign Implementation, with a square root of AVE of 0.849, shows that it is also well-differentiated from other constructs, as its correlations are lower than this value.Marketing Performance Measurement has a square root of AVE of 0.902, which exceeds all its correlations with other constructs, demonstrating adequate discriminant validity.Marketing Strategy Development has an AVE square root of 0.896, which is higher than any of its correlations with other constructs, thereby meeting the Fornell-Larcker criterion.Product and Service Innovation's square root of AVE is 0.868, which again is higher than its correlations with other constructs, indicating good discriminant validity.Lastly, Strategic Marketing Management (SMM) has a square root of AVE of 0.720, which, while being the lowest square root of AVE on the diagonal, is still higher than its correlations with other constructs, albeit with a relatively smaller margin compared to others.Overall, the table indicates that each of the constructs in this study is distinct and captures unique aspects of Strategic Marketing Management.The constructs have been operationalized in such a way that they reflect different dimensions of SMM without significant overlap, a testament to the rigorous construct development and validation process inherent in the study.This clear differentiation is essential for the integrity of the research and contributes to the robustness of the theoretical framework underpinning the study.The 9 presents the results from a regression analysis exploring the relationship between various components of strategic marketing and the overarching construct of Strategic Marketing Management (SMM).The regression analysis aims to understand the impact of each individual component on the effectiveness of Strategic Marketing Management as a whole.The Sample Mean column indicates the average effect size of each component on SMM across multiple samples.A positive mean suggests a positive relationship, where increases in the component are associated with increases in SMM effectiveness.Standard Deviation provides a measure of the variability of the effect size across samples.Lower standard deviations indicate a more stable effect size across different samples, suggesting that the relationship is consistent.T Statistics are used to determine the statistical significance of the relationship between the components and SMM.A higher T statistic indicates stronger evidence against the null hypothesis, which in this context posits that there is no relationship between the component and SMM.P Values indicate the probability that the observed results occurred by chance.A P value of 0.000 suggests that the results are statistically significant and that there is a negligible probability that the observed relationships are due to random variation in the data Interpreting the results, the analysis shows that: • Customer Relationship has a positive effect on SMM with a sample mean of 0.168 and a highly significant T statistic of 25,489.

Corporate Financial Performance (CFP)
In the sphere of corporate finance, this study meticulously explores the multifaceted nature of Corporate Financial Performance (CFP), offering insights into how strategic financial undertakings contribute to the fiscal health and prowess of a company.The research encompasses a variety of financial aspects, beginning with an evaluation of the company's revenue growth and net profit trends, both of which are indicative of the firm's profitability and market competitiveness.Delving into the domain of Investment and Innovation, the study assesses the company's commitment to innovation-driven projects and whether these endeavors are supported by adequate resources.It also evaluates the role of innovation within the company's broader business strategy, underlining the significance of foresight and resource allocation in fostering long-term returns and maintaining a competitive edge.Financial Management practices of the company are scrutinized to ascertain the efficiency and effectiveness of policies in place.This includes examining the company's expenditure monitoring systems and cost structures, critical for optimizing financial operations and ensuring sustainable growth.The analysis further extends to the company's ability to leverage External Funding.It probes into how the company sources and utilizes external capital to support its growth trajectory while emphasizing the responsible and wise management of such funds.
Long-term Financial Planning is another critical focus of the study, which highlights the company's proficiency in strategizing for the future.The research investigates the depth and clarity of the company's financial forecasts and risk management strategies, essential for navigating the complexities of future financial landscapes.Finally, Investor Confidence is evaluated as a key indicator of the company's financial stewardship.By examining the trust investors place in the company's financial decisions and their perceptions of transparency and equity, the study provides a gauge of the company's reputation and relational capital in the investment community.This comprehensive analysis of Corporate Financial Performance paints a detailed picture of how a company's financial activities and strategies interweave to create a robust, resilient, and growth-oriented financial framework.It underscores the interconnectivity of revenue performance, investment in innovation, financial management acumen, savvy use of external funding, strategic long-term planning, and investor relations in building and sustaining financial excellence.Based on that, all of explanation describe below:   Modeling(PLS-SEM) analysis, assessing the relationship between observed variables and their associated constructs within the framework of Corporate Financial Performance (CFP).The observed variables, represented by the rows, are the individual items that measure constructs such as External Funding, Financial Management, Financial Performance, Investment and Innovation, Investor Confidence, and Long-term Financial Planning.Outer loadings indicate the strength of the association between these observed variables and their respective latent constructs.These loadings are ideally above the threshold of 0.7 to suggest a strong relationship, although values above 0.4 may still be considered acceptable in exploratory research settings.In the context of External Funding, items YEF1, YEF2, and YEF3 show strong loadings of 0.904, 0.949, and 0.902 respectively, suggesting that these items are very good indicators of the construct and reflect the company's approach to sourcing and managing external capital.Financial Management is well-represented by items YFM1, YFM2, and YFM3, with YFM1 and YFM2 showing strong loadings of 0.910 and 0.902, respectively, while YFM3 has a slightly lower loading of 0.663, indicating a weaker but still acceptable relationship with the construct.Financial Performance is assessed through items YFP1, YFP2, and YFP3.With loadings of 0.905 and 0.903 for the first two items, they are strongly indicative of the construct, while YFP3's loading is marginally lower at 0.686.Investment and Innovation, as measured by items YIAI1, YIAI2, and YIAI3, also display strong loadings, all well above the threshold, which suggests that these items effectively capture the company's investment in and focus on innovative practices.Investor Confidence is quantified by items YIC1, YIC2, and YIC3, with all three showing good loadings, particularly YIC3 at 0.889, emphasizing the investors' trust and perception of the company's financial health.Long-term Financial Planning is robustly measured by items YLFP1, YLFP2, and YLFP3, with particularly high loadings for YLFP2 at 0.947 and YLFP1 at 0.888, affirming the significance of this construct in the overall assessment of the company's financial strategy.It's notable that for each construct, the outer loadings are replicated with an association to the overarching construct of Corporate Financial Performance (CFP), with varying degrees of strength.These loadings demonstrate the extent to which each item contributes to the overall concept of CFP within the model.Table 11 illustrates the psychometric properties of the constructs measured in a study on Corporate Financial Performance (CFP).These properties are essential indicators of the soundness and trustworthiness of the measurement tools used in the research.Cronbach's Alpha values are presented to gauge the internal consistency of the constructs, which refers to the extent to which all items within a construct reliably measure the same concept.A value above 0.7 is generally considered acceptable, indicating that the items are well correlated and thus reliable.In this table, the Cronbach's Alpha values range from 0.766 for Financial Management to 0.955 for Corporate Financial Performance, indicating good to excellent internal consistency across all constructs.The rho_A values are similar to Cronbach's Alpha but are considered to be a more accurate reflection of internal consistency because they account for the differing reliabilities of individual items.Here, the rho_A values closely match the Cronbach's Alpha values, confirming the reliability of the constructs.Composite Reliability goes beyond Cronbach's Alpha by also considering the different loadings of the items.It is another measure of internal consistency, and values above 0.7 are considered good.In this case, all constructs show composite reliability values well above this threshold, ranging from 0.869 for Financial Management to 0.959 for Corporate Financial Performance, which suggests that each construct is measured consistently and accurately.Average Variance Extracted (AVE) is the measure of convergent validity, which assesses the extent to which items of a construct are correlated, hence, indicating that they measure the same construct.An AVE value above 0.5 is deemed satisfactory as it indicates that more than half of the variance in the items is accounted for by the construct.The AVE values in this analysis range from 0.568 for Corporate Financial Performance to 0.844 for External Funding, all exceeding the acceptable limit, which demonstrates strong convergent validity.Table 12 presents the results of a discriminant validity assessment using the Fornell-Larcker criterion.This criterion is used to ensure that each construct in a model is distinct and does not overlap too much with other constructs, which is crucial for the clarity and validity of the constructs being studied.In table 12, the diagonal values represent the square roots of the Average Variance Extracted (AVE) for each construct.According to the Fornell-Larcker criterion, for adequate discriminant validity, these diagonal values should be higher than the off-diagonal values in their respective rows and columns, indicating that a construct shares more variance with its own indicators than with other constructs in the model.For Corporate Financial Performance (CFP), the square root of the AVE is 0.754.This number is compared against correlations with other constructs such as External Funding, Financial Management, and so on.In this case, the square root of the AVE for CFP is lower than the correlation with Financial Performance, which is 0.929.This suggests that there may be a lack of discriminant validity between these two constructs, and they may not be as distinct as desired.External Funding has a square root of AVE of 0.919, which is higher than all its correlations with other constructs, indicating good discriminant validity.Financial Management has a square root of AVE of 0.833, and it also shows adequate discriminant validity as its correlations with other constructs do not exceed this value.Financial Performance has a square root of AVE of 0.837.However, as previously mentioned, it has a high correlation with CFP, which exceeds this value, suggesting potential issues with discriminant validity.Investment and Innovation has a square root of AVE of 0.898 and shows adequate discriminant validity as all the correlations with other constructs are lower than this value.Investor Confidence shows a square root of AVE of 0.846, which is lower than its correlation with Long-term Financial Planning, raising concerns about discriminant validity between these two constructs.Long-term Financial Planning has a high square root of AVE of 0.907, which is higher than all of its correlations except for the one with Investor Confidence, indicating good discriminant validity with most constructs but potentially not with Investor Confidence.Table 13 provides a detailed examination of how various financial constructs contribute to Corporate Financial Performance (CFP) within an organizational context.This regression analysis quantifies the strength and significance of the relationships between these constructs.The Sample Mean column indicates the average effect size, which is the expected change in CFP for a one-unit change in the predictor construct.The Standard Deviation measures the variability of the effect size across different samples or estimations, providing an understanding of the consistency of the relationship.T Statistics are used to determine the statistical significance of the relationships.They compare the size of the effect to the variability observed in the data.A larger T Statistic indicates stronger evidence against the null hypothesis, which posits that there is no relationship.P Values assess the probability that any observed relationship occurred by chance.A P value of 0.000, as shown across all constructs, indicates that the relationships are statistically significant at conventional levels and that the likelihood of these findings occurring by chance is extremely low.Interpreting the data, the table suggests the following: • External Funding has a positive and significant effect on CFP with a sample mean of 0.215, and a very high T statistic of 21,819, demonstrating that access to external funds is a strong predictor of financial performance.• Financial Management is associated with CFP with a sample mean of 0.171 and the highest T statistic of 26,896, indicating a robust and significant relationship, suggesting that efficient financial management is crucial for the financial health of the company.• Financial Performance shows a slightly higher effect size with a sample mean of 0.190 and an even higher T statistic of 29,607, underscoring the importance of fundamental financial achievements in contributing to the overall financial performance of the company.
• Investment and Innovation is positively related to CFP with a sample mean of 0.187 and a T statistic of 21,573, highlighting innovation and investment in new projects as important drivers of financial success.
• Investor Confidence has a sample mean of 0.182 and a T statistic of 21,987, suggesting that the confidence investors have in company is significantly associated with better financial performance.• Long-term Financial Planning also shows a strong positive relationship with CFP, having a sample mean of 0.211 and a T statistic of 27,234, indicating that strategic long-term financial decisions are integral to the company's financial success.

Second-Order Effect Between HRM, SMM, and CFP
After conducting meticulous tests on key variables such as Human Resource Management, Strategic Marketing Management, and Corporate Financial Performance (CFP), we have reached an in-depth stage of analysis concerning validity and reliability.The results obtained affirm that both the dimensions and indicators associated with each variable have been tested and meet the criteria for validity and reliability.With the confidence bestowed by these findings, we now advance to Section 4.3.4,where we will continue our exploration through testing using a second-order model.This allows us not only to understand the relationships between indicators and their constructs but also to uncover the more complex dynamics between the constructs themselves, offering further insights into their interactions and overall impact on the organization.Table 14 presents a refined evaluation of the measurement models for three critical constructs in organizational studies: Corporate Financial Performance (CFP), Human Resource Management (HRM), and Strategic Marketing Management (SMM).This second-order analysis goes beyond examining individual items and instead assesses the constructs at a higher level of abstraction, considering the dimensions that make up each construct.Cronbach's Alpha is a measure of internal consistency, and values above 0.7 are typically seen as satisfactory.The values presented for CFP, HRM, and SMM are all above 0.95, indicating extremely high internal consistency within each of these second-order constructs.This suggests that the subconstructs or dimensions that form these second-order constructs are highly interrelated and consistently represent their respective overarching concepts.rho_A, or the rho coefficient, is another measure of reliability that corrects for the potential bias in Cronbach's Alpha.The rho_A values closely match Cronbach's Alpha values, further confirming the high reliability of the constructs.Composite Reliability also measures internal consistency, considering the varying contributions of each dimension to the construct.Just like the previous two measures, the composite reliability values for all constructs are above 0.95, reinforcing the presence of strong internal consistency.Average Variance Extracted (AVE) assesses convergent validity at the construct level, indicating the extent to which a construct explains the variance in its indicators.Generally, an AVE above 0.5 is desirable, as it shows that a majority of the variance in the indicators can be attributed to the variance in the construct.The AVE values for all three constructs are above this threshold, with CFP at 0.566, HRM at 0.532, and SMM at 0.517, indicating acceptable levels of convergent validity.Table 15 provides a quantitative assessment of discriminant validity for three secondorder constructs within a structural equation model: Corporate Financial Performance (CFP), Human Resource Management (HRM), and Strategic Marketing Management (SMM).Discriminant validity evaluates whether these constructs are distinct from each other within the model.The diagonal elements display the square roots of the Average Variance Extracted (AVE) for each of the constructs.According to the Fornell-Larcker criterion, these diagonal values should be greater than the off-diagonal values in the corresponding rows and columns for satisfactory discriminant validity.This would demonstrate that each construct shares more variance with its own indicators than with other constructs, indicating clear differentiation.For Corporate Financial Performance (CFP), the square root of the AVE is 0.752.This value should be compared to the correlation between CFP and other constructs.In this case, CFP's square root of AVE is less than its correlation with HRM (0.952), suggesting potential issues with discriminant validity.Similarly, the correlation between CFP and SMM (0.882) is also higher than the square root of CFP's AVE, further indicating discriminant validity concerns.Human Resource Management (HRM) has a square root of the AVE of 0.729.It has a high correlation with CFP (0.952), which is higher than its own square root of AVE, signaling a lack of discriminant validity between HRM and CFP.However, the correlation between HRM and SMM (0.907) is higher than the square root of HRM's AVE, suggesting discriminant validity issues between these constructs as well.Strategic Marketing Management (SMM) shows a square root of the AVE of 0.719.The correlations of SMM with both CFP (0.882) and HRM (0.907) are higher than this value, indicating that SMM may not be sufficiently distinct from the other two constructs based on the Fornell-Larcker criterion.Table 16 provides the results of an F-square analysis, which is used to measure the effect size of one construct on another within the context of a structural equation model.Effect size is a critical consideration as it offers an understanding of the magnitude of impact one variable has over another beyond mere statistical significance.In table 16, the constructs of Corporate Financial Performance (CFP), Human Resource Management (HRM), and Strategic Marketing Management (SMM) are analyzed to assess the extent of their interrelationships.It is important to note that the F-square values can be interpreted as follows: values of 0.02 indicate a small effect, 0.15 a medium effect, and 0.35 a large effect.The analysis reveals that Human Resource Management (HRM) has a substantial effect on Corporate Financial Performance (CFP), with an F-square value of 1.417.This suggests a large and potentially impactful relationship, indicating that HRM practices might have a significant influence on the financial outcomes of an organization.Moreover, the HRM construct appears to have an even more pronounced effect on Strategic Marketing Management (SMM), with an F-square value of 4.662.This exceptionally high value points to a very strong effect, suggesting that HRM capabilities could be crucial in shaping and supporting strategic marketing initiatives within the company.On the other hand, the effect of Strategic Marketing Management (SMM) on Corporate Financial Performance (CFP) is indicated to be negligible, with an F-square value of 0.020.This value falls into the range of a small effect size, implying that the direct impact of SMM on CFP, within the model analyzed, is limited.As result summary, the F-square analysis underscores the pivotal role of human resources management in both financial performance and marketing management within an organizational setting.The findings suggest that investment in HRM could potentially yield significant returns in both these areas.However, the relationship between strategic marketing and financial performance, as captured in this model, appears to be less pronounced, highlighting the complexity of these constructs and their interactions.It is essential to consider that these effects are context-dependent and should be interpreted within the specific framework of the study.Table 17 presents the coefficients of determination (R Square) and the adjusted R Square for two key constructs: Corporate Financial Performance (CFP) and Strategic Marketing Management (SMM).These coefficients are vital in structural equation modeling as they provide insight into the explanatory power of the model.The R Square value represents the proportion of variance in the dependent variable that is predictable from the independent variables.In this context, an R Square value of 0.908 for Corporate Financial Performance indicates that the model explains 90.8% of the variance in CFP, which is exceptionally high, suggesting that the model's independent variables provide an almost complete explanation of the variation in CFP.The Adjusted R Square considers the number of predictors in the model and adjusts the R Square accordingly.For CFP, the Adjusted R Square is 0.907, nearly identical to the R Square, which implies that the inclusion of the number of predictors in the model is appropriate and does not overfit the data.Similarly, for Strategic Marketing Management, the R Square value is 0.823, signifying that 82.3% of the variance in SMM is accounted for by the model.The Adjusted R Square mirrors this value, confirming that the model's predictors robustly explain a substantial portion of the variance in SMM without being inflated by excessive or irrelevant predictors.Table 18 presents a series of model fit statistics for both the saturated and estimated models within a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework.These fit statistics are essential for evaluating how well the proposed model captures the data.SRMR (Standardized Root Mean Square Residual) is a measure of the average discrepancy between the observed correlations and the model's predicted correlations.Both the saturated and estimated models have an SRMR value of 0.097.While this value is slightly higher than the preferred threshold of 0.08, it does not exceed it by much, suggesting that the model has an acceptable fit.d_ULS (Unweighted Least Squares discrepancy) and d_G (Geodesic discrepancy) are less commonly reported fit indices in PLS-SEM, but they serve as additional measures of the model's fit.Both d_ULS and d_G are consistent across the saturated and estimated models, with values of 17,171 and 6,588, respectively.These values should be interpreted in the context of their respective scales and distributions.The Chi-Square statistic is traditionally used in covariance-based SEM to assess model fit, with lower values generally indicating a better fit.However, in PLS-SEM, the absolute Chi-Square value is less informative without the degrees of freedom and corresponding p-value.Both models report a Chi-Square of 7251,400, which, in isolation, does not provide enough information to assess model fit.NFI (Normed Fit Index) is a baseline comparison fit index that compares the fit of the estimated model to a null model.An NFI close to 1 suggests a good fit.Here, both models have an NFI of 0.979, which is very close to 1, indicating an excellent fit of the model relative to the null model.Based on that, the model fit statistics in this table 18, particularly the high NFI value, suggest that the estimated model has a good fit to the data.The consistent values across the saturated and estimated models for SRMR, d_ULS, d_G, and Chi-Square, combined with the high NFI, provide evidence that the model is robust and captures the underlying structure of the data well.It should be noted that PLS-SEM prioritizes prediction over fit, and while these fit indices are useful, they should be considered alongside other criteria such as predictive relevance (Q^2) and the significance of path coefficients.Table 19 provides the results from a regression analysis that explores the predictive relationships between Human Resource Management (HRM), Strategic Marketing Management (SMM), and Corporate Financial Performance (CFP).This type of analysis is central to understanding the influence that one construct may have over another within the framework of a structural equation model.The Sample Mean column reports the average effect size, or the average expected change in the dependent variable for a one-unit change in the predictor variable across multiple samples.A higher mean suggests a stronger predictive relationship.The Standard Deviation column gives insight into the variability of the effect size across the samples.Lower standard deviation values indicate that the effect size is consistent across different samples.T Statistics are used to assess the significance of relationships.They provide a measure of how many standard deviations the sample mean is away from the null hypothesis, which typically states that there is no relationship.Higher T statistics indicate a stronger rejection of the null hypothesis.P Values indicate the probability that the observed relationship occurred by random chance.A P value of 0.000 suggests that the relationship is statistically significant and not due to random variation in the data.In interpreting the results: • The regression coefficient for HRM predicting CFP is extremely high at 0.953 with a very low standard deviation of 0.005.This is accompanied by an exceptionally high T statistic of 194.504, indicating a very strong and statistically significant predictive relationship.This suggests that HRM is a critical predictor of financial performance in the studied context.• HRM's effect on SMM is also very strong, with a sample mean of 0.908.Despite a slightly higher standard deviation of 0.018, the T statistic of 51.789 is still very high, providing robust statistical evidence for a significant predictive relationship.• SMM's predictive relationship with CFP has a sample mean of 0.898, which is substantial.
The standard deviation is larger at 0.057, which may suggest a bit more variability across samples for this relationship.Nevertheless, the T statistic of 19.779 is quite high, signaling a strong and significant predictive effect.

Discussion
In the realm of Human Resource Management (HRM), the dimensions of Recruitment and Selection Management, Employee Development and Training, Performance Management, Internal Communication, Employee Satisfaction, Organizational Commitment, and Employee Welfare emerge as critical pillars supporting the overarching structure of organizational health and effectiveness.Recruitment and Selection Management serves as the gateway through which talent enters the organization.It is the first touchpoint for potential employees and sets the stage for organizational fit, ensuring that individuals with the requisite skills and cultural alignment are brought into the fold.The strategic importance of this dimension cannot be overstated, as it lays the foundation for future performance and retention.Employee Development and Training represents an organization's commitment to continuous improvement and learning.By investing in the growth of employees, companies not only enhance the skill sets and competencies within their workforce but also signal a dedication to the individual career trajectories of their staff.This investment can lead to increased innovation, adaptability, and preparedness for the evolving demands of the business landscape.Performance Management is the rudder that guides employees towards the achievement of strategic goals.It encompasses setting clear expectations, providing ongoing feedback, and aligning individual objectives with organizational priorities.Effective performance management is essential for maintaining a high-performance culture that drives business success.Internal Communication is the circulatory system of an organization, ensuring that information flows seamlessly across all levels.Transparent and effective communication can foster a sense of inclusion and awareness, which are vital for cohesive and coordinated efforts towards common goals.
Employee Satisfaction is a key indicator of the health of an organization.Satisfied employees are more likely to be productive, engaged, and loyal.They are the lifeblood of the organization, driving its operations and representing its brand.Ensuring employee satisfaction is critical for minimizing turnover and maintaining a competitive edge in attracting and retaining top talent.Organizational Commitment reflects the strength of the bond between an organization and its employees.A deep-seated commitment suggests a workforce that is motivated, engaged, and aligned with the organization's vision and values.This level of commitment can result in higher levels of discretionary effort, which can be the difference between mediocre and exceptional organizational performance.Lastly, Employee Welfare encapsulates the organization's responsibility towards the well-being of its employees.By safeguarding the physical and psychological health of the workforce, organizations can ensure that employees are able to perform at their best.Moreover, a focus on welfare can enhance an organization's reputation as a desirable employer, which is invaluable in the competition for talent.In the dynamic discipline of Strategic Marketing Management (SMM), the dimensions of Market Analysis, Marketing Strategy Development, Marketing Campaign Implementation, Product and Service Innovation, Customer Relationship, Marketing Performance Measurement, and Long-term Strategic Planning are integral components that drive the holistic success of an organization's marketing efforts.Market Analysis stands at the forefront of strategic marketing.It provides the intelligence needed to understand market trends, consumer behaviours, and competitive dynamics.This insight is the groundwork that informs all other aspects of marketing, allowing businesses to identify opportunities and anticipate challenges in the marketplace.Marketing Strategy Development is the process of translating insights gained from market analysis into actionable plans.It is where the strategic direction is set, defining target markets, positioning, and the unique value proposition.This stage is crucial as it outlines the approaches that will be utilized to reach and persuade customers, and ultimately achieve the company's marketing goals.Marketing Campaign Implementation brings the marketing strategy to life.This is where planning meets action and where concepts and designs are transformed into tangible marketing messages that reach the audience.The effectiveness of this dimension is seen in the execution of marketing campaigns that resonate with the target audience and compel them to act.
Product and Service Innovation is the lifeblood of long-term market relevance.In a world where customer preferences shift rapidly, the ability to innovate -to offer new or improved products and services -is what keeps a company at the leading edge of its industry.Innovation reflects a company's commitment to growth and its ability to adapt to changing market conditions.Customer Relationship encapsulates the ongoing engagement and rapport that a business builds with its customers.In the age of customer-centricity, fostering strong relationships is paramount for loyalty and retention.Satisfied customers can become advocates for the brand, further driving organic growth through word-of-mouth and repeat business.Marketing Performance Measurement is the compass that guides marketing efforts.It involves the tracking, analyzing, and interpreting of data resulting from marketing activities.This dimension ensures accountability and enables marketers to understand the efficacy of their strategies and campaigns, adjusting tactics accordingly to optimize ROI.Long-term Strategic Planning is the visionary element of SMM.It extends the focus beyond immediate marketing initiatives to consider the future landscape.Long-term planning is about ensuring sustainability and competitive advantage over time, setting out a roadmap for future marketing growth and innovation that aligns with the company's overarching strategic goals.In essence, these dimensions collectively form the pillars of a robust Strategic Marketing Management framework.Each dimension interlocks with the others, creating a cohesive and comprehensive approach that ensures not just short-term wins, but also long-term success and competitiveness.Their importance lies not only in their individual contributions but also in how they synergize to create a strategic marketing system that is responsive, adaptive, and results-oriented.By integrating these dimensions effectively, SMM becomes a potent tool that drives business growth and builds enduring brand strength.
In the intricate domain of Corporate Financial Performance (CFP), several dimensions have been identified as key contributors to the fiscal health and prosperity of an organization.The dimensions of Financial Performance, Investment and Innovation, Financial Management, External Funding, Long-term Financial Planning, and Investor Confidence each play a pivotal role in shaping the financial trajectory of a company.Financial Performance is often seen as the scoreboard of a company's economic health.It encompasses metrics such as revenue, profit margins, and return on investment, providing a snapshot of how well the company is converting its resources into financial gain.Robust financial performance is indicative of a company's efficacy in managing its operations and can signal market leadership.Investment and Innovation represent a company's commitment to future growth.Organizations that invest in innovative processes and products are investing in their future market position.Innovation drives competitive differentiation and can lead to the development of new markets, while investment in technology and processes can yield improvements in efficiency and productivity.Financial Management is the strategic backbone of a company, ensuring that all financial resources are managed wisely.This includes maintaining control over costs, optimizing cash flow, and ensuring that capital is employed effectively.Sound financial management is crucial for ensuring that a company can weather economic storms and capitalize on opportunities.External Funding is a lever for growth and expansion.Access to capital markets, loans, and other forms of external finance can provide the necessary resources for a company to invest in new projects, enter new markets, and upgrade infrastructure.How a company secures and uses external funding can significantly affect its financial sustainability and capacity for growth.Long-term Financial Planning is the dimension that aligns financial goals with business strategy.It involves forecasting financial results, planning for investment, and preparing for future financial needs.This forward-looking approach is essential for navigating the future business landscape and ensuring financial resilience.Investor Confidence reflects the market's trust in a company's financial management and its future prospects.High investor confidence can lead to a better valuation in the capital markets, lower the cost of capital, and make it easier to attract investment.Maintaining investor confidence is critical for a company's ability to secure funding and support for its strategic initiatives.Collectively, these dimensions form a comprehensive framework that underpins Corporate Financial Performance.They are not standalone factors; rather, they are interconnected, with each dimension reinforcing the others.A company's ability to manage these dimensions effectively is a testament to its financial acumen and strategic foresight, which are essential for achieving financial excellence and delivering shareholder value.The synthesis of these dimensions provides a multidimensional view of an organization's financial robustness and its potential for sustained economic success.
The intricate interplay between Human Resource Management (HRM), Strategic Marketing Management (SMM), and Corporate Financial Performance (CFP) is foundational to the comprehensive success of an organization.The positive and significant impact of HRM and SMM on CFP can be attributed to the synergistic effect these two domains exert on the operational and strategic facets of a company.Human Resource Management is the cornerstone of organizational capability, engendering a workforce that is not only skilled and efficient but also motivated and aligned with the company's goals.HRM strategies that focus on recruitment, training, and employee engagement ensure that the organization is equipped with a human capital base that can drive innovation, productivity, and service excellence.When employees are well-managed, their performance and satisfaction levels rise, leading to enhanced work output and efficiency.This, in turn, translates into better financial results, as productive employees are the engine behind reduced costs, increased sales, and improved customer satisfaction-all of which are vital contributors to CFP.Strategic Marketing Management, on the other hand, serves as the conduit through which a company's offerings are positioned and communicated in the marketplace.SMM strategies that effectively analyze the market, craft compelling value propositions, and execute engaging marketing campaigns are pivotal in capturing customer interest and loyalty.A strong brand and customer base, nurtured through strategic marketing, often lead to increased market share and sales revenues.Moreover, SMM's focus on product innovation and customer relationship management helps in the development of new revenue streams and the retention of existing ones, both critical aspects of sustained financial performance.The intersection of HRM and SMM is where the magic happens for CFP.HRM ensures that the right talent is in place, fostering an environment where strategic marketing initiatives can be created and realized.Concurrently, effective marketing strategies enhance the value of the organization's brand, which can attract top talent and create a virtuous cycle of growth and profitability.When employees understand and are committed to the brand's promise, they are more likely to deliver on it, resulting in customer experiences that reinforce positive market perceptions and drive financial success.In essence, HRM and SMM are not merely operational levers; they are strategic imperatives that, when executed well, can significantly elevate a company's financial standing.The positive and significant relationship between these management disciplines and CFP underscores the necessity for organizations to adopt a holistic and integrated approach to business management, where people and marketing are seen as central, interdependent elements in the financial equation.This holistic approach not only catalyses immediate financial outcomes but also sets the stage for sustainable growth and long-term financial health.

Concluding
The research conducted unveils a synergistic relationship between Human Resource Management (HRM), Strategic Marketing Management (SMM), and Corporate Financial Performance (CFP).From a theoretical perspective, the findings provide robust evidence supporting the theory that effective human resource and marketing strategies are key to enhancing a company's financial performance.Efficient HRM drives productivity and innovation, while effective SMM strengthens market positioning and revenue streams.Collectively, these elements play a crucial role in driving sustainable financial growth.Managerially, the implications of these findings are extensive.Business leaders must recognize that investments in human resource development and strategic marketing are not merely departmental responsibilities but strategic investments in the company's future.Therefore, budget allocations for employee training and development programs, as well as innovative customer-oriented marketing initiatives, should be viewed as long-term investments that will yield positive financial returns.From the theoretical standpoint, this research enriches management literature by demonstrating functional interconnections between divisions that may not have been fully appreciated before.It challenges conventional thinking that often segregates HRM and SMM functions, calling for a more integrative approach in corporate strategy.Furthermore, the findings offer new insights into how financial performance can be optimized through more strategic management of human resources and marketing.In conclusion, this study affirms the importance of effective human resource management and strategic marketing in producing superior financial performance.Companies that integrate HRM and SMM into their core business strategies tend to achieve greater financial success.Therefore, business leaders should adopt best practices in HRM and SMM to ensure financial growth and sustainability in today's competitive business environment.

Table 3 .
Outer Loading The company's net profit has seen a steady increase.45.We have competitive profit margins in our industry.Investors have high confidence in the financial management capabilities of this company.59.The company's financial decisions are considered transparent and fair by investors.60.I feel that this company maintains a good relationship with investors.
Long-term Strategic Planning 40.The company has a clear long-term strategic plan.41.I feel that the company has a strong long-term vision and goals.42.The company is focused on achieving its long-term goals.55.The company has detailed long-term financial planning.56.I feel that this company has a clear long-term financial vision.57.The company prioritizes managing financial risks for the future.

Table 3 .
Reliability and Validity

Table 3
presents a systematic evaluation of various constructs within an organizational context, measuring internal consistency, reliability, and validity.The constructs assessed include Employee Development and Training, Employee Satisfaction, Employee Welfare, Human Resource Management (HRM), Internal Communication, Organizational Commitment, Performance Management, and Recruitment and Selection Management.Cronbach's Alpha is a measure of internal consistency, indicating how closely related a set of items are as a group.It is considered an estimate of the reliability of a psychometric instrument.In this table, Cronbach's Alpha values range from 0.774 for Performance Management, which is the lowest, suggesting moderate reliability, to 0.957 for Human Resource Management (HRM), which is the highest, indicating a very high level of internal consistency.The rho_A, presumably a measure similar to Cronbach's Alpha or a corrected version of it, mirrors the Alpha values closely, with the lowest being 0.779 for Employee Satisfaction and the highest being 0.958 for HRM, corroborating the internal consistency of the constructs measured.Composite Reliability (CR), another measure of internal consistency, extends beyond Cronbach's Alpha by considering the different loadings of items in the construct.The CR values in the table suggest that all constructs have satisfactory to high reliability, with the lowest value at 0.870 for Performance Management and the highest at 0.971 for Employee Welfare, indicating that the constructs are measured with precision.Lastly, the Average Variance Extracted (AVE) measures the amount of variance that a construct captures from its indicators relative to the amount due to measurement error and serves as an indicator of convergent validity.AVE values above 0.5 are generally considered acceptable, indicating that, on average, the construct explains more than half of the variance of its indicators.In this table, all constructs meet this criterion, with the lowest AVE being 0.553 for HRM, which is just above the threshold, and the highest being 0.944 for Employee Welfare, suggesting a very strong convergent validity.

Table 5 .
HRM Regression Analysis

•
The relationship between Employee Development and Training and HRM has a coefficient of 0.175, with a high T statistic of 20,352, indicating a positive and statistically significant impact of employee development and training on HRM practices.•Employee Satisfaction has a coefficient of 0.164, suggesting that increased employee satisfaction is positively associated with HRM practices, with a T statistic of 19,529 providing strong evidence for this relationship.• Employee Welfare is associated with HRM with a coefficient of 0.132 and the highest T statistic of 24,407, implying a significant positive relationship between how well employees feel their welfare is managed and HRM.• Internal Communication has a coefficient of 0.173, which is substantial and statistically significant, with a T statistic of 20,008, indicating that effective internal communication is positively related to HRM practices.• Organizational Commitment has a coefficient of 0.170, with a T statistic of 16,145, suggesting a significant positive relationship with HRM.• Performance Management is associated with HRM with a coefficient of 0.172 and a high T statistic of 22,540, indicating a strong positive impact on HRM.• Recruitment and Selection Management shows a coefficient of 0.174 with a T statistic of 19,759, signifying a positive and significant relationship with HRM practices.

Table 6
presents the outer loading values from a Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis focusing on Strategic Marketing Management (SMM).

Table 7 .
SMM Validity and ReliabilityThe 7 presents a comprehensive assessment of the reliability and validity of constructs related to Strategic Marketing Management (SMM).The constructs examined include Customer Relationship, Long-term Strategic Planning, Market Analysis, Marketing Campaign Implementation, Marketing Performance Measurement, Marketing Strategy Development, Product and Service Innovation, and the overall Strategic Marketing Management (SMM).

Table 9 .
SMM Regression Analysis • Long-term Strategic Planning is shown to have a stronger positive effect, with a sample mean of 0.212 and an even higher T statistic of 26,745, suggesting it is a critical driver of SMM effectiveness.Market Analysis has a modest positive impact on SMM, with a sample mean of 0.095 and a lower T statistic of 4,840, indicating that while it is significant, its influence is less pronounced compared to other factors.•Marketing Campaign Implementation has a notable positive relationship with SMM, as evidenced by a sample mean of 0.157 and a T statistic of 18,563.• Marketing Performance Measurement shows a substantial positive effect on SMM, with a sample mean of 0.201 and a T statistic of 25,288, emphasizing the importance of measuring performance in strategic marketing.

Table 10
encapsulates the results from a Partial Least Squares Structural Equation

Table 11 .
CFP Validity and Reliability

Table 13 .
CFP Regression Analysis

Table 14 .
Second-Order Validity and Reliability Analysis

Table 16 .
Second-Order F-Square Analysis

Table 17 .
Second-Order R-Square Analysis

Table 18 .
Second-Order SMARTPLS Model Fit

Table 19 .
Second-Order Regression Analysis