Institutional theory’s concepts of the legal environment and cooperative governance are less researched variables, especially concerning their effect on SACCOS’s financial performance, despite researchers proposing that these variables have a major impact of these variables [20][21][46]. Second, this research also addresses the territorial borders of institutional theory, which proposed investigating the future of institutional theory, including studying the variables of the legal environment and governance as factors affecting organizational performance [27]. It is necessary to explicitly establish and understand the relationships between a firm’s financial performance and the variables of the legal environment and cooperative governance, which this research has determined. At the African level, there are virtually no publications on social institution theories relative to this continent, thus highlighting the necessity of expanding inquiry to produce and construct the theory with extended empirical studies from this region of the globe [20][46].

Borio and Drehmann acknowledge that there may not be enough innovation and competition in the banking industry after arguing that too much regulation (legal environment) may hamper both innovation and competition. Such excessive regulation has adverse impacts on bank performance [47]. Gomez employed a large sample size (29,839 companies) from 2005 to 2014 and evaluated the role of the legislative environment on the financial success of firms, in particular, sales growth [48]. By using econometric methods of analysis of time-series data (panel data analysis), the research found a statistically significant and positive influence of the legal setting on firms’ financial performance (sales growth). The potential limitation of this study is that the definition and measurement of the legal environment established by the study have not been stipulated, and there is also a high diversity of the sample with the inclusion of MFIs that represent diverse nations and economic contexts. Arias assessed the effects of legal and institutional challenges on the performance of banking systems in 52 countries over 10 years between 2005 and 2014 [49]. Using descriptive design in combination with correlation procedures and panel data regression, the researchers correlated banking performance (as gauged by Net Interest Margin and Return on Asset (ROA) in nations with different legal systems. Remarkably, they detected a strong positive correlation; that is, environments with good legal settings correlated with good performance by banks. However, there is a possible limitation to this study. The sample size (52 banking systems) used in the study may be deemed small in order to arrive at generalized findings since 31 of the 52 systems are emerging economies and the other 21 are advanced. Quayes and Joseph’s study was based on the idea of determining how the legal system is involved in the performance of microfinance institutions in terms of OSS and ROA [50]. Applying unbalanced panel data of 1272 microfinance institutions across 16 years in developing and developed nations, they discovered that the financial performance of MFI working under codified legal codes, which is clear, was better than MFI under a common law system (based on precedence and judicial interpretation). This implies that legal frameworks have the potential to influence the financial performance. Nonetheless, such extreme multiplicity of the sample (in that MFIs of many different countries and situations are involved) creates its own heterogeneity.

Despite the fact that there is not enough empirical research in Africa, specifically on SACCOS, to offer solid evidence of the link between the legal environment and SACCOS financial performance, the fourth hypothesis is proposed as follows:

H₁: The legal environment(LE)positively influences the financial performance(FP)of SACCOS in Tanzania.

Singh and Rastogi examined the association between corporate governance and financial performance in small and medium-sized businesses (SMEs) listed on the Bombay Stock Exchange (BSE) in India was examined by Singh and Rastogi [11]. The analysis was about the disclosure of information, which is a measure of corporate governance, and likewise on its implication to the financial health of the corporation. They used a correlational research design and a sample size of 88 to ascertain the statistical significance of the relationship by employing regression analysis. Interestingly, the analysis shows that information disclosure has a negative relationship with financial performance among Indian SMEs. This implies that stringent disclosure requirements may expose a company to greater costs and regulatory requirements that are likely to hamper financial performance. A weakness of this study is that it is inductive since it has concentrated too closely on the concept of disclosure of information as an indicator of corporate governance. Other important factors, namely, the ownership structure and board composition nature of leadership, have been overlooked. This makes it impossible to conduct a more thorough investigation into how corporate governance standards affect SMEs’ financial performance.

Hermawan examined the relationship between good corporate governance, corporate social responsibility, and intellectual capital on Indonesian banks’ financial performance [51]. They used 15 banks listed on the Indonesian Stock Exchange to examine their study and used linear regression as their analytical tool. The survey results indicate that although proper corporate governance may tend to positively affect financial performance, more bureaucracies and red tape in such systems can impede operational challenges and negatively affect financial performance. Temba examined whether corporate governance has any influence on the financial situation of commercial Tanzanian banks [52]. Their study examined how key financial performance indicators, including asset quality, profitability, liquidity, and capital sufficiency, are affected by corporate governance procedures such as oversight and board composition. Quality corporate governance, particularly in the form of an effective board of directors and oversight, has a favorable effect on enhanced financial performance in terms of increased profitability and high-quality assets, according to researchers’ findings. However, they also discovered a negative correlation between the effective use of equity and liquidity and some governance measures, such as board gender diversity and over boarding. The study conducted by Boachie examines the interaction between Ghanaian banks’ financial performance and corporate practices [53]. The research study of 23 banks was conducted on a correlational cross-sectional rather than regression analysis because it helps to establish the cause and effects of variables. The study established that there was a positive relationship between the researched constructs (financial performance and corporate governance) based on the ROA and OSS metrics for financial performance, the audit committee’s independence, the presence of non-executive directors, and the qualities of the Chief Executive Officer (CEO) for corporate governance. Nonetheless, there are limits: the generalizability issue arises due to the fairly small sample size of 23 banks compared to the whole banking sector in Ghana. In addition, the cross-sectional characteristics of the data implied that the direction of the association could not be determined by the research. Alobari examined the connection between Nigerian microfinance institutions’ financial performance and corporate governance [54]. They conducted a 13-year analysis of 33 microfinance institutions between 2005 and 2017, using OLS analysis and correlational design. They find that financial success and corporate governance are significantly and positively correlated. However, the small sample size of 33 banks may limit the usefulness of this study for generalization purposes. Based on the aforementioned points found in the literature, the first hypothesis is proposed as follows:

H₂:Corporate governance(CG)positively influences the financial performance(FP)of SACCOS in Tanzania.

Existing literature consistently highlights that corporate governance (CG) plays a significant role in influencing the relationships among various organizational factors and financial performance. For instance, Earnest and Sofian demonstrated the moderating influence of corporate governance on the relationship between a firm’s financial performance and intellectual capital, suggesting that effective CG can amplify how intangible assets translate into financial success [23]. This view is supported by the empirical findings of a recent study conducted by Ngatno et al. [24], whose moderation analysis specifically identified that strong corporate governance practices can strengthen the association between a firm’s financial performance and its capital structure in a strong way. This means that organizations with effective governance have better capabilities to exploit their financing decisions to generate better financial results.

Besides these defined spheres, corporate governance also enhances several other areas. According to Agyemang et al.’s study conducted on listed Ghanaian firms, corporate governance enhances the association between market returns on stock and accounting information [25]. This indicates that good governance can help financial information be viewed positively in the market, thus determining investors’ confidence and stock value. Subsequent research studies, including the work by Aguilera & Jackson and Daily et al., highlighted the role of governance mechanisms in influencing the output of organizations in addressing internal capacities and external forces that translate to overall performance [55][56].

As much as these findings are common in different settings and variables, they all indicate that corporate governance usually serves as a significant intervening mechanism, intensifying or moderating the effect of other internal and external factors in determining the financial success of a firm. This evidence provides a good reason to examine the mediating or moderating role of corporate governance within the framework of other influencers of financial performance. Based on these strong arguments in the literature, the third hypothesis is proposed to learn more about this dynamic (see Figure 1Figure 1).

H₃: Corporate governance(CG)mediatesthe association between SACCOS financial performance(FP)and legal environment(LE)in Tanzania.

Figure 1. Conceptual model.

The study employs a rigorous methodology rooted in a positivist philosophy and a deductive approach to specifically examine the role of corporate governance as a mediator in the relationship between the Tanzania regulatory environment’s impact on the financial performance of SACCOS. It will also use explanatory research design in which census survey design will be implemented in all active SACCOS, hence providing a powerful framework for defining a relationship between key variables and their significance to the financial outcome means that the study employs objective reality and attempts to experimentally prove the given theory (Institutional Theory), formulating hypotheses, and testing it empirically [57][58]. This study proposes or formulates theoretical models regarding the SACCOS financial performance and legal environment relationship based on quantitative statistics [59].

A research design was adopted to determine the causal relationships between different factors and SACCOS financial performance [60]. A quantitative survey was used as a research strategy because it is an effective approach for gathering general information; thus, it was considered sufficient to draw conclusions about all active SACCOS in Mbeya [58]. Structured questionnaires were used to collect primary data and were translated into Swahili and English to gain a wider understanding. The drop-and-pick method was convenient in terms of high response levels and ease of use by the participants [61][62]. A five-point Likert scale was adopted to quantify the responses, which measured the attitudes and perceptions of the study variables.

The study focused on the Mbeya region because of its large population and a significant number of dormant SACCOS, highlighting a research gap concerning their financial performance (TCDC, 2023) [6]. A census method was employed to survey all 105 active SACCOS in the region to eliminate sampling bias and ensure an accurate representation of the population [63]. This study achieved a 100% response rate for 88 eligible SACCOS in Mbeya, excluding 17 new SACCOs without financial data. Missing values were handled using SmartPLS means replacement [64]. To guarantee the quality of the data, outliers based on the SPSS Bootstrap procedures were discarded, and 83 responses remained, which was enough to follow the rule of ten times PLS-SEM analysis [64]. Three types of variables were considered in this study: independent, dependent, and mediating. The dependent variable was financial performance, which was assessed through return on equity and assets [65]. The legal environment obtained by institutional theory is an independent variable [66]. The mediating factor was corporate governance, which included association-dependent and independent variables [66]. The variables based on Institution Theory have an ordinal level of measurement using a Likert scale [60].

PLS-SEM was used to analyze data because of its capacity to manage intricate models; accommodate non-normal, categorical, and ordinal data; and manage smaller sample sizes and multicollinearity, making it superior to Covariance-Based SEM (CB-SEM) for this research [64][67][68]. Additionally, PLS-SEM makes it easier to analyze mediation effects, offering flexibility in exploring theoretical relationships [69]. This study recorded a full response to 88 eligible SACCOS in the district of Mbeya, excluding 17 new SACCOS that lacked financial information. SmartPLS was used to replace the missing values through mean replacement [64]. The outliers were initially spotted through the SPSS Bootstrap procedures and eliminated to guarantee the accuracy of the data, and 83 responses were considered valid, which adheres to the principles of the PLS-SEM analysis and fulfills the requirements of the so-called ten-times rule [64][68]. Data were analyzed with the help of SmartPLS 4 software, which allows easy work with the interface and expands the PLS-SEM modeling processes with new data import opportunities, great performance on such operations as bootstrapping, and support of complex mediation models [70][71]. The data analysis process considered several significant processes. First, missing values and outliers were dealt with in data cleaning, which automatically substituted the missing values with the mean using SmartPLS [64]. The following assumptions were then checked: linearity, normality, multicollinearity (with Variance Inflation Factor (VIF) values less than 5), and heteroscedasticity (with HTMT values less than 0.9) to guarantee data quality and strength, although such assumptions are less demanding in PLS-SEM [64][67][72]. After data completion, the measurement model test assessed the extent to which the indicators represented underlying constructs through factor loadings, Cronbach’s alpha, and convergent and discriminant validity tests [64][73]. Then, the structural model test was applied to estimate associations between latent constructs, and bootstrapping was used to calculate path coefficients, T-statistics, and p-values [64]. Finally, the ability to predict (PLS-Predict) and explain (R-square) structural model was determined. A full structural model was validated, including cross-factor loading (>0.7), SRMR (<0.9), path coefficient p-value (<0.05), AVE (>0.5), Goodness of Fit (GoF) index (>0.1), and exact model fit d_ULS (<0.95), to determine The Goodness of Fit (GoF) [64][67][74]. To ensure that the data would be analyzed comprehensively, a comprehensive approach to the interpretation of SEM-PLS indices, such as SRMR, Cronbach’s alpha, AVE, Fornell-Larcker, VIF measures of the measurement model, and T-value, Beta, and R-square, GoF, and Q-square measures of the structural model. All of the hypotheses were accepted when the T-score of a path coefficient was greater than 1.96 (two-tailed) and the p-value was less than 0.05 [75].

This study systematically examines the influence of legislative environmental factors on the financial performance of SACCOS. To achieve this, the study employed three main types of variables: an independent variable, a mediating variable, and a dependent variable, each meticulously defined and measured based on established theoretical foundations and empirical studies.

Legal Environment served as the independent variable in this study. As a variable based on institutional theory, it is evaluated by a set of indicators with modifications based on Mallett’s recommendation [66]. These indicators will help formulate questionnaire and attempts to capture different aspects of the interaction and perception of SACCOS toward legal environment in Tanzania.

In particular, they also include Identification-interpretation: aimed to measure the SACCOS management’s awareness, monitoring, and understanding of the regulatory landscape. The indicator measures the critical input for accurate interpretation, the active receipt of updates from bodies like the TCDC or COASCO, which is essential in Tanzania’s evolving regulatory environment [66][76]. Strategisation: This indicator was used measured the proactive planning, resource allocation, and systematic activities undertaken in day-to-day operation to ensure compliance to legal raws. Strategisation was about assessing the strategic commitment of material resources (financial, human, technological) by SACCOS management deemed to meeting regulatory demands [5][66][77]. Negotiation: this context does not refer to bargaining over a contract. Instead, it is a theoretical construct describing how SACCOS interacts with its legal environment, through interpretation of rules, regulation and laws and creating smooth relationship with regulatory authorities in the country. It’s the process by which SACCOS “negotiates” its relationship with the law. Adaptation: the indicator measures the extent resource adjustments and operational changes and in the firm to make sure legal and regulatory requirements are implemented [66][78]. The indicator measures beyond “Strategisation” and “Negotiation” looking on tangible resource used to meet legal compliance [66][78]. All of these were rated on a 5-point Likert scale ranging from strongly disagree to strongly agree.

Corporate Governance is utilized as the mediating variable, which is also derived from the institutional theory. This variable is important to determine how a legal environment and its effect on financial performance may be directed or modified through internal governance structures. Its measurement measures include major spheres of governance that have been outlined in previous studies, such as the Board of Directors, Chief Executive Officers (CEOs), audit committees, and transparency [79]-[81]. In the case of the Board of Directors, the questions are used to access transparency concerning the appointment of members of the board and allocation of time to the duties. Regarding CEOs, their interest lies in their interaction with employees. Independence and risk assurance are used to measure the effectiveness of the Audit Committee. Finally, Transparency indicators attempt to examine what it means to be nimble and truthful when reporting conflicts of interest and the disclosure of information to shareholders. These predictors were measured as five-point Likert scale.

Finally, in this study, financial performance was the dependent variable, reflecting the ultimate outcomes of SACCOS success. In line with the general financial analysis in previous studies, Profit Margin (PM), Return on Assets (ROA), and Return on Equity (ROE) are the measurement indicators of its measurements [65][82]-[84]. All these measurable variables provide a detailed picture of the financial standing and effectiveness of the SACCOS. As in the case of the other variables, the answers to these financial performance tests were measured on a 5-point Likert scale. This study offers strong empirical findings on the relationship among these variables by defining and measuring them precisely in light of SACCOS in Tanzania.

This survey analyzed 83 SACCOS manager responses in Tanzania’s Mbeya region. The sample comprised a slight male majority (59%), with ages skewed towards middle age (49% 31 - 45 years). Managers were generally well educated, with most holding diplomas (57%) or bachelor’s degrees (41%). Experience levels were split, with 58% having less than five years in their current SACCOS. The SACCOS themselves varied in age, with a significant proportion (37%) established between 10 - 20 years ago, indicating a mix of mature and newer institutions. These demographic data provide a valuable context for understanding the financial performance of SACCOs in the region. Figure 2Figure 2 below summarizes the demographic information of the study.

This passage outlines the meticulous data preparation and validation process for a study on SACCOS in Tanzania’s Mbeya region. The data cleaning and coding steps were also run to guarantee the accuracy and consistency of the data, which were then entered into Microsoft Excel and subsequently into SmartPLS 4 to perform the analysis. The overall effect of the study was a 100 percent response rate of eligible SACCOS, since it managed to deliver 88 out of the 105 questionnaires. The difference between the 105 distributed and 88 responses is explained by the fact that 17 new SACCOS were formed and these were not included in the exercise. These were excluded from the analysis because they did not require year-end financial statements. Any data that were not filled or the unfilled answers

Figure 2.Demographic information.

were processed through mean imputation in SmartPLS, according to the guidelines of Hair et al. [64]. Extreme values marked as outliers by means of bootstrap processes and IBM SPSS Statistics 25 were removed, which allowed us to obtain 83 quality responses. This sample size met the requirement of a ten-times rule in PLS-SEM analysis of PLS-SEM [85]. Although PLS-SEM is a non-parametric procedure that does not strictly demand it, normality testing was performed, and the data were distributed within the corrected acceptable limits of skewness/kurtosis [86]. There was also support for linearity between latent variables, and the quadratic effects were insignificant [72]. Finally, homoscedasticity was satisfied, consistent variance of errors was secured, and since all the Variance Inflation Factor (VIF) scores were less than 10, there was no multicollinearity among the independent variables [87].

Rigorous evaluation of the PLS-SEM model’s fit demonstrated strong consistency with the empirical data. It is important to remember that assessing model fit requires examining multiple criteria, not just a single measure [67][74]. Our key metrics consistently indicated an excellent performance.

All observable indicators showed robust reliability, with factor loadings consistently surpassing the threshold of 0.700 (Figure 3Figure 3). For instance, the factor loadings for Corporate Governance indicators included CG_1 (0.752), CG_2 (0.888), CG_3 (0.825), CG_4 (0.847), CG_5 (0.809), CG_6 (0.921), and CG_7 (0.891) (Figure 3Figure 3). For Financial Performance, the loadings were FP_1: (0.954), FP_2: (0.982), and FP_3: (0.980) (Figure 3Figure 3). Finally, the Legal Environment indicators showed strong loadings with LE_1: (0.841), LE_2: (0.863), LE_3: (0.816), LE_4: (0.860), LE_5: (0.860), LE_6: (0.913), and LE_7: (0.904) (Figure 3Figure 3). These high loadings confirmed that our measurement items effectively captured their respective underlying constructs [64].

Figure 3. The measurement model analysis.

The R² values underscored the substantial explanatory power of the independent latent constructs on endogenous constructs. Corporate Governance achieved an R² of 0.955 (adjusted R² = 0.954), while Financial Performance achieved an R² of 0.757 (adjusted R² = 0.751) (Table 1). This indicates that the model explains a considerable amount of the variance in SACCOS’ financial performance is explained by the model. Furthermore, all the Q² values were above zero, confirming the predictive relevance of the PLS-SEM model for the constructs (Figure 3Figure 3). The overall Goodness of Fit (GoF) index (0.830) exceeded the recommended 0.36 cut-off value, signifying a strong global model fit [67][74].

Table 1.PLS-SEM model’s explanatory power.

Complementary measures, such as SRMR and d_ULS, further validated the model’s good fit. Both the saturated and estimated models showed an SRMR of 0.1 and d_ULS values of 0.532, indicating an excellent fit (Table 2). In addition, the study assessed discriminant validity through cross-loadings, confirming that each indicator loaded more strongly on its intended variable, thus ensuring the distinctiveness of our variables [71][74].

Table 2. Saturated model and estimated model.

Finally, the direction and intensity of the links between the constructs are shown by the path coefficients. A strong positive path coefficient of 0.966 was observed from Corporate Governance (CG) to Financial Performance (FP) (Figure 3Figure 3). Similarly, the path from Legal Environment (LE) to Corporate Governance (CG) showed an equally strong positive coefficient of 0.977. Interestingly, the direct path from the (LE) to Financial Performance (FP) was −0.098, indicating a negligible negative direct effect (Figure 3Figure 3). These path values, along with all the other fit indicators, confirm the overall robustness and explanatory power of our model [64].

As shown in Table 3, all constructs (CG, FP, and LE) exhibited excellent reliability across all three measures (Composite Reliability, Cronbach’s Alpha and Composite Reliability). Cronbach’s alpha scores for all three variables (CG: 0.935, FP: 0.931, LE: 0.924) were well above the generally considered excellent scores (0.90 threshold) (Lutfi et al., 2023; Hair et al., 2022). These measures also confirmed robust internal consistency. Values for all constructs ranging from 0.928 to 0.947, consistently above the acceptable level score of 0.70, the good threshold of 0.80, and even the excellent score of 0.90 [64][72].

Table 3. Construct validity & reliability.

5.4.1. Convergent Validity

Furthermore, AVE values for all variables (CG: 0.721, FP: 0.915, LE: 0.750) were substantially above the recommended threshold of 0.50 (Table 3) [64][88]. This proves that more than 50 percent of the variance in the indicators is explained by their respective latent constructs, thus confirming strong convergent validity. Overall, these results unequivocally demonstrated the high quality and trustworthiness of the measurement mode [64].

5.4.2. Discriminant Validity

As shown in Table 4 and Table 5, AVE the square root of each variable is clearly higher than its correlation with any other construct. Corporate Governance (CG) is 0.849, greater than its correlations with FP (0.822) and LE (0.837). Financial Performance (FP) was 0.972, which was greater than its correlations with CG (0.822) and LE (0.846). The Legal Environment (LE) was 0.866. This is greater than its correlation with CG (0.837) and FP (0.846). These results confirm that discriminant validity is well established for all latent variables in the model, indicating that the constructs are distinct (Table 5) [64][88].

Table 4. Fornell-Larcker test results.

Table 5. Discriminant validity established test results.

Overall, as seen in Tables 1-5, the measuring model showed strong fitness, affirming the reliability and validity of the constructs (Corporate Governance, Financial Performance, and legal environment). High composite reliability and Cronbach’s alpha scores (all above 0.90) indicate strong internal consistency (Hair et al., 2022). Furthermore, excellent convergent validity was confirmed with all AVE values comfortably above 0.50 [64][88]. Crucially, the Fornell-Larcker criterion establishes discriminant validity for all latent variables, ensuring the distinctiveness of the measures [88]. This rigorous assessment provides a robust foundation for subsequent structural model analyses.

The study’s hypothesized relationships between variables were assessed through path models, developed using both structural models and measurement theory [67]. While structural theory explains the relationships between these variables, measurement theory defines how indicators measure the theoretical concepts. A two-phase methodology was adopted: first, validating the measurement theory (reliability and model fit) and then validating the structural theory, as faulty measures preclude accurate structural validation [67][89].

Table 6 presents VIF values for both the “outer model” (referring to the measurement model, where indicators relate to their respective constructs) and the “inner model” (referring to the structural model, where latent constructs relate to each other). Outer Model VIFs: CG_1 (2.235), LE_1 (1.671), FP_1 (2.061), CG_2 (2.879), LE_2 (1.195), FP_2 (2.05), CG_3 (1.477), LE_3 (2.777), FP_3 (1.454), CG_4 (2.761), LE_4 (1.250), CG_5 (2.572), LE_5 (1.120), CG_6 (1.167), LE_6 (2.749), CG_7 (2.847), LE_7 (1.012) (Table 6). All these values are well below the threshold of 5. This indicates that the indicators within each construct do not exhibit problematic levels of collinearity. This is good for the reliability and validity of the measurement model (Hair et al., 2023). Inner model Structural Path VIFs) CG → FP (3.073), LE → CG (1), LE → FP (2.073), all of which are well below 5 (and even below 3). This indicates that the predictor constructs in the structural model (CG when predicting FP, LE when predicting CG, and LE when predicting FP) do not suffer from significant multicollinearity. As shown in Table 6, all VIF values were below the suggested threshold of 5, indicating minimal collinearity concerns within the structural model [67][72][89].

Table 6.Collinearity test results.

The assessment of the path coefficients confirmed strong, statistically significant relationships within the structural model. Corporate governance (CG) profoundly and positively influences financial performance (FP) (Path Coefficient = 0.966, p < 0.001). Similarly, the legal environment (LE) exhibits an overwhelmingly strong and positive direct effect on corporate governance (Path Coefficient = 0.977, p < 0.001) (Table 7). While the direct path from the legal environment to financial performance was weakly negative (Path Coefficient = −0.098), its significance was established (p = 0.027) (Table 7). These path coefficients are well within the recommended range of −1 to +1, signifying robust and interpretable relationships crucial for understanding SACCOS’ financial dynamics [67][71][89].

Table 7. Specific direct path.

Table 8. PLS-SEM model’s coefficient of determination.

Table 9. Specific indirect path.

The model demonstrates substantial explanatory power for its endogenous latent variables, which are key indicators of its overall fitness. For Corporate Governance (CG), the R-squared value was 0.955 (adjusted R-squared = 0.954). This indicates that 95.5% of the variance in Corporate Governance is explained by the predictor(s) in the model (Table 8). Similarly, for Financial Performance (FP), the R-squared is 0.757 (adjusted R-squared = 0.751), meaning that 75.7% of its variance is accounted for by the predictor constructs (Table 8). These exceptionally high R-squared values confirm the model’s strong ability to explain the variation in its dependent variables, aligning with standards for substantial explanatory power in PLS-SEM [67][71][72][88].

Q² (Predictive Relevance) indicates how well the model can predict the data points of the endogenous latent variable. It is a measure of the out-of-sample prediction power. A higher Q² indicates better predictive accuracy for that construct [68]. Criterion: A Q² value greater than zero indicates predictive relevance [68]. Q² per endogenous construct shows that all Q² values signify predictive relevance, with magnitudes suggesting varying degrees of effects. This indicates that the model can predict new data points for endogenous variables.

Based on the path analysis results, a summary of the hypothesis testing is presented:

H1:The legal environment (LE) positively influences the financial performance (FP) of SACCOS in Tanzania. (LE -> FP). Results: The path coefficient (β) was −0.098. p-score = 0.027 (p < 0.05), the relationship is a significant (Table 7). Conclusion: Although significant, the relationship is negative, contradicting the hypothesized “positive relationship”. Therefore, H1, as stated (for a positive relationship), was rejected, although a significant (negative) relationship existed.

H2:Corporate governance (CG) positively influences the financial performance (FP) of SACCOS in Tanzania. (CG -> FP). Results: The path coefficient (β) was 0.966, with a p-value of 0.000 (p< 0.001) (Table 7). Conclusion: This finding indicates a highly significant and strongly positive direct relationship. Therefore, H2 was strongly supported.

H3:Corporate governance (CG) mediates the association between financial performance (FP) and the legal environment (LE) in Tanzania. (LE > CG > FP). Results: The indirect path coefficient (β) was 0.944, with a P-score of 0.000 (p < 0.001) (Table 9). This finding indicates a significant and positive indirect effect, confirming that corporate governance mediates this relationship. Therefore, H3 is strongly supported.

Hypothesis 1:

Hypothesis 1, which proposes that, legal environment (LE) positively influences the financial performance (FP) of SACCOS in Tanzania, is notsupported. Regression analysis revealed a path coefficient (β) of −0.098, with a p-value of 0.027 (p < 0.05) (Table 7). The relationship is not positive, but rather negative, although statistically significant. The opposite direction of quantification of path coefficients points in the negative direction between the variables [64][86]. Thus, the null hypothesis that there is no association is rejected, and the hypothesis about the positive association is rejected as well to underline the fact that there is a significant negative association not only between financial performance and the legal environment, but also directly between these two variables.

This observation is opposite to that of prior studies. The research by [90], on 199 medium-sized enterprises in the manufacturing, trade, and service industries of Ethiopia concluded that there was a positive relationship between performance results and the aspect of law. Indicatively, Gomez [48], as he observed at 29,839 companies and a period of 2005-2014, proved that the legal environment contributed greatly to the better financial performance of enterprises. The negative direct association observed in this analysis indicates that it is possible that certain facets of the Tanzanian legal environment present direct challenges or burdens that have some slight potential for adverse effects on the financial performance of SACCOS and that research needs to be conducted on the identities of these negative factors.

The negative relationship between the legal environment (LE) and financial performance (FP) of SACCOS in the Tanzanian study can be attributed to what the World Bank called legal and regulatory uncertainty, the complicated system of taxation, and unreasonable regulations for corporations, along with frequent alterations in tax laws and regulations [13]. Tanzania in particular has multiple regulatory agencies (TCDC, COASCO, TRA, BOT, and local councils); this creates an administrative burden for SACCOS to navigate multiple agencies, leading to financial drain [4][13]. Furthermore, this is the reflection of the high tax system, the 30% corporate tax on profits, draining SACCOS financially [4].

Hypothesis 2:

The findings of the study clearly confirm Hypothesis 2, suggesting that Corporate governance (CG) positively influences the financial performance (PF) of SACCOS in Tanzania. The data regression outcome presents a strong path coefficient (β) of 0.966, with a highly significant p-value of 0.000 (p < 0.001) (Table 7). This indicates that corporate governance practices enhance, and when this happens, the financial performance of SACCOS greatly improves. Such findings are consistent with academic rules, as the path coefficients in the range between 0 and +1 define a positive relationship, and p-values less than 0.05, signifying a significant path [67][71]. Therefore, we reject the null hypothesis, which states that there is no significant relationship between variables.

These findings align with studies conducted in different contexts. The study by [11], which was carried out in India on a sample of 88 SME companies, also found a strong positive correlation between corporate governance and the company’s financial performance; also the finding was supported by a recent study in Kenya that used factor analysis and linear regression to find a strong positive correlation between corporate governance and financial performance [91]. Similar strengths and positive correlations between these variables were also observed in the study by [53] conducted in Ghana, which involved 23 banks as the sample. Nevertheless, the results of this study contradict those of another empirical study, as shared by [92] which found that the association between corporate governance and financial performance was either binary or inconsequential in most cases. This discrepancy indicates the situational application of corporate governance effects.

Hypothesis 3:

The study strongly supports Hypothesis 3, demonstrating that Corporate governance (CG) mediates the association between SACCOS financial performance (FP) and the legal environment (LE) in Tanzania.Analysis of the mediation effect showed a remarkable transformation. The direct path coefficient between LE and FP is −0.098 (p = 0.027), indicating a significant but negative direct relationship. However, the indirect path coefficient from LE → CG → FP is highly significant and strong positive 0.944 (p < 0.001) (Table 9). This signifies a full mediation effect, in which the mediating variable, corporate governance, entirely transforms the relationship from a direct negative to a strong indirect positive [87]. The indirect path’s significantly improved p-value (from 0.027 to 0.000) and increased magnitude confirm the reinforcing role of corporate governance.

This observation concurs with those of earlier studies. The relationship between stock market returns and accounting data for firms in Ghana mediates the influence of corporate governance, in line with the fact that good corporate governance supports all positive business results [25]. The results of [93] are consistent with the role CG plays in insulating the legal environment effects on the performance of SACCOS by demonstrating that effective corporate governance practices defy the adverse effects of strict regulations on the performance of French listed companies. In addition, their study in emerging economies showed that strong corporate governance diminishes the adverse effects of poor institutional quality (such as the legal environment) on firm performance [94] which reflects the mitigation effect of corporate governance on SACCOS in Tanzania. On the other hand, publications such as [95] in Pakistan identified no strong mediating impact of corporate governance on other contexts, indicating that there may be contextual barriers to mediation, which underlines the relevance of institutional environments of SACCOS in Tanzania.

Institutional Theory has long served as a foundational framework for understanding how external pressures-coercive, normative, and mimetic shape organizational structures and behaviours, often leading to isomorphism, or similarity among firms within a given field [96]. Traditionally, this perspective can imply a somewhat deterministic relationship where organizations passively adapt to their institutional environment to gain legitimacy and ensure survival. This study’s findings, however, challenge and significantly extend this classical view by empirically demonstrating that within the challenging context of Tanzanian SACCOS, internal organizational agency, channelled through robust corporate governance, is the critical factor that determines whether institutional pressures lead to failure or success.

The study’s primary contribution lies in its revelation of a complete mediation effect. While the direct relationship between the coercive pressure of the legal environment and financial performance is negative (β = −0.098, p < 0.05), this path is entirely subsumed and transformed by the mediating role of corporate governance, resulting in a powerful positive indirect effect (β = 0.944, p < 0.001). This finding moves Institutional Theory beyond a model of direct conformity. It introduces a more dynamic and strategic model where organizations do not merely absorb or succumb to institutional constraints. Instead, they can actively employ internal structures as “institutional filters” or “strategic buffers”. As [94], suggested in emerging economies, strong internal governance can diminish the adverse effects of weak institutional quality. Our results provide robust empirical validation for this premise, showing that governance does not just mitigate but fully reverses the valence of the institutional pressure, transforming a potential threat into a catalyst for disciplined performance.

This leads to the second major theoretical extension: the elevation of internal governance from a beneficial attribute to an essential survival mechanism in high-pressure institutional settings. In stable, well-regulated environments, good governance may enhance performance incrementally. In contexts like Tanzania—characterized by regulatory complexity, compliance burdens, and fiscal uncertainty as noted by the World Bank [13], our findings suggest that such governance becomes non-optional. It is the core mechanism that allows SACCOS to navigate, interpret, and strategically comply with coercive demands without being crippled by them. This aligns with and extends the work of [93], who found that effective corporate governance helps firms defy the adverse effects of strict regulations. We demonstrate that in the most demanding settings, governance is the linchpin that decouples institutional pressure from organizational failure, thereby refining the theory’s understanding of organizational survival under duress.

Finally, the study contributes by highlighting the contingent nature of institutional effects. The stark contrast between our findings of full mediation and studies that found weak or no mediation [95] underscores that the predictive power of Institutional Theory is highly context-dependent. The theory is enriched by recognizing that the pathway from institutional environment to organizational outcome is not universal. It is critically mediated by the quality and capacity of internal structures, which vary across organizations and national contexts. This contingent view encourages a more nuanced application of Institutional Theory, one that must account for organizational-level agency and resilience, particularly in Sub-Saharan African business environments where formal institutions are often still evolving.

This study, grounded in Institutional Theory, proposes that the legal environment (an external institutional force) influences SACCOS’ financial performance. This influence is not direct; instead, corporate governance (internal structures) acts as a mediator. Effective corporate governance enables SACCOS to navigate legal challenges, foster legitimacy, and improve financial performance. This study empirically tests how the legal environment impacts outcomes through internal governance (see Figure 4Figure 4).

Suggested Mathematical Model

LE [Legal Environment] --> |a: β₁ = 0.977***| CG [Corporate Governance

CG [Corporate Governance] --> |b: β₂ = 0.966***| FP [Financial Performance]

LE [Legal Environment] --> |c: β₃ = −0.098***| FP [Financial Performance]

Figure 4. Suggested conceptual model diagram.

Sub Graph Mediation Analysis

Indirect Effect (“(a * b) = 0.944***”)

Total Effect (“Total Effect = c + (a * b) ≈ 0.846”)

Mathematical Model: FP=(β₂)beta_2CG +(β₃)beta_3LE + epsilon_2)=FP =β₂CG+β₃LE+ϵ2

The relationships observed in the study can be expressed mathematically as follows:

1) ϵ2 is the variation in Financial Performance (FP) that is not explained by Corporate Governance (CG) and Legal Environment (LE) in the model.

2) Effect of Legal Environment on Corporate Governance: CG = beta_1LE + epsilon_1.

Here, (β₁) beta_1 represents the legal environment’s effect on corporate governance. The study found this to be significantly positive (β₁) beta = 0.977, p < 0.001).

3) Effect of Legal Environment and Corporate Governance on Financial Performance: FP = beta_2CG + (β₃) beta_3 LE + epsilon_2.

Here, (β₂) beta_2 represents the effect of Corporate Governance on Financial Performance. This study found this to be significantly positive (β₂) beta_2 = 0.966, p < 0.001).(β₃) beta_3 represents the direct effect of the Legal Environment on Financial Performance after accounting for the effects of Corporate Governance. The study found this to be negative (β₃) beta_3 = −0.098, p = 0.027).

4) Mediation Effect: The total effect of LE on FP is the sum of direct and indirect effects. Total Effect = beta_3 + (beta_1 times beta_2).

Thus, Total Mediation Effect = c + (a * b) = (−0.098 + (0.977 * 0.966)) ≈ 0.846.