Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5, 1348-1361 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: hungbq2310@gmail.com The relation of leadership mindset capacity and worker cohesion to the organization influences the business efficiency of small and medium-sized enterprises (SME) In Ho Chi Minh City MBA Bui Quang Hung1*, Nguyen Minh Tuan2 1,2Ho Chi Minh University of Banking, Vietnam; hungbq2310@gmail.com (M.B.Q.H.) tuannm@hub.edu.vn (N.M.T.) Abstract: This study aims to help administrators perceive a specific perspective on the importance of cognitive capacity, from which they can take measures and make changes in work, study, training, and the managing process to improve workers’ cohesion in the organization and business efficiency. Research methods using a pre-made questionnaire, a survey is used to gather data for official quantitative research. According to Hair et al. (2006); Kline (2011); Tabachnik and Fidell (2011), factor analysis and linear structure were performed using Structural Equation Modeling (SEM) using the SmartPLS tool. This is because the theoretical model has interwoven relationships, so the linear structural model (Structural Equation Model, SEM) is used to test the above hypotheses (Lorence and Mortimer, 1985; Anderson and Gerbing, 1988). In the study, there are 3 variables that receive impact from other variables: HQ, NLLD, and SGK. The adjusted R-squared of Effectiveness (HQ) is 0.328, so the independent variables affecting it include Leadership Mindset Capacity (NLLD) and Cohesion (SGK) explains 32.8% variation (variance) of the HQ variable. The adjusted R-squared of Leadership Mindset Capacity (NLLD) is 0.784, so the independent variable affecting it explains 78.4% of the variation (variance) of the NLLD variable. Scientifically speaking, this study enriches the research on business efficiency, leadership mindset capacity, worker cohesion in the organization, and forming the thinking capacity scale in addition to business efficiency scale. Furthermore, this study can be used as a reference for other authors to suggest new research issues. Practical Implications, the Thinking Capacity scale, which was established from the research results, can be used to evaluate the administrators’ proficiency in many different goals such as learning, personnel recruiting, training, and improving personnel’s abilities. Keywords: Cohesion, Efficiency, Leadership mindset, Mindset capacity, 1. Introduction According to Perry (2001) and Beaver (2003), weak leadership is the cause of the failure of many SMEs. Gordon and Yuki (2004) advocate further research on leadership skills related to small yet volatile enterprise environments. Beaver (2003) examined the profile of 200 bankrupt SMEs and discovered the main failure cause was the lack of leadership knowledge and poor management. Leadership style is very important for the success of an SME. Pellerin (2007) reveals that the failing percentage of SMEs is extremely high, bankrupt after the third year is 62%, although Pellerin’s research did not mention the poor management issue as a cause. Another research shows that 90% of SMEs fall in their first 10 years (Scheers and Radipere, 2007). Scheers and Radipere claimed that the reason for the downfall of small enterprises lies in poor management and lack of leadership directing. A successful economy depends on productive and competitive small enterprises (Beaver, 2003). Small enterprises are the engine of economic growth through solving employment problems and job shortages in the innovative digital era (Fuller, 2003). 1349 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate The management skills of SME leaders are still limited, the number of SME leaders graduated from colleges and universities is low, as is the percentage of leaders who studied business administration. Therefore, in the process of making critical decisions, they often let emotions get the better of them, leading to major mistakes. Experimental results of decision-making situations in investment (representing the strategic level), recruiting (representing the tactical level), and cash flow management (representing the operational level) show that the common dominant psychological behavioral factors are overconfidence, experience, stereotypes, and crowd psychology, which are factors that often lead to mistakes. The globalization process is taking place very strongly, especially in the context of Vietnam participating in bilateral and multilateral trade agreements, so competition between businesses is becoming increasingly fierce. To integrate successfully, businesses need to improve their competitiveness. In order to resolve this status quo, measurements such as training and developing human resources, strategic consulting, and encouraging change and innovative ideas in management and business can be implemented. This will ensure SMEs can improve their work performance, and at the same time build long-term and more efficient strategic business plans in a more challenging business environment. (Nguyen Thi Như Quynh and Nguyen Thi Thuy, 2016). Based on all the shreds of evidence that have been presented above, we can consider a deeper and more specific study about the relationship between leadership mindset, worker cohesion in the organization and business efficiency. To carry out this research effectively, it is necessary to develop a research model and research methodology. Throughout the building a research model and selecting suitable research methods, the study will explore the relation between leadership mindset, worker cohesion in the organization and business efficiency in detail and with practical value in mind. “The relation of leadership mindset capacity and worker cohesion to the organization influences the business efficiency of small and medium-sized enterprises in Ho Chi Minh City” is immensely necessary and meaningful. 2. Research Overview and Research Model 2.1. Research Overview Noor Afza Amran (2011), “Influence of owner's gender and age on firm performance: a review of Malaysia's publicly listed family businesses”. Research on secondary data approach with a sample size of 182 Malaysian family companies listed on Bursa Malaysia during the period 2003 to 2007. The result from this research shows the difference between male-led and female-led companies on company efficiency. This research only focuses on the gender and the age of leadership affecting the organization’s activities. The limitation of this study is not taking into account the leader’s traits and specialties. Pang, NS-K., & Pisapia, J. (2012) “Hong Kong School Leaders' Strategic Thinking Skills: How to Use and Effectively”. The goal of this study is to determine strategic thinking skills that differentiate efficient school leaders in Hong Kong. Five hundred and forty-three (543) school leaders participated in this study. The factor structure of the strategic thinking questionnaire (STQ) in the Chinese context was validated. The result confirmed the existence of the relationship between strategic thinking skills and the efficient performance of the leader. School leaders’ strategic thinking skill in Hong Kong is formed around a strategic thinking system; it’s the strongest and the most efficient prediction factor of the leader. This mindset varies depending on the role assignment, the type of school, and the complexity of the environment. The dean applies the thinking system and restructuring in parallel while the vice- deans utilize the restructuring system more than every other skills. As for senior masters, they make use of strategic thinking skills less often than deans and vice-deans. This study has looked into leaders’ strategic thinking skills, however, aside from that of a leader, they need other thinking skills in administration and business. This is the restriction of this study that others will have to dig further into. Combe, IA, & Carrington, DJ (2015). “Leaders' perceptiveness in crises: Cognitive consensus emerging over time in management teams”. The result of the study points out that the thinking model of administrators has significantly affected how they conveyed themselves and acted in different situations. The result of 1350 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate this study indicates that their thinking mindset can influence their actions, yet this study has never specified what kind of thinking mindset and how it affects the leader’s actions. Michael D. Mumford (2017), “Cognitive skills and leadership performance: The nine critical skills”. The study presents nine major skills everyone in leading positions uses when working with the knowledge in advance based on scenarios and situations they are in to address leadership: 1) problem definition, 2) cause/goal analysis, 3) constraint analysis, 4) planning, 5) forecasting, 6) creative thinking, 7) idea evaluation, 8) wisdom, and 9) sensemaking/visioning. In the process of researching, a new skill acquired is a connecting skill. The study shows that thinking skill and connecting skill can influence the leader’s proficiency. However, in terms of thinking ability in the competitiveness framework, the findings of this study leave out other thinking capacities that have yet to be investigated. Vision and purpose seem to be important aspects in which every individual and personnel must keep in mind (Vision, 2020). In organizations, various aspects need to be taken into account such as resource management, performance of job duties, formulation of laws, policies, and regulations that need to be in line with the vision and purpose. When organizations are created, they develop a vision and purpose; however, when developments occur and contemporary, pioneering scientific and technical approaches are introduced, the vision and mission alter. And when changes were made to those visions and purposes, many things happened in other fields and aspects, including the activities of managing human, financial, technical and material resources, performing job duties and responsibilities, developing laws, policies, procedures and general environmental conditions activities. The leader needs to communicate with subordinates often and provide them with information about every change and detail that occurs in the location. Thus, vision and purpose are considered important characteristics on the basis of which the job tasks of the leader and other members are emphasized. Phan Bui Gia Thuy and partner (2017), “Ảnh hưởng của đặc điểm tổng giám đốc điều hành đến hiệu quả hoạt động doanh nghiệp”. The research was conducted with the purpose of estimating the impact of the CEO’s traits on the company's performance in Vietnam. Based on the sample data consisting of 120 companies listed on HOSE from 2009 to 2015, a total of 840 observations, the result of this research concludes and shows the impact of age and the CEO’s capital ownership ratio on the company performance is non-linear. Moreover, companies that have CEO-cum-Board of Directors (BOD) have far better performance and more efficiency than companies that are not maintaining this structure. The study's shortcoming is that it only looks at leaders' demographics and capital ownership ratios. It also did not investigate leaders' cognitive abilities. Dinh Phi Ho (2020), Mối quan hệ giữa Gắn kết - Trung thành - Hiệu quả tổ chức ở Agribank chi nhánh Tỉnh Bình Thuận”. The purpose of this study is to identify the relation between employee cohesion and loyalty impacting the organization's performance at Agribank, Binh Thuan Province branch. Especially in the context of strong competition to survive and develop, the issue of human resources, which is employee engagement and loyalty to the organization, is the key for commercial banks to develop sustainably. Based on the survey taken by 320 employees and using the linear model structure, the study shows a positive relation between cohesion and loyalty to the organization's performance. The factors that affect employee cohesion in the organization include: (i) Career Development; (ii) Faith in the organization; (iii) Work motivation; (iv) Colleague; (v) Income; with the importance of “Career Development” and “Faith in the organization”. The research results provide a scientific basis for managers who want to improve organizational effectiveness and need to pay attention to employee cohesion and loyalty to the organization. The limitation of this study is that it only studies cohesion and loyalty around employees, without the impact of leadership. Jindong and partner (2011), “Factors Affecting Organizational Commitment among Bank Officers in Pakistan”. The research was conducted based on a sample survey with 147 bank employees in Lahore City, Pakistan. The results provide there are 5 factors affecting engagement in the organization: Income and welfare, relationship with leader, training opportunity and promotion, work characteristics, and work-life balance. Research has revealed that ties with leaders influence bank officials' participation, but 1351 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate it has not convincingly demonstrated the importance of leadership in engagement, specifically thinking capability and particularly how the leader’s thinking skills will affect the abovementioned issues. Tran Kim Dung (2005), “Nhu cầu, sự thỏa mãn của nhân viên và mức độ gắn kết với tổ chức” making modifications and examing JDI scale (Job Descriptive Index) of Smith (1969) was added another factor (satisfaction and welfare), turning it into AJDI scale with six factors: Nature of work, Training opportunity – promotion, Leadership, Colleague, Income, Welfare influence the employee’s engagement to the organization. The engagement to the organization scale was conducted following Mowday & ctg (1979) with three factors: Loyalty, Pride in the organization, and Conscious Effort to suit the practical conditions of Vietnam. The research has determined the influence with statistics of the above six factors in the AJDI scale to the three factors in the engagement to the organization of different employee groups. The results aid leaders in having the estimated data on the employee’s satisfaction towards the job, their engagement with the organization, and finding out methods to improve and encourage them to not turn over and prolong their cohesion to the organization. This research presents that Leadership has impacted the worker’s engagement, yet does not point out the part of how this leader’s thinking skill affects the worker’s engagement, which is this research restriction. Tinh & ctg (2012) in “Phân tích các nhân tố ảnh hưởng đến sự gắn kết lâu dài của nhân viên trẻ với doanh nghiệp” ultize the theories about encouragement of F.W. Taylor (1915). Herzberg (1976), the theory about needs of Maslow (1943) to build the factor affecting the long-term engagement of young employees toward companies and enterprises. The research model suggests eight factors in total: Income, Purpose of work, Work condition, Colleague relationship, Relationship with the leader, Empowerment, Benefit and Welfare, and Promotion, all of which affect the employee’s engagement with the organization. The result views that there are five factor influence employee engagement toward the organization, which are: Promotion, Benefits and Welfare, Relationship with the leader, Workplace condition, and Relevance of career goals. Out of those five factors, promotion is the biggest and strongest one. The results from the research are an important suggestion for organizations in building appropriate strategies and plans to better maintain young human resources for their units. Nonetheless, this research still has its limitations, as it only points out there was an influence in the Relationship with the leader toward the employee’s long-term engagement yet never analyzes any other traits of the Leadership which also has Thinking skills. 2.2. Research Theories In recent years, global economic instability has begun to increase while the pace of economic recovery has not reached the desired level. Simultaneously, as businesses have grown more circumspect in their strategic decision-making, the risk factor has started to rise across industries. Companies such as Facebook, Apple, Samsung, and Alibaba, in particular, prioritize minimizing any negative effects on their performance while making long-term strategic decisions (Z. Yang & Zhu, 2016). Making decisions with this "risk" approach will impact how well CEOs lead and how information is shared across the company. Trust and vision are essential for effective leadership, and workers should believe that a capable leader is working with them to advance sustainability and critical steps toward the objective (Zhang et al., 2011). The perceived effectiveness of leaders is a criterion that is conveyed through assessments of their leaders and seeks to explain how leaders impact the organization (Prati et al., 2003). Stated differently, the efficacy of a leader is determined by how well they manage and steer their actions toward achieving their objectives (Dabke, 2016). According to Koçel (2003), a company's performance dictates its future state, the position and scope of its goals, and the industry it will pursue. The outcome of a given amount of time, effort, or research in completing the duties or goals of the organization is the efficiency of the business. According to Zerenler (2005), corporate performance in this sense is the assessment of all company goals that are attained. Successful performance reviews, obviously, guarantee the expansion of the business, and determining the success and failure rates of the company's leaders and staff is crucial to boosting the 1352 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate enterprise's long-term prosperity (Kara, 2010). The significance of performance measurement is growing in the modern economy, and shifting market dynamics have shown that, in addition to financial performance indicators, market-oriented indicators, such as competitiveness, likewise are necessary to evaluate company performance. Regardless of how leadership is defined in detail, the fundamental idea is that followers are greatly impacted by their leaders in terms of their attitudes and actions. As a result, "the quality of the developing relationship between leader and follower is to predict outcomes at the individual, group, and organizational levels of analysis" (Gerstner & Day, 1997, p. 827)). In another way, leadership is a significant precursor to numerous succeeding factors, such as staff engagement (Xu & Thomas, 2011), which in turn leads to desired results like efficient organizational performance or individual welfare. With the aforementioned data, the author proposes two hypotheses as follows: Hypothesis H1: Leadership mindset positively influences business efficiency of small and medium-sized enterprises in Ho Chi Minh City Hypothesis H2: Worker engagement in the organization positively impacts business efficiency of small and medium- sized enterprises in Ho Chi Minh City In the world and in the country today, there are many studies on the influence of leadership on the engagement of employees or workers in general in the organization, especially the leadership style that affects the engagement of employees. In this context, the author only cites the studies most closely related to the topic. In order to have a better understanding of the connection between psychological ownership, job engagement, and authentic leadership in businesses, Alok and Israel (2012) reported on research of 117 employees in India, comprising 82 men and 35 women. The results reveal that authentic leadership indirectly impacted worker engagement. Xu and Thomas (2011) looked into the connection between 414 New Zealand employees' engagement and leadership. An online survey was carried out to measure leader competency using the John Robertson & Associates (JRA) 360-degree feedback metric and to evaluate employee engagement using the attitude scale at JRA. The findings demonstrate that employee engagement is significantly positively impacted by leadership capacity. Tran Kim Dung's (2005) study demonstrated the influence of leadership on employee engagement. This study's shortcoming is that it doesn't address how leaders' ability to think critically affects employee engagement. With the abovementioned data, the author introduces the following hypothesis: Hypothesis H3: Leadership mindset positively influences worker engagement in the organization Based on the aforementioned theories, the author suggests the following three components make up a research model: Figure 1. Research model. Business Performance Leadership Mindset Capacity Worker Engagement To The Organization H3(+) (+) H2(+) (+) H1(+) (+) 1353 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate 3. Research Method The two stages of quantitative research methodology are official quantification and preliminary quantification. A structured questionnaire was utilized to survey 78 managers for the preliminary quantitative research. Data was analyzed using SPSS 22 software and tested using Cronbach's Alpha coefficient and exploratory factor analysis (EFA). This pilot study aims to assess the validity of the previously created scale using qualitative methods. The scale will be incorporated into the official scale for the official study after being modified based on the preliminary quantitative research findings. Using a pre-made questionnaire, a survey is used to gather data for official quantitative research. According to Hair et al. (2006); Kline (2011); Tabachnik and Fidell (2011), factor analysis and linear structure were performed using Structural Equation Modeling (SEM) using the SmartPLS tool. This is because the theoretical model has interwoven relationships, so the linear structural model (Structural Equation Model, SEM) is used to test the above hypotheses (Lorence and Mortimer, 1985; Anderson and Gerbing, 1988). 3.1. Selecting Sample Studies The sample was chosen using a non-representational method with convenient techniques, to ensure representativeness, so the survey was conducted in all areas in HCM City. The study used PLS-SEM analyses such as scale evaluation, discriminant validity testing, model fit testing, and estimation results via SmartPLS. PLS-SEM is a technology requiring huge samples. As suggested by many researchers, the minimum size used for these analysis techniques is 5*q (q is the number of observed variables in the study), while many authors suggest a ratio of 10*q. Since the total study group was too large to select a 10*q ratio, the study selected 5*q as the ratio. The expected number of samples detected in this study is 500 votes. 3.2. Testing the Scale Reliability Alpha was developed by Lee Cronbach in 1951 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1. Internal consistency describes the extent to which all items in a test measure the same concept or construct and, therefore, is connected to the relationship between items on the test. Internal consistency must be determined before using a test for research or testing purposes to ensure validity. Furthermore, tests' measurement inaccuracy is indicated by reliability estimates. In a nutshell, this view of reliability is the test's connection with itself. Squaring this correlation and subtracting 1.00 produces an index of measurement error. It should be noted that test reliability indicates the impact of measurement error on the observed scores of a group of students, not on individual students. To calculate the effect of measurement error on each student's observed score, the standard error of measurement (SEM) must be calculated. An excessively high Cronbach's Alpha coefficient (>0.95) shows that there are variables in the scale that are not different, meaning they measure the same content of the research concept, and this is the phenomenon of overlap in measurement. If a measurement variable has a total correlation coefficient r≥ 0.3, that variable meets the requirements (Nunnally & Bernstein, 1994). If r = 1, then the two measurement variables become one and we only need to use one of the two variables to meet the requirements. There are various reports on acceptable values of alpha, ranging from 0.70 to 0.95. Low alpha values may be due to a small number of questions, poor relationships between items, or due to structural heterogeneity. For example, if a low alpha index is due to poor correlations between items, then some items need to be revised or eliminated. The easiest method to find them is to calculate the correlation of each test item with the total test score; Items with low correlation (close to 0) are removed. If the alpha index is too high, it may suggest that some items are redundant because they are testing the same question but in a different form. A maximum alpha value of 0.90 has been recommended, with good reliability ranging from [0.7-0.8] (Nunnally & Bernstein, 1994). 1354 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate To evaluate the scale reliability on SMARTPLS through two main indicators: Cronbach's Alpha and Composite Reliability, Composite Reliability (CR) is preferred by many researchers over Cronbach's Alpha because Cronbach's Alpha underestimates reliability compared to CR. Chin (1998) believes that in exploratory research, CR must be 0.6 or higher. With confirmatory studies, the threshold of 0.7 is the appropriate level of the CR index (Henseler & Sarstedt, 2013). Many other researchers also agree that 0.7 is the appropriate assessment threshold for the vast majority of cases such as Hair et al (2010), Bagozzi & Yi (1988). Thus, Cronbach's Alpha ≥ 0.7 (DeVellis, 2012) and Composite Reliability CR ≥ 0.7 (Bagozzi & Yi, 1988). If a scale does not meet the reliability threshold, it is necessary to eliminate each observed variable with the lowest outer loading in turn to improve reliability. In the event that the scale remains unreliable after removing all unqualified variables, we will draw the conclusion that the scale is unreliable and won't be using it for any further quantitative analysis. In addition, based on the AVE index (Average Variance Extracted), Hock & Ringle (2010) believe that a scale has convergent validity if AVE is 0.5 or higher. This level of 0.5 (50%) means that the average parent latent variable will explain at least 50% of the variation of each child observed variable. 3.3. Examining the Discriminant Values Discriminant validity shows the distinctiveness of a construct when compared to other constructs in the model. The traditional approach to evaluate discrimination is to use the AVE square root index proposed by Fornell and Larcker (1981). This traditional method has shortcomings and requires a more accurate assessment method. Henseler and colleagues (2015) used simulation studies to demonstrate that discriminant validity is better assessed by the HTMT index they developed. SMARTPLS uses both of these methods of assessing discrimination but still focuses more on HTMT. With the HTMT index, Garson (2016) believes that the discriminatory value between two latent variables is guaranteed when the HTMT index is less than 1. 3.4. Testing the Model Fit (Explanation Level of the Independent Variable for the Dependent). To evaluate the impact of one or more independent variables on a dependent variable in the SEM model, the author will use the R squared index or adjusted R squared index (preferably using the adjusted value would be more accurate). 3.5. PLS-SEM Estimation Model Analysis PLS-SEM is a discrete least squares structural means model. PLS-SEM estimates model parameters based on method error. This is the preferred method for researchers when the goal of their research is to develop theory and explain variation in the dependent variable. Currently, the second- generation analysis technique (PLS-SEM) has software that has been developed and used effectively. Many researchers choose PLS-SEM when the research model contains latent variables measured through observed variables that reflect causes, small sample sizes, and non-normally distributed data. 3.6. PLS-SEM Linear Structural Model Analysis The relationships between research variables as well as between research variables and their indicator variables are represented by arrows. In PLS-SEM, the arrows are always unidirectional, representing a direct relationship. Unidirectional arrows are viewed as predictive relationships, which, with strong theoretical support, can be interpreted as cause-and-effect relationships. The PLS path model consists of two elements: 1. First, there is the structural model (also called the inner model) that represents the research variables (circle or oval). The structural model also displays the relationships (paths) between study variables. 2. Second, it is the measurement model (also known as the outer model) of the research variables, showing the relationships between the research variables and the observed variables. Japan). 1355 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate • Endogenous latent variables and exogenous latent variables There are two types of measurement models: one is exogenous latent variables (are research variables that explain other research variables in the model, also called independent variables) and another is endogenous latent variables (are the research variables being explained in the model, also known as dependent variables). Instead of referring to the measurement model of exogenous and endogenous latent variables, researchers frequently refer to the measurement model of a particular latent variable. For instance, x1 to x3 are observed variables used in the measurement model of latent variable Y1, while Y4 only has the observed variable x10 in the measurement model. • Error and residual The error term (for example, e7 or e8) is connected to the (endogenous) variable and the measured (outcome) variable by one-way arrows. The error term represents the unexplained variance when the path model is estimated. The errors e7 to e9, lie on observed variables whose relationship goes from the research variable to the observed variables (the observed variable measures the outcome). In contrast, for the variables measuring causes x1 to x6, the relationship goes from observed variables to research variables, without error. The structural model also contains errors (residuals). The two residuals z3 and z4 are related to the endogenous latent variables Y3 and Y4. In contrast, exogenous latent variables only explain other latent variables in the structural model without error. The path model is built based on theory. A theory is a set of systematically related hypotheses developed according to the scientific method that can be used to explain and predict outcomes. Thus, a hypothesis is a personal guess, while a theory consists of many hypotheses that are logically linked together and can be tested experimentally. Two types of theory are needed to build a path model: measurement theory and structural theory. Structural theory determines how research variables are related to each other in the structural model, while measurement theory specifies how each research variable is measured. Table 1. The internally reliability test results (CA, CR, AVE). Cronbach's alpha rho_A Composite reliability Average variance extracted (AVE) CSKT 0.938 0.949 0.956 0.844 DDCV 0.939 0.950 0.956 0.846 DTTT 0.934 0.946 0.953 0.836 HQ 0.891 0.894 0.925 0.754 KN 0.875 0.878 0.914 0.727 MTLV 0.938 0.952 0.955 0.843 NLLD 0.918 0.922 0.939 0.756 NT 0.918 0.921 0.942 0.803 SGK 0.868 0.870 0.911 0.720 ST 0.928 0.929 0.946 0.777 TH 0.942 0.710 0.929 0.769 TN 0.909 0.912 0.936 0.785 TNPL 0.938 0.941 0.956 0.846 1356 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate 4. Measurement Scale From the aforementioned table, all variables have Cronbach’s Alpha ≥ 0,7 (DeVellis, 2012) and Composite Reliability CR ≥ 0,7 (Bagozzi & Yi, 1988). Thus, the model satisfies the measurement scales' reliability requirements. Additionally, as can be seen from the statistics above, AVE is greater than 0,5, fulfilling the conditions. Remarks: The analysis's findings demonstrate the reliability of each component, with a Cronbach's Alpha coefficient between 0,868 to 0,942, which is greater than 0,7. All components have good composite reliability (CR), with coefficients above 0,7 and falling between 0,911 and 0,956. Ultimately, the components' average variance extracted (AVE) coefficient, which ranges from 0,720 to 0,846, is more than 0,5 and satisfies the testing conditions (Hair et al., 2014). 4.1. Determining Discriminant Validity By comparing a construct to other constructs in the model, discriminant validity demonstrates how unique a construct is. AVE, the square root index established by Fornell and Larcker (1981), is the traditional method used to assess discrimination. A more precise assessment technique is needed because the conventional approach has flaws. The HTMT index created by Henseler and colleagues (2015) provides a superior means of assessing discriminant validity, as demonstrated by simulation studies. HTMT is given additional attention in SMARTPLS, which employs both of these techniques to evaluate discrimination. Garson (2016) holds the opinion that when the HTMT index is less than 1, the discriminant value between two latent variables is assured. Table 2. Determine discriminant validity. CSKT DDCV DTTT HQ KN MTLV NLLD NT SGK ST TH TN TNPL CSKT DDCV 0.608 DTTT 0.652 0.679 HQ 0.310 0.349 0.593 KN 0.284 0.286 0.424 0.569 MTLV 0.664 0.632 0.595 0.277 0.369 NLLD 0.304 0.314 0.465 0.623 0.978 0.333 NT 0.396 0.350 0.496 0.601 0.649 0.344 0.629 SGK 0.368 0.328 0.345 0.187 0.154 0.321 0.157 0.210 ST 0.256 0.240 0.367 0.502 0.496 0.210 0.500 0.570 0.158 TH 0.041 0.077 0.060 0.059 0.070 0.058 0.044 0.106 0.052 0.024 TN 0.354 0.369 0.509 0.691 0.700 0.295 0.680 0.784 0.178 0.611 0.045 TNPL 0.615 0.627 0.965 0.544 0.439 0.599 0.475 0.502 0.327 0.524 0.052 0.533 Source: Smart PLS analysis result (2024) As can be seen in the above table, HTMT values are all lower than 1. 4.2. Testing Model Compatibility The author will use the R squared index or adjusted R squared index (ideally using the adjusted value) would be more accurate to assess the effect of one or more independent variables on a dependent variable in the SEM model. 1357 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate Table 3. Determine Model Compability. R square R Square adjusted HQ 0.330 0.328 NLLD 0.786 0.784 SGK 0.143 0.134 Source: Smart PLS analysis result (2024) SGK, HQ, and NLLD are the three research variables that are impacted by other variables. As can be seen from the data above: • The adjusted R squared of HQ is 0,328, meaning that 32.8% of the variation (variance) of the HQ variable is explained by the independent variables that affect it, such as NLLD and SGK. • With an adjusted R-squared of 0,784, the independent variable that influences the NLLD variable accounts for 78.4% of its variance or variation. • The adjusted R-squared of Employee Attachment to the Organization (SGK) is 0.134, so the independent variable affecting it explains 13.4% of the variation in the variable Employee Attachment to the Organization position 4.3. SmartPLS Estimated Result We will utilize the Bootstrap analysis results to assess the impact relationships. The table that follows displays the outcomes of examining the link between the factors. These results demonstrate that employee commitment to the company is influenced by NLLD (leadership thinking capability), with a P-value of 0,003 < 0,05. NLLD (Leadership Thinking Capacity) affects HQ (Business efficiency) of SMEs in Ho Chi Minh City, has P-value = 0,000 < 0,05; SGK (Worker Engagement to the Organization) influences HQ (Business efficiency) of SMEs in Ho Chi Minh City, has P Value = 0,012 < 0,05. Thus, the estimated model result shows that the impact of the relation of leadership mindset to the business efficiency of small and medium enterprises in Ho Chi Minh City with the impact factor is 0,555. Meaning if Leadership Mindset rises or falls by 1 unit then Business efficiency rises or falls by 0,555 units in the condition that other factors do not change. On top of that, the estimated model result also presents the impact of the relation of leadership mindset to workers’ cohesion in the organization of SMEs in Ho Chi Minh City with the impact factor is 0,141. Meaning if Leadership Mindset rises or falls by 1 unit then Workers’ Engagement in the organization rises or falls by 0,141 units in the condition that other factors are unchanged. Table 4. The relationship impact result. Impact coefficient Medium sample (M) Standard deviation (STDEV) Statistical T (|O/STDEV|) P Values KN -> NLLD 0.783 0.780 0.050 15.761 0.000 NLLD -> HQ 0.555 0.555 0.048 11.463 0.000 NLLD -> SGK 0.141 0.147 0.047 3.012 0.003 NT -> NLLD 0.045 0.045 0.043 1.058 0.290 SGK -> CSKT 0.338 0.340 0.046 7.305 0.000 SGK -> DDCV 0.303 0.306 0.049 6.198 0.000 SGK -> DTTT 0.318 0.322 0.045 7.001 0.000 SGK -> HQ 0.089 0.091 0.035 2.515 0.012 1358 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate SGK -> MTLV 0.296 0.299 0.049 6.029 0.000 SGK -> TNPL 0.300 0.304 0.044 6.758 0.000 ST -> NLLD 0.046 0.047 0.047 0.990 0.323 TH -> NLLD -0.068 -0.055 0.034 1.988 0.047 TN -> NLLD 0.076 0.076 0.034 2.200 0.028 Source: Smart PLS analysis result (2024) The estimated model result displays the impact of the relation of Workers’ Engagement in the organization to the Business efficiency of SMEs in Ho Chi Minh City with an impact factor is 0,089. Meaning if Workers’ Cohesion rises or falls by 1 unit then Business Efficiency rises or falls by 0,089 units in the condition that other factors remain unchanged. Figure 2. The estimated result evaluating the relationship between leadership mindset capacity and worker engagement in the organization which influences business efficiency of small and medium-sized enterprises in Ho Chi Minh City. Source: Smart PLS analysis result (2024) 4.4. Examining The Distinction The result of Levene's test gives the value F = 0.240 with a significance level Sig. = 0.624> 0.05 so the variance of the two populations is identical. The t-test result in the line Assuming equal variances has the value Sig. = 0.321 > 0.05 shows no gender difference in Leadership Mindset Capacity at a statistical confidence level of 95%. Levene's test confirms the difference in the variance of Leadership Mindset Capacity between age groups, with sig = 0.000<0.05. The results of ANOVA analysis with sig = 0.034 < 0.05 should indicate that Leadership Mindset Capacity is impacted by Age differences. The ANOVA study results indicate that Leadership Mindset Capacity varies statistically significantly between age groups. The findings of the ANOVA analysis can be effectively used because Levene's test, with sig = 0.662 > 0.05, reveals that there is no difference in the variance of Leadership Mindset Capacity between Level groups. The ANOVA analysis indicating a significance level of 0.828 > 0.05 suggests that the variation in qualifications has no impact on Leadership Mindset Capacity. The variance of the two populations is equal, according to Levene's test result, which yields the value F = 0.166 with a significance level Sig. = 0.684> 0.05. At a 95% statistical confidence level, the t- 1359 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate test result in the line Assuming Equal Variances has the value Sig. = 0.550 > 0.05, which indicates that there is no gender difference in Business Efficiency. Levene's test with sig = 0.000<0.05 confirms that the variance of Business Efficiency between age groups is dissimilar. ANOVA analysis with sig = 0.014 < 0.05 should conclude that the difference in Age affects Business Efficiency. From ANOVA analysis results, it shows that there is a statistically significant difference in the Business Efficiency factor between age groups. Because the variance of Business Efficiency between Level groups is the same, as confirmed by Levene's test with sig = 0.441 > 0.05, the results of the ANOVA study can be effectively utilized. There should be no difference in Qualification and no impact on Business Efficiency, according to an ANOVA analysis with sig = 0.879 > 0.05. Independent Sample T-test was performed to test the distinction between gender groups on Employee Engagement with the organization. The result of Levene's test gives the value F = 0.001 with a critical level of Sig. = 0.972> 0.05 so the variance of the two populations is identical. At a 95% statistical confidence level, the t-test result in the line Assuming equal variances with the value Sig. = 0.798 > 0.05 indicates that there is no gender variance in Employee Engagement with the company. It is confirmed by Levene's test (sig = 0.000<0.05) that there is a dissimilarity in the variance of Employee Engagement with the company between age groups. An ANOVA study with sig = 0.013 < 0.05 is expected to suggest that Employee Engagement with the business is impacted by age differences. Age group differences in the factor of Employee engagement with the organization are statistically significant, according to the results of the ANOVA study. According to the analysis results in the table above, the Levene test with sig = 0.551 > 0.05 confirms that the variance of Employee Engagement with the organization between groups of Qualifications is not different, so the ANOVA analysis results are able to be utilized. ANOVA analysis with sig = 0.260 > 0.05 should conclude that there is no difference in Qualifications and does not influence Employee engagement. 5. Conclusion Of the total 591 investigators, 462 were male (accounting for 78,2%), and the rest were female. Among them, the number of interviewed people aged 31 - 45 accounted for the highest proportion at 65%. Analysis results show that all factors are reliable with Cronbach's Alpha coefficient greater than 0,7, specifically ranging from 0,868 to 0,942. The composite reliability (CR) of all factors is satisfactory with coefficients greater than 0,7 and in the range of 0,911 - 0,956. Finally, the total variance extracted (AVE) coefficient of the factors is greater than 0,5, meeting the testing requirements, ranging from 0,720 - 0,846 (Hair et al., 2014). The adjusted R-squared of Employee Attachment to the Organization (SGK) is 0.134, so the independent variable affecting it explains 13.4% of the variation in the variable Employee Attachment to the Organization position In the study, there are 3 variables that receive impact from other variables: HQ, NLLD, and SGK. The adjusted R-squared of Effectiveness (HQ) is 0.328, so the independent variables affecting it include Leadership Mindset Capacity (NLLD) and Cohesion (SGK) explains 32.8% variation (variance) of the HQ variable. The adjusted R-squared of Leadership Mindset Capacity (NLLD) is 0.784, so the independent variable affecting it explains 78.4% of the variation (variance) of the NLLD variable. The estimated model results show that the relationship between employee engagement and the organization has an impact on the business performance of small and medium-sized enterprises in Ho Chi Minh City with an impact coefficient of 0.089. That is if the employee's commitment to the organization increases or decreases by 1 unit, business performance increases or decreases by 0.089 units under the condition that other factors remain unchanged. 1360 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 1348-1361, 2024 DOI: 10.55214/25768484.v8i5.1836 © 2024 by the authors; licensee Learning Gate . Additionally, the estimated model results demonstrate the relationship between Leadership Mindset Capacity and employee attachment in the context of SMEs in Ho Chi Minh City with the impact factor of 0.141. That is, given other things stay constant, employee involvement in the organization will grow or decrease by 0.141 units if leadership thinking ability rises or decreases by one unit. The estimated model result shows that the impact of the relation of leadership mindset to the business efficiency of small and medium enterprises in Ho Chi Minh City with the impact factor is 0,555. Meaning if Leadership Mindset rises or falls by 1 unit then Business efficiency rises or falls by 0,555 units in the condition that other factors do not change Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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