Navigating Social Impact: Assessing Sustainability through UTAUT Model in India's Social Good Landscape

Navigating Social Impact: Assessing Sustainability through UTAUT Model in India's Social Good Landscape | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Navigating Social Impact: Assessing Sustainability through UTAUT Model in India's Social Good Landscape Anisha Arora, Prashant kumar Siddhey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3933523/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research investigates the transformative impact of social media on driving positive societal change, focusing on users in Punjab within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The study, encompassing 422 participants, employs a combination of surveys, interviews, and social media interaction observations. Findings highlight social media's pivotal role in shaping decisions for social good, influenced by performance expectations, social influence, effort, and a conducive environment. Risk and attitude emerge as crucial factors connecting social media use to engagement in charitable initiatives. The research adds originality by contextualizing insights within the Punjab region, contributing significantly to the understanding of technology acceptance in the realm of social good. Quantitative techniques reveal patterns, while qualitative data undergoes thematic analysis for nuanced insights. Social Media UTAUT Model Social Good Sustainability Smart PLS Figures Figure 1 Figure 2 1. Introduction There is a rapid advancement of mobile internet (Alalwan et al., 2017 )(C. Sun et al., 2023 ). The expression "social media," for instance, has many different definitions. The Encyclopaedia of the Polish Language explains this phrase(Urzędowska, 2021 ). It can be found widely in foreign literature (Katsoni, 2014 )as well as in Polish literature in the works of (Gryszel et al., 3923 );(Klepek & Starzyczná, 2018 ). Social media is described by (Kaplan & Haenlein, 2010 ), among other things, as a collection of Web 2.0-based tools that allow users to produce and share user-generated content (UGC) (Kaplan & Haenlein, 2010 ). Social media is an interconnected tool to interact (Cartwright et al., 2021 );(Dong & Lian, 2021)that helps acquaintances stay in touch, and its channel serves as a vital conduit enabling information as well as news(Zagidullin et al., 2021 ). The extensive adoption of social media as an instrument for promotion during the past ten years has drawn a sizable amount of research study, however, it can be said that this body of work is extremely dispersed and lacks distinct goals and insights(Li et al., 2023 ). Researches in the field of social media can be utilised to investigate variations in interests and views(Hardt & Glückstad, 2024 ). Everybody's everyday lives are progressively embracing more and more social networking. Recent years have seen remarkable growth in "social networking sites like Facebook, Instagram, Twitter, etc"(Katsoni, 2014 ). The literature related to marketing has given a lot of emphasis to online social media marketing(Lin et al., 2020 ). Customization, entertainment, trendiness, and interactivity are examples of social media marketing activities (SMMAs) that may significantly affect followers' perceptions of value and customer behavioural intentions(Bushara et al., 2023). Consumer interaction with business-to-business organisations on social media platforms is heavily influenced by social media message strategy. (Balaji et al., 2023 ) looked at how messaging sources, such as company versus employee-generated substance, and the contents of messages, such as emojis and factual information, affected social media engagement. The rapid and vast adoption of these technologies is changing how find partners, access information from the news, and organize to demand political change. During 2004, My Space emerged the initial social networking site to have a million engaged monthly users. Perhaps this marks the start of social media (Ortiz-Ospina & Roser, 2023 ). Knowing the growing importance of social networking sites in SE, an apparent increase in literature within this field is obvious, encompassing an array of fields of study, which include business, finance, and accounting (Cheung et al., 2020 ), ecological science (Ridley-Duff & Bull, 2015 ), psychology (Burger et al., 2018 ), technical fields. (Corradini et al., 2006 ), the humanities and social sciences (Chou et al., 2018 ), among others as well . With the social good inducement igniting people's inner motivations and promoting social media engagement, the social good incentive has a substantially stronger influence on social media engagement among consumers for advertisements(Siuki & Webster, 2021 ). People use social media platforms as a component of society to advocate for social change by denouncing bad social norms and/or advancing charitable causes(Bhatti et al., 2020 ). Decisions regarding purchases are positively and significantly impacted by social media marketing(Sudirjo et al., 2023 ). Data don't actively interact with those who distribute erroneous on social media(Micallef et al., 2020 ). However, non-expert social media users, often known as the general public or regular users, serve as on-the-ground eyes and actively challenge and refute disinformation, mainly new falsehoods(Micallef et al., 2020 );(Micallef et al., 2021 );(Miyazaki et al., 2022 );(Tully et al., 2020 );(Zhou et al., 2022 );(Vo & Lee, 2018 )Furthermore, On the UTAUT paradigm, numerous current investigations are based in various fields(Scur et al., 2023 );(Hanif & Lallie, 2021 ). The present study, nevertheless, adds additional variables to the UTAUT model, such as risk and attitude which will offer more insights. As a result, the following questions are addressed in the current study: Q1 What impact does social media have on volunteers, social groups or ventures as they choose social media for social good? Q2 Does risk affect how the elements of UTAUT and behavioural intention relate to one another? The paper was meticulously divided into several sections. The following section a literature review provides an outline of topics as well as a theoretical framework. The final section discusses the research strategy used to explore the phenomenon. Section four discusses the study's findings. Sections five and six outline the discussions and discoveries, whereas sections seven and eight examine the repercussions and restrictions. 2.1 Review of Literature UTAUT According to the literature evaluation, "Venkatesh et al., 2003 established UTAUT as an exhaustive compilation of existing technology acceptance research." The four UTAUT notions are social influence, performance expectancy, effort expectancy, and facilitating conditions(Venkatesh et al., 2003). It emphasises ongoing usage (Edo et al., 2023). (Vega et al., 2019) sheds further light on the most likely factors that influence how well technologies are received. With the UTAUT, issues like the risk of utilising current instructional methods will be addressed. UTAUT consolidates and extends various prior technology acceptance models, such as the Technology Acceptance Model (TAM) (Gupta et al., 2022), Theory of Planned Behaviour (TPB)(Xia et al., 2020), and the Diffusion of Innovations theory. According to the UTAUT model, every one of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions four elements have an impact on a person's intention to use a technology, which in turn has an impact on their intention. In other words, someone is more likely to plan to use a technology and that intention is likely to transfer into real usage if they believe it to be beneficial, simple to use, socially supported, and supported by their surroundings(Chatterjee et al., 2023). In order to explain and forecast user behaviour in a variety of scenarios, such as the acceptance of new software programmes, online platforms, and other electronic devices, the UTAUT model has been widely employed in the field of technology adoption research. It offers a thorough framework that academics and practitioners may use to examine and change the elements that motivate the adoption and use of technology. Researchers employ UTAUT's underlying principles and ideas for the acceptance and utilisation of technology for consumers. 2.2 Conceptual Model and Hypothesis Development Performance Expectancy (PE) “It is the degree to which using technology will provide benefits to consumers in performing certain activities" (Venkatesh et al., 2003). PE is closely associated with how well someone performs on their position and how much they believe using a certain system would aid them in meeting performance standards. PEbeen driven to have the greatest impact on someone's decision to use a certain system(Hoque & Sorwar, 2017). Users' perceptions of how useful and advantageous the technology is important. (PE) has a significant influence on the Behavioural Intention (BI)(Nikolopoulou et al., 2021). It is intriguing and proven that there is a relationship between behavioural intention (BI) and performance expectation (PE) (Guillén‐Gámez et al., 2023). Effort Expectancy (EE) EE is defined as the level of comfort users experience with the system when utilising it(Boontarig et al., 2012). EE according to(Venkatesh et al., 2003) is “the degree of ease associated with the use of the system”. It evaluates if consumers think the technology is easy to use and won't involve too much complexity or effort on their behalf. Behavioural intention (BI) is related to (EE)(Guillén‐Gámez et al., 2023). Effort expectancy and performance expectancy constructs data positively related to behavioural intentions(Aydin, 2023). Social Influence (SI) The term of SI is the conviction of an individual's significant others on the use of an innovative system(Wills & El-Gayar, 2008). (Venkatesh et al., 2003) defined SI as “the degree to which an individual perceives that important others believe he or she should use the new system”. Numerous studies demonstrate the significant importance of social influence on behavioural goals in social media(Puriwat & Tripopsakul, 2021);(Shi et al., 2022). It encompasses elements like peer pressure, supervisor approval, and other outside forces that might promote or prevent adoption.(SI) affected BI to use technology(Nikolopoulou et al., 2021). The theory has been proven since there is a statistically significant correlation among social influence (SI) and behavioural intention (BI).(Guillén‐Gámez et al., 2023) Facilitating Conditions (FI) The precise meaning of a FC is the existence of the administrative and technological components required to make an apparatus usable(Y. Sun et al., 2013). (Venkatesh et al., 2003) demonstrated “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system”. It covers topics including facilities, assistance with technology, development, and ensuring the accessibility of essential resources.(FC) on students’ intentions to use mobile internet technology was supported by Jayanth and Murugan(Jacob & Pattusamy, 2020). the influence of Facilitating Conditions (FC) on the Behavioural Intention (BI)(Yeop et al., 2019). Attitude An attitude toward a behaviour is defined as “the degree to which a person has a favourable or unfavourable evaluation or appraisal of behaviour in question”(Ajzen, 1991).(Ajzen & Fishbein, 1977) review and theoretical research revealed a weak and erratic relationship between attitude and behaviour. "The extent to which an individual has a favourable or negative evaluation or appraisal of the behaviour" is what the phrase "attitude towards a behaviour" refers to(Ajzen, 1991). Behavioural intention (BI) (Venkatesh et al., 2003) proposed BI is impacted by the constructs PE, EE, and SI. Actual use is significantly influenced by behavioural intention(Wills & El-Gayar, 2008). The person's readiness or purpose to employ the technology. BI, which (Davis, 1989)described as "a measure of the strength of one's intention to perform a specified behaviour" transferred from TRA to UTAUT. usage behaviour was not expressly described in UTAUT since system records contained the usage (Venkatesh et al., 2003). Use Behaviour The modified version of the UTAUT model illustrates how customers' behavioural intentions for utilising technology and their actual utilisation behaviours relate to one another(Kim & Kang, 2023). The actual use of the technology by the individual. The primary variables currently used as focal constructs to define technology adoption are use behaviour (i.e., usage) and behavioural intents(Aydin, 2023). Risk (Sharma et al., 2023) The risk was pointed out in the study as an integral determining element for social media sites. PR is frequently associated with risks associated with online payment transfers, particularly if actually making the transaction is impeding the financial transactions. Online consumers are aware of these worries, yet e-commerce companies still struggle with internet privacy and the protection of personal data (Amin et al., 2009). The risks or adverse effects that might result from using the technology. (Aydin, 2023)Privacy risk has a bad relationship with behavioural intentions. The research ((Alam et al., 2020);(Leong et al., 2020);(Alqahtani & Orji, 2020);(Guo et al., 2016) is supported by this finding. 3. Objectives The study will have following objectives: - To study the effect of utaut on use behavior. To identify the mediating effect of attitude on use behavior. To study the mediating effect of risk. To study the mediating effect of attitude. To validate the effect of behavior intention towards use behavior. 4. Research Methodology As the Punjab offer a wide range of social media users these cities are chosen as the study's target audience. Users who use SM platforms to obtain information regarding social good make up the sampled group. Using an adaptable questionnaire, data collected over the course of three months duration from July 2023 to September 2023, from The following is how the confessions employed during the examination data are modified: Performance Expectancy, Expected Effort, and Behavioural Intention ,facilitating conditions each have 4 components(Venkatesh et al., 2003);(Chao, 2019) social influence from(Tak & Panwar, 2017) (Venkatesh et al., 2012). Risk and attitude as mediating variables (Chayomchai et al., 2020);(Nguyen & Nguyen, 2017);(Harmon & Reddy-Best, 2020) .A "5-point Likert scale (1 = strongly disagree to 5 = strongly agree)" was used to measure the results. Overall, 422 responses data collected, and "Smart PLS Software 4 version using Partial Least Square Structural Equational Modelling" was used to conduct the ultimate evaluation. The choice of the population target being limited to Punjab in the research abstract might have been based on several justifiable reasons: Geographical Focus: Research often focuses on specific geographical regions to draw meaningful conclusions. Punjab is a well-defined and distinct region, and by concentrating on it, the research can provide in-depth insights into the impact of social media on social good activities within this particular context. Relevance: The researchers might have chosen Punjab because it is a region with specific social, economic, and cultural dynamics. By narrowing the scope to one region, they can provide more contextually relevant findings that are directly applicable to the local population's needs and experiences. Practical Considerations: Limiting the study to a specific region can make data collection and analysis more manageable. Researchers may have constraints on resources, time, and personnel, and focusing on one region allows for a more thorough and detailed investigation. Comparative Studies: Researchers may intend to use Punjab as a case study or baseline for future comparative research. By establishing a clear understanding of social media's impact in this region, they can later compare it with other regions or conduct cross-cultural analyses to identify similarities and differences. Policy Implications: The research could be designed to inform or influence policies and initiatives within Punjab specifically. By concentrating on one region, the findings can be used more effectively to shape local policies and strategies for social change. 5. Results 422 Samples were bootstrapped using SmartPLS 4.0 software for the estimation of parameters and verification of the hypothesis.(Abdulhakim et al., 2021)(Sarstedt et al., 2017)(Hair et al., 2014) data collected via Google forms from Punjab region. 5.1 Descriptive Analysis Table 1 presents details regarding the demographics of the study. Among the population, males constituted 47.1%, while females comprised 52.1%. which stated women use more social media than men results are similar to (Karatsoli & Nathanail, 2020).A majority of respondents, accounting for 64.3%, fell within the age range of 18 to 24, whereas 26.7% data aged between 24 and 34. In terms of marital status, 83.8% of participants data single, in contrast to 16.2% data are married. The largest segment of respondents (60.5%) held post-graduate degrees, with 67.1% being students and 17.6% employed. Within the respondent pool, 68.6% belonged to families consisting of three to five members and engage in doing more social media activities, while 67.6% resided in urban settings and they are more concerned about social media. Regarding religious affiliation, the majority (89%) identified as Hindus. Approximately 28.1% of individuals still relied on family members similar to (Karatsoli & Nathanail, 2020) students use more social media than employed, while 12.4% earned more than 10 lakhs independently. Among the respondents, 61% data involved in moderate levels of social good, and a subset data higher score of 35.2. 5.2 Results of Measurement model Table 2. Reliability and validity of the instrument Items Cronbach’s alpha Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) A 0.844 0.845 0.906 0.762 BI 0.876 0.879 0.910 0.668 EE 0.862 0.868 0.906 0.707 FC 0.884 0.886 0.928 0.811 PE 0.887 0.888 0.914 0.641 R 0.899 0.901 0.937 0.832 SI 0.880 0.887 0.912 0.675 The statistical measures in Table 2 assess the reliability and validity of a measurement instrument or scale. The dual Cronbach's alpha values for each item reflect strong internal consistency, exceeding the commonly accepted threshold of 0.7. The indicators used for the validation of the reliability were Cronbach α coefficient ≥0.7(Cronbach, 1951), the composite reliability index CR ≥0.7 (Hair Jr et al., 2017);(Alalwan et al., 2017), Similarly, both rho_a and rho_c values for each item also demonstrate robust internal consistency. The Average Variance Extracted (AVE) values ranging from 0.641 to 0.832 indicate that the items effectively measure their respective constructs with minimal measurement error, surpassing the guideline of AVE above 0.5. Extracted - AVE ≥0.5 (Fornell & Larcker, 1981). All five constructs meet the required criteria as the loading values are above 0.7 (Carmines & Zeller, 1979)(Hair et al., 2014). rho_a and rho_c above 0.6 accepted In summary, the data strongly suggests that the measurement instrument exhibits high internal consistency and construct validity, making it a well-constructed and reliable tool for assessing the underlying constructs represented by the items (A, BI, EE, FC, PE, R, SI). 5.2.1 Discriminant Validity Table 3. HTMT A BI EE FC PE R SI A BI 0.944 EE 0.870 0.874 FC 0.907 0.925 0.892 PE 0.810 0.848 0.864 0.769 R 0.359 0.380 0.400 0.278 0.289 SI 0.841 0.915 0.827 0.882 0.865 0.318 Table 3 Discriminant validity(Fornell & Larcker, 1981) HTMT suggested by (Henseler et al., 2016) Table displays a correlation matrix showing the relationships between various variables and sub-items within constructs (A, BI, EE, FC, PE, R, SI). The correlation coefficients range from -1 (perfect negative correlation) to 1 (perfect positive correlation), reflecting the strength and direction of linear associations. Positive correlations are predominant within individual constructs, such as "A," "BI," "EE," "FC," "PE," and "SI," indicating that the items within these constructs tend to move in the same direction. HTMT suggested by (Henseler et al., 2016)where the threshold value for conceptually different construct is <0.85 increased to <.95 is also okay (Gold et al., 2001) Conversely, the "R" construct exhibits negative correlations with the others, signifying an inverse relationship. The pattern of higher correlations within the same construct compared to those between different constructs suggests good discriminant validity, supporting the idea that the constructs measure distinct aspects. This correlation matrix offers valuable insights for researchers, enabling them to delve deeper into the interrelationships among these variables and potentially enhance their measurement instrument or research approach. 5.2.2 Outer Loading Table 4 Outer Loading A BI EE FC PE R SI A1 0.886 A2 0.869 A3 0.864 BI1 0.812 BI2 0.805 BI3 0.833 BI4 0.793 BI5 0.842 EE1 0.843 EE2 0.854 EE3 0.812 EE4 0.855 FC1 0.896 FC2 0.916 FC3 0.891 PE1 0.794 PE2 0.804 PE3 0.792 PE4 0.713 PE5 0.859 PE6 0.833 R1 0.880 R2 0.920 R3 0.936 SI1 0.789 SI2 0.781 SI3 0.869 SI4 0.855 SI5 0.809 Table 4 The provided table represent the strength of the relationships between observed variables and their respective latent constructs within a structural equation model (SEM). These numbers serve as indicators of how effectively the observed variables (A1, A2, BI1, BI2, etc.) measure and represent the underlying latent constructs (A, BI, EE, etc.). Higher loading values, closer to 1, indicate a stronger and more reliable connection, suggesting that the observed variables are good indicators of the constructs they are meant to represent. In this context, these outer loadings are essential for assessing the validity and accuracy of the structural model, ensuring that the chosen variables appropriately capture the intended latent concepts(Kee jiar & Yap, 2020). 5.2.3 F2 Table 5 F2 A BI EE FC PE R SI A 0.119 BI EE 0.036 0.005 0.055 FC 0.148 0.076 0.007 PE 0.029 0.027 0.002 R 0.017 SI 0.020 0.086 0.009 0.02 indicates a small effect, 0.15 a medium effect, and 0.35 a large effect. (Liu et al., 2021) The table 5 represents a matrix of numerical values, possibly reflecting relationships or interactions between various entities denoted by the row and column labels, such as A, BI, EE, FC, PE, R, and SI. The numbers in the table seem to suggest some form of association or similarity between these entities, with higher values indicating stronger connections. For example, there is a relatively high value of 0.148 between FC and PE, indicating a strong relationship, while other values like 0.036 between EE and A are relatively lower. This matrix could represent various aspects, such as correlations, similarities, or interactions, but without additional context or specific information about what these entities represent, it's challenging to provide a precise interpretation. 5.3.4 R-square Table 6 R 2 R-square A 0.688 BI 0.800 R 0.135 Table 6 The R-squared and adjusted R-squared values provide insights into the goodness of fit for regression models. In this context, for the variable "A," the R-squared value of 0.688 indicates that approximately 68.8% of the variance in the dependent variable can be explained by the independent variables in the model. The adjusted R-squared, slightly lower at 0.682, adjusts for the number of independent variables in the model, making it a more conservative measure of goodness of fit. For "BI," the R-squared is higher at 0.800, suggesting that around 80% of the variance in the dependent variable is accounted for by the independent variables, with the adjusted R-squared at 0.794. Conversely, for "R," the R-squared is notably lower at 0.135, indicating that only about 13.5% of the variance is explained by the independent variables, and the adjusted R-squared, even lower at 0.118, reflects the model's lower explanatory power. These statistics offer a concise assessment of how well the independent variables in the models explain the variation in the dependent variables, with "BI" having the highest explanatory power, "A" falling in between, and "R" having the least explained variance. 5.4 Structural Model Assessment In SmartPLS, a bootstrapping analysis was conducted with 10,000 subsamples to assess the significance of parameter estimates using a two-tailed test at a 0.05 significance level, while simultaneously generating bias-corrected confidence intervals. This robust resampling technique allows for a comprehensive examination of the reliability and precision of structural equation model parameters, facilitating a more thorough understanding of the relationships between variables in the analysed dataset. 5.4.1 Path Coefficient Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values A -> BI 0.278 0.273 0.080 3.465 0.001 EE -> A 0.196 0.188 0.095 2.054 0.040 EE -> BI 0.062 0.068 0.073 0.848 0.397 EE -> R 0.407 0.418 0.125 3.267 0.001 FC -> A 0.400 0.403 0.097 4.112 0.000 FC -> BI 0.248 0.252 0.083 2.981 0.003 FC -> R -0.149 -0.150 0.123 1.214 0.225 PE -> A 0.166 0.168 0.089 1.876 0.061 PE -> BI 0.131 0.127 0.067 1.970 0.049 PE -> R -0.072 -0.079 0.126 0.575 0.566 R -> BI 0.063 0.060 0.036 1.750 0.080 SI -> A 0.150 0.154 0.081 1.854 0.064 SI -> BI 0.250 0.250 0.074 3.363 0.001 SI -> R 0.161 0.158 0.136 1.190 0.234 Table 7 Path Coefficient Table 7 presents structural relationships between three latent constructs: A, BI, and R, assessed using structural equation modeling (SEM) or a similar statistical analysis. The table provides essential information, including estimated coefficients (O), sample means (M), standard deviations (STDEV), t-statistics (|O/STDEV|), and p-values for these relationships. The "FC > A -> BI" relationship stands out as highly statistically significant, supported by a t-statistic with an absolute value greater than 2 and a very low p-value (p A -> BI" and "PE -> A -> BI" relationships exhibit marginal significance, with t-statistics exceeding 1.5 but p-values slightly above 0.05 (p A -> BI" and "SI -> A -> BI," are deemed non-significant as their t-statistics fall below 2, and p-values exceed 0.05. In summary, this structural model assessment unveils the statistical significance of specific pathways between constructs, with the "FC -> A -> BI" relationship standing out as highly significant, while other relationships either marginally reach significance or do not meet the criteria at the given significance level. Researchers typically prioritize the significant relationships when interpreting and discussing the implications of their structural models. 5.4.2 Mediating Effect Table 8 Specific indirect effects PE -> A -> BI 0.046 SI -> A -> BI 0.042 EE -> R -> BI 0.026 FC -> R -> BI -0.009 EE -> A -> BI 0.055 PE -> R -> BI -0.005 SI -> R -> BI 0.010 FC -> A -> BI 0.111 Table 8 utlines specific indirect effects involving different variables. These effects describe how changes in one variable indirectly impact another through an intermediate variable. For instance, the positive indirect effect of 0.046 from PE to A and then to BI implies that an increase in PE is associated with an increase in BI through A. Conversely, the negative indirect effect of -0.009 from FC to R and then to BI suggests that an increase in FC is linked to a decrease in BI through R. These indirect effects help us understand the complex relationships and pathways between these variables in the overall system, shedding light on the consequences of changes in one variable on another through intermediary factors. 5.9 Model fit- Saturated model Estimated model SRMR 0.061 0.061 d_ULS 1.626 1.645 d_G 0.926 0.928 Chi-square 1085.113 1086.173 NFI 0.785 0.785 Table 9 The "Saturated Model" and the "Estimated Model" fit statistics, often employed in structural equation modeling (SEM) or similar analyses, are used to evaluate the model-data fit. The SRMR (Standardized Root Mean Square Residual) for both models are 0.061, indicating a similar level of fit regarding the standardized discrepancies between observed and expected correlations; lower SRMR values imply better fit. The d_ULS (Unweighted Least Squares) and d_G (Bentler's Comparative Fit Index) values are closely aligned between the models, with the Estimated Model having slightly higher values; these indices are less commonly used. The Chisquare statistic, a traditional measure of model fit, is very close for both models, reflecting similar fit levels. Notably, the NFI (Normed Fit Index) is identical at 0.785 for both models, suggesting a similar degree of fit improvement over a null model. In summary, various fit indices in the Saturated and Estimated Models yield similar results, signifying comparable model-we fit. It's advisable to consider multiple fit indices collectively rather than relying solely on one to assess model fit comprehensively, ensuring confidence in the structural models' validity. 5.10 Pls predict Q²predict A1 0.536 A2 0.433 A3 0.547 BI1 0.491 BI2 0.403 BI3 0.563 BI4 0.427 BI5 0.606 R1 0.088 R2 0.065 R3 0.072 The values provided in the "Q²predict" table appear to represent coefficients or statistics associated with the predictive quality of different variables or models. These values, which range from 0.065 to 0.606(Mat Roni, 2014), indicate the ability of the respective variables or models to predict or explain variations in the data. Higher values, such as 0.606 for "BI5," suggest a stronger predictive quality, while lower values, like 0.065 for "R2," indicate a weaker predictive ability. These statistics are valuable in assessing the performance of predictive models or variables in explaining variations in a given dataset, with the higher Q²predict values signifying a better predictive fit. 6. Discussion and Conclusion The study examined how social media (SM) impacts individuals' choices related to social good initiatives, utilizing factors from the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The findings of the study we consistent with prior research, indicating significant associations between Perceived Ease of Use (PE) and Behavioural Intention (BI) (Gutiérrez & Herrero-Crespo, 2012 )and between Effort Expectancy (EE) and BI(Tak & Panwar, 2017 ). This implies that individuals not only consider the perceived benefits of using social media for social good but also take into account the effort required to engage with these platforms. Study shows that individuals are largely impacted by attitude and decisions are influenced by risk factor similar to(Arifin et al., 2018 ) (Karatsoli & Nathanail, 2020 ) Furthermore, the study confirmed the influence of Social Influence (SI) on BI, aligning with earlier research. This suggests that peer groups and social interactions on SM platforms play a significant role in individuals' decision-making processes related to social good initiatives. People seek guidance and are influenced by the activities and opinions of their social media connections concerning charitable or socially beneficial causes. Additionally, the study underscored the importance of Risk in shaping intentions for technology adoption, including the use of social media for promoting social good. The results supported the impact of risk and attitude on behavioural intentions, indicating that individuals are cautious about sharing information and participating in activities related to social good online, particularly on social media platforms. Furthermore, the study examined risk as a mediator and found that it significantly influenced the impact of on BI. This suggests that risk and attitude play a more crucial role when it comes to their engagement in social media-driven social good activities. Performance expectancy, effort expectancy, social influence and facilitating conditions have direct relation with risk results are similar to (Shaikh et al., 2018 );(Xie et al., 2021 ). Attitude influences the decisions of individual similar to (Dwivedi et al., 2019 );(Iqbal et al., 2020 ) In conclusion, the study revealed that social media platforms have a substantial influence on individuals' behavioural intentions in social good initiatives. People turn to social media to gather information and participate in various activities related to charitable causes and social betterment. This underscores the importance of leveraging social media for promoting social good and suggests that organizations and initiatives can expand their reach and impact by effectively utilizing social media in their efforts to bring about positive social change. 7. Implication 7.1 Theoretical The current study offers valuable insights into the adoption behaviour of individuals when using social media for social good initiatives. It confirms the factors that influence people's behaviour when utilizing social media for such purposes. Additionally, this study contributes to the theoretical understanding in three distinct ways. Firstly, this research can be considered one of the pioneering empirical investigations into individuals' use of social media as a source of information for supporting social causes. This opens up opportunities for academia to delve deeper into various aspects of human behaviour related to social media and its impact on societal well-being. Secondly, this study introduces new variables like risk, attitude into the Unified Theory of Acceptance and Use of Technology (UTAUT) model, shedding more light on individuals' behavioural intentions to engage with social media for social good. Attitude and risk are critical factors that can significantly influence adoption behaviour, particularly in the context of technology for social good. The incorporation of these variables and the resulting findings suggest possibilities for extending or modifying the UTAUT model by integrating these factors into it. Thirdly, the study examines the mediating effect of attitude and risk on the relationship between various variables, highlighting potential differences in behaviour when it comes to using social media for social good. This aspect provides an avenue for in-depth exploration of how risk influences participation in social causes through social media. 7.2 Managerial The study's outcomes are aimed at offering valuable insights to key stakeholders in the realm of social media for social good, including organizations focused on social causes, nonprofits, government agencies, and policymakers. These research findings can be a valuable resource for these entities, aiding them in understanding user behaviour and optimizing their efforts to promote social causes through social media. It is imperative for social cause organizations and advocates to recognize the significance of social media in their endeavours. Social media networking platforms play a pivotal role in connecting with individuals who prefer technology-based solutions and web-based engagement for social good initiatives. Furthermore, incorporating social media into their outreach strategies enables these organizations to efficiently disseminate crucial information to their target audiences. There are several activities that can be undertaken to promote social causes effectively through social media. These include creating dedicated social media profiles or accounts on various platforms that resonate with the cause, sharing sought-after information on prominent social media platforms, and fostering a virtual community where past and current supporters can engage and share their valuable experiences. Attitude and risk are vital factors influencing engagement in social causes through social media. Advocates and organizations can proactively work to build trust and reduce risk perceptions among their target audience. Collecting and sharing testimonials from a diverse range of individuals who have been positively impacted by the social cause can help build trust and alleviate concerns. Government agencies can also play a role by actively using social media to communicate their commitment to addressing social issues and providing assistance to those in need. Additionally, government entities and policymakers can explore initiatives aimed at branding and promoting social causes on social media platforms. They can collaborate with businesses known for their strong social reputations to further these causes and drive positive change within society through digital channels. 8. Limitation and Future Research This study's findings should be interpreted in the context of several limitations from a social media perspective focused on driving positive societal change. Firstly, it's important to recognize that this study employed a cross-sectional design, which offers a snapshot in time. Future research could potentially adopt longitudinal approaches to provide a deeper understanding of how social media impacts societal change over time. Secondly, this study primarily examined the overall use of social media for promoting positive societal change. However, different social media platforms may play distinct roles and exert various influences on these efforts. Subsequent research could investigate the specific contributions of individual platforms in the context of fostering societal benefits. Thirdly, to advance our understanding, future studies might consider incorporating additional variables such as hedonic motivation or other relevant theories into the conceptual model. This could help to better elucidate the underlying mechanisms driving social media's impact on societal change. Additionally, considering the role of risk and attitude as a potential mediator in the relationship between social media and societal benefits could offer a more comprehensive view of the dynamics at play in the realm of social media-driven positive societal change. Investigating how risk perceptions may influence the effectiveness of social media campaigns aimed at societal improvement could yield valuable insights for future research in this domain. Investigating social media for Social Good: Exposing Restrictions We have learned a lot on our road towards using social media for the greater good, but we shouldn't ignore the challenges we've faced along the way. Here is a list of the things we need to remember: Snapshots in Time : Although our research has given readers a clear image of the present, it's crucial to remember that we have also caught a specific point in time. Future studies should include taking the long view using the longitudinal study to fully understand the progression and impact. By doing so, we can follow the progress over time and identify any dynamic changes. Full Spectrum of SM : Our attention was focused on the global perspective of social media for social good. However, the SM landscape is a rich mosaic, with diverse platforms serving a variety of purposes. Future investigations could concentrate on other social media sites. Expanding the Canvas : Although the colours in our changeable palette have been vivid, there is always potential for new hues. Hedonic drive and facilitating conditions are simply two colours that might add depth to our conceptual masterpiece. In addition to them, attitude and risk may function as mediating, directing the good effects of social media. Actual Usage Behaviour : The future researchers can beyond assessing behavioural intentions and also examines the actual usage behaviour in the context of social media for social good. Declarations Author Contribution "Anisha Arora wrote the main manuscript.""Prashant Kumar Siddhey reviewed the manuscript. " References Abdulhakim, A., Amponsah, S., Patrick, O.-D., & Addo, S. (2021). Ali et al 2018 . 50425– 50427. Abdur Rahman, K. (2012). 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Government policies and attitudes to social media use among users in Turkey: The role of awareness of policies, political involvement, online trust, and party identification. Technology in Society , 67 , 101708. https://doi.org/https://doi.org/10.1016/j.techsoc.2021.101708 Zhou, X., Shu, K., Phoha, V., Liu, H., & Zafarani, R. (2022). “This is Fake! Shared it by Mistake”:Assessing the Intent of Fake News Spreaders . https://doi.org/10.1145/3485447.3512264 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1DemographicProfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3933523","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271596686,"identity":"3e15bed9-1e9f-4ba3-aae8-917a742fc164","order_by":0,"name":"Anisha Arora","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYFACxgZmGPNAQoWNHJjxgEgtjA8enEkzhuglYA9MC7Phw7bDiQ0gJj4t/LObmz8X1Njl80sffiaR2HY4fX7Y4YdAW+zkdBuwa5G4c7BNesaxZMuZfWlmEgnn0nM33k4zAGpJNjY7gMOaG4ltzDxszAYGZxiAWsqsczfOTgBpOZC4DYcW+RuJzZ95/tUDtbB/k0hgY043nJ3+Aa8WgxuJDdK8bYeBWniMDRLanBPkpXPw22IIdJg0b99xA8kensIHCWfSDDdI5xQcSDDA7Re5G+mPP/N8qzbg52HfcPBHhY28/Oz0zR8+VNjJ4fQ+plPBKg2IVQ4C8g2kqB4Fo2AUjIKRAABgv2SHbGqYgwAAAABJRU5ErkJggg==","orcid":"","institution":"Chandigarh University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Anisha","middleName":"","lastName":"Arora","suffix":""},{"id":271596687,"identity":"c7305ccc-da10-4484-8a0c-9108cdfa3b94","order_by":1,"name":"Prashant kumar Siddhey","email":"","orcid":"","institution":"Chandigarh University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"kumar","lastName":"Siddhey","suffix":""}],"badges":[],"createdAt":"2024-02-06 10:14:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3933523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3933523/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50937829,"identity":"db4759dc-7505-40f9-a552-0efaf4547a21","added_by":"auto","created_at":"2024-02-09 21:22:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193776,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3933523/v1/52db2a769af747039b46cef6.png"},{"id":50937828,"identity":"c21f207a-8898-48f0-935b-8b57b138eb19","added_by":"auto","created_at":"2024-02-09 21:22:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171260,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3933523/v1/772787270f7b62da0d3c54cc.png"},{"id":51431949,"identity":"eb372211-a8f6-43df-b366-0699e2733b2c","added_by":"auto","created_at":"2024-02-21 13:44:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1098513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3933523/v1/b10b3720-c79a-4476-b706-30facb49f61e.pdf"},{"id":50937827,"identity":"d2bcb666-d7f4-4512-ab6d-af9d363e2b75","added_by":"auto","created_at":"2024-02-09 21:22:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22618,"visible":true,"origin":"","legend":"","description":"","filename":"Table1DemographicProfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-3933523/v1/cbd630410cb791a1c504e967.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Navigating Social Impact: Assessing Sustainability through UTAUT Model in India's Social Good Landscape","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThere is a rapid advancement of mobile internet (Alalwan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)(C. Sun et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The expression \"social media,\" for instance, has many different definitions. The Encyclopaedia of the Polish Language explains this phrase(Urzędowska, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can be found widely in foreign literature (Katsoni, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)as well as in Polish literature in the works of (Gryszel et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e3923\u003c/span\u003e);(Klepek \u0026amp; Starzyczn\u0026aacute;, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Social media is described by (Kaplan \u0026amp; Haenlein, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), among other things, as a collection of Web 2.0-based tools that allow users to produce and share user-generated content (UGC) (Kaplan \u0026amp; Haenlein, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial media is an interconnected tool to interact (Cartwright et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e);(Dong \u0026amp; Lian,\u003c/p\u003e \u003cp\u003e2021)that helps acquaintances stay in touch, and its channel serves as a vital conduit enabling information as well as news(Zagidullin et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The extensive adoption of social media as an instrument for promotion during the past ten years has drawn a sizable amount of research study, however, it can be said that this body of work is extremely dispersed and lacks distinct goals and insights(Li et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearches in the field of social media can be utilised to investigate variations in interests and views(Hardt \u0026amp; Gl\u0026uuml;ckstad, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Everybody's everyday lives are progressively embracing more and more social networking. Recent years have seen remarkable growth in \"social networking sites like Facebook, Instagram, Twitter, etc\"(Katsoni, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The literature related to marketing has given a lot of emphasis to online social media marketing(Lin et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Customization, entertainment, trendiness, and interactivity are examples of social media marketing activities (SMMAs) that may significantly affect followers' perceptions of value and customer behavioural intentions(Bushara et al., 2023).\u003c/p\u003e \u003cp\u003eConsumer interaction with business-to-business organisations on social media platforms is heavily influenced by social media message strategy. (Balaji et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) looked at how messaging sources, such as company versus employee-generated substance, and the contents of messages, such as emojis and factual information, affected social media engagement. The rapid and vast adoption of these technologies is changing how find partners, access information from the news, and organize to demand political change. During 2004, My Space emerged the initial social networking site to have a million engaged monthly users. Perhaps this marks the start of social media (Ortiz-Ospina \u0026amp; Roser, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Knowing the growing importance of social networking sites in SE, an apparent increase in literature within this field is obvious, encompassing an array of fields of study, which include business, finance, and accounting (Cheung et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), ecological science (Ridley-Duff \u0026amp; Bull, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), psychology (Burger et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), technical fields. (Corradini et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the humanities and social sciences (Chou et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), among others as well .\u003c/p\u003e \u003cp\u003eWith the social good inducement igniting people's inner motivations and promoting social media engagement, the social good incentive has a substantially stronger influence on social media engagement among consumers for advertisements(Siuki \u0026amp; Webster, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). People use social media platforms as a component of society to advocate for social change by denouncing bad social norms and/or advancing charitable causes(Bhatti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Decisions regarding purchases are positively and significantly impacted by social media marketing(Sudirjo et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Data don't actively interact with those who distribute erroneous on social media(Micallef et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, non-expert social media users, often known as the general public or regular users, serve as on-the-ground eyes and actively challenge and refute disinformation, mainly new falsehoods(Micallef et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e);(Micallef et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e);(Miyazaki et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e);(Tully et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2020\u003c/span\u003e);(Zhou et al., \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2022\u003c/span\u003e);(Vo \u0026amp; Lee, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)Furthermore, On the UTAUT paradigm, numerous current investigations are based in various fields(Scur et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2023\u003c/span\u003e);(Hanif \u0026amp; Lallie, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study, nevertheless, adds additional variables to the UTAUT model, such as risk and attitude which will offer more insights. As a result, the following questions are addressed in the current study:\u003c/p\u003e \u003cp\u003e \u003cb\u003eQ1 What impact does social media have on volunteers, social groups or ventures as they choose social media for social good?\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eQ2 Does risk affect how the elements of UTAUT and behavioural intention relate to one another?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe paper was meticulously divided into several sections. The following section a literature review provides an outline of topics as well as a theoretical framework. The final section discusses the research strategy used to explore the phenomenon. Section four discusses the study's findings. Sections five and six outline the discussions and discoveries, whereas sections seven and eight examine the repercussions and restrictions.\u003c/p\u003e "},{"header":"2.1 Review of Literature","content":"\u003cp\u003e\u003cstrong\u003eUTAUT \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the literature evaluation, \u0026quot;Venkatesh et al., 2003 established UTAUT as an exhaustive compilation of existing technology acceptance research.\u0026quot; The four UTAUT notions are social influence, performance expectancy, effort expectancy, and facilitating conditions(Venkatesh et al., 2003). It emphasises ongoing usage (Edo et al., 2023). (Vega et al., 2019) sheds further light on the most likely factors that influence how well technologies are received. With the UTAUT, issues like the risk of utilising current instructional methods will be addressed. UTAUT consolidates and extends various prior technology acceptance models, such as the Technology Acceptance Model (TAM) (Gupta et al., 2022), Theory of Planned Behaviour (TPB)(Xia et al., 2020), and the Diffusion of Innovations theory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;According to the UTAUT model, every one of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions four elements have an impact on a person\u0026apos;s intention to use a technology, which in turn has an impact on their intention. In other words, someone is more likely to plan to use a technology and that intention is likely to transfer into real usage if they believe it to be beneficial, simple to use, socially supported, and supported by their surroundings(Chatterjee et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to explain and forecast user behaviour in a variety of scenarios, such as the acceptance of new software programmes, online platforms, and other electronic devices, the UTAUT model has been widely employed in the field of technology adoption research. It offers a thorough framework that academics and practitioners may use to examine and change the elements that motivate the adoption and use of technology. Researchers employ UTAUT\u0026apos;s underlying principles and ideas for the acceptance and utilisation of technology for consumers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Conceptual Model and Hypothesis Development\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePerformance Expectancy (PE)\u0026nbsp;\u003cbr\u003e\u0026ldquo;It is the degree to which using technology will provide benefits to consumers in performing certain activities\u0026quot; (Venkatesh et al., 2003). PE is closely associated with how well someone performs on their position and how much they believe using a certain system would aid them in meeting performance standards. PEbeen driven to have the greatest impact on someone\u0026apos;s decision to use a certain system(Hoque \u0026amp; Sorwar, 2017). Users\u0026apos; perceptions of how useful and advantageous the technology is important. (PE) has a significant influence on the Behavioural Intention (BI)(Nikolopoulou et al., 2021). It is intriguing and proven that there is a relationship between behavioural intention (BI) and performance expectation (PE) (Guill\u0026eacute;n‐G\u0026aacute;mez et al., 2023).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEffort Expectancy (EE)\u0026nbsp;\u003c/strong\u003eEE is defined as the level of comfort users experience with the system when utilising it(Boontarig et al., 2012). EE according to(Venkatesh et al., 2003) is \u0026ldquo;the degree of ease associated with the use of the system\u0026rdquo;. It evaluates if consumers think the technology is easy to use and won\u0026apos;t involve too much complexity or effort on their behalf. Behavioural intention (BI) is related to (EE)(Guill\u0026eacute;n‐G\u0026aacute;mez et al., 2023). Effort expectancy and performance expectancy constructs data positively related to behavioural intentions(Aydin, 2023).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSocial Influence (SI)\u0026nbsp;\u003c/strong\u003eThe term of SI is the conviction of an individual\u0026apos;s significant others on the use of an innovative system(Wills \u0026amp; El-Gayar, 2008). (Venkatesh et al., 2003) defined SI as \u0026ldquo;the degree to which an individual perceives that important others believe he or she should use the new system\u0026rdquo;. Numerous studies demonstrate the significant importance of social influence on behavioural goals in social media(Puriwat \u0026amp; Tripopsakul, 2021);(Shi et al., 2022). It encompasses elements like peer pressure, supervisor approval, and other outside forces that might promote or prevent adoption.(SI) affected BI to use technology(Nikolopoulou et al., 2021). The theory has been proven since there is a statistically significant correlation among social influence (SI) and behavioural intention (BI).(Guill\u0026eacute;n‐G\u0026aacute;mez et al., 2023)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFacilitating Conditions (FI)\u003c/strong\u003e The precise meaning of a FC is the existence of the administrative and technological components required to make an apparatus usable(Y. Sun et al., 2013). (Venkatesh et al., 2003) demonstrated \u0026ldquo;the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system\u0026rdquo;. It covers topics including facilities, assistance with technology, development, and ensuring the accessibility of essential resources.(FC) on students\u0026rsquo; intentions to use mobile internet technology was supported by Jayanth and Murugan(Jacob \u0026amp; Pattusamy, 2020). the influence of Facilitating Conditions (FC) on the Behavioural Intention (BI)(Yeop et al., 2019).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAttitude\u0026nbsp;\u003c/strong\u003eAn attitude toward a behaviour is defined as \u0026ldquo;the degree to which a person has a favourable or unfavourable evaluation or appraisal of behaviour in question\u0026rdquo;(Ajzen, 1991).(Ajzen \u0026amp; Fishbein, 1977) review and theoretical research revealed a weak and erratic relationship between attitude and behaviour. \u0026quot;The extent to which an individual has a favourable or negative evaluation or appraisal of the behaviour\u0026quot; is what the phrase \u0026quot;attitude towards a behaviour\u0026quot; refers to(Ajzen, 1991).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBehavioural intention (BI)\u0026nbsp;\u003c/strong\u003e(Venkatesh et al., 2003) proposed BI is impacted by the constructs PE, EE, and SI. Actual use is significantly influenced by behavioural intention(Wills \u0026amp; El-Gayar, 2008). The person\u0026apos;s readiness or purpose to employ the technology. BI, which (Davis, 1989)described as \u0026quot;a measure of the strength of one\u0026apos;s intention to perform a specified behaviour\u0026quot; \u0026nbsp;transferred from TRA to UTAUT. usage behaviour was not expressly described in UTAUT since system records contained the usage (Venkatesh et al., 2003).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUse Behaviour\u0026nbsp;\u003c/strong\u003eThe modified version of the UTAUT model illustrates how customers\u0026apos; behavioural intentions for utilising technology and their actual utilisation behaviours relate to one another(Kim \u0026amp; Kang, 2023).\u0026nbsp;The actual use of the technology by the individual. The primary variables currently used as focal constructs to define technology adoption are use behaviour (i.e., usage) and behavioural intents(Aydin, 2023).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRisk \u0026nbsp;\u003c/strong\u003e(Sharma et al., 2023) The risk was pointed out in the study as an integral determining element for social media sites. PR is frequently associated with risks associated with online payment transfers, particularly if actually making the transaction is impeding the financial transactions. Online consumers are aware of these worries, yet e-commerce companies still struggle with internet privacy and the protection of personal data (Amin et al., 2009). The risks or adverse effects that might result from using the technology. (Aydin, 2023)Privacy risk has a bad relationship with behavioural intentions. The research ((Alam et al., 2020);(Leong et al., 2020);(Alqahtani \u0026amp; Orji, 2020);(Guo et al., 2016) \u0026nbsp;is supported by this finding.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. Objectives ","content":"\u003cp\u003eThe study will have following objectives: -\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo study the effect of utaut on use behavior.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo identify the mediating effect of attitude on use behavior.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo study the mediating effect of risk.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo study the mediating effect of attitude.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo validate the effect of behavior intention towards use behavior.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"4. Research Methodology ","content":"\u003cp\u003e\u003cstrong\u003eAs the Punjab\u0026nbsp;\u003c/strong\u003eoffer a wide range of social media users these cities are chosen as the study's target audience. Users who use SM platforms to obtain information regarding social good make up the sampled group. Using an adaptable questionnaire, data collected over the course of three months duration from July 2023 to September 2023, from The following is how the confessions employed during the examination data are modified: Performance Expectancy, Expected Effort, and Behavioural Intention ,facilitating conditions each have 4 components(Venkatesh et al., 2003);(Chao, 2019) social influence from(Tak \u0026amp; Panwar, 2017) (Venkatesh et al., 2012). Risk and attitude as mediating variables (Chayomchai et al., 2020);(Nguyen \u0026amp; Nguyen, 2017);(Harmon \u0026amp; Reddy-Best, 2020) .A \"5-point Likert scale (1 = strongly disagree to 5 = strongly agree)\" was used to measure the results. Overall, \u003cstrong\u003e422\u0026nbsp;\u003c/strong\u003eresponses data collected, and \"Smart PLS Software 4 version using Partial Least Square Structural Equational Modelling\" was used to conduct the ultimate evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe choice of the population target being limited to Punjab in the research abstract might have been based on several justifiable reasons:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographical Focus: Research often focuses on specific geographical regions to draw meaningful conclusions. Punjab is a well-defined and distinct region, and by concentrating on it, the research can provide in-depth insights into the impact of social media on social good activities within this particular context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelevance: The researchers might have chosen Punjab because it is a region with specific social, economic, and cultural dynamics. By narrowing the scope to one region, they can provide more contextually relevant findings that are directly applicable to the local population's needs and experiences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePractical Considerations: Limiting the study to a specific region can make data collection and analysis more manageable. Researchers may have constraints on resources, time, and personnel, and focusing on one region allows for a more thorough and detailed investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparative Studies: Researchers may intend to use Punjab as a case study or baseline for future comparative research. By establishing a clear understanding of social media's impact in this region, they can later compare it with other regions or conduct cross-cultural analyses to identify similarities and differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolicy Implications: The research could be designed to inform or influence policies and initiatives within Punjab specifically. By concentrating on one region, the findings can be used more effectively to shape local policies and strategies for social change.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Results ","content":"\u003cp\u003e422 Samples were bootstrapped using SmartPLS 4.0 software for the estimation of parameters and verification of the hypothesis.(Abdulhakim et al., 2021)(Sarstedt et al., 2017)(Hair et al., 2014) data collected via Google forms from Punjab region.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e5.1 Descriptive Analysis \u0026nbsp;\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents details regarding the demographics of the study. Among the population, males constituted 47.1%, while females comprised 52.1%. which stated women use more social media than men results are similar to (Karatsoli \u0026amp; Nathanail, 2020).A majority of respondents, accounting for 64.3%, fell within the age range of 18 to 24, whereas 26.7% data aged between 24 and 34. In terms of marital status, 83.8% of participants data single, in contrast to 16.2% data are married. The largest segment of respondents (60.5%) held post-graduate degrees, with 67.1% being students and 17.6% employed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin the respondent pool, 68.6% belonged to families consisting of three to five members and engage in doing more social media activities, while 67.6% resided in urban settings and they are more concerned about social media. Regarding religious affiliation, the majority (89%) identified as Hindus. Approximately 28.1% of individuals still relied on family members similar to (Karatsoli \u0026amp; Nathanail, 2020) students use more social media than employed, while 12.4% earned more than 10 lakhs independently. Among the respondents, 61% data involved in moderate levels of social good, and a subset data higher score of 35.2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Results of Measurement model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Reliability and validity of the instrument\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s alpha \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026apos;s alpha \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ereliability (rho_a)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ereliability (rho_c)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage variance extracted (AVE)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.844\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.845\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.906\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.762\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.876\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.879\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.910\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.668\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.862\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.868\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.906\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.707\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.884\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.886\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.928\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.811\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.887\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.888\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.914\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.641\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.899\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.901\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.937\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.832\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.880\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.887\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.912\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.675\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe statistical measures in Table 2 assess the reliability and validity of a measurement instrument or scale. The dual Cronbach\u0026apos;s alpha values for each item reflect strong internal consistency, exceeding the commonly accepted threshold of 0.7. The indicators used for the validation of the reliability were Cronbach \u0026alpha; coefficient \u0026ge;0.7(Cronbach, 1951), the composite reliability index CR \u0026ge;0.7 (Hair Jr et al., 2017);(Alalwan et al., 2017), Similarly, both rho_a and rho_c values for each item also demonstrate robust internal consistency. The Average Variance Extracted (AVE) values ranging from 0.641 to 0.832 indicate that the items effectively measure their respective constructs with minimal measurement error, surpassing the guideline of AVE above 0.5. Extracted - AVE \u0026ge;0.5 (Fornell \u0026amp; Larcker, 1981). All five constructs meet the required criteria as the loading values are above 0.7 (Carmines \u0026amp; Zeller, 1979)(Hair et al., 2014). rho_a and rho_c above 0.6 accepted \u0026nbsp;In summary, the data strongly suggests that the measurement instrument exhibits high internal consistency and construct validity, making it a well-constructed and reliable tool for assessing the underlying constructs represented by the items (A, BI, EE, FC, PE, R, SI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.1 Discriminant Validity \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. HTMT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.944\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.870\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.874\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.907\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.925\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.892\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.810\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.848\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.864\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.769\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.359\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.380\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.400\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.278\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.289\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.841\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.915\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.827\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.882\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.865\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.318\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Discriminant validity(Fornell \u0026amp; Larcker, 1981) HTMT suggested by (Henseler et al., 2016)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable displays a correlation matrix showing the relationships between various variables and sub-items within constructs (A, BI, EE, FC, PE, R, SI). The correlation coefficients range from -1 (perfect negative correlation) to 1 (perfect positive correlation), reflecting the strength and direction of linear associations. Positive correlations are predominant within individual constructs, such as \u0026quot;A,\u0026quot; \u0026quot;BI,\u0026quot; \u0026quot;EE,\u0026quot; \u0026quot;FC,\u0026quot; \u0026quot;PE,\u0026quot; and \u0026quot;SI,\u0026quot; indicating that the items within these constructs tend to move in the same direction. HTMT suggested by (Henseler et al., 2016)where the threshold value for conceptually different construct is \u0026lt;0.85 increased to \u0026lt;.95 is also okay \u0026nbsp;(Gold et al., 2001) Conversely, the \u0026quot;R\u0026quot; construct exhibits negative correlations with the others, signifying an inverse relationship. The pattern of higher correlations within the same construct compared to those between different constructs suggests good discriminant validity, supporting the idea that the constructs measure distinct aspects. This correlation matrix offers valuable insights for researchers, enabling them to delve deeper into the interrelationships among these variables and potentially enhance their measurement instrument or research approach.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.2 Outer Loading \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 Outer Loading \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.886\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.864\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.812\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.805\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.793\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.842\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.843\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.854\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.812\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.855\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.896\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.916\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.891\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.794\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.804\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.792\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.713\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.936\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.781\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.855\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.809\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 The provided table represent the strength of the relationships between observed variables and their respective latent constructs within a structural equation model (SEM). These numbers serve as indicators of how effectively the observed variables (A1, A2, BI1, BI2, etc.) measure and represent the underlying latent constructs (A, BI, EE, etc.). Higher loading values, closer to 1, indicate a stronger and more reliable connection, suggesting that the observed variables are good indicators of the constructs they are meant to represent. In this context, these outer loadings are essential for assessing the validity and accuracy of the structural model, ensuring that the chosen variables appropriately capture the intended latent concepts(Kee jiar \u0026amp; Yap, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.3 F2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 F2\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.119\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eBI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eEE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e0.02 indicates a small effect, 0.15 a medium effect, and 0.35 a large effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Liu et al., 2021)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe table 5 represents a matrix of numerical values, possibly reflecting relationships or interactions between various entities denoted by the row and column labels, such as A, BI, EE, FC, PE, R, and SI. The numbers in the table seem to suggest some form of association or similarity between these entities, with higher values indicating stronger connections. For example, there is a relatively high value of 0.148 between FC and PE, indicating a strong relationship, while other values like 0.036 between EE and A are relatively lower. This matrix could represent various aspects, such as correlations, similarities, or interactions, but without additional context or specific information about what these entities represent, it\u0026apos;s challenging to provide a precise interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3.4 R-square\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"401\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-square\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.688\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.800\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.135\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe R-squared and adjusted R-squared values provide insights into the goodness of fit for regression models. In this context, for the variable \u0026quot;A,\u0026quot; the R-squared value of 0.688 indicates that approximately 68.8% of the variance in the dependent variable can be explained by the independent variables in the model. The adjusted R-squared, slightly lower at 0.682, adjusts for the number of independent variables in the model, making it a more conservative measure of goodness of fit. For \u0026quot;BI,\u0026quot; the R-squared is higher at 0.800, suggesting that around 80% of the variance in the dependent variable is accounted for by the independent variables, with the adjusted R-squared at 0.794. Conversely, for \u0026quot;R,\u0026quot; the R-squared is notably lower at 0.135, indicating that only about 13.5% of the variance is explained by the independent variables, and the adjusted R-squared, even lower at 0.118, reflects the model\u0026apos;s lower explanatory power. These statistics offer a concise assessment of how well the independent variables in the models explain the variation in the dependent variables, with \u0026quot;BI\u0026quot; having the highest explanatory power, \u0026quot;A\u0026quot; falling in between, and \u0026quot;R\u0026quot; having the least explained variance.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5.4 Structural Model Assessment\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn SmartPLS, a bootstrapping analysis was conducted with 10,000 subsamples to assess the significance of parameter estimates using a two-tailed test at a 0.05 significance level, while simultaneously generating bias-corrected confidence intervals. This robust resampling technique allows for a comprehensive examination of the reliability and precision of structural equation model parameters, facilitating a more thorough understanding of the relationships between variables in the analysed dataset.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e5.4.1 Path Coefficient \u0026nbsp;\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOriginal sample (O)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample mean (M)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard deviation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(STDEV)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT \u0026nbsp; \u0026nbsp; \u0026nbsp; statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(|O/STDEV|)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP values\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.278\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.273\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.080\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.465\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE -\u0026gt; A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.196\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.188\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.095\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.054\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.062\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.068\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.073\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.848\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.397\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE -\u0026gt; R\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.407\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.418\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.125\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.267\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC -\u0026gt; A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.400\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.403\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.097\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.112\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.248\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.252\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.083\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.981\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC -\u0026gt; R\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.149\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.150\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.123\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.214\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.225\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE -\u0026gt; A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.166\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.168\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.089\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.876\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.061\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.131\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.127\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.970\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE -\u0026gt; R\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.072\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.079\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.126\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.575\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.566\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.063\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.060\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.750\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.080\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI -\u0026gt; A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.150\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.154\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.081\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.854\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.064\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.250\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.250\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.074\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.363\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.177693761814744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI -\u0026gt; R\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8241965973535%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.161\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.446124763705104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.158\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.136\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.281663516068054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.190\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.879017013232515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.234\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 Path Coefficient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7 presents structural relationships between three latent constructs: A, BI, and R, assessed using structural equation modeling (SEM) or a similar statistical analysis. The table provides essential information, including estimated coefficients (O), sample means (M), standard deviations (STDEV), t-statistics (|O/STDEV|), and p-values for these relationships. The \u0026quot;FC \u0026gt; A -\u0026gt; BI\u0026quot; relationship stands out as highly statistically significant, supported by a t-statistic with an absolute value greater than 2 and a very low p-value (p \u0026lt; 0.005). In contrast, the \u0026quot;EE \u0026gt; A -\u0026gt; BI\u0026quot; and \u0026quot;PE -\u0026gt; A -\u0026gt; BI\u0026quot; relationships exhibit marginal significance, with t-statistics exceeding 1.5 but p-values slightly above 0.05 (p \u0026lt; 0.10)(Abdur Rahman, 2012). Several other relationships, such as \u0026quot;PE -\u0026gt; A -\u0026gt; BI\u0026quot; and \u0026quot;SI -\u0026gt; A -\u0026gt; BI,\u0026quot; are deemed non-significant as their t-statistics fall below 2, and p-values exceed 0.05. In summary, this structural model assessment unveils the statistical significance of specific pathways between constructs, with the \u0026quot;FC -\u0026gt; A -\u0026gt; BI\u0026quot; relationship standing out as highly significant, while other relationships either marginally reach significance or do not meet the criteria at the given significance level. Researchers typically prioritize the significant relationships when interpreting and discussing the implications of their structural models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4.2 Mediating Effect\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 8\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific indirect effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE -\u0026gt; A -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI -\u0026gt; A -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE -\u0026gt; R -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC -\u0026gt; R -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.009\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEE -\u0026gt; A -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.055\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE -\u0026gt; R -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.005\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI -\u0026gt; R -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC -\u0026gt; A -\u0026gt; BI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.111\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8 utlines specific indirect effects involving different variables. These effects describe how changes in one variable indirectly impact another through an intermediate variable. For instance, the positive indirect effect of 0.046 from PE to A and then to BI implies that an increase in PE is associated with an increase in BI through A. Conversely, the negative indirect effect of -0.009 from FC to R and then to BI suggests that an increase in FC is linked to a decrease in BI through R. These indirect effects help us understand the complex relationships and pathways between these variables in the overall system, shedding light on the consequences of changes in one variable on another through intermediary factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.9 Model fit-\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Saturated model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRMR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.061\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.061\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ed_ULS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.626\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.645\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ed_G\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.926\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.928\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1085.113\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1086.173\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39622641509434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.58490566037736%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.785\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.785\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 9 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;Saturated Model\u0026quot; and the \u0026quot;Estimated Model\u0026quot; fit statistics, often employed in structural equation modeling (SEM) or similar analyses, are used to evaluate the model-data fit. The SRMR (Standardized Root Mean Square Residual) for both models are 0.061, indicating a similar level of fit regarding the standardized discrepancies between observed and expected correlations; lower SRMR values imply better fit. The d_ULS (Unweighted Least Squares) and d_G (Bentler\u0026apos;s Comparative Fit Index) values are closely aligned between the models, with the Estimated Model having slightly higher values; these indices are less commonly used. The Chisquare statistic, a traditional measure of model fit, is very close for both models, reflecting similar fit levels. Notably, the NFI (Normed Fit Index) is identical at 0.785 for both models, suggesting a similar degree of fit improvement over a null model. In summary, various fit indices in the Saturated and Estimated Models yield similar results, signifying comparable model-we fit. It\u0026apos;s advisable to consider multiple fit indices collectively rather than relying solely on one to assess model fit comprehensively, ensuring confidence in the structural models\u0026apos; validity.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e5.10 Pls predict \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u0026sup2;predict\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.536\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.433\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.547\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.491\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.403\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.563\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI4\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.427\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBI5\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.606\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.088\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.065\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.072\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe values provided in the \u0026quot;Q\u0026sup2;predict\u0026quot; table appear to represent coefficients or statistics associated with the predictive quality of different variables or models. These values, which range from 0.065 to 0.606(Mat Roni, 2014), indicate the ability of the respective variables or models to predict or explain variations in the data. Higher values, such as 0.606 for \u0026quot;BI5,\u0026quot; suggest a stronger predictive quality, while lower values, like 0.065 for \u0026quot;R2,\u0026quot; indicate a weaker predictive ability. These statistics are valuable in assessing the performance of predictive models or variables in explaining variations in a given dataset, with the higher Q\u0026sup2;predict values signifying a better predictive fit.\u0026nbsp;\u003c/p\u003e"},{"header":"6. Discussion and Conclusion","content":"\u003cp\u003eThe study examined how social media (SM) impacts individuals' choices related to social good initiatives, utilizing factors from the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The findings of the study we consistent with prior research, indicating significant associations between Perceived Ease of Use (PE) and Behavioural Intention (BI) (Guti\u0026eacute;rrez \u0026amp; Herrero-Crespo, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)and between Effort Expectancy (EE) and BI(Tak \u0026amp; Panwar, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This implies that individuals not only consider the perceived benefits of using social media for social good but also take into account the effort required to engage with these platforms.\u003c/p\u003e \u003cp\u003eStudy shows that individuals are largely impacted by attitude and decisions are influenced by risk factor similar to(Arifin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Karatsoli \u0026amp; Nathanail, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFurthermore, the study confirmed the influence of Social Influence (SI) on BI, aligning with earlier research. This suggests that peer groups and social interactions on SM platforms play a significant role in individuals' decision-making processes related to social good initiatives. People seek guidance and are influenced by the activities and opinions of their social media connections concerning charitable or socially beneficial causes.\u003c/p\u003e \u003cp\u003eAdditionally, the study underscored the importance of Risk in shaping intentions for technology adoption, including the use of social media for promoting social good. The results supported the impact of risk and attitude on behavioural intentions, indicating that individuals are cautious about sharing information and participating in activities related to social good online, particularly on social media platforms.\u003c/p\u003e \u003cp\u003eFurthermore, the study examined risk as a mediator and found that it significantly influenced the impact of on BI. This suggests that risk and attitude play a more crucial role when it comes to their engagement in social media-driven social good activities.\u003c/p\u003e \u003cp\u003ePerformance expectancy, effort expectancy, social influence and facilitating conditions have direct relation with risk results are similar to (Shaikh et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e);(Xie et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Attitude influences the decisions of individual similar to (Dwivedi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e);(Iqbal et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) In conclusion, the study revealed that social media platforms have a substantial influence on individuals' behavioural intentions in social good initiatives. People turn to social media to gather information and participate in various activities related to charitable causes and social betterment. This underscores the importance of leveraging social media for promoting social good and suggests that organizations and initiatives can expand their reach and impact by effectively utilizing social media in their efforts to bring about positive social change.\u003c/p\u003e"},{"header":"7. Implication","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Theoretical\u003c/h2\u003e \u003cp\u003eThe current study offers valuable insights into the adoption behaviour of individuals when using social media for social good initiatives. It confirms the factors that influence people's behaviour when utilizing social media for such purposes. Additionally, this study contributes to the theoretical understanding in three distinct ways.\u003c/p\u003e \u003cp\u003eFirstly, this research can be considered one of the pioneering empirical investigations into individuals' use of social media as a source of information for supporting social causes. This opens up opportunities for academia to delve deeper into various aspects of human behaviour related to social media and its impact on societal well-being.\u003c/p\u003e \u003cp\u003eSecondly, this study introduces new variables like risk, attitude into the Unified Theory of Acceptance and Use of Technology (UTAUT) model, shedding more light on individuals' behavioural intentions to engage with social media for social good. Attitude and risk are critical factors that can significantly influence adoption behaviour, particularly in the context of technology for social good. The incorporation of these variables and the resulting findings suggest possibilities for extending or modifying the UTAUT model by integrating these factors into it.\u003c/p\u003e \u003cp\u003eThirdly, the study examines the mediating effect of attitude and risk on the relationship between various variables, highlighting potential differences in behaviour when it comes to using social media for social good. This aspect provides an avenue for in-depth exploration of how risk influences participation in social causes through social media.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Managerial\u003c/h2\u003e \u003cp\u003eThe study's outcomes are aimed at offering valuable insights to key stakeholders in the realm of social media for social good, including organizations focused on social causes, nonprofits, government agencies, and policymakers. These research findings can be a valuable resource for these entities, aiding them in understanding user behaviour and optimizing their efforts to promote social causes through social media.\u003c/p\u003e \u003cp\u003eIt is imperative for social cause organizations and advocates to recognize the significance of social media in their endeavours. Social media networking platforms play a pivotal role in connecting with individuals who prefer technology-based solutions and web-based engagement for social good initiatives. Furthermore, incorporating social media into their outreach strategies enables these organizations to efficiently disseminate crucial information to their target audiences.\u003c/p\u003e \u003cp\u003eThere are several activities that can be undertaken to promote social causes effectively through social media. These include creating dedicated social media profiles or accounts on various platforms that resonate with the cause, sharing sought-after information on prominent social media platforms, and fostering a virtual community where past and current supporters can engage and share their valuable experiences.\u003c/p\u003e \u003cp\u003eAttitude and risk are vital factors influencing engagement in social causes through social media. Advocates and organizations can proactively work to build trust and reduce risk perceptions among their target audience. Collecting and sharing testimonials from a diverse range of individuals who have been positively impacted by the social cause can help build trust and alleviate concerns. Government agencies can also play a role by actively using social media to communicate their commitment to addressing social issues and providing assistance to those in need.\u003c/p\u003e \u003cp\u003eAdditionally, government entities and policymakers can explore initiatives aimed at branding and promoting social causes on social media platforms. They can collaborate with businesses known for their strong social reputations to further these causes and drive positive change within society through digital channels.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Limitation and Future Research","content":"\u003cp\u003eThis study's findings should be interpreted in the context of several limitations from a social media perspective focused on driving positive societal change. Firstly, it's important to recognize that this study employed a cross-sectional design, which offers a snapshot in time. Future research could potentially adopt longitudinal approaches to provide a deeper understanding of how social media impacts societal change over time.\u003c/p\u003e \u003cp\u003eSecondly, this study primarily examined the overall use of social media for promoting positive societal change. However, different social media platforms may play distinct roles and exert various influences on these efforts. Subsequent research could investigate the specific contributions of individual platforms in the context of fostering societal benefits.\u003c/p\u003e \u003cp\u003eThirdly, to advance our understanding, future studies might consider incorporating additional variables such as hedonic motivation or other relevant theories into the conceptual model. This could help to better elucidate the underlying mechanisms driving social media's impact on societal change.\u003c/p\u003e \u003cp\u003eAdditionally, considering the role of risk and attitude as a potential mediator in the relationship between social media and societal benefits could offer a more comprehensive view of the dynamics at play in the realm of social media-driven positive societal change. Investigating how risk perceptions may influence the effectiveness of social media campaigns aimed at societal improvement could yield valuable insights for future research in this domain.\u003c/p\u003e \u003cp\u003eInvestigating social media for Social Good: Exposing Restrictions We have learned a lot on our road towards using social media for the greater good, but we shouldn't ignore the challenges we've faced along the way. Here is a list of the things we need to remember:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSnapshots in Time\u003c/b\u003e: Although our research has given readers a clear image of the present, it's crucial to remember that we have also caught a specific point in time. Future studies should include taking the long view using the longitudinal study to fully understand the progression and impact. By doing so, we can follow the progress over time and identify any dynamic changes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFull Spectrum of SM\u003c/b\u003e: Our attention was focused on the global perspective of social media for social good. However, the SM landscape is a rich mosaic, with diverse platforms serving a variety of purposes. Future investigations could concentrate on other social media sites.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExpanding the Canvas\u003c/b\u003e: Although the colours in our changeable palette have been vivid, there is always potential for new hues. Hedonic drive and facilitating conditions are simply two colours that might add depth to our conceptual masterpiece. In addition to them, attitude and risk may function as mediating, directing the good effects of social media.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eActual Usage Behaviour\u003c/b\u003e: The future researchers can beyond assessing behavioural intentions and also examines the actual usage behaviour in the context of social media for social good.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"Anisha Arora wrote the main manuscript.\"\"Prashant Kumar Siddhey reviewed the manuscript. \"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdulhakim, A., Amponsah, S., Patrick, O.-D., \u0026amp; Addo, S. (2021). \u003cem\u003eAli et al 2018\u003c/em\u003e. 50425\u0026ndash; 50427. \u003c/li\u003e\n\u003cli\u003eAbdur Rahman, K. (2012). 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Shared it by Mistake\u0026rdquo;:Assessing the Intent of Fake News Spreaders\u003c/em\u003e. https://doi.org/10.1145/3485447.3512264 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social Media, UTAUT Model, Social Good, Sustainability, Smart PLS","lastPublishedDoi":"10.21203/rs.3.rs-3933523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3933523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research investigates the transformative impact of social media on driving positive societal change, focusing on users in Punjab within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The study, encompassing 422 participants, employs a combination of surveys, interviews, and social media interaction observations. Findings highlight social media's pivotal role in shaping decisions for social good, influenced by performance expectations, social influence, effort, and a conducive environment. Risk and attitude emerge as crucial factors connecting social media use to engagement in charitable initiatives. The research adds originality by contextualizing insights within the Punjab region, contributing significantly to the understanding of technology acceptance in the realm of social good. Quantitative techniques reveal patterns, while qualitative data undergoes thematic analysis for nuanced insights.\u003c/p\u003e","manuscriptTitle":"Navigating Social Impact: Assessing Sustainability through UTAUT Model in India's Social Good Landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 21:21:55","doi":"10.21203/rs.3.rs-3933523/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f256bac1-5fd0-4fc3-9a58-4cc949816860","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-21T13:36:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 21:21:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3933523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3933523","identity":"rs-3933523","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}