Assessing Citizen Use Behavior of Digital Land Services in Bangladesh: The Mediating Role of Public Trust and Satisfaction

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Shariful Islam, Nahida Shaulin, Anas Al Masud, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9104671/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 study assesses the factors affecting citizen behavior toward using Digital Land Services (DLS) in Bangladesh. The study is basically cross-sectional quantitative research using the survey method based on a structured questionnaire. The data collected from 448 primary users of DLS were considered for the analysis, where the Structural Equation Modeling (SEM), run by Smart-PLS and M-plus, was used for testing the research model containing several hypotheses. This study reveals that Service Quality (SQ) significantly impacts Public Trust (PUBT) and Satisfaction (SAT), and PUBT and SAT directly influence Use Behavior (USEB). The mediation analysis furthermore highlights that PUBT and SAT as important mediators between SQ and USEB. Total effects analysis underscores SQ, PUBT, and SAT as the most influential constructs in determining the USEB of DLS service recipients in Bangladesh. This study introduces a novel research model aimed at understanding citizen use behavior of DLS, which is one of the pioneering works to combine a service quality model with satisfaction and public trust. Moreover, it marks the first instance in the e-service domain to utilize public trust as a mediator alongside satisfaction. Service Quality Public Trust: Satisfaction Use Behavior Digital Land Service Bangladesh Figures Figure 1 Figure 2 1. Introduction Land, the concept primarily defines the relationship between people and the environment, whereas land services management deals with resource sustainability concerns respectively (Reenberg, 2006 ). Where rights and access to land for all citizens are mostly vital for global investment, economic growth, and resilience, preventing land seizures, displacement vulnerability, and forced migration also fall under the common concern for countries around the world (World Bank, 2023 ). Increasing population, urbanization, and climate change with an increased number of natural disasters- are a few of many pressuring factors that pulled the concern of improving rights and access to land-property information (Chehrehbargh et al., 2024 ; Burns & Shojaei, 2023). In the present years, the concept and practices of E-governance have evolved and appeared as a paradigm shift in delivering land-property information, prioritizing transparency, reliability, and accountability across both developing and developed countries (Wandaogo, 2022 ; Khanra & Joseph, 2019 ; Mosse & Whitley, 2009 ). The digital transformation of land administration has mostly altered concerned services into swift, user-friendly, and capable of producing reliable outputs that ensure legal certainty within a contemporary land administration framework, mostly critical for achieving the Sustainable Development Goals (SDGs) as well (Kusmiarto et al., 2021 ). Nowadays, traditional land registries are increasingly shifting towards digital systems to enhance respective transparency and efficiency, minimizing repeated visits to land offices with unnecessary bureaucratic procedures and incompetent intermediaries (Arifuzzaman & Islam, 2024 ; Todorovski & Potel, 2019 ). The world is now experiencing a growing trend with about one-third of countries around the world, adopting digital land record systems (Rodima-Taylor, 2021 ). Research works also show that this digitization of land administration is simplifying the monitoring of land procedures, ensuring an updated service with reduced challenges by service seekers (Todorovski & Potel, 2019 ). India is now leading the game with digitized land records and computerized property registration offices (Thakur et al., 2020 ). Bangladesh, on the other side of the coin, covering an area of roughly 148,460 square kilometers, is facing challenges in accommodating and managing its vast population where land is still being treated as an increasingly valuable source of fixed capital (Alam et al., 2022 ; Viana et al., 2022 ). However, the existing land use policy in Bangladesh struggles with major issues stemming from outdated and fragmented land records (Joysoyal et al., 2024 ). Even in this age of technological development, Bangladesh relies on an inefficient, traditional, paper-based land management system. The process of registering ownership, possession, or other land rights is mostly dependent on various physical records, such as deeds, purchases, registrations, and mutation documents, which require the participation of multiple ministries and departments in the process additionally (Rahman & Hossain, 2020 ). In practice, this is often treated as a cumbersome job as it is quite challenging to coordinate among various departments in a 3rd world developing country like- Bangladesh. This outdated system poses significant challenges for the government in managing and updating land registration records with practices such as limiting access to reliable land information and preventing real-time transaction record updates (Ameyaw & de Vries, 2020 ; Islam et al., 2020 ). The limited synchronization among departments often leads to diverse forms of fraud, reducing transparency among stakeholders in land transactions. Additionally, completing the Record of Rights (ROR) transfer takes several months, and identifying court cases or bank leases during the registration of ownership transfer is equally difficult (Alam et al., 2022 ). Acknowledging such a plethora of challenges as well as the potential of digitizing the land management system, the Government of Bangladesh (GoB) has taken a few initiatives followed by highlighting an urgent necessity for digitizing its land records, registration processes, paperwork, and associated data (Akter, 2022 ). The first land management service to undergo digitization was “E-Mutation,” launched in 2018, which allowed citizens to apply for mutations online, significantly reducing the time, cost, and number of visits required compared to traditional methods (Rabbani & Hossain, 2019 ). Upazila land offices in Bangladesh are leading these digital land services in Bangladesh to streamline land-related processes and improve citizen satisfaction. Highlighting the potential, the government has plans to expand the digital land services system to all Upazila land offices to enhance the quality of land services (Akter, 2023 ). However, in many instances, the general population remains uninformed about these advancements, limiting their ability to benefit from them indeed (The Daily Star, 2023 ). The existing body of literature demonstrates a significant focus on digital land services globally. However, in the context of Bangladesh, most studies concentrate on traditional land service systems, with limited scholarly attention given to the digital land services provided by Upazila Land Offices (ULOs). Therefore, this research seeks to address this gap and enrich the academic discourse on digital land services in Bangladesh. Accordingly, the primary aim of this study is to evaluate user’s use behavior of digital land services provided by ULO. The study further aims to assess citizens’ satisfaction with digital land services, thereby providing insights into their effectiveness and implications for improving service delivery in the Bangladeshi context. 2. Literature Review: Development of Hypotheses and Research Model 2.1 Antecedents of Service Quality: Service quality reflects the perceived performance of services compared to recipient expectations, with perceived ease of use and usage patterns being key factors (Chohan & Hu, 2020 ). It plays a vital role in fostering public trust across sectors like law enforcement, healthcare, and education. High-quality services enhance trust by promoting responsiveness, professionalism, and transparency, as seen in studies on digital police services (Chaeruddin et al., 2024 ). In healthcare, inefficiencies erode trust, prompting many to seek treatment abroad (Yulianto, 2024 ). Research also shows that service quality and innovation boost satisfaction, strengthening public trust (Muzaki et al., 2023 ). Therefore, service quality is pivotal for building and sustaining trust in institutions. Thus, the hypothesis in this regard is as follows; H1 Service Quality has a positive impact on the Public Trust Service quality differs from citizen satisfaction, as it precedes and influences satisfaction in the assessment process, with quality leading to satisfaction (Parasuraman et al., 1988 ). Rita et al. ( 2019 ) emphasized that service quality shapes ultimate satisfaction and influences whether recipients remain loyal or seek alternatives. In e-government, studies have consistently shown a strong positive relationship between service quality and satisfaction (Biswas et al., 2024 ; Saha, 2022 ; Hoque, 2020 ). Aryanty et al. ( 2024 ) and Ghimire et al. ( 2024 ) found that dissatisfaction arises from lower-quality services, underscoring service quality’s role in satisfaction. Reliability, a key dimension of service quality, ensures consistent and accurate service delivery, significantly enhancing satisfaction (Albar, 2024 ; Halika & Kharisma, 2024 ). In the context of ULO’s digital land services, “service quality” refers to citizens’ evaluation of these services, which directly impacts overall satisfaction. Consequently, the proposed hypotheses are as follows; H2 Service quality has a direct positive impact on user’s satisfaction Service quality influences both satisfaction and customer loyalty, with improved quality driving higher satisfaction and use behavior, as seen in e-commerce (Hidayat et al., 2024 ; Halika & Kharisma, 2024 ; Nur Ullah & Biswas, 2024 ). It is a key factor in fostering loyalty (Fatiha et al., 2024 ), particularly in digital environments where website quality encourages return visits, engagement, and recommendations. This applies to both online and traditional services, as studies confirm that perceived service quality significantly impacts consumer behavior (Saputra et al., 2022 ). In e-government, research shows a direct link between service quality and use behavior (Yapinski et al., 2024 ; Latifah et al., 2023). Therefore, this study posits that high service quality is essential for sustaining consistent use behavior in e-government platforms. Thus, the hypothesis in this case is as follows; H3 Service Quality has a direct positive impact on Use Behavior 2.2 Antecedents of Satisfaction: Specific satisfaction refers to satisfaction with individual transactions by measuring the gap between expected and actual services, while accumulative satisfaction reflects overall satisfaction built over time since the initial transaction (Zhang, 2009 ). Some prior studies (Biswas et al., 2024 ; Alsuwaidi, 2023 ; Ferdous et al., 2022 ; Biswas & Roy, 2020 ) have found that specific satisfaction significantly influences accumulative satisfaction. Satisfaction with each service interaction donates to overall satisfaction with an organization and the cumulative effect determining the final satisfaction level. Citizens’ evaluations are influenced by both the halo effect and recent experiences by emphasizing the dynamic interaction between specific and accumulative satisfaction. Satisfaction plays an important role for building sustainable and reciprocal relationships between government and citizens in digital services system (Sukma & Leelasantitham, 2022 ). It is also a key driver of use behavior on digital government platforms (Fatiha et al., 2024 ). DeLone and McLean ( 2003 ) found that there has a positive relationship between satisfaction and reuse intention and citizen loyalty. Recent studies identify satisfaction as a primary determinant of use behavior in service organizations (Biswas et al., 2024 ; Oematan et al., 2024 ; Yu et al., 2024 ; Jo, 2024 ). Similarly, Prior research on e-loyalty has shown that satisfaction shapes use intentions and enhance the long-term relationships with service providers (Jin & Ryu, 2024 ; Pramudita et al., 2023 ). The literature consistently highlights that satisfaction drives e-government service use and loyalty and both specific and accumulative satisfaction significantly contributing to citizen loyalty. Therefore, the hypothesis proposed in this context reflects as follows; H4 User’s Satisfaction has a direct positive impact on Use Behavior 2.3 Antecedents of Trust: Citizen trust in government agencies is crucial for driving use behavior and satisfaction with e-government services. Trust develops through social interactions and experiences with government programs (Cai, 2023 ). Transparent, fair, and ethical conduct by agencies fosters confidence, enhancing satisfaction and continued engagement (Li & He, 2024 ; Qatawneh et al., 2024 ). ICT advancements enable governments to build trust by offering effective, citizen-centric e-services that boost satisfaction and promote consistent use (Taufiqurokhman et al., 2024 ; Li & Shang, 2020 ; and Alzahrani et al., 2018 ). For example, in Saudi Arabia, trust in the Ministry of Education enhances satisfaction with university admissions and increases engagement with e-services. Research also shows that trust directly influences satisfaction and reuse intentions in e-commerce (Zheng et al., 2017 ; Bilgihan, 2016 ) and e-government, fostering long-term relationships by sustaining satisfaction and use behavior (Tegethoff et al., 2019 ). Therefore, the proposed hypotheses are H5 Public Trust has a direct positive impact on Use Behavior 2.4 Antecedents of Use Behavior: Behavioral intention towards technology significantly impacts actual usage and long-term organizational performance (Hossain et al., 2023 ; Jiakui et al., 2023 ; and Li et al., 2022 ). Continuance intention is a key indicator of IS success model that reflects users’ ongoing adoption behavior and helps to measure user loyalty as well as the success of IS implementation (Hossain et al., 2023 ; Wang et al., 2022 ). In the context of e-government, continuous use behavior of citizens is essential for maximizing service value and overall performance of the system (Qatawneh et al., 2024 ; Li & Shang, 2020 ). Many scholars argued that user engagement also influences behavioral intention toward e-services (Asagbra et al., 2018 ; Brock & Von, 2019 ). However, factors that influencing initial adoption may reduce continuance usage and also emphasizing that the importance of post-adoption behavior in ensuring long-term technology adoption and satisfaction (Oliver, 1980 ; and Bhattacherjee, 2001 ). 2.5 Model Development: The measurement of service quality research started significantly in the 1980s. Haywood-Farmer ( 1988 ) introduced three key attributes: physical facilities and processes, people’s behavior, and professional judgment in his Attribute Service Quality model. In 1994, Taylor and Cronin developed the SERVPERF model to measure performance based on the service quality matrices. In 1985, Parasuraman initially identified 97 attributes across 10 dimensions for service quality assessment, and later, in 1988 refined the model with seven dimensions. Further (Parasuraman et al., 1994 ) revised their model with a five-dimension SERVQUAL model. The SERVQUAL model is widely used to assess service quality by measuring the gap between expected and perceived service quality (Raza et al., 2020 ; Yadav & Rai, 2019 ). It has been applied in healthcare (Teshnizi et al., 2018 ; Abuosi & Atinga, 2013 ), education (Ali & Gulliver, 2017; Wang & Chiu, 2011 ), and banking (Negi, 2009 ; and Nyeck et al., 2002 ). Many recent studies (Biswas et al., 2024 ; Nur Ullah & Biswas, 2024 ; Pandey, 2024 ; Raza et al., 2020 ; and Yadav & Rai, 2019 ) suggested that the SERVQUAL model is also effective for evaluating digital e-government services. In this study, the five-dimensional SERVQUAL model is used to assess the service quality of digital land services provided by the ULO in Bangladesh. Citizen satisfaction is measured using Zhang’s Two-Dimensional Satisfaction Model (SAT) (Zhang, 2009 ), while Public Trust (PUBT) is conceptualized based on Colesca ( 2009 ). Additionally, the Use Behavior (USEB) is examined following the framework of Biswas & Roy ( 2020 ). This study proposes an integrated model to examine the factors influencing use behavior toward government digital services. The model aims to explore the theoretical relationships among SQ, SAT, and PUBT and their combined impact on USEB regarding the digital land services of the ULO in Bangladesh. These relationships are illustrated in Fig. 1 . 3. Methodology of the Study 3.1 Research Method: This study is a cross-sectional quantitative study based on primary and secondary data sources. The quantitative method helps to measure impacts, causes, and relationships where statistical significance among the constructs of latent variables is necessary to unveil (Palys, 1997 ). As this study tests predetermined hypothetical paths among the constructs, i.e., SQ, PUBT, SAT, and USEB, towards the digital land service of Upazila Land Office (ULO), the quantitative method is logical to apply. Biswas et al. ( 2024 ) and many researchers used cross-sectional quantitative methods to measure service quality, satisfaction, and use intention. 3.2 Study context: The Government of Bangladesh introduced the digital land service system under the Digital Bangladesh Manifesto of 2008 (Akter, 2022 ). This initiative aims to digitize land records and documents using ICT tools, simplifying service management and enhancing accessibility for citizens (Islam et al., 2015 ). The transition from traditional paper-based land services to a modern e-land system has significantly increased demand among citizens (Hossain, 2015 ). According to The Business Standard (September 30, 2022), 3.5 lakh people accessed digital land services via the land service hotline in just eight months. This innovation in land management offers a compelling context for research. The study examines 20 upazila land offices (ULOs) across Bangladesh, representing various regions to ensure generalizability where five ULOs each from the northeastern, northwestern, southeastern, and southwestern parts of the country. 3.3 Measurement Instruments: This study evaluates SQ, PUBT, SAT, and USEB related to the digital land services of ULO in Bangladesh. The five-dimensional SERVQUAL framework (Tangibility, Reliability, Responsiveness, Assurance, and Empathy) has been used to measure SQ. SAT was measured based on Biswas et al. ( 2024 ), USEB was measured using items from Biswas & Roy ( 2020 ), and PUBT was assessed using measures from Colesca ( 2009 ). All constructs were measured on a five-point Likert scale (1 = Very Disagree to 5 = Very Agree). Additionally, demographic characteristics such as age, gender, education, occupation, and income of the respondents were also collected. 3.4 Sampling and Sample Selection: This study has been primarily used purposive sampling because it is cost-effective and time-efficient technique as well as more effective method when a limited and specific group of people can serve as primary data sources that are closely related to specific research objectives (Campbell et al., 2020 ; and Ovi et al., 2023 ). A convenient sampling technique is also used due to limited funding opportunities and participants’ time scheduling constraints (Etikan et al., 2016 ). The sample was the users who had visited and got services from ULO’s digital land services at least once. Based on Yamane’s ( 1967 ) formula for populations exceeding 100,000 at a 95% confidence level, the recommended sample size is 400. Tabachnick and Fidell’s ( 2007 ) rule-of-thumb suggests a minimum sample of 82 for four independent variables. Memon et al. ( 2020 ) recommend a range of 160 to 300 for multivariate analysis methods like PLS-SEM and CB-SEM. Considering these guidelines, this study analyzed data from 448 respondents that fulfilled the minimum requirements. 3.5 Questionnaire development and Data Collection and Analysis: A structured survey questionnaire was developed based on the research model constructed. Initially designed in English, it was translated into Bengali for better respondent comprehension. After piloting, the finalized questionnaire was used for data collection between February 20 and March 10, 2025. A total of 500 questionnaires were distributed, and 460 were returned. Following quality checks, 12 were excluded due to missing data, leaving 448 for analysis. Descriptive statistics were analyzed using IBM SPSS-25, while SmartPLS-4.0 was employed for Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), a widely accepted method for testing and validating theoretical models. 3.6 Model Specification: In order to confirm the relationships between latent variables and their respective observed indicators, this study first estimate the measurement model as follows; $$SQ={{\lambda}}_{SQ1}{X}_{1}+{{\lambda}}_{SQ2}{X}_{2}+{{\lambda}}_{SQ3}{X}_{3}\dots\dots\dots.+{{\lambda}}_{SQ22}{X}_{22}+{ϵ}_{SQ}$$ $$PUBT={{\lambda}}_{PUBT1}{Y}_{1}+{{\lambda}}_{PUBT2}{Y}_{2}+{{\lambda}}_{PUBT3}{Y}_{3}+{{\lambda}}_{PUBT4}{Y}_{4}+{{\lambda}}_{PUBT5}{Y}_{5}+{{\lambda}}_{PUBT6}{Y}_{6}+{ϵ}_{PUBT}$$ $$SAT={{\lambda}}_{ACCS1}{Z}_{1}+{{\lambda}}_{ACCS2}{Z}_{2}+{{\lambda}}_{ACCS3}{Z}_{3}+{{\lambda}}_{ACCS4}{Z}_{4}+{{\lambda}}_{ACCS5}{Z}_{5}+{ϵ}_{ACCS}$$ $$USEB={{\lambda}}_{USEB1}{W}_{1}+{{\lambda}}_{USEB2}{W}_{2}+{{\lambda}}_{USEB3}{W}_{3}+{ϵ}_{USEB}$$ Where, X 1, X 2, X 3 ……….X 22 ; Y 1, Y 2, Y 3, Y 4, Y 5, Y 6 ; Z 1, Z 2, Z 3, Z 4, Z 5 ; Z 6, Z 7, Z 8 ; and W 1, W 2 W 3 are the observed items of latent variable- SQ; PUBT; SAT and USEB respectively. λ SQ1, λ SQ2, λ SQ3 …. λ SQ22 ; λ PUBT1, λ PUBT2, λ PUBT3, λ PUBT4, λ PUBT5, λ PUBT6 ; λ ACCS1, λ ACCS2, λ ACCS3, λ ACCS4, λ ACCS5 ; λ SPES1, λ SPES2, λ SPES3 ; and λ USEB1, λ USEB2, λ USEB3 are the factor loadings of all items of latent variable- SQ; PUBT; ACCS; SPES; and USEB respectively. ϵ SQ, ϵ PUBT, ϵ ACCS, ϵ SPES, and ϵ USEB are the measurement error of latent variables- SQ; PUBT; ACCS; SPES; and USEB respectively. As part of the measurement model, this study calculates Composite Reliability (CR) and Convergent Validity through Average Variance Extracted (AVE) based on the following equations; CR= \(\frac{\left[{\sum}_{i=1}^{n}{\lambda}_{i}^{2}\right]}{\left[{\sum}_{i=1}^{n}{\lambda}_{i}^{2}\right]+\left[{\sum}_{i=1}^{n}{\delta}_{i}\right]}\) AVE= \(\frac{\left[{\sum}_{i=1}^{n}{\lambda}_{i}^{2}\right]}{n}\) Where n is the number of items, λ i is the standardized factor loading for item i , and δ i is the measurement error for item i . And then this study calculated the structural model that shows hypothetical relationships among the constructs i.e. SQ; PUBT; ACCS; SPES; and USEB. The equitation is; $$USEB={\beta}_{0}+{\beta}_{1}SQ+{\beta}_{2}PT+{\beta}_{3}AS+{ϵ}_{UB}$$ $$PUBT={\beta}_{0}+{\beta}_{4}SQ+{ϵ}_{PT}$$ $$SPES={\beta}_{0}+{\beta}_{5}SQ+{\beta}_{6}PT+{ϵ}_{SS}$$ Where, \({\beta}_{0}\) is the intercept or constant term in each equation, representing the baseline value when all independent variables (predictors) are zero? \({\beta}_{1},{\beta}_{2},{\beta}_{3}\dots\dots\dots..{\beta}_{10}\) , are the path coefficients. ϵ is the error term. Then, the model calculates direct, indirect and total effects on the UB. In this case, the value of \({\beta}_{1},{\beta}_{2},{\beta}_{3},{\beta}_{4}\text{a}\text{r}\text{e}\) the direct effects of SQ, PUBT, ACCS, and SPES on USEB respectively. Alongside, the value of \({\beta}_{5}\) is the direct effect of SQ on PUBT; and the value of \({\beta}_{6},{\beta}_{7},{\beta}_{8}\) are the direct effects of SQ, PUBT, SPES on ACCS respectively. And the value of \({\beta}_{9},{\beta}_{10}\) are the direct effects of SQ, PUBT on SPES respectively. The model then calculates indirect and total effects of SQ on USEB based on the following equations; SQ -> PUBT -> USEB = \(({\beta}_{4}.{\beta}_{2})\) SQ -> SAT -> USEB = \(({\beta}_{5}.{\beta}_{3})\) Total Indirect Effects of SQ -> USEB = \(\left({\beta}_{4}.{\beta}_{2}\right)+({\beta}_{5}.{\beta}_{3})\) Total effect of SQ -> USEB= \({\beta}_{1}+\left({\beta}_{4}.{\beta}_{2}\right)+({\beta}_{5}.{\beta}_{3})\) 3.7 Ethical Statement: This research was conducted following the highest ethical standards to ensure integrity, transparency, and academic honesty. Ethical approval (Ref: 23.0.902.858.07.786.24/30) was obtained from the Research Ethical Committee (REC) of Bangladesh University of Professionals (BUP), Dhaka, and all necessary permissions were secured before data collection. Informed consent was obtained from all participants, ensuring their voluntary participation and confidentiality. The authors declare that there are no conflicts of interest related to this study. All data presented in this article are accurate and have not been manipulated or fabricated. The authors confirm their adherence to ethical research and publishing practices, ensuring that all contributions were properly acknowledged and that the study maintains academic integrity. 4. Results 4.1 Demographic Information: The demographic and behavioral characteristics of the study’s respondents (users of DLS) are summarized in Table 1. Most of the users are middle-aged, with 27.7% aged 31-40 and 41-50 years, while younger (60 years) users comprise only 2.2% and 5.1%, respectively. The majority of them are male (73.2%), and 49.1% hold at least a Bachelor’s degree, though 4.2% lack formal education. Among all of the respondents, the businesspeople (25.9%) represent the largest occupational group, followed by private employees (20.5%) and students (12.3%). The monthly incomes of the respondents primarily range from 30,001-40,000 BDT (33.7%) and 20,001-30,000 BDT (22.1%), with fewer users earning 50,000 BDT (8.9%). The user’s awareness of DLS is largely driven by personal interactions (38.4%), followed by ULO officials (21.9%), with additional contributions from public representatives (16.3%), advertisements (13.6%), and websites (9.8%). Regarding usage, 17.6% are first-time users, while 40.5% have used the service for 6 months to 2 years, and only 8.9% are long-term users (>3 years). This data reveals that digital land services in Bangladesh are primarily utilized by educated, middle-aged males with moderate-to-high incomes and varied experience levels. Table 1: Demographic Data (N=448) Variables Categories Frequency Percentage Age 60 Years 23 5.1 Gender Male 328 73.2 Female 120 26.8 Education No education 19 4.2 Primary 44 9.8 High school 81 18.1 College 84 18.8 Bachelor 128 28.6 Masters or above 92 20.5 Occupation Teacher 39 8.7 Farmer 50 11.2 Private employee 92 20.5 Housewife 38 8.5 Students 55 12.3 Business man 116 25.9 Retired 42 9.4 No job 16 3.6 Income (Monthly) 50000 40 8.9 Knowing ULO Digital Services People 172 38.4 Advertisement 61 13.6 ULO officials 98 21.9 Public Representative 73 16.3 Website 44 9.8 Tenure of Using This is the first time 79 17.6 Less than 6 months 65 14.5 6 -12 months 97 21.7 1-2 84 18.8 2-3 83 18.5 >3 years 40 8.9 Source: Field Survey (2025) 4.2 Measurement Model In order to evaluate the measurement model, this study calculated the validity and reliability of the four-factor model (i.e., SQ, PUBT, SAT, and USEB). Conformity Factor Analysis (CFA), was conducted for factor loadings of the items of four constructs and their covariance. Construct reliability was confirmed through Cronbach’s Alpha and Composite Reliability (CR). Convergent and divergent (Discriminant) validity was also checked through Average Variance Extracted (AVE), the Fornell-Larcker Criterion, and the Heterotrait-Monotrait (HTMT) Ratio. Table no 2 presents the construct validity and reliability of the data used in this study. The factor loadings of all items have shown a significant contribution in explaining respective constructs since they exceed the critical value of 0.50 (Hair et al., 2020). In the case of reliability, all constructs show acceptable reliability, with Cronbach’s Alpha and CR values exceeding the threshold of 0.70 (Hair et al., 2020) that indicate strong internal consistency and confirming the strong reliability of the model. In convergent validity, the value of AVE of all constructs exceeded the critical value of 0.50 (Fornell & Larcker, 1981). Furthermore, this study confirmed discriminant validity using the Fornell-Larcker Criterion and the Heterotrait-Monotrait (HTMT) ratio that the constructs are distinct from one another. According to the Fornell-Larcker Criterion, the square root of the AVE (diagonal values) for each construct exceeds the correlations with other constructs (off-diagonal values), shown in Table no 3. According to HTMT ratio, all construct’s pair values are within the threshold value of 0.85 shown in table 4, indicating strong discriminant validity (Fornell & Larcker, 1981 and Henseler et al., 2015). Together, these results confirm that the constructs are sufficiently independent. Hence, the measurement model is reliable and valid for further analysis. Table 2: Construct reliability and validity Constructs Items Loadings Cronbach's Alpha CR AVE SAT1 0.853 0.850 0.899 0.690 Satisfaction SAT2 0.821 SAT3 0.843 SAT4 0.804 PUBT1 0.805 0.879 0.908 0.623 PUBT2 0.816 Public Trust PUBT3 0.772 PUBT4 0.787 PUBT5 0.763 PUBT6 0.793 SQAS1 0.855 Assurance SQAS2 0.828 0.831 0.887 0.663 SQAS3 0.787 SQAS4 0.786 SQEM1 0.956 SQEM2 0.930 Empathy SQEM3 0.942 0.959 0.970 0.890 SQEM4 0.945 SQRL1 0.834 SQRL2 0.842 Reliability SQRL3 0.812 SQRL4 0.835 0.885 0.916 0.684 SQRL5 0.813 SQRS1 0.887 Responsiveness SQRS2 0.879 SQRS3 0.898 0.934 0.950 0.791 SQRS4 0.874 SQRS5 0.908 SQTA1 0.857 SQTA2 0.842 0.856 0.902 0.698 Tangible SQTA3 0.804 SQTA4 0.838 USEB1 0.829 Use Behavior USEB2 0.758 0.748 0.855 0.664 Source: Field Survey (2025) Note: CR= Composite Reliability, AVE= Average Variance Extracted Table 3: Discriminant validity- Fornell and Larcker Criterion SAT PUBT SQAS SQEM SQRL SQRS SQTA USEB SAT 0.830 PUBT 0.492 0.789 SQAS 0.274 0.332 0.814 SQEM 0.230 0.390 0.017 0.943 SQRL 0.490 0.351 0.236 0.012 0.827 SQRS 0.319 0.268 0.056 0.090 0.170 0.889 SQTA 0.285 0.353 0.337 0.033 0.360 0.148 0.835 USEB 0.407 0.468 0.334 0.260 0.255 0.286 0.274 0.815 Source: Field Survey (2025) Table 4: Discriminant validity-Heterotrait- monotrait ratio (HTMT) matrix SAT PUBT SQAS SQEM SQRL SQRS SQTA USEB SAT PUBT 0.567 SQAS 0.323 0.383 SQEM 0.254 0.422 0.052 SQRL 0.566 0.393 0.273 0.035 SQRS 0.357 0.293 0.066 0.095 0.187 SQTA 0.335 0.401 0.401 0.048 0.413 0.168 USEB 0.507 0.570 0.413 0.303 0.309 0.335 0.342 Source: Field Survey (2025) However, this study used service quality constructs that requires higher order constructs (HOC) validity as service quality measured as the higher order construct in this study based on fiver lower order construct-responsiveness, reliability, tangible, assurance and empathy. In order to establish higher-order construct validity, outer weights, outer loading, and VIF were calculated. Furthermore, outer loading was found to be greater than .50 for each of the lower-order constructs (Sarstedt et al., 2019). VIF values were assessed to check multicollinearity and found to be at a very minimal level, which is acceptable (Hair et al., 2016). Since all criteria were met, the HOC validity was established . Table 5: Higher order construct validity results HOC LOC Outer weight T-statistics P values Outer loading VIF SQ SQAS 0.351 6.508 0.000 0.547 1.148 SQEM 0.471 10.128 0.000 0.519 1.009 SQRL 0.431 7.982 0.000 0.650 1.188 SQRS 0.346 6.927 0.000 0.510 1.047 SQTA 0.199 3.326 0.001 0.539 1.257 Source: Field Survey (2025) 4.3 Structural Model Analysis: As this study used Structural Equation Modeling (SEM), and the measurement model was found reliable and valid, this study tested the structural model for analyzing the statistical significance of path relationships among the constructs of the research model shown in Figure 1. The results of the direct path relationships presented in Table 6 and Figure 2 indicate that all constructs have substantial positive effects on use behavior as assumed in the hypotheses and research of the model. SQ significantly influences SAT (β = 0.583, t = 16.566, p = 0.000); PUBT (β = 0.614, t = 17.440, p = 0.000); and USEB (β = 0.285, t = 4.452, p = 0.000) respectively, accepting the H1, H2, H5. SAT also exhibits a significant positive effect on USEB (β = 0.126, t = 2.210, p = 0.027), assuming in the H3. The study also shows a strong positive impact of PUBT on USEB (β = 0.234, t = 3.844, p = 0.000) supporting the H4. Table 6: Hypotheses Testing Results (Direct Effects) Hypothesis path β SE T statistics P values PUBT -> USEB 0.234 0.061 3.844 0.000 SAT -> USEB 0.126 0.057 2.210 0.027 SQ -> PUBT 0.614 0.035 17.440 0.000 SQ -> SAT 0.583 0.035 16.566 0.000 SQ -> USEB 0.285 0.064 4.452 0.000 Source: Field Survey (2025) In this study, calculated potential indirect effects shown in Table 6 reveal the significant mediating effects of SAT and PUBT on the relationship of SQ and USEB. SQ also significantly influences USEB indirectly through SAT (β = 0.073, t = 2.176, p = 0.030) and PUBT (β = 0.143, t = 3.740, p = 0.000), highlighting the mediating roles of satisfaction and trust, therefore, accepting the H6 and H7. It is noted that as SQ has a direct effect on the USEB in presence of mediators like PUBT and SAT, there is a partial but significant mediating effects of public trust and satisfaction in shaping the relationship between service quality and citizen use behavior considering the context of DLS in Bangladesh. Table 7: Mediation Results (Indirect Effects) Indirect Path β SE T value P value Percentile bootstraps 95% confidence interval Lower Upper SQ → SAT → USEB 0.073 0.034 2.176 0.030 0.017 0.128 SQ → PUBT → USEB 0.143 0.038 3.740 0.000 0.081 0.208 Source: Field Survey (2025) Table 7 presents the total effects analysis of SQ on USEB. The indirect effect of SQ on USEB through mediators (β = 0.217, t = 4.684, p = 0.000) indicates a meaningful mediating influence. And the total effect, which includes both direct and indirect impacts (β = 0.501, t = 12.788, p = 0.000), reinforces the strong overall effect of SQ on USEB. The results suggest that while a significant portion of the relationship is mediated, SQ still has a substantial total effect on USEB. Table 8: Total Effects (Direct + Indirect) Path Direction β SE T statistics P values SQ -> USEB (Through Mediators) 0.217 0.046 4.684 0.000 SQ -> USEB (Total Effects) 0.501 0.039 12.788 0.000 Source: Field Survey (2025) 5. Discussion This study investigates the determinants of use behavior among users of digital land services in Bangladesh, focusing on key constructs such as SQ, PUBT, SAT, and USEB and their interrelationships. The results provide significant insights into the theoretical and practical implications of digital service adoption, revealing both strengths and gaps in the measurement and structural models. The study demonstrates strong reliability and validity of the measurement model. The Cronbach’s alpha values for all constructs exceeded the 0.70 threshold, confirming internal consistency, while Composite Reliability (CR) values supported the construct’s reliability. Average Variance Extracted (AVE) values also met the minimum standard of 0.50, ensuring convergent validity. Furthermore, the Fornell-Larcker criterion and HTMT ratio confirmed discriminant validity, with constructs clearly distinguished from one another. These results affirm the robustness of the model and the reliability of the instruments used in this study. The structural model sheds light on the pathways influencing USEB. SQ emerges as the most pivotal construct, exerting both direct and indirect effects. Its direct effect on USEB is statistically significant, exposing similarity to the existing studies of Nur Ullah & Biswas (2024), Latifah et l (2023), and Saputra et al (2022), reinforcing the idea that better service quality encourages users to continue using digital services. SQ also significantly influences PUBT and SAT, admitting the existing studies of Chaeruddin et al. (2024), Yulianto (2024), Biswas et al. (2024), Albar (2024), Ghimire et al. (2024), Muzaki et al., (2023); and Hoque, (2020), highlighting its foundational role in shaping users’ perceptions and attitudes toward digital land services. The effect of SQ on PUBT is highly significant, which highlights that higher service quality leads to increased public trust. The efficiency, reliability, and accessibility of digital land services play a crucial role in shaping citizens’ trust in government service delivery. A strong positive relationship is observed between SQ and SAT, which suggests that service quality is a major determinant of user satisfaction. When digital land services perform well in terms of responsiveness, reliability, and accessibility, users tend to be more satisfied with their experiences. PUBT also plays a critical role, directly affecting USEB, which supports the existing studies of Li & He (2024), Qatawneh et al. (2024), Taufiqurokhman et al. (2024), Cai (2023), and Tegethoff et al., (2019). This finding indicates that public trust in the digital land service strongly influences users’ engagement and continued use. Higher trust in service provider’s fosters confidence and reliance on the system. SAT also plays a critical role in determining user behavior in using digital land services. This study found that the relationship between SAT and USEB positively matches with the existing studies of Biswas et al. (2024) and Alsuwaidi (2023) but weaker than PUBT’sPUBT’s effect, which indicates that user satisfaction significantly contributes to continued use but is a relatively weaker predictor compared to trust. While satisfaction enhances user engagement, trust appears to be a stronger determinant of behavioral outcomes. Furthermore, the mediation analysis in this study also explores the indirect effects of service quality (SQ) on use behavior (USEB) through satisfaction (SAT) and public trust (PUBT). The results provide deeper insights into the underlying mechanisms driving user engagement with digital land services. The path between SQ and USEB through SAT has a significant indirect effect (β = 0.073, p = 0.030), indicating that satisfaction partially mediates the relationship between service quality and use behavior. However, the relatively small coefficient suggests that while service quality enhances satisfaction, its impact on use behavior through this route is modest. This supports the direct effect, where satisfaction had a weaker influence on use behavior compared to public trust. However, the path between SQ and USEB through PUBT shows a stronger mediation effect (β = 0.143, p = 0.000), with a 95% confidence interval (0.081, 0.208) confirming its significance. This suggests that public trust is significant mediator between service quality and use behavior. Users are more likely to engage with digital land services when they perceive high service quality and strengthens their trust in the system. This emphasizes the idea that trust plays a dominant role in determining user behavior. The findings of this study found that that service quality (SQ) is the most influential factor in driving user engagement with digital land services directly and indirectly. Public trust (PUBT) and satisfaction (SAT) significantly mediate the relationship between SQ and use behavior (USEB). The results of direct effects show that public trust has a greater impact on use behavior than satisfaction. The mediation analysis further confirms that the indirect effect of SQ on USEB via PUBT is stronger than through SAT. These results emphasize the need for trust-building strategies alongside service quality improvements to enhance public confidence and engagement with digital government services in Bangladesh. 6. Research Implications 6.1 Theoretical Implications: This study has a substantial theoretical implication for the e-governance service delivery and management field of study because it offers a deeper new understanding and dynamics of citizen use behavior through service quality, trust, and satisfaction by analyzing both direct and indirect effects. The empirical results of this study challenge the traditional e-service adoption models by adding trust as a crucial mediator alongside satisfaction, providing new insight into the debate on the use behavior of digital services. The previous research argued that service quality is a key driver of citizen use behavior, not only in digital services but also in traditional public services. However, this study redesigns and extends these existing theoretical frameworks by adding trust as a mediator with satisfaction. In contrast, the higher mediating effect of trust over satisfaction indicates that service quality enhances trust more effectively than satisfaction, which in turn drives citizen use behavior. This new insight suggests the SERVQUAL, the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) by emphasizing service quality as a fundamental precursor to trust rather than just a determinant of satisfaction. Besides, the Expectation-Confirmation Theory (ECT) claims satisfaction is an important factor in user behavior. Still, this study disagrees that public trust plays a stronger mediating role in shaping the citizen use intention. This study raises a question on the limited direct influence of satisfaction on citizen use behavior, suggesting that satisfaction alone is insufficient to drive sustained engagement in digital services, especially in public-sector settings where trust in institutions plays a crucial role. This study urges a shift from a satisfaction-centric approach to a trust-focused model in e-governance research. By extending e-governance theories with new mediators like trust, this study provides a holistic perspective on digital public service engagement, emphasizing the importance of trust-building strategies alongside technological advancements. 6.2 Practical implications: This study has also a practical implication as it provides some significant insights for policymakers, digital service providers, and public administrators striving to enhance user engagement with e-government services, particularly in digital land services in Bangladesh. By demonstrating the theoretical model tested positive, where SQ through SERVQUAL, SAT, and PUBT works as the key drivers of USEB, this research provides actionable strategies to improve the effectiveness of digital land services in Bangladesh. Given that SQ directly influences trust, satisfaction, and use behavior, digital service providers should focus on enhancing system efficiency by reducing processing times, minimizing errors, and ensuring reliable service delivery, user-friendly design, and accessibility to cater to diverse demographics, including those with low digital literacy; and proactive customer support to resolve issues promptly and improve the overall user experience. Since public trust plays a stronger role than satisfaction, policymakers must prioritize trust-building initiatives such as enhancing transparency in digital services through clear policies, open data, and accountability mechanisms; strengthening data security and privacy protections to reassure users that their information is safe, and implementing feedback mechanisms to address concerns and demonstrate responsiveness. As satisfaction alone does not strongly predict continued use behavior, therefore, authority should focus on a trust-focused citizen engagement strategy. Policymakers should adopt context-specific strategies such as localizing digital literacy programs to bridge knowledge gaps in rural and underserved communities, introducing public-private partnerships to leverage expertise in digital service delivery while maintaining government oversight, and formulating policy frameworks that align with institutional trust theories, ensuring that government-led digital initiatives foster long-term engagement. 7. Limitations While this study offers some crucial arguments in the debate on determinants of use behavior for digital land services in Bangladesh, it has several limitations. Firstly, it is a cross-sectional survey research capturing user perceptions at a single point in time, which limits the ability to establish causal relationships among the constructs. Thus, a longitudinal research method could be done for deeper understanding on how these relationships are developed. Secondly, this study is based on self-reported responses, which may introduce biases; users may have overestimated or underestimated their level of trust, satisfaction, or use behavior, affecting the accuracy of the findings. Thirdly, this study’s context limits its wider applicability, meaning the findings may not be directly applicable to other digital government services or contexts, particularly in countries with different socio-economic, political, or technological landscapes. Fourthly, while the study considers key determinants of the use behavior of digital land service, other factors such as digital literacy, internet accessibility, legal frameworks, and government policies were not explicitly analyzed. Other psychological or behavioral factors, such as perceived ease of use, perceived risk, or prior digital experience, may also play crucial roles. These factors could significantly influence users’ use behavior with digital land services and should be explored in future studies. Fifthly, the users of digital land services across Bangladesh are unlimited, so, the sample size considered in this study may limit the generalizability of the findings of this study. Therefore, research with a large sample could be conducted in future. Lastly, although the survey ensures reliability and validity through Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE), certain latent constructs might still be subject to measurement limitations. Future studies could refine the scales or employ mixed-method approaches, such as qualitative interviews, to capture more nuanced user experiences. 8. Conclusion This study investigated the role of service quality, satisfaction, and trust in determining the use behavior of digital land services in Bangladesh. The results of this study confirm that SQ serves as the most influential factor, exerting both direct and indirect effects on USEB through PUBT and SAT. It reinforces prior studies that demonstrate the strong link between high-quality digital services and user retention. Additionally, it has been tested that SQ significantly impacts PUBT and SAT, which argues that the efficiency, reliability, and accessibility of digital services play a crucial role in shaping user perceptions and attitudes toward digital land services. This study unveiled an interesting insight that while both PUBT and SAT mediate the relationship between SQ and USEB, trust (PUBT) emerges as the stronger mediator than the SAT, which illustrates that trust-building measures should be prioritized to enhance citizens’ reliance on digital land services. It also suggests that while users appreciate high service quality and express satisfaction, their continued use behavior is more significantly driven by their confidence in the system. This study is an important piece of research for its unique contribution in both theoretical and practical domains. Theoretically, this study proposes an extended e-governance model by including trust as a mediator alongside satisfaction. Practically, the study findings offer crucial implications for policymakers and digital service providers. More efforts should focus on improving service quality while simultaneously enhancing user engagement and sustaining the adoption of digital land services, implementing trust-building initiatives. Besides, transparency, data security, and responsive service mechanisms can improve public trust and encourage continued usage. Future research can further explore these relationships in different digital governance contexts and examine additional moderating factors such as demographic variables, digital literacy, and regulatory frameworks. By addressing these aspects, policymakers and service providers can refine their strategies to ensure higher user engagement and satisfaction with digital land services in Bangladesh and beyond. Declarations Compliance with Ethical Standards Conflict of Interest: The author(s) declared no potential conflicts of interest. Informed consent: Informed consent was secured from each participant, ensuring voluntary participation and the protection of confidentiality. Funding information : This research receives no funding Ethical approval: This study was conducted in strict adherence to established ethical guidelines to uphold academic integrity, transparency, and research credibility. Ethical clearance (Ref: 23.0.902.858.07.786.24/30) was granted by the Research Ethical Committee (REC) of Bangladesh University of Professionals (BUP), Dhaka, prior to the commencement of data collection. Data availability: Data can be shared upon request from the corresponding author. Authors' contributions Mohammad Nur Ullah contributed to the conceptualization, methodology, supervision, validation, and review and editing of the manuscript. Md. Shariful Islam contributed to the investigation, resources, and review and editing of the manuscript. Nahida Shaulin contributed to the supervision, and editing of the manuscript. All authors read and approved the final manuscript. Anas Al Masud contributed to project administration, data collection and review. Bikram Biswas contributed to the conceptualization, formal analysis, investigation, and data curation. References Akter, M. (2022). Digitalization in the Land Service Delivery: Comparison Between Bangladesh and Indonesia. 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H., Aghamolaei, T., Kahnouji, K., Teshnizi, S. M. H., & Ghani, J. (2018). Assessing Quality of Health Services with the SERVQUAL Model in Iran. A Systematic Review and Meta-Analysis. International Journal for Quality in Health Care , 30 (2), 82-89. Thakur, V., Doja, M. N., Dwivedi, Y. K., Ahmad, T., & Khadanga, G. (2020). Land Records on Blockchain for implementation of Land Titling in India. International Journal of Information Management , 52 , 101940. https://doi.org/10.1016/j.ijinfomgt.2019.04.013 The Daily Star. (2023, March 2). ‘Digitalisation of Land Management Crucial for Good Governance.’ .https://www.thedailystar.net/business/news/digitalisation-land-management-crucial-good-governance-3261541 Todorovski, D., & Potel, J. (2019). Exploring the Nexus Between Displacement and Land Administration: The Case of Rwanda. Land , 8 (4), Article 4. https://doi.org/10.3390/land8040055 Uppal, M. A., Ali, S., & Gulliver, S. R. (2018). Factors Determining E‐learning Service Quality. British Journal of Educational Technology , 49 (3), 412-426. Viana, C. M., Freire, D., Abrantes, P., Rocha, J., & Pereira, P. (2022). Agricultural Land Systems Importance for Supporting Food Security and Sustainable Development Goals: A Systematic Review. Science of The Total Environment , 806 , 150718. https://doi.org/10.1016/j.scitotenv.2021.150718 Volkov, S. N., Papaskiri, T. V., Alekseenko, N. N., Ananicheva, E. P., & Rudinova, Y. I. (2020). Land-Property and Land-Resource Information Obtained as a Result of Land Management. IOP Conference Series: Earth and Environmental Science , 579 (1), 012132. https://doi.org/10.1088/1755-1315/579/1/01213 Wang, H. C., & Chiu, Y. F. (2011). Assessing E-learning 2.0 System Success. Computers & Education , 57 (2), 1790-1800. Wang, Y., Zhang, M., Luo, N., & Guo, L. (2022). Understanding How Participating Behaviours Influenced by Individual Motives Affect Continued Generating Behaviours in Product-Experience-Shared Communities. Behaviour & Information Technology , 41 (14), 3044-3064. Worldometer. (2024). Bangladesh Population . Retrieved December 18, 2024, from https://www.worldometers.info/world-population/bangladesh-population/ World Bank. (2023). Why Secure Land Rights Matter . Retrieved December 18, 2024, from https://www.worldbank.org/en/news/feature/2017/03/24/why-secure-land-rights-matter Wandaogo, A. (2022). Does Digitalization Improve Government Effectiveness? Evidence from Developing and Developed Countries. Applied Economics , 54 (33), 3840–3860. https://doi.org/10.1080/00036846.2021.2016590 Yadav, M. K., & Rai, A. K. (2019). An Assessment of the Mediating Effect of Customer Satisfaction on the Relationship Between Service Quality and Customer Loyalty. IUP Journal of Marketing Management , 18 (3), 7-23. Yamane, Taro,(1967). Statistics: An Introductory Analysis, 2 nd . ed., NewYork: Harper and Row Yapinski, J. J., Nursanti, T. D., & Scoth, J. (2024, August). Optimizing E-Service Quality and User Experience to Enhance Customer Loyalty Via Satisfaction. In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT) (pp. 1-6). IEEE. Yu, C., Yan, J., & Cai, N. (2024, May). ChatGPT in Higher Education: Factors Influencing ChatGPT User Satisfaction and Continued Use Intention. In Frontiers in Education (Vol. 9, p. 1354929). Frontiers Media SA. Yulianto, Y., & Rugaiyah, R. (2024). Examining the Impact of Education in Entrepreneurship on Character Development among Students at University. Scope: Journal of English Language Teaching , 9 (1), 434-439. Zafarullah, H., & Ferdous, J. (2021). Cyberspace at the Grassroots: E-governance and Citizen/Stakeholder Perceptions at the Local Level in Bangladesh. Journal of Development Policy and Practice , 6 (2), 168-187. Zhang, L. (2009). The Study on Mobile Service Quality and Customer Satisfaction (Doctoral dissertation). Huazhong University of Science and Technology, Wuhan. Retrieved from http://www.cnki.net/KCMS/detail/detailall.aspx?filename=2010042299.nh&dbcode=CDFD&dbname=CDFD0911. Zheng, X., Lee, M. and Cheung, C. (2017), “Examining E-loyalty Towards Online Shopping Platforms: the Role of Coupon Proneness and Value Consciousness”, Internet Research, Vol. 27 No. 3, pp. 709-726. Supplementary Information Q 2 result Q²predict RMSE MAE PUBT 0.366 0.801 0.598 SAT 0.328 0.824 0.654 USEB 0.241 0.876 0.653 R 2 R-square R-square adjusted PUBT 0.377 0.375 SAT 0.340 0.338 USEB 0.304 0.299 Model fit indices Estimated model χ2 /df 1.244 RMSEA 0.023 GFI 0.924 AGFI 0.910 SRMR 0.031 NFI 0.934 TLI 0.985 CFI 0.986 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .908 Bartlett's Test of Sphericity Approx. Chi-Square 9728.821 df 595 Sig. <.001 Harman’s single-factor test was applied across all ten constructs, following the recommendation of Podsakoff et al. (2003). The eight constructs were combined into one factor. The results concluded that a single factor explained 26.22% of the total variance, which was below the commonly recommended threshold of 50%. (Podsakoff et al., 2003), indicating that no evidence of CMB was presented in this study. Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9104671","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605066967,"identity":"eacdc331-95e0-4bd7-8a3d-62df4bfdf5ef","order_by":0,"name":"Mohammad Nur Ullah","email":"","orcid":"","institution":"Department of Public Administration, Bangladesh University of Professionals (BUP), Mirpur Cantonment, Dhaka-1216, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Nur","lastName":"Ullah","suffix":""},{"id":605073348,"identity":"a76a8d56-415b-4348-9982-41aaa586752b","order_by":1,"name":"Md. Shariful Islam","email":"","orcid":"","institution":"Department of Economics, Bangladesh University of Professionals (BUP), Mirpur Cantonment, Dhaka-1216, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Shariful","lastName":"Islam","suffix":""},{"id":605073349,"identity":"e18a862b-16dd-4bcb-9949-a4e3558e5b97","order_by":2,"name":"Nahida Shaulin","email":"","orcid":"","institution":"Department of Public Administration, Bangladesh University of Professionals (BUP), Mirpur Cantonment, Dhaka-1216, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Nahida","middleName":"","lastName":"Shaulin","suffix":""},{"id":605073350,"identity":"e255194c-a065-4c6d-9b5e-0ed1aeaf008e","order_by":3,"name":"Anas Al Masud","email":"","orcid":"","institution":"Department of Public Administration, Bangladesh University of Professionals (BUP), Mirpur Cantonment, Dhaka-1216, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Anas","middleName":"Al","lastName":"Masud","suffix":""},{"id":605073351,"identity":"6d04ed18-ee4a-4585-8c27-1a7877661c98","order_by":4,"name":"Bikram Biswas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACA2Y2EMXGwMbMfODABxCTnVgtfOxtiQ9ngJjMhLQwsEEYcjxnjI15QCxCWszZ2dI+/MzhS2yTSDCTtvm1TZ6PmYHxw8cc3Fosm9kOz+zdxgbSkiad23fbsI2ZgVly5jY8DjvM3szAC9FyTDq35zYjUAsbMy8BLYx/wVoS26Qte27bE6GF7TAz2Baew8zGDD9uJxLUAvRLMrPsNjbjNvY2xoe9DbeT25gZm/H6xZz/mDHj223HZOc383848OPPbdv57c0HP3zEowUKjkEoxjYw2UBQPRDUQOk/xCgeBaNgFIyCkQYA07RMjEGOZA8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5429-3916","institution":"College of Public administration, Huazhong University of Science and Technology, Wuhan, Hubei, China \u0026 Department of Educational Administration, Noakhali Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Bikram","middleName":"","lastName":"Biswas","suffix":""}],"badges":[],"createdAt":"2026-03-12 12:17:08","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9104671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9104671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104586128,"identity":"e074e793-eab2-4095-bfef-6497aca14840","added_by":"auto","created_at":"2026-03-13 15:52:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9104671/v1/81ffeada31797065c4c66a4d.jpeg"},{"id":104586129,"identity":"26ec48a4-574a-496d-86ab-ed8e20ff6348","added_by":"auto","created_at":"2026-03-13 15:52:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9104671/v1/b2652d954dce87aa12edb3d9.png"},{"id":104782045,"identity":"593050f6-6ae1-430c-99b8-7d2e096c8559","added_by":"auto","created_at":"2026-03-17 07:56:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1920100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9104671/v1/abe6ae20-9162-46ee-a978-387b347ab691.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing Citizen Use Behavior of Digital Land Services in Bangladesh: The Mediating Role of Public Trust and Satisfaction\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand, the concept primarily defines the relationship between people and the environment, whereas land services management deals with resource sustainability concerns respectively (Reenberg, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Where rights and access to land for all citizens are mostly vital for global investment, economic growth, and resilience, preventing land seizures, displacement vulnerability, and forced migration also fall under the common concern for countries around the world (World Bank, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Increasing population, urbanization, and climate change with an increased number of natural disasters- are a few of many pressuring factors that pulled the concern of improving rights and access to land-property information (Chehrehbargh et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Burns \u0026amp; Shojaei, 2023). In the present years, the concept and practices of E-governance have evolved and appeared as a paradigm shift in delivering land-property information, prioritizing transparency, reliability, and accountability across both developing and developed countries (Wandaogo, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khanra \u0026amp; Joseph, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mosse \u0026amp; Whitley, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The digital transformation of land administration has mostly altered concerned services into swift, user-friendly, and capable of producing reliable outputs that ensure legal certainty within a contemporary land administration framework, mostly critical for achieving the Sustainable Development Goals (SDGs) as well (Kusmiarto et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nowadays, traditional land registries are increasingly shifting towards digital systems to enhance respective transparency and efficiency, minimizing repeated visits to land offices with unnecessary bureaucratic procedures and incompetent intermediaries (Arifuzzaman \u0026amp; Islam, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Todorovski \u0026amp; Potel, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The world is now experiencing a growing trend with about one-third of countries around the world, adopting digital land record systems (Rodima-Taylor, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research works also show that this digitization of land administration is simplifying the monitoring of land procedures, ensuring an updated service with reduced challenges by service seekers (Todorovski \u0026amp; Potel, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). India is now leading the game with digitized land records and computerized property registration offices (Thakur et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Bangladesh, on the other side of the coin, covering an area of roughly 148,460 square kilometers, is facing challenges in accommodating and managing its vast population where land is still being treated as an increasingly valuable source of fixed capital (Alam et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Viana et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the existing land use policy in Bangladesh struggles with major issues stemming from outdated and fragmented land records (Joysoyal et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Even in this age of technological development, Bangladesh relies on an inefficient, traditional, paper-based land management system. The process of registering ownership, possession, or other land rights is mostly dependent on various physical records, such as deeds, purchases, registrations, and mutation documents, which require the participation of multiple ministries and departments in the process additionally (Rahman \u0026amp; Hossain, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In practice, this is often treated as a cumbersome job as it is quite challenging to coordinate among various departments in a 3rd world developing country like- Bangladesh. This outdated system poses significant challenges for the government in managing and updating land registration records with practices such as limiting access to reliable land information and preventing real-time transaction record updates (Ameyaw \u0026amp; de Vries, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The limited synchronization among departments often leads to diverse forms of fraud, reducing transparency among stakeholders in land transactions. Additionally, completing the Record of Rights (ROR) transfer takes several months, and identifying court cases or bank leases during the registration of ownership transfer is equally difficult (Alam et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Acknowledging such a plethora of challenges as well as the potential of digitizing the land management system, the Government of Bangladesh (GoB) has taken a few initiatives followed by highlighting an urgent necessity for digitizing its land records, registration processes, paperwork, and associated data (Akter, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The first land management service to undergo digitization was \u0026ldquo;E-Mutation,\u0026rdquo; launched in 2018, which allowed citizens to apply for mutations online, significantly reducing the time, cost, and number of visits required compared to traditional methods (Rabbani \u0026amp; Hossain, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Upazila land offices in Bangladesh are leading these digital land services in Bangladesh to streamline land-related processes and improve citizen satisfaction. Highlighting the potential, the government has plans to expand the digital land services system to all Upazila land offices to enhance the quality of land services (Akter, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in many instances, the general population remains uninformed about these advancements, limiting their ability to benefit from them indeed (The Daily Star, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe existing body of literature demonstrates a significant focus on digital land services globally. However, in the context of Bangladesh, most studies concentrate on traditional land service systems, with limited scholarly attention given to the digital land services provided by Upazila Land Offices (ULOs). Therefore, this research seeks to address this gap and enrich the academic discourse on digital land services in Bangladesh. Accordingly, the primary aim of this study is to evaluate user\u0026rsquo;s use behavior of digital land services provided by ULO. The study further aims to assess citizens\u0026rsquo; satisfaction with digital land services, thereby providing insights into their effectiveness and implications for improving service delivery in the Bangladeshi context.\u003c/p\u003e"},{"header":"2. Literature Review: Development of Hypotheses and Research Model","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Antecedents of Service Quality:\u003c/h2\u003e \u003cp\u003eService quality reflects the perceived performance of services compared to recipient expectations, with perceived ease of use and usage patterns being key factors (Chohan \u0026amp; Hu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It plays a vital role in fostering public trust across sectors like law enforcement, healthcare, and education. High-quality services enhance trust by promoting responsiveness, professionalism, and transparency, as seen in studies on digital police services (Chaeruddin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In healthcare, inefficiencies erode trust, prompting many to seek treatment abroad (Yulianto, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research also shows that service quality and innovation boost satisfaction, strengthening public trust (Muzaki et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, service quality is pivotal for building and sustaining trust in institutions. Thus, the hypothesis in this regard is as follows;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eService Quality has a positive impact on the Public Trust\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eService quality differs from citizen satisfaction, as it precedes and influences satisfaction in the assessment process, with quality leading to satisfaction (Parasuraman et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Rita et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) emphasized that service quality shapes ultimate satisfaction and influences whether recipients remain loyal or seek alternatives. In e-government, studies have consistently shown a strong positive relationship between service quality and satisfaction (Biswas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saha, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hoque, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Aryanty et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Ghimire et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that dissatisfaction arises from lower-quality services, underscoring service quality\u0026rsquo;s role in satisfaction. Reliability, a key dimension of service quality, ensures consistent and accurate service delivery, significantly enhancing satisfaction (Albar, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Halika \u0026amp; Kharisma, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the context of ULO\u0026rsquo;s digital land services, \u0026ldquo;service quality\u0026rdquo; refers to citizens\u0026rsquo; evaluation of these services, which directly impacts overall satisfaction. Consequently, the proposed hypotheses are as follows;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eService quality has a direct positive impact on user\u0026rsquo;s satisfaction\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eService quality influences both satisfaction and customer loyalty, with improved quality driving higher satisfaction and use behavior, as seen in e-commerce (Hidayat et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Halika \u0026amp; Kharisma, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nur Ullah \u0026amp; Biswas, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is a key factor in fostering loyalty (Fatiha et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly in digital environments where website quality encourages return visits, engagement, and recommendations. This applies to both online and traditional services, as studies confirm that perceived service quality significantly impacts consumer behavior (Saputra et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In e-government, research shows a direct link between service quality and use behavior (Yapinski et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Latifah et al., 2023). Therefore, this study posits that high service quality is essential for sustaining consistent use behavior in e-government platforms. Thus, the hypothesis in this case is as follows;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eService Quality has a direct positive impact on Use Behavior\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Antecedents of Satisfaction:\u003c/h2\u003e \u003cp\u003eSpecific satisfaction refers to satisfaction with individual transactions by measuring the gap between expected and actual services, while accumulative satisfaction reflects overall satisfaction built over time since the initial transaction (Zhang, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Some prior studies (Biswas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alsuwaidi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ferdous et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Biswas \u0026amp; Roy, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have found that specific satisfaction significantly influences accumulative satisfaction. Satisfaction with each service interaction donates to overall satisfaction with an organization and the cumulative effect determining the final satisfaction level. Citizens\u0026rsquo; evaluations are influenced by both the halo effect and recent experiences by emphasizing the dynamic interaction between specific and accumulative satisfaction. Satisfaction plays an important role for building sustainable and reciprocal relationships between government and citizens in digital services system (Sukma \u0026amp; Leelasantitham, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is also a key driver of use behavior on digital government platforms (Fatiha et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). DeLone and McLean (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) found that there has a positive relationship between satisfaction and reuse intention and citizen loyalty. Recent studies identify satisfaction as a primary determinant of use behavior in service organizations (Biswas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oematan et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, Prior research on e-loyalty has shown that satisfaction shapes use intentions and enhance the long-term relationships with service providers (Jin \u0026amp; Ryu, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pramudita et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The literature consistently highlights that satisfaction drives e-government service use and loyalty and both specific and accumulative satisfaction significantly contributing to citizen loyalty. Therefore, the hypothesis proposed in this context reflects as follows;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003e \u003cem\u003eUser\u0026rsquo;s Satisfaction has a direct positive impact on Use Behavior\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Antecedents of Trust:\u003c/h2\u003e \u003cp\u003eCitizen trust in government agencies is crucial for driving use behavior and satisfaction with e-government services. Trust develops through social interactions and experiences with government programs (Cai, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Transparent, fair, and ethical conduct by agencies fosters confidence, enhancing satisfaction and continued engagement (Li \u0026amp; He, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Qatawneh et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). ICT advancements enable governments to build trust by offering effective, citizen-centric e-services that boost satisfaction and promote consistent use (Taufiqurokhman et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Shang, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; and Alzahrani et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, in Saudi Arabia, trust in the Ministry of Education enhances satisfaction with university admissions and increases engagement with e-services. Research also shows that trust directly influences satisfaction and reuse intentions in e-commerce (Zheng et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bilgihan, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and e-government, fostering long-term relationships by sustaining satisfaction and use behavior (Tegethoff et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the proposed hypotheses are\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePublic Trust has a direct positive impact on Use Behavior\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Antecedents of Use Behavior:\u003c/h2\u003e \u003cp\u003eBehavioral intention towards technology significantly impacts actual usage and long-term organizational performance (Hossain et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiakui et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; and Li et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Continuance intention is a key indicator of IS success model that reflects users\u0026rsquo; ongoing adoption behavior and helps to measure user loyalty as well as the success of IS implementation (Hossain et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of e-government, continuous use behavior of citizens is essential for maximizing service value and overall performance of the system (Qatawneh et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li \u0026amp; Shang, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many scholars argued that user engagement also influences behavioral intention toward e-services (Asagbra et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Brock \u0026amp; Von, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, factors that influencing initial adoption may reduce continuance usage and also emphasizing that the importance of post-adoption behavior in ensuring long-term technology adoption and satisfaction (Oliver, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; and Bhattacherjee, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Model Development:\u003c/h2\u003e \u003cp\u003eThe measurement of service quality research started significantly in the 1980s. Haywood-Farmer (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) introduced three key attributes: physical facilities and processes, people\u0026rsquo;s behavior, and professional judgment in his Attribute Service Quality model. In 1994, Taylor and Cronin developed the SERVPERF model to measure performance based on the service quality matrices. In 1985, Parasuraman initially identified 97 attributes across 10 dimensions for service quality assessment, and later, in 1988 refined the model with seven dimensions. Further (Parasuraman et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) revised their model with a five-dimension SERVQUAL model. The SERVQUAL model is widely used to assess service quality by measuring the gap between expected and perceived service quality (Raza et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yadav \u0026amp; Rai, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It has been applied in healthcare (Teshnizi et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abuosi \u0026amp; Atinga, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), education (Ali \u0026amp; Gulliver, 2017; Wang \u0026amp; Chiu, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and banking (Negi, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; and Nyeck et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Many recent studies (Biswas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nur Ullah \u0026amp; Biswas, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pandey, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; and Yadav \u0026amp; Rai, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggested that the SERVQUAL model is also effective for evaluating digital e-government services. In this study, the five-dimensional SERVQUAL model is used to assess the service quality of digital land services provided by the ULO in Bangladesh. Citizen satisfaction is measured using Zhang\u0026rsquo;s Two-Dimensional Satisfaction Model (SAT) (Zhang, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while Public Trust (PUBT) is conceptualized based on Colesca (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, the Use Behavior (USEB) is examined following the framework of Biswas \u0026amp; Roy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study proposes an integrated model to examine the factors influencing use behavior toward government digital services. The model aims to explore the theoretical relationships among SQ, SAT, and PUBT and their combined impact on USEB regarding the digital land services of the ULO in Bangladesh. These relationships are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology of the Study","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Method:\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional quantitative study based on primary and secondary data sources. The quantitative method helps to measure impacts, causes, and relationships where statistical significance among the constructs of latent variables is necessary to unveil (Palys, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). As this study tests predetermined hypothetical paths among the constructs, i.e., SQ, PUBT, SAT, and USEB, towards the digital land service of Upazila Land Office (ULO), the quantitative method is logical to apply. Biswas et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and many researchers used cross-sectional quantitative methods to measure service quality, satisfaction, and use intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study context:\u003c/h2\u003e \u003cp\u003eThe Government of Bangladesh introduced the digital land service system under the Digital Bangladesh Manifesto of 2008 (Akter, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This initiative aims to digitize land records and documents using ICT tools, simplifying service management and enhancing accessibility for citizens (Islam et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The transition from traditional paper-based land services to a modern e-land system has significantly increased demand among citizens (Hossain, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). According to The Business Standard (September 30, 2022), 3.5 lakh people accessed digital land services via the land service hotline in just eight months. This innovation in land management offers a compelling context for research. The study examines 20 upazila land offices (ULOs) across Bangladesh, representing various regions to ensure generalizability where five ULOs each from the northeastern, northwestern, southeastern, and southwestern parts of the country.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Measurement Instruments:\u003c/h2\u003e \u003cp\u003eThis study evaluates SQ, PUBT, SAT, and USEB related to the digital land services of ULO in Bangladesh. The five-dimensional SERVQUAL framework (Tangibility, Reliability, Responsiveness, Assurance, and Empathy) has been used to measure SQ. SAT was measured based on Biswas et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), USEB was measured using items from Biswas \u0026amp; Roy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and PUBT was assessed using measures from Colesca (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). All constructs were measured on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;Very Disagree to 5\u0026thinsp;=\u0026thinsp;Very Agree). Additionally, demographic characteristics such as age, gender, education, occupation, and income of the respondents were also collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sampling and Sample Selection:\u003c/h2\u003e \u003cp\u003eThis study has been primarily used purposive sampling because it is cost-effective and time-efficient technique as well as more effective method when a limited and specific group of people can serve as primary data sources that are closely related to specific research objectives (Campbell et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; and Ovi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A convenient sampling technique is also used due to limited funding opportunities and participants\u0026rsquo; time scheduling constraints (Etikan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The sample was the users who had visited and got services from ULO\u0026rsquo;s digital land services at least once. Based on Yamane\u0026rsquo;s (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) formula for populations exceeding 100,000 at a 95% confidence level, the recommended sample size is 400. Tabachnick and Fidell\u0026rsquo;s (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) rule-of-thumb suggests a minimum sample of 82 for four independent variables. Memon et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) recommend a range of 160 to 300 for multivariate analysis methods like PLS-SEM and CB-SEM. Considering these guidelines, this study analyzed data from 448 respondents that fulfilled the minimum requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Questionnaire development and Data Collection and Analysis:\u003c/h2\u003e \u003cp\u003eA structured survey questionnaire was developed based on the research model constructed. Initially designed in English, it was translated into Bengali for better respondent comprehension. After piloting, the finalized questionnaire was used for data collection between February 20 and March 10, 2025. A total of 500 questionnaires were distributed, and 460 were returned. Following quality checks, 12 were excluded due to missing data, leaving 448 for analysis. Descriptive statistics were analyzed using IBM SPSS-25, while SmartPLS-4.0 was employed for Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), a widely accepted method for testing and validating theoretical models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Model Specification:\u003c/h2\u003e \u003cp\u003eIn order to confirm the relationships between latent variables and their respective observed indicators, this study first estimate the measurement model as follows;\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$SQ={{\\lambda}}_{SQ1}{X}_{1}+{{\\lambda}}_{SQ2}{X}_{2}+{{\\lambda}}_{SQ3}{X}_{3}\\dots\\dots\\dots.+{{\\lambda}}_{SQ22}{X}_{22}+{ϵ}_{SQ}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$PUBT={{\\lambda}}_{PUBT1}{Y}_{1}+{{\\lambda}}_{PUBT2}{Y}_{2}+{{\\lambda}}_{PUBT3}{Y}_{3}+{{\\lambda}}_{PUBT4}{Y}_{4}+{{\\lambda}}_{PUBT5}{Y}_{5}+{{\\lambda}}_{PUBT6}{Y}_{6}+{ϵ}_{PUBT}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$SAT={{\\lambda}}_{ACCS1}{Z}_{1}+{{\\lambda}}_{ACCS2}{Z}_{2}+{{\\lambda}}_{ACCS3}{Z}_{3}+{{\\lambda}}_{ACCS4}{Z}_{4}+{{\\lambda}}_{ACCS5}{Z}_{5}+{ϵ}_{ACCS}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$USEB={{\\lambda}}_{USEB1}{W}_{1}+{{\\lambda}}_{USEB2}{W}_{2}+{{\\lambda}}_{USEB3}{W}_{3}+{ϵ}_{USEB}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, X\u003csub\u003e1,\u003c/sub\u003e X\u003csub\u003e2,\u003c/sub\u003e X\u003csub\u003e3\u003c/sub\u003e\u0026hellip;\u0026hellip;\u0026hellip;.X\u003csub\u003e22\u003c/sub\u003e ; Y\u003csub\u003e1,\u003c/sub\u003e Y\u003csub\u003e2,\u003c/sub\u003e Y\u003csub\u003e3,\u003c/sub\u003e Y\u003csub\u003e4,\u003c/sub\u003e Y\u003csub\u003e5,\u003c/sub\u003eY\u003csub\u003e6\u003c/sub\u003e ; Z\u003csub\u003e1,\u003c/sub\u003e Z\u003csub\u003e2,\u003c/sub\u003e Z\u003csub\u003e3,\u003c/sub\u003e Z\u003csub\u003e4,\u003c/sub\u003e Z\u003csub\u003e5\u003c/sub\u003e ; Z\u003csub\u003e6,\u003c/sub\u003e Z\u003csub\u003e7,\u003c/sub\u003e Z\u003csub\u003e8\u003c/sub\u003e ; and W\u003csub\u003e1,\u003c/sub\u003e W\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e are the observed items of latent variable- SQ; PUBT; SAT and USEB respectively. λ\u003csub\u003eSQ1,\u003c/sub\u003e λ\u003csub\u003eSQ2,\u003c/sub\u003e λ\u003csub\u003eSQ3\u003c/sub\u003e\u0026hellip;. λ\u003csub\u003eSQ22\u003c/sub\u003e ; λ\u003csub\u003ePUBT1,\u003c/sub\u003e λ\u003csub\u003ePUBT2,\u003c/sub\u003e λ\u003csub\u003ePUBT3,\u003c/sub\u003e λ\u003csub\u003ePUBT4,\u003c/sub\u003e λ\u003csub\u003ePUBT5,\u003c/sub\u003e λ\u003csub\u003ePUBT6\u003c/sub\u003e ; λ\u003csub\u003eACCS1,\u003c/sub\u003e λ\u003csub\u003eACCS2,\u003c/sub\u003e λ\u003csub\u003eACCS3,\u003c/sub\u003e λ\u003csub\u003eACCS4,\u003c/sub\u003e λ\u003csub\u003eACCS5\u003c/sub\u003e ; λ\u003csub\u003eSPES1,\u003c/sub\u003e λ\u003csub\u003eSPES2,\u003c/sub\u003e λ\u003csub\u003eSPES3\u003c/sub\u003e ; and λ\u003csub\u003eUSEB1,\u003c/sub\u003e λ\u003csub\u003eUSEB2,\u003c/sub\u003e λ\u003csub\u003eUSEB3\u003c/sub\u003e are the factor loadings of all items of latent variable- SQ; PUBT; ACCS; SPES; and USEB respectively. ϵ\u003csub\u003eSQ,\u003c/sub\u003e ϵ\u003csub\u003ePUBT,\u003c/sub\u003e ϵ\u003csub\u003eACCS,\u003c/sub\u003e ϵ\u003csub\u003eSPES,\u003c/sub\u003e and ϵ\u003csub\u003eUSEB\u003c/sub\u003e are the measurement error of latent variables- SQ; PUBT; ACCS; SPES; and USEB respectively. As part of the measurement model, this study calculates Composite Reliability (CR) and Convergent Validity through Average Variance Extracted (AVE) based on the following equations;\u003c/p\u003e \u003cp\u003eCR= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\left[{\\sum}_{i=1}^{n}{\\lambda}_{i}^{2}\\right]}{\\left[{\\sum}_{i=1}^{n}{\\lambda}_{i}^{2}\\right]+\\left[{\\sum}_{i=1}^{n}{\\delta}_{i}\\right]}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eAVE= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\left[{\\sum}_{i=1}^{n}{\\lambda}_{i}^{2}\\right]}{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003en\u003c/em\u003e is the number of items, λ \u003csub\u003ei\u003c/sub\u003e is the standardized factor loading for item \u003cem\u003ei\u003c/em\u003e, and δ\u003csub\u003ei\u003c/sub\u003e is the measurement error for item \u003cem\u003ei\u003c/em\u003e. And then this study calculated the structural model that shows hypothetical relationships among the constructs i.e. SQ; PUBT; ACCS; SPES; and USEB. The equitation is;\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$USEB={\\beta}_{0}+{\\beta}_{1}SQ+{\\beta}_{2}PT+{\\beta}_{3}AS+{ϵ}_{UB}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$PUBT={\\beta}_{0}+{\\beta}_{4}SQ+{ϵ}_{PT}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$SPES={\\beta}_{0}+{\\beta}_{5}SQ+{\\beta}_{6}PT+{ϵ}_{SS}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept or constant term in each equation, representing the baseline value when all independent variables (predictors) are zero? \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{1},{\\beta}_{2},{\\beta}_{3}\\dots\\dots\\dots..{\\beta}_{10}\\)\u003c/span\u003e\u003c/span\u003e, are the path coefficients. ϵ is the error term. Then, the model calculates direct, indirect and total effects on the UB. In this case, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{1},{\\beta}_{2},{\\beta}_{3},{\\beta}_{4}\\text{a}\\text{r}\\text{e}\\)\u003c/span\u003e\u003c/span\u003ethe direct effects of SQ, PUBT, ACCS, and SPES on USEB respectively. Alongside, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{5}\\)\u003c/span\u003e\u003c/span\u003e is the direct effect of SQ on PUBT; and the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{6},{\\beta}_{7},{\\beta}_{8}\\)\u003c/span\u003e\u003c/span\u003e are the direct effects of SQ, PUBT, SPES on ACCS respectively. And the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{9},{\\beta}_{10}\\)\u003c/span\u003e\u003c/span\u003e are the direct effects of SQ, PUBT on SPES respectively. The model then calculates indirect and total effects of SQ on USEB based on the following equations;\u003c/p\u003e \u003cp\u003eSQ -\u0026gt; PUBT -\u0026gt; USEB = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(({\\beta}_{4}.{\\beta}_{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eSQ -\u0026gt; SAT -\u0026gt; USEB = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(({\\beta}_{5}.{\\beta}_{3})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTotal Indirect Effects of SQ -\u0026gt; USEB = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({\\beta}_{4}.{\\beta}_{2}\\right)+({\\beta}_{5}.{\\beta}_{3})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTotal effect of SQ -\u0026gt; USEB= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta}_{1}+\\left({\\beta}_{4}.{\\beta}_{2}\\right)+({\\beta}_{5}.{\\beta}_{3})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.7 Ethical Statement:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted following the highest ethical standards to ensure integrity, transparency, and academic honesty. Ethical approval (Ref: 23.0.902.858.07.786.24/30) was obtained from the Research Ethical Committee (REC) of Bangladesh University of Professionals (BUP), Dhaka, and all necessary permissions were secured before data collection. Informed consent was obtained from all participants, ensuring their voluntary participation and confidentiality. The authors declare that there are no conflicts of interest related to this study. All data presented in this article are accurate and have not been manipulated or fabricated. The authors confirm their adherence to ethical research and publishing practices, ensuring that all contributions were properly acknowledged and that the study maintains academic integrity.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1 Demographic Information:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic and behavioral characteristics of the study\u0026rsquo;s respondents (users of DLS) are summarized in Table 1. Most of the users are middle-aged, with 27.7% aged 31-40 and 41-50 years, while younger (\u0026lt;20 years) and older (\u0026gt;60 years) users comprise only 2.2% and 5.1%, respectively. The majority of them are male (73.2%), and 49.1% hold at least a Bachelor\u0026rsquo;s degree, though 4.2% lack formal education. Among all of the respondents, the businesspeople (25.9%) represent the largest occupational group, followed by private employees (20.5%) and students (12.3%). The monthly incomes of the respondents primarily range from 30,001-40,000 BDT (33.7%) and 20,001-30,000 BDT (22.1%), with fewer users earning \u0026lt;10,000 BDT (5.6%) and \u0026gt;50,000 BDT (8.9%). The user\u0026rsquo;s awareness of DLS is largely driven by personal interactions (38.4%), followed by ULO officials (21.9%), with additional contributions from public representatives (16.3%), advertisements (13.6%), and websites (9.8%). Regarding usage, 17.6% are first-time users, while 40.5% have used the service for 6 months to 2 years, and only 8.9% are long-term users (\u0026gt;3 years). This data reveals that digital land services in Bangladesh are primarily utilized by educated, middle-aged males with moderate-to-high incomes and varied experience levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Demographic Data (N=448)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCategories\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; Frequency\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePercentage\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026lt;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026gt;60 Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cem\u003eMale\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eMasters or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003ePrivate employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eBusiness man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eNo job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eIncome (Monthly)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026lt;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e10001 -20000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e20001-30000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e30001-40000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e40001-50000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026gt;50000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eKnowing ULO Digital Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003ePeople\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eAdvertisement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eULO officials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003ePublic Representative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eWebsite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTenure of Using\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eThis is the first time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eLess than 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e6 -12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e1-2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e2-3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026gt;3 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2 Measurement Model\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the measurement model, this study calculated the validity and reliability of the four-factor model (i.e., SQ, PUBT, SAT, and USEB). Conformity Factor Analysis (CFA), was conducted for factor loadings of the items of four constructs and their covariance. Construct reliability was confirmed through Cronbach\u0026rsquo;s Alpha and Composite Reliability (CR). Convergent and divergent (Discriminant) validity was also checked through Average Variance Extracted (AVE), the Fornell-Larcker Criterion, and the Heterotrait-Monotrait (HTMT) Ratio. Table no 2 presents the construct validity and reliability of the data used in this study. The factor loadings of all items have shown a significant contribution in explaining respective constructs since they exceed the critical value of 0.50 (Hair et al., 2020). In the case of reliability, all constructs show acceptable reliability, with Cronbach\u0026rsquo;s Alpha and CR values exceeding the threshold of 0.70 (Hair et al., 2020) that indicate strong internal consistency and confirming the strong reliability of the model. In convergent validity, the value of AVE of all constructs exceeded the critical value of 0.50 (Fornell \u0026amp; Larcker, 1981). Furthermore, this study confirmed discriminant validity using the Fornell-Larcker Criterion and the Heterotrait-Monotrait (HTMT) ratio that the constructs are distinct from one another. According to the Fornell-Larcker Criterion, the square root of the AVE (diagonal values) for each construct exceeds the correlations with other constructs (off-diagonal values), shown in Table no 3. According to HTMT ratio, all construct\u0026rsquo;s pair values are within the threshold value of 0.85 shown in table 4, indicating strong discriminant validity (Fornell \u0026amp; Larcker, 1981 and Henseler et al., 2015). Together, these results confirm that the constructs are sufficiently independent. Hence, the measurement model is reliable and valid for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Construct reliability and validity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eConstructs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eItems\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLoadings\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCronbach\u0026apos;s Alpha\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAVE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSAT1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.853\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.850\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.899\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.690\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSAT2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.821\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSAT3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.843\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSAT4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.804\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.805\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.879\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.908\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.623\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.816\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePublic Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.772\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.787\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.763\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePUBT6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.793\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQAS1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.855\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAssurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQAS2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.887\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.663\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQAS3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.787\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQAS4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.786\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQEM1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.956\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQEM2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.930\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEmpathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQEM3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.942\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.959\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.970\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.890\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQEM4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.945\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRL1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.834\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRL2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.842\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRL3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.812\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRL4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.835\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.885\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.916\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.684\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRL5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.813\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRS1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.887\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eResponsiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRS2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.879\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRS3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.898\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.934\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.950\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRS4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.874\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQRS5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.908\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQTA1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.857\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQTA2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.842\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.856\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.902\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.698\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTangible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQTA3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.804\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSQTA4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.838\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eUSEB1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.829\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eUse Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eUSEB2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.758\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.748\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.855\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.664\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003eNote: CR= Composite Reliability, AVE= Average Variance Extracted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Discriminant validity- Fornell and Larcker Criterion\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePUBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSEB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSAT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.830\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePUBT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.789\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQAS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.814\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.943\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.827\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.889\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQTA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.835\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUSEB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.815\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDiscriminant validity-Heterotrait- monotrait ratio (HTMT) matrix\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePUBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSQTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSEB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePUBT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQAS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQTA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUSEB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003eHowever, this study used service quality constructs that requires higher order constructs (HOC) validity as service quality measured as the higher order construct in this study based on fiver lower order construct-responsiveness, reliability, tangible, assurance and empathy. In order to establish higher-order construct validity, outer weights, outer loading, and VIF were calculated. Furthermore, outer loading was found to be greater than .50 for each of the lower-order constructs (Sarstedt et al., 2019). VIF values were assessed to check multicollinearity and found to be at a very minimal level, which is acceptable (Hair et al., 2016). Since all criteria were met, the HOC validity was established\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Higher order construct validity results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHOC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLOC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOuter weight\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eT-statistics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP values \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOuter loading\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSQAS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.351\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6.508\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.547\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSQEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.519\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSQRL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.431\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e7.982\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.650\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.188\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSQRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.346\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6.927\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.047\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSQTA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.199\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3.326\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.539\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.257\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3 Structural Model Analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study used Structural Equation Modeling (SEM), and the measurement model was found reliable and valid, this study tested the structural model for analyzing the statistical significance of path relationships among the constructs of the research model shown in Figure 1. The results of the direct path relationships presented in Table 6 and Figure 2 indicate that all constructs have substantial positive effects on use behavior as assumed in the hypotheses and research of the model. SQ significantly influences SAT (\u0026beta; = 0.583, t = 16.566, p = 0.000); PUBT (\u0026beta; = 0.614, t = 17.440, p = 0.000); and USEB (\u0026beta; = 0.285, t = 4.452, p = 0.000) respectively, accepting the H1, H2, H5. SAT also exhibits a significant positive effect on USEB (\u0026beta; = 0.126, t = 2.210, p = 0.027), assuming in the H3. The study also shows a strong positive impact of PUBT on USEB (\u0026beta; = 0.234, t = 3.844, p = 0.000) supporting the H4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Hypotheses Testing Results (Direct Effects)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypothesis path\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eT statistics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP values\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePUBT -\u0026gt; USEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSAT -\u0026gt; USEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQ -\u0026gt; PUBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQ -\u0026gt; SAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSQ -\u0026gt; USEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003eIn this study, calculated potential indirect effects shown in Table 6 reveal the significant mediating effects of SAT and PUBT on the relationship of SQ and USEB. SQ also significantly influences USEB indirectly through SAT (\u0026beta; = 0.073, t = 2.176, p = 0.030) and PUBT (\u0026beta; = 0.143, t = 3.740, p = 0.000), highlighting the mediating roles of satisfaction and trust, therefore, accepting the H6 and H7. It is noted that as SQ has a direct effect on the USEB in presence of mediators like PUBT and SAT, there is a partial but significant mediating effects of public trust and satisfaction in shaping the relationship between service quality and citizen use behavior considering the context of DLS in Bangladesh.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Mediation Results (Indirect Effects)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndirect Path\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eT value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePercentile bootstraps 95% confidence interval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLower\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUpper\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSQ\u0026nbsp;\u003cstrong\u003e\u003cem\u003e\u0026rarr;\u003c/em\u003e\u003c/strong\u003e SAT\u003cstrong\u003e\u003cem\u003e\u0026rarr;\u003c/em\u003e\u003c/strong\u003eUSEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSQ\u0026nbsp;\u003cstrong\u003e\u003cem\u003e\u0026rarr;\u003c/em\u003e\u003c/strong\u003ePUBT\u003cstrong\u003e\u003cem\u003e\u0026rarr;\u003c/em\u003e\u003c/strong\u003eUSEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.081\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.208\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e\n\u003cp\u003eTable 7 presents the total effects analysis of SQ on USEB. The indirect effect of SQ on USEB through mediators (\u0026beta; = 0.217, t = 4.684, p = 0.000) indicates a meaningful mediating influence. And the total effect, which includes both direct and indirect impacts (\u0026beta; = 0.501, t = 12.788, p = 0.000), reinforces the strong overall effect of SQ on USEB. The results suggest that while a significant portion of the relationship is mediated, SQ still has a substantial total effect on USEB.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Total Effects (Direct + Indirect)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePath Direction\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eT statistics\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP values\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSQ -\u0026gt; USEB (Through Mediators)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.217\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.046\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.684\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSQ -\u0026gt; USEB (Total Effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.501\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.788\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey (2025)\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study investigates the determinants of use behavior among users of digital land services in Bangladesh, focusing on key constructs such as SQ, PUBT, SAT, and USEB and their interrelationships. The results provide significant insights into the theoretical and practical implications of digital service adoption, revealing both strengths and gaps in the measurement and structural models. The study demonstrates strong reliability and validity of the measurement model. The Cronbach\u0026rsquo;s alpha values for all constructs exceeded the 0.70 threshold, confirming internal consistency, while Composite Reliability (CR) values supported the construct\u0026rsquo;s reliability. Average Variance Extracted (AVE) values also met the minimum standard of 0.50, ensuring convergent validity. Furthermore, the Fornell-Larcker criterion and HTMT ratio confirmed discriminant validity, with constructs clearly distinguished from one another. These results affirm the robustness of the model and the reliability of the instruments used in this study. The structural model sheds light on the pathways influencing USEB. SQ emerges as the most pivotal construct, exerting both direct and indirect effects. Its direct effect on USEB is statistically significant, exposing similarity to the existing studies of Nur Ullah \u0026amp; Biswas (2024), Latifah et l (2023), and Saputra et al (2022), reinforcing the idea that better service quality encourages users to continue using digital services. SQ also significantly influences PUBT and SAT, admitting the existing studies of Chaeruddin et al. (2024), Yulianto (2024), Biswas et al. (2024), Albar (2024), Ghimire et al. (2024), Muzaki et al., (2023); and Hoque, (2020), highlighting its foundational role in shaping users\u0026rsquo; perceptions and attitudes toward digital land services. \u0026nbsp;The effect of SQ on PUBT is highly significant, which highlights that higher service quality leads to increased public trust. The efficiency, reliability, and accessibility of digital land services play a crucial role in shaping citizens\u0026rsquo; trust in government service delivery. A strong positive relationship is observed between SQ and SAT, which suggests that service quality is a major determinant of user satisfaction. When digital land services perform well in terms of responsiveness, reliability, and accessibility, users tend to be more satisfied with their experiences. PUBT also plays a critical role, directly affecting USEB, which supports the existing studies of Li \u0026amp; He (2024), Qatawneh et al. (2024), Taufiqurokhman et al. (2024), Cai (2023), and Tegethoff et al., (2019). This finding indicates that public trust in the digital land service strongly influences users\u0026rsquo; engagement and continued use. Higher trust in service provider\u0026rsquo;s fosters confidence and reliance on the system. SAT also plays a critical role in determining user behavior in using digital land services. This study found that the relationship between SAT and USEB positively matches with the existing studies of Biswas et al. (2024) and Alsuwaidi (2023) but weaker than PUBT\u0026rsquo;sPUBT\u0026rsquo;s effect, which indicates that user satisfaction significantly contributes to continued use but is a relatively weaker predictor compared to trust. While satisfaction enhances user engagement, trust appears to be a stronger determinant of behavioral outcomes.\u003c/p\u003e\n\u003cp\u003eFurthermore, the mediation analysis in this study also explores the indirect effects of service quality (SQ) on use behavior (USEB) through satisfaction (SAT) and public trust (PUBT). The results provide deeper insights into the underlying mechanisms driving user engagement with digital land services. The path between SQ and USEB through SAT has a significant indirect effect (\u0026beta; = 0.073, p = 0.030), indicating that satisfaction partially mediates the relationship between service quality and use behavior. However, the relatively small coefficient suggests that while service quality enhances satisfaction, its impact on use behavior through this route is modest. This supports the direct effect, where satisfaction had a weaker influence on use behavior compared to public trust. However, the path between SQ and USEB through PUBT shows a stronger mediation effect (\u0026beta; = 0.143, p = 0.000), with a 95% confidence interval (0.081, 0.208) confirming its significance. This suggests that public trust is significant mediator between service quality and use behavior. Users are more likely to engage with digital land services when they perceive high service quality and strengthens their trust in the system. This emphasizes the idea that trust plays a dominant role in determining user behavior. The findings of this study found that that service quality (SQ) is the most influential factor in driving user engagement with digital land services directly and indirectly. Public trust (PUBT) and satisfaction (SAT) significantly mediate the relationship between SQ and use behavior (USEB). \u0026nbsp;The results of direct effects show that public trust has a greater impact on use behavior than satisfaction. The mediation analysis further confirms that the indirect effect of SQ on USEB via PUBT is stronger than through SAT. These results emphasize the need for trust-building strategies alongside service quality improvements to enhance public confidence and engagement with digital government services in Bangladesh.\u0026nbsp;\u003c/p\u003e"},{"header":"6. Research Implications","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e6.1 Theoretical Implications:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has a substantial theoretical implication for the e-governance service delivery and management field of study because it offers a deeper new understanding and dynamics of citizen use behavior through service quality, trust, and satisfaction by analyzing both direct and indirect effects. The empirical results of this study challenge the traditional e-service adoption models by adding trust as a crucial mediator alongside satisfaction, providing new insight into the debate on the use behavior of digital services. The previous research argued that service quality is a key driver of citizen use behavior, not only in digital services but also in traditional public services. However, this study redesigns and extends these existing theoretical frameworks by adding trust as a mediator with satisfaction. In contrast, the higher mediating effect of trust over satisfaction indicates that service quality enhances trust more effectively than satisfaction, which in turn drives citizen use behavior. This new insight suggests the SERVQUAL, the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) by emphasizing service quality as a fundamental precursor to trust rather than just a determinant of satisfaction. Besides, the Expectation-Confirmation Theory (ECT) claims satisfaction is an important factor in user behavior. Still, this study disagrees that public trust plays a stronger mediating role in shaping the citizen use intention. This study raises a question on the limited direct influence of satisfaction on citizen use behavior, suggesting that satisfaction alone is insufficient to drive sustained engagement in digital services, especially in public-sector settings where trust in institutions plays a crucial role. \u0026nbsp;This study urges a shift from a satisfaction-centric approach to a trust-focused model in e-governance research. By extending e-governance theories with new mediators like trust, this study provides a holistic perspective on digital public service engagement, emphasizing the importance of trust-building strategies alongside technological advancements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e6.2 Practical implications: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has also a practical implication as it provides some significant insights for policymakers, digital service providers, and public administrators striving to enhance user engagement with e-government services, particularly in digital land services in Bangladesh. By demonstrating the theoretical model tested positive, where SQ through SERVQUAL, SAT, and PUBT works as the key drivers of USEB, this research provides actionable strategies to improve the effectiveness of digital land services in Bangladesh. Given that SQ directly influences trust, satisfaction, and use behavior, digital service providers should focus on enhancing system efficiency by reducing processing times, minimizing errors, and ensuring reliable service delivery, user-friendly design, and accessibility to cater to diverse demographics, including those with low digital literacy; and proactive customer support to resolve issues promptly and improve the overall user experience. Since public trust plays a stronger role than satisfaction, policymakers must prioritize trust-building initiatives such as enhancing transparency in digital services through clear policies, open data, and accountability mechanisms; strengthening data security and privacy protections to reassure users that their information is safe, and implementing feedback mechanisms to address concerns and demonstrate responsiveness. As satisfaction alone does not strongly predict continued use behavior, therefore, authority should focus on a trust-focused citizen engagement strategy. Policymakers should adopt context-specific strategies such as localizing digital literacy programs to bridge knowledge gaps in rural and underserved communities, introducing public-private partnerships to leverage expertise in digital service delivery while maintaining government oversight, and formulating policy frameworks that align with institutional trust theories, ensuring that government-led digital initiatives foster long-term engagement.\u003c/p\u003e"},{"header":"7. Limitations","content":"\u003cp\u003eWhile this study offers some crucial arguments in the debate on determinants of use behavior for digital land services in Bangladesh, it has several limitations. Firstly, it is a cross-sectional survey research capturing user perceptions at a single point in time, which limits the ability to establish causal relationships among the constructs. Thus, a longitudinal research method could be done for deeper understanding on how these relationships are developed. Secondly, this study is based on self-reported responses, which may introduce biases; users may have overestimated or underestimated their level of trust, satisfaction, or use behavior, affecting the accuracy of the findings. Thirdly, this study\u0026rsquo;s context limits its wider applicability, meaning the findings may not be directly applicable to other digital government services or contexts, particularly in countries with different socio-economic, political, or technological landscapes. Fourthly, while the study considers key determinants of the use behavior of digital land service, other factors such as digital literacy, internet accessibility, legal frameworks, and government policies were not explicitly analyzed. Other psychological or behavioral factors, such as perceived ease of use, perceived risk, or prior digital experience, may also play crucial roles. These factors could significantly influence users\u0026rsquo; use behavior with digital land services and should be explored in future studies. Fifthly, the users of digital land services across Bangladesh are unlimited, so, the sample size considered in this study may limit the generalizability of the findings of this study. Therefore, research with a large sample could be conducted in future. Lastly, although the survey ensures reliability and validity through Cronbach\u0026rsquo;s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE), certain latent constructs might still be subject to measurement limitations. Future studies could refine the scales or employ mixed-method approaches, such as qualitative interviews, to capture more nuanced user experiences.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis study investigated the role of service quality, satisfaction, and trust in determining the use behavior of digital land services in Bangladesh. The results of this study confirm that SQ serves as the most influential factor, exerting both direct and indirect effects on USEB through PUBT and SAT. It reinforces prior studies that demonstrate the strong link between high-quality digital services and user retention. Additionally, it has been tested that SQ significantly impacts PUBT and SAT, which argues that the efficiency, reliability, and accessibility of digital services play a crucial role in shaping user perceptions and attitudes toward digital land services. This study unveiled an interesting insight that while both PUBT and SAT mediate the relationship between SQ and USEB, trust (PUBT) emerges as the stronger mediator than the SAT, which illustrates that trust-building measures should be prioritized to enhance citizens\u0026rsquo; reliance on digital land services. It also suggests that while users appreciate high service quality and express satisfaction, their continued use behavior is more significantly driven by their confidence in the system. This study is an important piece of research for its unique contribution in both theoretical and practical domains. Theoretically, this study proposes an extended e-governance model by including trust as a mediator alongside satisfaction. Practically, the study findings offer crucial implications for policymakers and digital service providers. More efforts should focus on improving service quality while simultaneously enhancing user engagement and sustaining the adoption of digital land services, implementing trust-building initiatives. Besides, transparency, data security, and responsive service mechanisms can improve public trust and encourage continued usage. Future research can further explore these relationships in different digital governance contexts and examine additional moderating factors such as demographic variables, digital literacy, and regulatory frameworks. By addressing these aspects, policymakers and service providers can refine their strategies to ensure higher user engagement and satisfaction with digital land services in Bangladesh and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe author(s) declared no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eInformed consent was secured from each participant, ensuring voluntary participation and the protection of confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis research receives no funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThis study was conducted in strict adherence to established ethical guidelines to uphold academic integrity, transparency, and research credibility. Ethical clearance (Ref: 23.0.902.858.07.786.24/30) was granted by the Research Ethical Committee (REC) of Bangladesh University of Professionals (BUP), Dhaka, prior to the commencement of data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData can be shared upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammad Nur Ullah contributed to the conceptualization, methodology, supervision, validation, and review and editing of the manuscript.\u0026nbsp;Md. Shariful Islam\u0026nbsp;contributed to the investigation, resources, and review and editing of the manuscript. Nahida Shaulin contributed to the supervision, and editing of the manuscript. All authors read and approved the final manuscript. Anas Al Masud contributed to\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eproject administration, data collection and review. Bikram Biswas contributed to the conceptualization, formal analysis, investigation, and data curation.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAkter, M. (2022). Digitalization in the Land Service Delivery: Comparison Between Bangladesh and Indonesia. \u003cem\u003eSoutheast Asia: A Multidisciplinary Journal, 22\u003c/em\u003e(1), 79-91. https://doi.org/10.1108/SEAMJ-01-2022-B1006\u003c/p\u003e\n\u003cp\u003eAnderson, J. C., \u0026amp; Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. \u003cem\u003ePsychological Bulletin, 103\u003c/em\u003e(3), 411.\u003c/p\u003e\n\u003cp\u003eAsagbra, O. E., Burke, D., \u0026amp; Liang, H. (2018). Why Hospitals Adopt Patient Engagement Functionalities at Different Speeds? 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(2017), “Examining E-loyalty Towards Online Shopping Platforms: the Role of Coupon Proneness and Value Consciousness”, Internet Research, Vol. 27 No. 3, pp. 709-726.\u003c/p\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003e\u003cstrong\u003eQ\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQ²predict\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRMSE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMAE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n 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\u003cp\u003e0.375\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSAT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.340\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.338\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUSEB\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.304\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.299\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel fit indices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"77%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\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\u003e\n \u003cp\u003e\u003cstrong\u003eχ2 /df\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.244\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.924\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAGFI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.910\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSRMR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.985\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.986\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"441\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMO and Bartlett's Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBartlett's Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9728.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHarman’s single-factor test was applied across all ten constructs, following the recommendation of Podsakoff et al. (2003). The eight constructs were combined into one factor. The results concluded that a single factor explained 26.22% of the total variance, which was below the commonly recommended threshold of 50%. \u0026nbsp;(Podsakoff et al., 2003), indicating that no evidence of CMB was presented in this study.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Service Quality, Public Trust: Satisfaction, Use Behavior, Digital Land Service, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-9104671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9104671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assesses the factors affecting citizen behavior toward using Digital Land Services (DLS) in Bangladesh. The study is basically cross-sectional quantitative research using the survey method based on a structured questionnaire. The data collected from 448 primary users of DLS were considered for the analysis, where the Structural Equation Modeling (SEM), run by Smart-PLS and M-plus, was used for testing the research model containing several hypotheses. This study reveals that Service Quality (SQ) significantly impacts Public Trust (PUBT) and Satisfaction (SAT), and PUBT and SAT directly influence Use Behavior (USEB). The mediation analysis furthermore highlights that PUBT and SAT as important mediators between SQ and USEB. Total effects analysis underscores SQ, PUBT, and SAT as the most influential constructs in determining the USEB of DLS service recipients in Bangladesh. This study introduces a novel research model aimed at understanding citizen use behavior of DLS, which is one of the pioneering works to combine a service quality model with satisfaction and public trust. Moreover, it marks the first instance in the e-service domain to utilize public trust as a mediator alongside satisfaction.\u003c/p\u003e","manuscriptTitle":"Assessing Citizen Use Behavior of Digital Land Services in Bangladesh: The Mediating Role of Public Trust and Satisfaction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 15:52:25","doi":"10.21203/rs.3.rs-9104671/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":"9dc3a09c-8e6c-40e6-a7b2-a8c85e080c69","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T15:52:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 15:52:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9104671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9104671","identity":"rs-9104671","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}