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Open Access

Peer-reviewed

Research Article

Facilitators and inhibitors of attitude and word-of-mouth intention toward adoption of digital municipal service systems: A stimulus-organism-response approach

Abstract

Increased technology adoption has significantly transformed how governments interact with citizens. Today, e-government services and tools are integral to modern public administration. Factors affecting users’ adoption of e-government services have been studied in the past. However, this study focuses on citizens’ acceptance and resistance to such services, which have not been thoroughly explored. This study addresses the gap by examining the facilitators and inhibitors affecting users’ perceptions of the Digital Municipal Service System (DMSS). An integrated research model was developed based on the Stimulus-Organism-Response (SOR) framework. Using Smart-PLS, the study validated 353 respondents’ data collected from Bangladesh. The study revealed that platform quality, convenience, social, and inclusiveness values significantly influenced attitudes toward DMSS adoption. On the other hand, tradition and usage barriers significantly negatively impacted attitudes toward DMSS adoption. These findings offer important insights for policymakers involved in developing and implementing e-government services in emerging economies such as Bangladesh. The study also provides a foundation for further research on technology adoption.

Citation: Talukder MS, Islam QT, Karim Z (2024) Facilitators and inhibitors of attitude and word-of-mouth intention toward adoption of digital municipal service systems: A stimulus-organism-response approach. PLoS ONE 19(12): e0315009. https://doi.org/10.1371/journal.pone.0315009

Editor: Anu Sayal, Taylor’s University - Lakeside Campus: Taylor’s University, MALAYSIA

Received: July 29, 2023; Accepted: November 19, 2024; Published: December 18, 2024

Copyright: © 2024 Talukder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The funding for this project has been provided jointly by United International University and North South University, with the grant number UIU/IAR/01/2021/BE/28. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

In today’s digital age, e-government services have emerged as an essential mechanism for governments to deliver efficient and effective services to citizens. Public service digitalization has improved significantly in terms of quality and accessibility, which has led to improved citizen-government interactions [1]. Digitizing government services has several benefits, as it reduces cost, enhances service delivery speed and decreases administrative costs, and allows citizens to interact with government services using a single tool [2, 3]. Digital Municipal Service System (DMSS) has been introduced as a one-stop platform aimed at improving citizen-government interactions in Bangladesh. However, as with many other e-government platforms in developing nations [4, 5], the success of DMSS is dependent on the willingness and enthusiasm of citizens to adopt the system. Even though DMSS has the potential to improve the delivery of government services, there is a lack of knowledge about why some citizens are inclined to adopt and use DMSS, whereas others are unwilling to do so.

While E-government service delivery platforms continue to evolve and expand, Information systems (IS) researchers have consistently explored how citizens perceive e-government services. It has been argued that previous studies have only focused on the factors that promote the adoption or usage, known as enablers while ignoring the factors that hinder adoption. Numerous factors have been identified in earlier research as enablers of e-government service adoption, such as perceived usefulness, perceived ease of use, trust in government, technological infrastructure, social influence, and citizen awareness of the services [1, 6–10]. Despite the focus on enablers, understanding the inhibitors is equally crucial for comprehensive insights into e-government adoption. Factors such as low digital literacy, privacy and security concerns, lack of trust in online platforms, inadequate infrastructure, and cultural resistance often act as significant barriers to adoption [11–13]. Some studies in recent years have shown data privacy and the complexity of using digital platforms deter citizens in emerging economies from fully embracing e-government services [14]. Besides, socio-economic factors, such as income disparities and access to reliable internet, are identified as factors that widen the digital divide [15]. This makes e-government platforms less accessible to certain segments of the population.

In order to understand the acceptance and resistance factors in e-government adoption from both theoretical and practical perspectives, it is important to study facilitators and inhibitors together. Theoretically, existing models such as the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA) emphasize facilitators that explain why citizens may adopt e-government services [8]. However, these models are inadequate as they do not account for inhibitor or resistance factors such as privacy concerns, digital literacy gaps, and trust issues [16]. In reality, focusing solely on facilitators risks creating policies that neglect critical barriers, thereby limiting the success of real-world implementation. Considering both enablers and inhibitors allows governments to design more inclusive e-government systems. This helps address the diverse needs of people while mitigating factors that contribute to resistance [17]. Policies that consider both viewpoints are crucial for increasing adoption rates and ensuring the long-term success of e-government platforms. Only then can we develop a model combining facilitators and barriers into a unified framework.

This study intends to investigate the facilitators and inhibitors of attitude and word-of-mouth intention toward adopting DMSS. The following research questions are proposed to fulfill the research objective:

  1. What are the critical factors influencing citizens’ attitudes and word-of-mouth toward intentions to use DMSS, and how are these factors related to enablers and inhibitors? and
  2. How can this integrated aspect be used to draw users toward e-government services in Bangladesh and other developing countries?

This study used the stimulus-organism-response (S-O-R) paradigm proposed by Mehrabian and Russell [18] to investigate the research questions. The S-O-R framework integrates concepts from consumer behavior, information systems, and psychology to understand the significance of DMSS in influencing users’ attitudes and word-of-mouth (WOM) intentions. Attitude represents an individual’s positive or negative evaluation of a technology or service, which can shape their intention to adopt or use it [19]. As e-government services are voluntary, users formed their intentions to use the system primarily based on their attitude toward using the system [20]. Thus, this study highlights the crucial role of attitude in predicting the intention to use DMSS, given that its use is voluntary. Besides, consumers’ positive perception was regarded as an enabler, and negative perception was considered an inhibitor, similar to the dual-factor model of Herzberg’s Two Factor Theory [21]. We also applied the theory of consumption values (TCV) [22] as enablers and the innovation resistance theory (IRT) as an inhibitor (S). Our research suggests that enablers drive the adoption of DMSS, but inhibitors may hinder their usage, which acts as the organism (O). The study evaluates user organismic states and WOM as a response (R) to the stimuli.

WOM plays a crucial role in engaging a larger number of citizens and fostering trust in government initiatives [23]. As citizens increasingly share their opinions and experiences with e-governance services across various social platforms, WOM serves to increase citizen engagement and interactions with governments [24]. WOM provides citizens with information, enabling their engagement with government entities and influencing their future behavior toward government services [25, 26]. So, the application of WOM in this study helps extend the existing e-government body of literature. By using the S-O-R framework, this research aims to provide a complete understanding of the factors shaping citizens’ attitudes toward using DMSS. It offers insights on encouraging the use of e-government services in developing countries. Thus, this research is essential in fully comprehending the facilitator’s and inhibitors’ perspectives of e-government systems.

2. Background literature

2.1. E-government acceptance and resistance

E-government adoption is complex because it involves multiple stakeholders with different and sometimes conflicting interests [27]. Citizens, government bodies, and service providers have changing expectations and needs as technology and user demands also evolve. This makes it difficult to adopt a single solution, as stakeholders must put a combined effort to adjust to these shifting demands [28]. As new e-government platforms are introduced, existing theories can barely explain the complexity of consumer acceptance. The rapid advancement of technology has created a landscape where user expectations change frequently, highlighting continuous adjustments to electronic services and delivery methods [17]. This constantly changing environment underlines the need for developing novel theoretical frameworks to better understand and predict e-government service adoption, especially since existing frameworks do not always account for this variability [29].

The majority of the existing literature on e-government adoption focuses on enablers that drive user acceptance and applied well-established theoretical frameworks (e.g., UTAUT, TAM, SCT, IS success model) [8, 20, 30]. These studies mainly emphasize the role of perceived usefulness, ease of use, social influence, and trust in shaping users’ intentions to adopt e-government services. Despite the extensive focus on enablers, a smaller body of research has attempted to explore the barriers or inhibitors to e-government adoption [11–13, 31]. Factors such as digital literacy gaps, privacy concerns, lack of trust, and resistance to change have been found in distinct studies as critical barriers in e-government adoption research. However, these studies often lack a structured theoretical model (e.g., innovation resistance theory, status quo bias, decision avoidance theory) to adequately capture the full complexity of inhibitors. Moreover, a key limitation in current research is the lack of studies that examine enablers and inhibitors simultaneously. This has left a significant gap in understanding how these two opposing forces interact and shape user adoption decisions in real-world e-government implementations. Therefore, this research should aim to bridge this gap by developing integrated models that simultaneously consider facilitators and barriers, providing a more comprehensive understanding of e-government adoption dynamics.

2.2. The stimulus-organism-response (S-O-R) model

Environmental psychology literature often applied the S-O-R paradigm, first introduced in 1974 by Mehrabian and Russell. The framework suggests that environmental cues (stimuli) perceived by individuals can prompt internal evaluative processes (organism), leading to either positive or negative behaviors (response) toward the stimuli [18, 32]. The stimulus is the input that prompts the organism to act, such as by launching a new e-government service, when it comes to adopting e-government services. The person who is exposed to the stimuli and can respond to it, in this situation, the user’s attitude, is the organism. The organism’s output is the response, which might take the form of behavioral, emotional, or cognitive reactions [33, 34].

The S-O-R model highlights the importance of understanding how the stimulus, organism, and response interact when studying citizens’ acceptance of e-government services. The model has been successfully utilized to understand consumer innovation adoption in earlier literature.

The model was used by Tak and Gupta [35] to explore the intentions to use the travel app and Yuan to examine the adoption of mobile payment loyalty. Chopdar and Balakrishnan [36] employed the model to investigate the repurchase intention and satisfaction experience of Indian consumers in m-commerce. These studies show the ongoing relevance of the S-O-R model in examining consumer behavior. Therefore, the S-O-R model is a strong foundation for our research as it helps examine different factors specific to the application and their overall effect on user behavior.

2.3. Dual factor theory (DFT)

The dual-factor model explains that both positive and negative factors influence how quickly consumers accept or reject a product or service [31]. Positive factors, called facilitators or enablers, encourage greater use of the product or service [37]. For example, the usefulness of an e-government platform may lead to a higher adoption rate, while a lack of usefulness may discourage usage. On the other hand, some factors may be uniquely negative and discourage usage without being simply the opposite of an enabler. These factors are known as inhibitors and only discourage usage [37]. For instance, if paying an electricity bill goes smoothly, it will be viewed positively. Still, if the process is hindered by negative perceptions such as transaction risk, it may discourage the usage of the e-government system.

In this study, we choose the enabler-inhibitor viewpoint for four reasons. Firstly, prior IS research has mainly highlighted the enablers, with limited consideration for inhibitors [38]. Secondly, adverse events tend to be more noticeable and significant than positive events, implying that inhibitors may significantly affect user adoption more than enablers [39]. Thirdly, inhibitors may be more evident due to their harmful nature, potentially overwhelming the positive impact of enablers. Lastly, by analyzing enablers and inhibitors, we aim to uncover the unique factors affecting the use of e-government services and provide recommendations for promoting and hindering the usage.

3. Research model and hypothesis development

3.1. Theory of consumption values as enablers

As previously mentioned, enablers are positive factors that promote or encourage using a product or service [40]. The Theory of Consumption Values (TCV) explains how different factors shape consumer behavior, such as adopting e-government systems. It has three key ideas: people’s actions are influenced by various values, the importance of these values changes based on the situation, and these values work independently of each other [22].

The TCV framework acknowledges that contextual or situational factors can influence consumer behavior rather than focusing solely on predefined factors [16, 41, 42]. Therefore, understanding the usage context is vital for implementing the TCV successfully. In this study, we build upon the TCV’s categorization of consumption values and apply it to our context of DMSS adoption. We identify six dimensions of value that may facilitate citizens’ use of the DMSS platform: quality of the platform, convenience value, social value, inclusivity value, conditional value, and epistemic value. This expanded framework allows for a more comprehensive understanding of the various factors contributing to adopting DMSS.

Functional value refers to the usefulness of a product or service based on how well it performs specific tasks, such as being durable and reliable [22]. In e-government platforms, functional value is reflected in two key areas: platform quality and convenience. Platform quality is judged by how durable and reliable it is, ensuring it provides timely information and services that efficiently meet citizens’ needs. The quality and convenience of e-government platforms significantly influence citizens’ attitudes and intentions to use such services [10]. Past studies have shown that quality is essential to functional value [41]. Convenience, in this context, refers to how easy the platform is to access, navigate, and use and how quickly citizens can access services. Talwar, Dhir [43] highlighted that convenience plays a significant role in shaping consumer behavior and improving satisfaction. For DMSS, the convenience value is vital because citizens can use the platform to complete tasks like paying taxes, getting certificates, paying bills, and managing properties.

Functional value is an essential aspect of e-government adoption, and prior studies have demonstrated the significance of perceived quality and convenience in shaping users’ attitudes toward services or products across various contexts [41, 44]. This could lead to a positive attitude toward using the DMSS for their e-government needs. We assume the following hypotheses based on the preceding discussion.

  1. H1. The quality of the DMSS platform positively impacts citizens’ attitudes.
  2. H2. Convenience value has a positive impact on citizen’s attitudes toward DMSS.

The term "social value" describes how much utilizing a product or service improves a user’s connections, identity, or standing in society [22]. This study utilizes two concepts, social image, and inclusiveness value, to capture the dimensions of social value. First, the use of new technology, such as DMSS, can lead to an increase in social status and image, as they are considered trendy in developing countries. Earlier research has shown that social image can impact individuals’ behavioral intentions to use a specific IS [10, 45, 46].

E-government platforms can provide all citizens’ services regardless of income or education level [10]. This way, e-government platforms can provide public services equitably and increase accessibility for all citizens. Research supports the notion that inclusiveness in e-government systems positively impacts citizens’ attitudes toward Digital Municipal Service Systems (DMSS). The inclusiveness value is tied to individuals’ perceptions that e-government platforms make public services more accessible and delivery more transparent, fostering positive attitudes Hornung and Baranauskas [47]. Studies by Al-Hujran et al. [20] confirm that public value, ease of use, and demographic factors enhance inclusiveness, thereby influencing citizen engagement. A recent study by Li and Shang [10] found that inclusiveness value is a significant dimension of an e-government system’s overall value and plays a vital role in the broader adoption of public services. Therefore, it is suggested that the inclusiveness value will result in a positive attitude toward using the DMSS. As a result, the following hypotheses are proposed:

  1. H3. Social value has a positive impact on citizen’s attitudes toward DMSS.
  2. H4. Inclusiveness value has a positive impact on citizen’s attitudes toward DMSS.

The conditional value represents how useful a service or product is in specific situations [22]. Wang, Liao [48] further describe how the value of a good or service is directly linked to its relevance in specific situations. This can be the temporary functional or social value that arises from a need created by the circumstances [22]. This creates a positive attitude towards using the platform, as supported by the work of Savoldelli, Codagnone [49], who illustrate how the perceived usefulness in specific contexts influences adoption behavior.

The government can promote platform use by offering discounts on services compared to traditional methods. For example, e-filing taxes is cheaper than manual submission. When citizens find services on the platform at a lower cost, this creates conditional value, which is expected to encourage a positive attitude toward using DMSS. As a result, the following hypotheses are proposed:

  1. H5. Conditional value has a positive impact on citizen’s attitudes toward DMSS.

The perceived benefit from gaining knowledge or information through goods or services is called epistemic value [22]. According to prior literature, acquiring knowledge can increase self-efficacy and self-esteem. This increase can lead to positive attitudes toward using a product or service [17, 50]. In the context of DMSS, providing knowledge and information through these services can lead to a positive attitude towards their usage. For instance, if citizens feel that DMSS will provide them with relevant and trustworthy information on government policies, services, and procedures, they may be more likely to use it [10, 47]. Additionally, accessing and using e-government services can be viewed as a means of gaining knowledge and empowerment, which can also lead to positive attitudes towards their usage [17, 44]. Based on existing research, we assume that epistemic value can positively affect user attitudes towards using DMSS. Therefore, we propose the following hypothesis:

  1. H6. Epistemic value has a positive impact on citizens’ attitudes toward DMSS.

3.2. Innovation resistance theory (IRT) as inhibitors

The IRT suggests that various factors may lead to consumers’ resistance to using an innovation [51, 52]. According to Kushwah, Dhir [53], it is identified that there are two significant aspects of consumer resistance. They are functional barriers and psychological barriers. Changes in consumption patterns cause functional obstacles. On the other hand, conflicts between consumers’ beliefs and particular products cause psychological barriers. The most common functional barriers are related to usage, risk, and values. At the same time, psychological barriers are related to image and tradition. This study identifies the risks, usage, and traditional barriers preventing consumers from embracing a new system such as DMSS.

A tradition barrier is a reluctance to adopt new technologies because of a preference for familiar practices and cultural norms [54]. Polites and Karahanna [55] identified that users often persist with an existing system despite incentives and better outcomes associated with change. Hoque and Sorwar [56] suggest that past behavior influences people’s decisions. It makes them overestimate the benefits of new systems. This bias reduces their engagement with new technology and lowers their intention to use it. Those with a tradition barrier tend to rely on past behavior and limit the information they consider [17]. In this context, citizens may visit local government offices physically instead of using the recent DMSS, resulting in lower intentions to use the platform. Based on these arguments, we propose the following hypothesis.

  1. H7. Tradition barrier has a negative impact on citizens’ attitudes toward DMSS.

Perceived risk is the subjective assessment of potential adverse outcomes or uncertainties associated with a decision [57]. According to Saxena [58], data security, privacy, lack of control over personal information, and the threat of online fraud can influence the adoption of e-government services. These concerns often increase the perceived risk, leading to a negative attitude among users and reducing their likelihood of using such services. Research has shown that around 80% of internet users express concerns about revealing their identities online [59]. This highlights how risk perception can be a significant barrier to adopting and using electronic services [17]. Thus, individuals may hesitate to use DMSS due to concerns about the safety and confidentiality of their personal information. Based on this argument, we hypothesize that;

  1. H8. Risk barrier has a negative impact on citizens’ attitudes toward DMSS.

The term "usage barrier" refers to the incompatibility of consumers modifying their usual behaviors, habits, and routines to adopt an innovation [60]. This concept suggests that the usage barrier manifests when customers must adjust their typical usage patterns to accommodate a new product or service, requiring effort and adaptation. In e-government services, these barriers often involve a lack of technical skills, limited technological access, and complex website designs. These challenges and lack of self-confidence can deter adoption and halt the effective use of technology [44].

Research shows that usage barriers negatively impact users’ behavioral intentions toward adopting new technologies. For example, barriers like unfamiliarity or complexity hinder individuals from embracing new technologies [10, 61]. In the context of mobile payment services, Kaur, Dhir [52] found that the usage barrier was a crucial factor influencing whether individuals adopted these services. Also, recent studies confirm that usage barriers, such as limited computer literacy and high internet access costs, pose significant challenges to e-government adoption, particularly in developing nations [44]. Based on this argument, we hypothesize that;

  1. H9. Usage barrier has a negative impact on citizens’ attitudes toward DMSS.

According to the S-O-R framework, response (R) represents the final behavioral outcome of the organism [18]. In the context of this study, the response variable examined was word of mouth (WOM). Attitude towards DMSS is the cognitive state (O) discussed in this study. Attitude is a learned, persistent, and evaluative mental and neural state. It shapes an individual’s feelings, beliefs, and behavioral intentions toward a specific object, person, or event. This attitude impacts psychological and behavioral responses [62, 63]. Likewise, WOM refers to informal communication about products, services, or brands that occurs between consumers without the direct involvement of the company or brand [64]. It is considered one of the most powerful marketing tools as it is based on personal experiences and opinions, which can significantly influence the purchasing decisions of other consumers. Previous studies have shown that positive WOM can enhance individuals’ perceptions of the value and simplicity of e-government services. As a result, their attitude towards the technology becomes more positive [65]. WOM is a powerful informal communication tool that can persuade users to make decisions by providing others’ experiences and recommendations about a product or service [66]. It is incredibly influential for individuals who perceive high risks and have limited information about their choice of a service or a product. Therefore, WOM is crucial in determining users’ acceptance of DMSS. Thus, we propose the following hypothesis:

  1. H10. A positive user attitude towards DMSS leads to an increase in the intention to spread word-of-mouth (WOM) about the system.

Based on the above discussion and proposed hypotheses, the research model is presented with their hypothetical relationships in Fig 1 below.

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Fig 1. Proposed research model.

https://doi.org/10.1371/journal.pone.0315009.g001

4. Methodology

4.1. Study context

The digital municipality services system (DMSS) project, launched in 2019, automated five citizen services, including councillor’s certificate, property management, trade licenses, holding tax, and water billing services. Korea International Cooperation Agency (KOICA) plans to grant $8.5 million to expand the pilot to all 329 municipalities in Bangladesh. The project, running from 2022 to 2026, will establish the national municipality digital service system, upgrade and expand the existing municipality services by providing equipment, and strengthening the capacity of Bangladeshi officers. The platform aims to simplify and streamline the process of accessing government services, making it easier and more convenient for citizens. DMSS was introduced to address the challenges faced by citizens in accessing traditional government services, which were often time-consuming, bureaucratic, and non-transparent. However, e-government initiatives, including DMSS, usually fail in developing countries due to the disparity between the idealized design and the practical implementation of e-government systems [20, 44]. The Bangladeshi government must consider the societal, technological, and security factors that impact the success of e-government in their specific context. This consideration will help bridge the gap between the envisioned e-government design and its implementation. In the context of Bangladesh, understanding the factors that influence citizen adoption of e-government services is of paramount importance for policymakers. This knowledge can inform the design of e-government services, enhance service delivery processes for public organizations, and attract and retain users for government agencies.

4.2. Measurement of constructs

The research team used existing measurement items from previous studies, which they adapted to the specific context of DMSS. Each statement describing every single item was measured on a 7-point Likert scale ranging from 1 –“Strongly disagree” to 7 –“Strongly agree”. Participants were asked to respond to a series of statements by indicating their level of agreement. In S1 Appendix, the measuring items are listed. Among the two survey sections, the first section contained 37 items associated with the 11 constructs in the proposed research model. The second section of the survey questionnaire asked about the participant’s gender, age, education, experience, and region. According to the guideline by Rahi [67], a limited-scale pre-test involving 20 graduate students was conducted to refine our survey questionnaire. Based on their valuable feedback, we slightly adjusted the questionnaire’s structure and wording, enhancing its clarity and relevance for the subsequent study.

4.3. Sample selection and survey method

The study focused on Bangladeshi citizens who had prior experience using the DMSS. The ethics committee has confirmed that no formal ethical approval was required because the data is anonymous, not sensitive or confidential, and no vulnerable or dependent groups were included. This study has been performed by the Declaration of Helsinki. The research team used non-probability convenience sampling; a method commonly used in studies analyzing the usage of e-government services [7, 17, 68]. This type of sampling allows for efficient data collection, improved data quality, and increased control over the sample selection process [69]. Even though it is not typical of the population, it allows the researcher to gather a representative sample of the population of interest, leading to more excellent knowledge and insightful discoveries [70].

Recruiting participants in the study involved approaching individuals at different strategic locations such as parks, marketplaces, and local government offices. Participants who agreed to participate were given a comprehensive explanation of the purpose of the study and their rights as participants, including the provision of an informed written consent form. The participants were then notified of the data collection method involving a survey. To ensure the confidentiality of the participants’ information, the researcher explained the measures that would be taken, such as using coding the data. Participants were given information about the anticipated time needed to complete the survey and were offered a token of appreciation for their participation. This approach enhanced participant engagement and facilitated a smoother data collection process [71]. The data collection for this study took place over a period of three months, from April 2021 to June 2021.

The sample size was determined based on standard guidelines for SEM, which recommend a minimum of 200–300 responses for valid model estimation [72]. We also used G*Power software to determine the minimum sample size requirement [73]. Based on the analysis, a minimum of 88 participants was required to achieve an acceptable level of statistical power for detecting significant relationships between variables. Therefore, we plan to collect more than 300 sample to satisfy the minimum sample size requirement for this study. We distributed 380 physical copies of the questionnaire to the respondents who agreed to participate in the survey. All the participants completed the survey during the data collection process. To minimize statistical biases due to missing data, 27 responses were excluded from the analysis as they contained more than 10% missing responses across all items, following the guidelines outlined by Joseph, Black [74]. Eliminating responses with excessive missing data helped ensure the results’ validity and reliability. This led to a final sample of 353 usable responses.

4.4. Data analysis

The proposed model was analyzed using partial least square structural equation modeling (PLS-SEM), the analytical approach recommended for analyzing a complex path model [63]. We conducted the PLS-SEM analysis using SmartPLS version 3, a widely employed theory testing and validation tool. PLS assesses psychometric properties and offers substantial evidence regarding the presence or absence of relationships [75]. Notably, it delivers more dependable results when dealing with non-normally distributed data and small sample sizes compared to component-based SEM [76]. In this study, we followed a two-stage methodology outlined by Anderson and Gerbing [77]. In the first stage, we tested reliability and validity tests using a measurement model, and secondly, we assessed the structural model to validate the hypotheses.

4.5. Demographics

Gender distribution shows a relatively balanced representation, with 53.54% male and 46.46% female respondents. In terms of age distribution, the majority of respondents are between 18 and 35 years old (63.73%), while smaller percentages fall into older age brackets. When it comes to education, a significant proportion of respondents hold Bachelor’s degrees (58.07%), followed by Master’s degrees (34.84%). The data also highlight respondents’ experience with e-government services, with a substantial portion indicating usage of less than a year (41.93%), followed by 1–2 years (40.51%). Geographically, the majority of respondents reside in urban areas (53.54%), while the remaining respondents live in semi-urban areas. It is worth noting that there are no respondents from rural areas in our sample, as the DMSS platform is only available in selected urban and semi-urban areas. An outline of the respondents’ profiles can be found in S2 Appendix.

5. Results

5.1. Common method bias

This study applied several solutions to limit common method bias concerns [78]. Firstly, the respondents’ personal information is kept confidential, and the order of questions is changed to avoid revealing the research structure. Second, Harman’s single-factor test was conducted to evaluate the likelihood of the standard bias method occurring. The analysis results of Harman’s single-factor test showed that the single factor explained 36.45 (<50%) of the total variance, so the common method bias did not seem to occur in this study [79]. Moreover, the variance inflation factor (VIF) values have been assessed for collinearity analysis between the indicators before undertaking the research model path analysis. The multicollinearity problem may have affected the model if the VIF values are equal to or greater than 3.3 [72, 80]. According to Table 1, the indicators’ VIF values are below 3.3, and the collinearity between the indicators employed in the research model study has been considered without any problems.

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Table 1. Measurement model.

https://doi.org/10.1371/journal.pone.0315009.t001

5.2. Measurement model assessment

The measurement model was evaluated using convergent validity, indicator reliability, internal consistency, and discriminant validity criteria. As shown in Table 1, factor loadings, which represent the correlations between observed variables and their underlying constructs, ranged from 0.716 to 0.977, all exceeding the recommended threshold of 0.7, indicating strong indicator reliability. The internal consistency of each construct was measured using Cronbach’s alpha (α) and Composite Reliability (CR). α values ranged from 0.714 to 0.881, while CR values ranged from 0.840 to 0.926, both surpassing the minimum acceptable threshold of 0.7, confirming the reliability of the constructs. Average Variance Extracted (AVE) values ranged from 0.597 to 0.807, exceeding the required threshold of 0.5, thus demonstrating convergent validity [72].

Discriminant validity, which ensures that constructs are distinct from one another, was assessed using two methods. First, according to Fornell and Larcker [75] criterion, the square roots of the AVE values for each construct were higher than the correlations between constructs (Table 2). Second, the Heterotrait-Monotrait (HTMT) ratio of correlations was calculated, with all values below the 0.90 threshold (Table 3), meeting the discriminant validity criteria [81].

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Table 2. The correlation matrix and the square root of the AVE.

https://doi.org/10.1371/journal.pone.0315009.t002

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Table 3. Heterotrait-Monotrait ratio (HTMT).

https://doi.org/10.1371/journal.pone.0315009.t003

5.3. Structural model analysis

Hair, Hult [72] suggested that the research model path analysis was performed after the measurement model assessment. The hypothesized relationships have been tested using the bootstrapping method (5000 resamples) with a significance threshold of 0.05. The model accounted for 32.9 percent of the variance in user attitudes toward word-of-mouth and 73 percent of the variance in user attitudes toward DMSS adoption. Further, the Q2 value was measured to assess the model’s predictive validity [82]. The measured Q2 value in SEM must be higher than zero for a particular endogenous latent construct [83]. This study shows that Q2 for attitude is 0.437 and WOM is 0.238, which is greater than the recommended threshold. In a nutshell, among the ten proposed hypotheses we found eight hypotheses were supported as follows: PQ -> ATT (β = 0.123, t = 3.524); COV -> ATT (β = 0.230, t = 4.928); SV -> ATT (β = 0.234, t = 4.711); INV -> ATT (β = 0.153, t = 2.763); CV -> ATT (β = 0.134, t = 3.441); TB -> ATT (β = -0.170, t = 3.219); UB -> ATT (β = -0.082, t = 3.035); and ATT -> WOM (β = 0.574, t = 15.936). However, the results showed that two hypotheses were not statistically significant: EPV -> ATT (β = 0.012, t = 0.265) and RB -> ATT (β = -0.011, t = 0.297). The results of the hypotheses are shown in Table 4 and Fig 2.

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Fig 2. Results of PLS-SEM.

** = significance at p < 0.01, * = significance at p < 0.05, ns = not significant.

https://doi.org/10.1371/journal.pone.0315009.g002

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Table 4. Hypothesis test.

https://doi.org/10.1371/journal.pone.0315009.t004

6. Discussion

The results of our study shed light on the complex relationships between various factors that influence citizens’ initial adoption and post-adoption behavior of DMSS. Our analysis revealed several key enablers and barriers affecting users’ attitudes toward and intention to use the platform. First and foremost, it’s evident that quality and convenience are significant enablers for citizens looking to engage with DMSS. Users who perceive the e-government platform as high quality and easy to use are more likely to adopt it [10]. Social value and inclusiveness also emerged as significant factors that positively impact users’ attitudes toward DMSS [84]. These findings support previous research [8, 10] and indicate that individuals are more inclined to adopt DMSS if they perceive its use as compatible with their social identity. Furthe, the inclusion of diverse age groups, such as the elderly [17] and the younger population [85], on the platform also contributes to this likelihood.

According to the study, the conditional value significantly improves the public’s perception of DMSS by offering superior alternative services. The findings suggest that citizens have a more positive attitude toward DMSS when they consider reliability, security, and ease of use essential. This is especially true when DMSS allows them to complete transactions without needing to visit government offices in person, making them more likely to use DMSS [17, 44]. However, the findings revealed that epistemic value was unexpectedly not a significant predictor of the attitude toward the intention to use DMSS. This result aligns with earlier research conducted in China, which found that urban residents lacked the expertise to accurately recognize products with green certification [86]. Prior research also indicated epistemic value is not a significant predictor of consumers’ attitudes or intentions to adopt digital services, with utilitarian and economic factors playing a more prominent role [87, 88].

On the other hand, our study identified that both traditional and usage barriers negatively impact users’ attitudes toward DMSS [51, 52]. In other words, citizens who believe that DMSS goes against established norms and practices or find the platform difficult to use are less likely to adopt it. Bangladesh’s citizens may prefer traditional channels when accessing public services due to their past experiences [17]. This is consistent with earlier studies that suggested a relationship between prior behavior and intention to use new technologies [16, 56]. Surprisingly, the risk barrier did not significantly impact users’ attitudes and contradicted much earlier research [8, 61]. However, this result is also consistent with earlier research. For instance, earlier studies have shown that risk perception is not a significant predictor of technology acceptance and use [89, 90]. This result suggests that users may not perceive the DMSS platform as highly risky or uncertain. It could also indicate that the potential benefits of using DMSS outweigh any concerns regarding security or privacy. Likewise, if users are satisfied with the DMSS and their families and friends have a favorable opinion about using DMSS, it can be considered a safe way to obtain services. Therefore, the importance of perceived risk in this study was not a significant concern for Bangladesh’s citizens due to the abovementioned reasons [17].

Finally, the noteworthy influence of attitude toward behavioral intention to use DMSS on WOM intention highlights the crucial role of user perceptions in influencing their advocacy behaviors [25, 65]. This result relates to prior studies that underscore the impact of user attitudes on their intention to recommend technology-driven solutions [91]. When citizens have a positive attitude toward using DMSS, they are more likely to engage in favorable word-of-mouth (WOM) communication. This is often influenced by their satisfaction with the platform’s usability, functionality, and overall experience [24, 26]. This finding emphasizes the significance of establishing a positive user experience and cultivating positive attitudes toward the platform. Doing so can result in organic growth and heightened awareness of the platform.

6.1. Theoretical implications

This study offers several novel contributions to e-government and technology adoption research by applying an innovative approach through the SOR model. Specifically, it brings new insights into how facilitators and inhibitors concurrently shape user attitudes and behavior toward adopting DMSS. While previous studies have often focused on either enablers or barriers, our research integrates these factors into a single, unified framework, offering a more comprehensive understanding of e-government adoption.

An essential contribution of this study is integrating facilitators (enablers) and inhibitors (barriers) within a dual-factor model, addressing a significant gap in the existing literature. Previous research in e-government adoption focused on either enablers, such as perceived ease of use and usefulness, or inhibitors, such as resistance to change. Those studies did not explore their combined influence on user behavior. By incorporating both positive drivers through TCV and negative barriers through IRT into a single model, this study provides a more holistic and nuanced perspective on how users form attitudes toward e-government platforms [92]. This dual-process approach allows for a deeper understanding of users’ complex decision-making process, offering a more accurate reflection of real-world behavior than traditional models that focus solely on enablers [93].

In this study, TCV and IRT are crucial for understanding the enablers and inhibitors of e-government adoption, each offering unique theoretical insights. TCV suggests that consumer choices are influenced by different types of value, which are crucial in understanding how users perceive the benefits of services like DMSS. By applying TCV, this study shows how perceived convenience, social recognition, and knowledge shape users’ attitudes toward adopting DMSS. This framework helps explain why users find these services appealing, especially in public services, where value perceptions vary based on individual needs and priorities [93].

On the other hand, IRT explains the barriers to adoption by identifying sources of user resistance associated with new technologies. In the context of DMSS, IRT helps clarify why certain users may hesitate to transition from traditional government interactions to digital platforms. By examining inhibitors through IRT, this study offers insights into the psychological and cultural factors that slow down e-government adoption. This is more befitting in environments where users may feel uncomfortable or skeptical about technological change. TCV and IRT provide a comprehensive lens for understanding both the positive motivations and negative barriers shaping user behavior in adopting e-government platforms [92].

Another novel aspect of this study is emphasizing WOM intention as a key ‘Response’ variable. While previous studies have primarily focused on direct adoption or usage intention, we explore WOM as a critical driver for the broader diffusion of e-government services. In e-government research, WOM has been underexplored, but it can have a powerful impact on service uptake, as users’ recommendations can significantly influence others’ decisions to adopt digital services [94]. WOM also helps build trust, which is a key facilitator in adopting digital platforms [9]. This study, therefore, highlights the social influence of WOM and its potential to extend the reach of e-government platforms.

Although the SOR model has been used in various domains, its application to e-government adoption is still relatively rare. By applying the SOR framework in this context, we illustrate how external stimuli (enablers and inhibitors) influence internal states (attitudes) and lead to behavioral outcomes (WOM intention). This approach offers a more dynamic view of how users engage with e-government services, making it possible to better understand both the emotional and cognitive processes involved in adoption decisions [95]. WOM intention is not only an outcome of user satisfaction but also a mechanism through which e-government platforms can achieve long-term sustainability through network effects.

6.2. Practical implications

Our study provides valuable insights for policymakers creating and implementing e-government services. The findings suggest that the quality of the e-government platform, convenience value, social value, inclusiveness value, and conditional value all positively impact users’ attitudes toward adopting the DMSS. To achieve this, the government needs to invest in developing e-government systems that are user-friendly, accessible, secure, and reliable. Besides, continuous efforts should be made to improve the quality of these systems through regular maintenance and upgrading, user feedback, and system performance monitoring. This finding can also serve as a benchmark for e-government system providers and developers, who can use it to ensure that their systems meet the quality expectations of the citizens [10].

Specifically, convenience can be improved through user-friendly interfaces, easy access to information, and efficient service delivery processes [9]. Policymakers can use transparent and well-structured service designs to increase convenience and effectiveness in such services. Incorporating multiple channels of access to e-government services can also improve convenience and accessibility, positively impacting citizens’ attitudes [20, 68]. Furthermore, the findings suggest that policymakers should focus on the perception and reputation of e-government services among citizens to enhance the social value of these services. Policymakers can achieve this by creating positive social values of e-government services through marketing and public relations efforts. Ultimately, this will encourage citizens to adopt these services and increase their usage [8, 68].

Moreover, the finding emphasizes the significance of developing and executing inclusive e-government services that address all citizens’ various requirements and preferences when using such services. Collaborating with technology providers, user experience experts, and other stakeholders can assist policymakers in designing and implementing accessible, user-friendly, and inclusive services. E-government services must offer clear benefits to citizens and be user-friendly, convenient, and efficient to improve their perception of conditional value [44].

On the other hand, to overcome traditional and usage barriers, policymakers should invest in raising awareness and understanding of DMSS services through training and support for citizens. Regular consultations with citizens to understand their needs and preferences can also help overcome barriers to adopting the services. Improving citizens’ digital literacy and awareness through education can reduce barriers to usage. Providing user-friendly interfaces and adequate technical support can promote inclusivity and empower citizens to engage in e-government services [17, 44]. Finally, the positive relationship between user attitude and word-of-mouth intention highlights the importance of encouraging users to perceive e-government services positively. Policymakers should prioritize investing in communication and marketing strategies that enhance awareness and understanding of e-government services. These strategies should also aim to promote the benefits of these services, encouraging users to speak positively about them and encourage others to utilize them [8, 68].

7. Limitations and future research directions

It is essential to recognize the limitations of the current study. Firstly, the study was limited in generalizing to other people or locations because it was conducted in a particular community in Bangladesh. Secondly, the data was collected through self-administered questionnaires, which may have introduced a social desirability bias in the responses. Given these limitations, there are several areas for future research to further build on the insights gained from this study. Future studies could extend the present research to different populations and regions to examine the generalizability of the findings. In addition, it would be valuable to collect data using more objective measures, such as usage logs, to overcome the limitations of self-reported measures. Thirdly, future studies should consider additional elements that can affect people’s attitudes regarding e-government services, such as sociodemographic traits, prior technology-related experience, and perceptions of privacy and security. Finally, it would be helpful to study the impact of policy interventions designed to address usage barriers on citizens’ attitudes toward adopting e-government services.

8. Conclusion

In conclusion, the present study aimed to shed light on the drivers and hindrances of citizens’ acceptance and utilization of DMSS, a one-stop e-government service platform. The results showed that the quality of the platform, convenience value, social value, and inclusiveness value were the critical drivers for DMSS adoption. On the other hand, the traditional and usage barriers were identified as the main barriers that hindered citizens from using the platform. The study also revealed that users’ attitude significantly affects their word-of-mouth intention towards the DMSS, emphasizing improving citizens’ perception of the platform. These findings hold significant implications for policymakers in developing and implementing e-government services. By addressing the barriers and enhancing the drivers, policymakers can increase the likelihood of citizens’ adoption and successful implementation of e-government services. Future research can provide a more detailed exploration of the factors and their impact on e-government service adoption in diverse contexts and countries.

Acknowledgments

We are grateful to the reviewers for their valuable feedback and insightful suggestions. We also acknowledge the use of generative and assistive AI tools, which were instrumental in improving this article’s grammer, language, and overall communication quality.

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