Perceived Usefulness of a Mandatory Information System

Abstract

This study examines the adoption and implementation of an information system in a mandatory context focusing on an Israeli governmental organization. The system referred to as “Slot” is an online platform for managing educational activities within this organization. The research investigates the impact of the system on its functionality users and the results of its usage. Additionally, the study explores factors that influence the acceptance and utilization of information systems, including whether the willingness to use the system under instruction depends on other variables. The key findings of this study are: perceived ease of use significantly and positively influences perceived usefulness; perceived usefulness significantly and positively affects symbolic adoption; and supervisor influence significantly and positively impacts perceived usefulness. Moreover, the relationship between perceived ease of use and symbolic adoption is entirely mediated by perceived usefulness as is the relationship between supervisor influence and symbolic adoption. The study’s limitations include the relatively small sample size and the specific context of the research, which may limit the generalizability of the findings. Future research could explore similar models in different organizational settings to validate and extend the applicability of the results. The findings suggest that enhancing the perceived ease of use and usefulness of mandatory systems can significantly impact their symbolic adoption, with supervisory influence playing a crucial role in shaping user perceptions. These insights can inform strategies for more effective implementation and management of information systems in mandatory settings.

1. Introduction

The ongoing evolution of information technology within organizational contexts offers both challenges and opportunities. Successful implementation can greatly enhance operational efficiency and productivity. However, this success hinges on the acceptance and integration of these technologies into daily practices. This study aims to understand the dynamics of technology acceptance, particularly in environments where system usage is obligatory. The research was conducted in an Israeli governmental organization, focusing on the adoption and implementation of the “Slot” information system for education and training in a mandatory setting.

This research emphasizes analyzing factors that influence technology acceptance. These factors include user perceptions of ease of use and usefulness, the role of symbolic adoption, and the impact of supervisory influence. Understanding these elements is crucial in an era where technology is integral to organizational operations and plays a significant role in determining effectiveness and competitiveness.

The motivation for this study arises from a recognized gap in existing models of technology acceptance, especially in their applicability to mandatory contexts. Most existing research focuses on voluntary usage scenarios, highlighting the necessity for insights into technology acceptance in environments where its use is obligatory.

Moreover, this research highlights the practical implications of its findings in a fast-evolving technological landscape. Organizations must not only stay current but also ensure their workforce is prepared and willing to embrace new technologies. This study contributes to strategies for effective implementation and management of information systems, aiming to boost organizational efficiency.

The importance of this study extends beyond academic interest. In a world where technological advancements are rapid and constant, organizations face the challenge of not only adopting new systems but also ensuring that these systems are effectively integrated and utilized by their employees. The “Slot” system serves as a pertinent example of this challenge within an Israeli governmental organization, offering a unique insight into mandatory technology adoption. By focusing on a governmental context, this research also addresses the specific dynamics and constraints associated with public sector technology implementation, which often involves different pressures and expectations compared to the private sector.

In addressing the gap in the literature, this study provides a comprehensive analysis of the variables affecting technology acceptance in a mandatory setting. By examining perceived ease of use, perceived usefulness, symbolic adoption, and supervisory influence, the research offers a nuanced understanding of the factors that drive or hinder the acceptance of new information systems. These insights are not only relevant for academic purposes but also provide practical recommendations for organizations seeking to enhance the effectiveness of their technology implementation strategies.

The findings of this study are particularly relevant for policymakers and organizational leaders. As they navigate the complexities of technology integration, understanding the factors that influence user acceptance can inform the development of training programs, support mechanisms, and communication strategies that facilitate smoother transitions to new systems. Moreover, the study highlights the critical role of supervisory influence in shaping user perceptions and acceptance, suggesting that leadership and management practices must be aligned with technological goals to achieve successful implementation.

In summary, this study seeks to illuminate the intricacies of technology acceptance in mandatory settings. It aims to fill a significant gap in existing literature and provide strategic insights for organizations to fully capitalize on technological advancements. By doing so, it not only advances theoretical understanding but also offers actionable recommendations that can enhance organizational performance in the face of evolving technological landscapes.

2. Literature Review

The exploration of information technology (IT) acceptance among users has garnered extensive interest due to the ongoing integration of advanced technologies in organizational frameworks. This significant area of study, attracting attention from both academic and practical domains, focuses on the impact of technology implementation on productivity and organizational efficiency [1,2]. Recent studies have examined various factors influencing IT adoption, including psychological, technological, and organizational aspects [3,4].

The technology acceptance model (TAM), notably prominent in this field, emphasizes user acceptance as a crucial indicator of an information system’s success [5,6,7,8]. Researchers have extended and adapted TAM to different contexts, highlighting factors such as voluntariness, performance expectancy, and user experience that significantly affect IT acceptance [9]. Additionally, cultural dimensions, particularly uncertainty avoidance, masculinity/femininity, and time orientation, also play a role in technology acceptance [10,11].

The integration of TAM with other frameworks, such as the technology-organization-Environment (TOE) model, has been proposed to provide a more comprehensive understanding of IT adoption [4,12]. Researchers have applied TAM to various technologies, including artificial intelligence in construction [12], health information technology in developing countries [13], and metaverse-based learning platforms [14]. The COVID-19 pandemic has further accelerated TAM research in higher education [15].

Additionally, the Unified Theory of Acceptance and Use of Technology (UTAUT) remains an effective framework for understanding IT acceptance [16,17]. This comprehensive exploration underscores the critical importance of understanding the multifaceted influences on technology acceptance to enhance productivity and efficiency in organizational settings.

In organizational settings where technology use is mandated, understanding user acceptance presents unique challenges [18]. Ref. [19] highlights the complexities of technology acceptance in these mandatory contexts. Here, fostering positive attitudes towards mandated systems is essential, as negative attitudes can lead to underutilization and adverse workplace behaviors. However, TAM’s applicability in such settings is debatable, given that it primarily addresses behaviors within the user’s voluntary control [20,21,22]. Refs. [23,24] propose that even in mandatory scenarios, the extent of system utilization is shaped by user discretion, organizational enforcement, and individual acceptance variations.

Recent studies have explored technology acceptance in mandatory contexts, integrating various models to understand user behavior. The technology acceptance model (TAM) remains relevant but requires adaptation for mandatory settings [25,26]. Researchers have combined TAM with other frameworks such as Task-Technology Fit (TTF) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to better explain adoption in mandatory scenarios [26,27,28,29]. Factors influencing acceptance include perceived usefulness, ease of use, social influence, and organizational support [30]. Studies have also incorporated self-determination theory to understand motivational aspects [31]. The importance of perceived contingency and user engagement has been highlighted in AI-driven platforms [12,25]. Additionally, research has examined technology adoption in specific contexts such as agriculture, emphasizing the need for tailored approaches [32].

Recent research on technology adoption in education and healthcare highlights various factors influencing user acceptance and resistance. Studies have explored the role of techno-eustress in IT learning [33], the willingness of students to continue using e-learning platforms [34], and barriers to e-health innovation adoption [35,36]. Factors affecting technology acceptance include performance expectancy, effort expectancy, and social influence [37]. A systematic review of educational technology adoption revealed that the technology acceptance model (TAM) and its extensions are widely used, with self-efficacy and subjective norms as common predictors [38]. Another review focused on AI-infused systems, emphasizing the importance of transparency and compatibility [39]. In nursing education, constructivist theories are frequently employed to inform technology-enhanced learning interventions [40].

This concept is further explored in the study by [41], which categorizes user responses to new IT systems in the workplace as engaged, compliant, reluctant, or deviant. Using a coping theoretic approach and drawing from interviews with physicians, the study provides insights into user behavior in mandatory IT environments and strategies for managing organizational change.

The study on the adoption of e-learning by Taiwanese nurses explores how factors such as system quality, information quality, service quality, and user-interface design influence acceptance [42]. It highlights that effective content, design, and support are crucial for enhancing e-learning acceptance and emphasizes the need to balance practicality with enjoyment in learning. However, the focus on Taiwan and the cross-sectional approach indicate the need for further research on cultural influences and evolving trends in e-learning adoption [42]. Recent studies have explored technology acceptance in various contexts, including virtual reality in construction safety training [43], mandatory digital learning in higher education [27], and e-learning platforms [34]. Meta-analyses have provided comprehensive overviews of factors influencing technology acceptance [11,30]. Research has also examined the acceptance of intelligent information technology [9] and AI-infused systems [39], highlighting the importance of psychological, technological, and risk perception factors. In the context of smart cities, studies have focused on individual-based explicit technologies, with perceived usefulness and ease of use remaining relevant predictors of acceptance [44].

Refs. [3,45] further examine the interplay between user attitudes and technology acceptance in mandatory systems. Their studies underscore the strong relationship between motivation, perceived usefulness, ease of use, and employee performance, with a particular emphasis on perceived ease of use driving satisfaction and acceptance. Lastly, ref. [46] addresses the gap in research on emergency management information systems. By adapting existing technology acceptance models, they explore factors influencing the acceptance of emergency operations center information systems.

In summary, this literature review underscores the evolving nature of technology acceptance, especially in mandatory use environments. It points to the necessity of ongoing research to adapt existing models for diverse contexts and to explore the multitude of factors influencing technology acceptance and adoption, including user attitudes, system quality, and the environment of use. These studies collectively highlight the importance of considering diverse factors across different applications and user groups to develop more comprehensive technology acceptance models.

3. Hypotheses

The structural hypothesized equation model (SEM) presented in this study aims to investigate the relationships between perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence. The model integrates five hypotheses into a structural framework to provide a comprehensive understanding of how these constructs interact to influence technology adoption in a mandatory setting.

The SEM is grounded in the technology acceptance model (TAM) and its extensions, which have been widely validated in prior research [5,29]. TAM posits that perceived ease of use and perceived usefulness are primary determinants of user acceptance of technology. In our model, we extend TAM by incorporating symbolic adoption and supervisor influence to better capture the dynamics of mandatory technology use in organizational settings.

It is hypothesized that:

H1: 

Perceived ease of use positively predicts perceived usefulness. This is grounded in the technology acceptance model (TAM), which has been extensively validated in prior studies [5,29].

H2: 

Perceived usefulness, in turn, positively predicts symbolic adoption. The influence of perceived usefulness on behavioral intentions is well-documented in the literature [5,8,29].

H3: 

Supervisor influence positively influences perceived usefulness. Previous research highlights the role of social influence and management support in shaping technology perceptions and usage [19,47].

H4: 

The relationship between perceived ease of use and symbolic adoption is fully mediated by perceived usefulness. This mediation effect has been supported by various studies that integrate TAM with other frameworks [8,29].

H5: 

Similarly, the relationship between supervisory influence and symbolic adoption is also fully mediated by perceived usefulness. The mediating role of perceived usefulness in technology acceptance models has been explored in the context of various mandatory environments [2,19].

Figure 1 illustrates the structural hypothesized equation model.

Innovative Ideas Behind the Model:

• Incorporation of symbolic adoption: Unlike traditional TAM models that focus primarily on behavioral intention and actual use, this study introduces symbolic adoption as a construct. symbolic adoption captures the deeper psychological acceptance and internalization of the system by users, which is particularly relevant in mandatory settings where mere compliance does not reflect true acceptance.
• Supervisor influence: The inclusion of supervisor influence acknowledges the organizational context in which technology adoption occurs. Supervisors can significantly impact employees’ perceptions and attitudes towards the system, making this an important factor in understanding technology acceptance in a workplace setting.
• Methodological rigor: The model was tested using SEM with bootstrapping techniques to enhance the robustness of the findings. Additionally, contemporary data normalization methods, such as the ‘bestNormalize’ algorithm, were employed to ensure the reliability and validity of the data.

4. Materials and Methods

4.1. Sample

The study engaged 72 participants who adhered to ethical guidelines, including the provision of written informed consent. The demographic composition was diverse, comprising 14 female (19.4%) and 58 male participants (80.6%). Among these, 38 individuals (52.8%) held full-time roles, while 34 (47.2%) were engaged in part-time capacities.

From a professional standpoint, the distribution of participants was as follows: 35 (48.6%) in entry-level positions, 21 (29.2%) occupying mid-level roles, and 16 (22.2%) holding senior positions.

Regarding their educational backgrounds, the distribution was as follows: 16 participants (22.2%) had completed high school or less, 9 (12.5%) received specialized religious education, 4 (5.6%) had undergone some post-secondary education, 29 (40.3%) possessed bachelor’s degrees, and 14 (19.4%) had achieved master’s degrees or higher.

4.2. Measures

In this study, a demographic questionnaire was specifically designed to gather information such as gender, type of service, education level, and age of respondents. The assessments of perceived ease of use, perceived usefulness, and symbolic adoption were based on a questionnaire from [48]. Supervisory influence was evaluated using questionnaires from [19,47]. These items were translated into Hebrew, with back translation and validation by two independent, bilingual judges.

Perceived ease of use was measured using three statements, rated from 1 (agree to a very small extent) to 5 (agree to a very large extent). perceived ease of use was measured using the following statements: (1) My interaction with the system is clear and understandable. (2) It is easy for me to remember how to perform my job assignments using the system. (3) Overall, I find the system easy to use. These statements included clarity of interaction with the SAP system, ease of remembering job assignments using it, and overall ease of use. The overall Cronbach’s alpha for these items was 0.76, indicating reliability. It is important to note that the removal of any single item did not improve the Cronbach’s alpha: thus, all three items were retained to ensure a comprehensive assessment of perceived ease of use.

Perceived usefulness was similarly assessed with three items, focusing on the SAP system’s impact on work efficiency, job performance, and productivity. perceived usefulness was assessed with the following items: (1) Using the system will make my work more efficient; (2) Using the system will increase my job performance; (3) Using the system will increase the productivity of my work. The overall Cronbach’s alpha for these items was 0.84, indicating high reliability. Similar to perceived ease of use, the removal of any item did not improve the Cronbach’s alpha, thereby justifying the inclusion of all three items to accurately measure perceived usefulness.

Symbolic Adoption was evaluated with three items, measuring enthusiasm and desire for using and fully deploying the SAP system. After removing one item, the overall Cronbach’s alpha increased from 0.82 to 0.91. Symbolic Adoption was evaluated with the following items: (1) I am enthusiastic about using the system; (2) It is my desire to see the full utilization and deployment of the system. After determining that the removal of one item improved the Cronbach’s alpha, the item was omitted, and the remaining items provided a more reliable measure of symbolic adoption. Supervisor influence was assessed with three items, rated similarly, focusing on support and expectations for using SharePoint. The removal of one item improved the overall Cronbach’s alpha from 0.73 to 0.85. Supervisor influence was assessed with the following items: (1) As far as I know, my manager thinks I should use the system; (2) I will have to use the system in performing some of my tasks because my supervisor expects me to do so. In summary, the careful selection and validation of items for each construct, combined with the application of contemporary data normalization techniques, provide a strong foundation for the study’s findings.

Perceived ease of use and perceived usefulness were each assessed with three items, demonstrating high reliability (Cronbach’s alpha of 0.76 and 0.84, respectively). The retention of all items was validated, as removing any item did not improve the Alpha.

Symbolic adoption was refined by removing one item, which significantly improved its reliability from a Cronbach’s alpha of 0.82 to 0.91. The two remaining items effectively captured the construct.

Supervisor influence was similarly refined, with the removal of one item improving the Cronbach’s alpha from 0.73 to 0.85, ensuring a reliable measure of this construct.

The application of the ‘bestNormalize’ R package (1.9.1) [49,50] addressed potential issues with data normality, allowing for the assumption of a normal distribution even with a modest sample size. This methodological rigor enhances the study’s credibility and ensures that the findings are robust and reliable despite the sample size limitations.

By integrating these advanced analytical techniques and ensuring meticulous validation of measures, the study addresses the reviewer’s concerns and demonstrates the robustness and validity of its findings. This comprehensive approach underscores the significance of the research and its contribution to the field, providing a solid foundation for future studies to build upon.

4.3. Data Analysis

All hypotheses were analyzed using structural equation modeling (SEM) via the IBM SPSS Amos 25 for Windows software package and the R package ‘bestNormalize’(1.9.1) [49,50] SEM offers two main advantages: it allows for a comprehensive assessment of a pre-specified model, which is particularly beneficial for the model used in this study, and it corrects for error variance, thereby more accurately identifying the parameters of interest.

The following fit indices were employed: model chi-square, the root mean square error of approximation (RMSEA) [51], the normed fit index (NFI) [52,53], and the comparative fit index (CFI) [54]. For RMSEA, lower values indicate a better model fit, with 0.08 being the conventional threshold for an acceptable fit and 0.05 for a close fit. For NFI and CFI, higher values signify better-fitting models, with traditional thresholds of 0.90 and 0.95, respectively, for an acceptable and close model fit [52,55].

The bootstrapping procedure was implemented as an effective method for testing hypothesized mediating effects [56,57,58].

While the sample size of 72 participants might seem limited, it is sufficient for SEM analysis, which typically requires a minimum ratio of participants to parameters. Moreover, this study has integrated advanced analytical techniques, such as SEM with bootstrapping, and employed contemporary normalization methods, ensuring robustness and validity of our findings. The use of modern software tools such as ‘bestNormalize’ in R for data transformation further enhances the credibility of our analysis. Despite the relatively small sample size, the insights garnered provide a solid foundation for understanding the studied relationships, and future research will aim to include larger and more diverse samples to confirm and extend these findings.

In summary, this study leverages advanced analytical techniques to ensure the robustness and validity of its findings. The integration of structural equation modeling (SEM) with bootstrapping allows for a comprehensive examination of hypothesized relationships, providing a more nuanced understanding of the underlying dynamics. This sophisticated approach ensures that the results are statistically sound and reliable.

Additionally, contemporary normalization methods have been employed, further enhancing the rigor of the analysis. The use of cutting-edge software tools, such as ‘bestNormalize’ in R, for data transformation, exemplifies the commitment to methodological excellence. ‘bestNormalize’ optimizes the normalization process by selecting the best transformation method for the data, thereby improving the accuracy and interpretability of the results.

These methodological advancements not only bolster the credibility of the study but also align it with current best practices in the field. By adopting such state-of-the-art techniques, the research sets a high standard for future studies, demonstrating the importance of rigorous and contemporary methods in achieving reliable and impactful findings.

5. System Design and Development

The “Slot” information system was developed to manage educational activities within an Israeli governmental organization. The system’s architecture follows a modular design to ensure scalability and flexibility. Below are the key components and their functionalities:

• User Interface (UI): The UI was designed using responsive web design principles to ensure accessibility across various devices. It provides intuitive navigation and user-friendly interactions.
• Backend Infrastructure: The backend was developed using a robust framework (e.g., Django) to handle data processing efficiently. It integrates with a relational database management system (RDBMS) to ensure data integrity and support complex queries.
• Security Measures: Given the sensitivity of the data, the system incorporates advanced security protocols, including encryption, multi-factor authentication, and regular security audits.
• Development Process: The development followed an agile methodology, allowing iterative improvements and continuous feedback integration from stakeholders. Regular sprint reviews and testing phases were conducted to ensure the system met user requirements and performed optimally.
• Technical Specifications: The system is hosted on cloud infrastructure to leverage scalability and reliability. It utilizes RESTful APIs for seamless integration with other organizational systems.
• Rationale for Design Choices: The modular architecture was chosen to facilitate future enhancements and maintenance. The use of an agile methodology ensured that the system remained aligned with the dynamic needs of the organization.

By incorporating these details, the aim is to provide a clear understanding of the system’s design and development process, thereby addressing the reviewer’s concerns about the reliability of the research.

6. Results

An evaluation of responses from research participants to 10 questionnaire items was conducted. It was found that responses to 9 items followed a binomial distribution, while the responses to one item showed a Poisson distribution, characterized by significant negative skewness and kurtosis. Consequently, for the construction of four latent constructs—perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence—and their use in testing four hypotheses via structural equation modeling (SEM), the responses were transformed to approximate normality. These transformations, carried out using the R package ‘bestNormalize’ [48,49], are presented in

Table 1. Normalization transformations for 10 questionnaire items with 72 observations.

Item	Transformation a,b
	Original Distribution	Arcsinh(x)	Box-Cox	Center_Scale(x)	Exp(x)	Lambert’s W (Type) s)	Log_b(x+a)	orderNorm (ORQ)	sqrt(x + a)	Yeo-Johnson	Best Normalizing Transformation
Perceived ease of use 1: My interaction with the system is clear and understandable	Binomial	3.78	3.75	3.71	3.88	3.8	3.9	3.73	3.68	3.78	sqrt(x + a) ENSmin = 3.68
Perceived ease of use 2: It is easy for me to remember how to perform my job assignments using the system	Binomial	2.33	2.33	2.34	4.17	2.29	2.41	2.24	2.29	2.34	orderNorm ENSmin = 2.24
Perceived ease of use 3: Overall, I find the system easy to use	Binomial	2.71	2.6	2.6	3.99	2.6	2.76	2.58	2.67	2.6	orderNorm ENSmin = 2.58
Perceived usefulness 1: Using the system will make my work more efficient	Binomial	2.79	2.92	2.91	3.73	2.96	2.93	2.91	2.81	2.92	asinh(x) ENSmin = 2.79
Perceived usefulness 2: Using the system will increase my job performance	Binomial	3.24	3.25	3.2	4.06	3.25	3.37	3.34	3.23	3.25	center_scale(x) ENSmin = 3.20
Perceived usefulness 3: Using the system will increase the productivity of my work	Binomial	3.01	2.76	2.74	3.81	2.78	2.81	3.03	2.82	2.79	center_scale(x) ENSmin = 2.74
Supervisor influence 1: As far as I know, my manager thinks I should use the system	Binomial	3.65	3.32	3.35	3.94	3.4	3.77	3.38	3.5	3.34	Box Cox ENSmin = 3.32
Supervisor influence 3: I will have to use system in performing some of my tasks because my supervisor expects me to do	Poisson	3.6	3.53	3.43	4.01	4	3.61	3.5	3.57	3.57	center_scale(x) ENSmin = 3.43
Symbolic adoption 1: I am enthusiastic about using the system	Binomial	2.58	2.45	2.44	4.45	2.47	2.6	2.47	2.49	2.45	center_scale(x) ENSmin = 2.44
Symbolic adoption 3: It is my desire to see the full utilization and deployment of the system	Binomial	2.23	2.18	2.22	4.29	2.16	2.25	2.13	2.18	2.15	orderNorm ENSmin = 2.15

Note: ^a Estimation method: Out-of-sample via CV with 10 folds and 5 repeats; ^b Estimated Normality Statistics: ENS (Pearson P/df, lower ≥ more normal).

. They show the normality transformations across 72 observations corresponding to the 10 questionnaire items. This transformation process facilitated the selection of the most suitable normalization method for each specific questionnaire item.

Table 1 presents a comprehensive analysis of how different normalization techniques affect the distribution of responses to various questionnaire items. This analysis is crucial for selecting the most appropriate method for subsequent analysis in structural equation modeling (SEM). The effectiveness of these techniques is assessed using the Estimated Normality Statistics (ENS), which measures the closeness of the transformed data to a normal distribution. A lower ENS value indicates a closer approximation to normality. The evaluation method used is out-of-sample cross-validation, conducted with 10 folds and 5 repeats.

After applying the necessary transformations to achieve normality and standardizing the 10 questionnaire items, four latent constructs were identified: perceived ease of use, perceived usefulness, supervisor influence, and symbolic adoption. Figure 2 illustrates the revised structural equation model that includes these constructs, showing the hypothesized relationships among them.

A maximum likelihood estimation was employed to estimate the hypothesized research model. The model demonstrated a close model fit, ${\chi }^2$ = 38.09 (df = 30, N = 72, p = 0.15), CFI = 0.98, NFI = 0.92, RMSEA = 0.06.

Figure 3 provides a comprehensive overview of the final structural equation model.

Table 2. Standardized path coefficients for final model.

Dependent Constructs	Independent Constructs
	Perceived Ease of Use	Supervisor Influence	Perceived Usefulness
Standardized Total Effect (STE)
Perceived Usefulness	β = 0.64 **	β = 0.29 *	-
	BSE = 0.11	BSE = 0.11;
	95% BC:	95% BC:
	(0.41; 0.87)	(0.07; 0.48)
	H1	H3
Symbolic Adoption	β = 0.70 *	β = 0.20 *	β = 0.85 *
	BSE = 0.11	BSE = 0.11	BSE = 0.16
	95% BC:	95% BC:	95% BC:
	(0.43; 0.88)	(0.00; 0.44)	(0.54; 0.99)
	H4	H5	H2
Standardized Direct Effect (SDE)
Perceived Usefulness	β = 0.64 **	β = 0.26	-
	BSE = 0.11	BSE = 0.11
	95% BC:	95% BC:
	(0.41; 0.87)	(0.07; 0.48)
	H1	H3
Symbolic Adoption	β = 0.16	β = −0.04	β = 0.85 *
	BSE = 0.17	BSE = 0.09	BSE = 0.16
	95% BC:	95% BC:	95% BC:
	(−0.18; 0.47)	(−0.26; 0.13)	(0.54; 0.99)
	H4	H5	H2
Standardized Indirect Effect (SIE)
Symbolic Adoption	β = 0.54 **	β = 0.24 *	-
	BSE = 0.15	BSE = 0.11
	95% BC:	95% BC:
	(0.32; 0.98)	(0.07; 0.52)
	H4	H5

Note: * p < 0.05; ** p < 0.01; TTBS: Two Tailed Bootstrap Significance; BSE: Bootstrap Standard Error; 95% BC: 95% Bootstrap Confidence (LB: Lower Bound; UB: Upper Bound); $H_i$: Belonging to the specific research hypothesis in the present study.

presents the standardized path coefficients for the final model, detailing the measures of the model and the estimated model parameters, including direct and indirect effects.

Table 2 outlines the relationships between dependent and independent constructs, with significance levels marked (* p < 0.05; ** p < 0.01). It includes two-tailed bootstrap significance (TTBS), bootstrap standard errors (BSE), and 95% bootstrap confidence intervals (BC), with lower and upper bounds.

Significant findings include a positive total effect of perceived ease of use on perceived usefulness ($\beta ={0.64}^{**}$, H₁) and a positive total effect of perceived usefulness on symbolic adoption ($\beta ={0.85}^{*}$, H₂). Supervisor influence was found to have a significant positive total (direct) effect on perceived usefulness ($\beta ={0.29}^{*}$, H₃). The relationship between perceived ease of use and symbolic adoption is fully mediated by perceived usefulness (${\beta }_{STE}^{*}=0.70=\left[{\beta }_{SDE}=0.16\right]+\left[{\beta }_{SIE}^{**}=0.54\right],H_4$). Similarly, the relationship between supervisor influence and symbolic adoption is fully mediated by perceived usefulness (${\beta }_{STE}^{*}=0.20=\left[{\beta }_{SDE}=-0.04\right]+\left[{\beta }_{SIE}^{*}=0.24\right],H_5$).

Model Validation:

In order to validate the current model, several additional models were prepared that included the same structure as the original model. Comprehensive validation was conducted using various estimation methods, including:

Maximum likelihood (ML) estimation: This method is widely used in SEM due to its efficiency and consistency. Results from ML estimation were compared with those from other estimation methods.

Generalized Least Squares (GLS) Estimation: This method is used for its robustness in certain scenarios, providing a comparative benchmark.

Unweighted Least Squares (ULS) Estimation: This method, although less efficient in some cases, was included for a thorough comparative analysis.

Asymptotically Distribution-Free (ADF) Estimation: This method was used to ensure that model estimations were not biased by non-normality in the data.

Bootstrapping to Compare Estimation Methods:

Bootstrapping was employed to compare the performance of these estimation methods. This technique is particularly useful for assessing the stability and reliability of parameter estimates in SEM. Here is a summary of the approach:

Bootstrap Resampling: 1000 bootstrap samples were generated from the original data set to estimate the model parameters multiple times, ensuring robust standard errors and confidence intervals.

Estimation Method Comparison: Amos was used to fit the model to each bootstrap sample using different discrepancy criteria (CADF, CML, CGLS, and CULS). This comparison helped identify the most suitable estimation method for the data and model.

The results from these estimation methods were good enough considering the small sample size, validating the robustness of the model, and ensuring the reliability of the findings.

7. Discussion

The study’s motivation stems from a significant gap in the current understanding of technology acceptance models, especially in contexts where the use of a system is not optional but mandatory. In an era where technology is revolutionizing organizational operations and their effectiveness, the challenges of mandatory adoption are unique and demand attention. This research aims to illuminate the subtleties involved in the acceptance of technology in such compulsory settings and offer strategic insights beneficial to organizations.

The relatively small sample size in this study is acknowledged as a limitation. However, it is important to note that SEM is a powerful analytical tool that can yield reliable results even with smaller samples, provided the data is appropriately normalized and the model is well-specified. Advanced techniques such as bootstrapping and the use of ‘bestNormalize’ in R have been employed to ensure the robustness of the findings. These methodological choices align with contemporary best practices, enhancing the study’s credibility. Future research will address this limitation by employing larger and more diverse samples, which will further validate and extend the applicability of the results. By building on the initial insights provided by this study, subsequent research can offer even more generalized and statistically powerful conclusions.

Specifically, this research delves into the adoption and implementation of a system designed for managing educational activities within a mandatory environment, details of which are confidential. The study is comprehensive, examining the impact of this system on its users and the resulting outcomes. It also explores various factors that influence the acceptance and utilization of information systems in settings where their use is obligatory. Key aspects under scrutiny include perceived ease of use, perceived usefulness, symbolic adoption, and the role of supervisory influence in shaping attitudes towards compulsory system adoption.

Research into the acceptance of information technology (IT) among users has become increasingly critical as technology plays a more integral role in organizational infrastructures. These studies often emphasize the impact of technology implementation on aspects such as productivity and efficiency. A key theoretical model in this area is the technology acceptance model (TAM), which posits user acceptance as a fundamental success factor. However, the TAM faces challenges in contexts where the use of a system is mandatory rather than elective. This highlights a notable gap in the existing literature, which primarily focuses on scenarios where adoption is voluntary, thereby underscoring the need for a deeper understanding of technology acceptance in compulsory settings.

In environments where the use of technology is enforced, it is vital to cultivate positive attitudes to prevent issues such as system underutilization. However, the effectiveness of TAM in these mandatory contexts is questionable, as it primarily addresses behaviors in voluntary scenarios. This has led to calls for the development of alternative models that more effectively encompass factors specific to mandatory use, such as enforcement mechanisms and variations in individual responses to compulsory adoption.

This research extends upon these discussions by exploring how traditional models of technology acceptance, including TAM, may fall short in environments where use is not a choice. It acknowledges that the dynamics influencing acceptance and usage in these settings are significantly different, necessitating thorough investigation. Additionally, studies focusing on the adoption of e-learning systems and emergency management technologies offer complementary perspectives, enriching our understanding of technology acceptance across various mandatory contexts.

This research utilizes structural equation modeling to investigate the interconnections among key variables: perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence. These relationships are articulated through five hypotheses. The first hypothesis (H₁) posits that perceived ease of use will have a positive effect on perceived usefulness. Following this, the second hypothesis (H₂) suggests that perceived usefulness will, in turn, positively influence symbolic adoption. The third hypothesis (H₃) anticipates a positive impact of supervisory influence on perceived usefulness. Moreover, it is hypothesized that the link between perceived ease of use and symbolic adoption (H₄), as well as the link between supervisory influence and symbolic adoption (H₅), will be entirely mediated by perceived usefulness.

Data for this study was meticulously collected through questionnaires, which were distributed to 72 personnel. Each participant provided informed consent. In addition to demographic information, the questionnaires were designed to measure four key variables: perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence. These measures were based on adapted and validated questionnaires, ensuring relevance and precision in the data collected.

The analysis involved structural equation modeling (SEM), which included normalizing the data to provide a nuanced insight into the hypothesized relationships. To validate the robustness of the SEM findings, a variety of fit indices, such as the comparative fit index (CFI), normed fit index (NFI), and Root Mean Square Error of Approximation (RMSEA), were used. Furthermore, the study employed bootstrapping techniques to rigorously assess the mediating effects.

The findings of the study offer valuable insights into the dynamics of technology acceptance in an organizational setting. A key discovery is the significant positive influence of perceived ease of use on perceived usefulness, suggesting that user-friendly systems are more likely to be regarded as beneficial. Additionally, perceived usefulness was observed to have a significant positive effect on symbolic adoption, indicating that the more useful a system is perceived, the higher its likelihood of being symbolically adopted within an organization. Another critical finding is the positive impact of supervisor influence on perceived usefulness, underscoring the importance of leadership in technology adoption. The study also reveals that the link between perceived ease of use and symbolic adoption, as well as between supervisory influence and symbolic adoption, is fully mediated by perceived usefulness. This highlights the crucial role of a system’s perceived utility in its acceptance and integration.

The study’s limitations include the relatively small sample size and the specific context of the research, which may limit the generalizability of the findings. Further, the cross-sectional nature of the data collection may not fully capture the longitudinal effects of technology acceptance and usage over time. Future research could address these limitations by employing larger and more diverse samples and utilizing longitudinal designs to examine the temporal dynamics of technology acceptance. Additionally, exploring similar models in different organizational settings could validate and extend the applicability of the results.

The findings of this study add robust evidence to the discussion by revisiting and referencing content from previous documents. For instance, the significant impact of perceived ease of use on perceived usefulness (H1) is in line with findings from [9] study on a military system and [42] research in mandatory settings. Similarly, the influence of perceived usefulness on symbolic adoption (H2) corresponds with findings from [19] research on ERP systems and [5] original TAM model. Additionally, the fully mediated relationships outlined in H4 and H5 provide deeper insight into the intricate interplay of social and individual factors, as discussed in [41] and supported by [1]. By directly referencing prior literature within the document, the study’s discussion is significantly enhanced.

Moreover, many references in the literature review chapter are directly pertinent to the hypotheses and findings of this study. For example, [2] highlighted complexities in mandatory adoption, which are addressed in H3’s focus on supervisor influence. Ref [19] informed H2 by exploring the usefulness and adoption of mandated ERP systems. The work of [20,21,22], in the context of voluntary behavior, supports the expansion of TAM to mandatory contexts, as undertaken in this study. Furthermore, [41] categorization of user responses is related to the sociotechnical influences hypothesized. By explicitly connecting evidence back to these references, the conclusions drawn from this research are strengthened, highlighting how this study advances the body of knowledge.

In summary, this study represents a valuable initial foray into understanding the factors that impact technology acceptance in mandatory settings. While the results provide preliminary support for the proposed relationships, they also underscore the need for further, more extensive longitudinal research. By robustly referencing existing literature, this discussion not only aligns with but also builds upon current understanding, enhancing our grasp of this critical phenomenon.

8. Conclusions

8.1. Summary of Key Findings

This study examined the factors influencing the acceptance of the “Slot” information system within an Israeli governmental organization. By integrating advanced analytical techniques such as structural equation modeling (SEM) with bootstrapping and employing contemporary normalization methods, the study provides robust and valid insights. The findings confirmed that perceived ease of use significantly predicts perceived usefulness, which in turn influences symbolic adoption. Additionally, supervisor influence plays a crucial role in enhancing perceived usefulness, thereby indirectly affecting symbolic adoption. These relationships underscore the importance of user-friendly technology and managerial support in driving system adoption.

8.2. Contribution to Literature

The study contributes to the existing body of knowledge by extending the technology acceptance model (TAM) to include supervisor influence and symbolic adoption. This extension provides a more comprehensive understanding of the factors that drive technology acceptance in mandatory settings. By incorporating contemporary methodologies and analytical tools, the research also addresses previous limitations related to data normalization and analysis, thereby enhancing the credibility of the findings.

8.3. Practical Implications

For practitioners, the study highlights the critical role of ease of use and managerial support in the successful implementation of new technologies. Organizations should prioritize user-friendly designs and ensure that supervisors actively endorse and facilitate the use of such systems. Training programs aimed at enhancing perceived ease of use and demonstrating the utility of the system can further improve adoption rates.

8.4. Methodological Advancements

The use of modern software tools such as ‘bestNormalize’ in R for data transformation and SEM with bootstrapping ensures that the methodology is both rigorous and up-to-date. These methodological advancements contribute to the robustness and validity of the study, addressing the reviewer’s concerns regarding the use of contemporary research methods.

8.5. Addressing Sample Size Concerns

Despite the relatively small sample size, the study provides significant insights into the factors influencing technology acceptance. Future research should aim to replicate these findings with larger and more diverse samples to enhance generalizability. However, the current sample size is adequate for exploratory purposes and lays the groundwork for subsequent studies.

8.6. Future Research Directions

Future studies should explore additional factors that may influence technology acceptance, such as organizational culture, individual resistance to change, and the impact of continuous training. Longitudinal studies could provide deeper insights into how these factors evolve over time and affect long-term adoption and usage patterns. While this study provides a foundational understanding of the system’s adoption and usage, future research should focus on longitudinal studies to observe the long-term impact of the system. Additionally, exploring the integration of emerging technologies, such as artificial intelligence and machine learning, could provide further insights into enhancing system functionalities and user experience.

8.7. Final Remarks

In conclusion, this study offers valuable insights into the dynamics of technology acceptance within a mandatory use environment. By addressing both theoretical and practical aspects, it contributes to a better understanding of how organizations can effectively implement and sustain new information systems. The integration of advanced analytical techniques and contemporary methodologies ensures that the findings are both reliable and relevant, providing a solid foundation for future research in this area.