Currently, information systems constitute an essential component of organizational management for improving efficiency and performance. However, many systems fail to achieve the expectations of their users, which negatively impacts individual and organizational performance [3] [4] [5]. Therefore, the need for research on the successful operationalization of information systems emerged from these challenges and still continues to be an important subject of inquiry [6]. Thus, the evaluation of an IS being operationalized is a worthwhile undertaking to ensure its success and benefits [7]. One of the key aspects of IS success is the perceived satisfaction of its users [6].
2.1. IS user satisfaction as a measure of IS success
The IS user satisfaction reflects the degree to which a user feels that an IS meets his needs and expectations [8]. IS user satisfaction has been considered an important element or even a surrogate of an information system’s success, especially when the use of MIS is mandatory for work [9] [7]. This assertion stems from the fact that users interact with an IS and know its functionality strengths and weaknesses, thus playing the role of evaluators [6] [10]. In addition to these intuitive findings, extensive studies have been conducted that have shown a correlation between user satisfaction and IS success [10]. Therefore, as measuring IS success is difficult, satisfaction is usually used as a proxy for IS success [11] [12].
2.2. Factors affecting user satisfaction
A variety of models have been developed to understand the factors that affect user satisfaction with an IS. Among these, the most widely used are the DeLone and McLean (D&M) original and revised multidimensional and interdependent construct models [13] [7] [14]. According to the updated D&M model, user satisfaction has been theorized to be associated with system quality, information quality and service quality [14] [15]. The model stipulates that the quality dimension of an IS has a positive effect on its use or intention to use and on its user satisfaction, which in turn brings about IS benefits, as shown in Fig. 1 below [14].
According to the D&M model, system quality reflects the aspects of usability, availability, reliability, adaptability and response time; information quality relates to interface content issues, including personalization, completeness, relevance, ease of understanding and security; service quality is concerned with support provided to users, whether coming from internally or externally to the organization; use measures the aspects of navigation, retrieval of information and execution of operations; user satisfaction captures users’ opinions through their experience while interacting with the system; and finally, net benefits direct them to impacts, whether they are positives or negatives [14] [15].
The model suggests that the results of IS quality are iteratively interrelated [14]. In fact, system use must precede user satisfaction, but a good experience of use (user satisfaction) can also lead to the intention to use the system [7] [14] [15]. On the other hand, system use and user satisfaction result in net benefits, but these net benefits also result in intention to use and user satisfaction [14]. A variety of empirical studies have been conducted to validate the D&M IS model. Most of these studies used a quantitative design and revealed significant relationships between the quality of IS and user satisfaction as well as a positive relationship between user satisfaction and IS benefits [16] [17] [18] [19] [20] [21] [22].
The D&M IS model was criticized for its difficulties in application due to the different contexts of IS research, modeling processes with causal explanations resulting in confusing meanings, and limited coverage of dependent variables, as there are many other possible categories of measures varying from user to user [23] [14] [15] [10] [24] [25]. Thus, a variety of other IS success models have been proposed, and others have surged, including the technology acceptance model, human-organizational-technology and fit model, technology-organization-environment model, and innovation dissemination theory [13] [7] [26].
2.3. Other main models of IS success
a. Technology acceptance model (TAM)
According to this model, IS success is characterized by the extent of user acceptance measured through actual use, while the underlying factors of that acceptance are perceived usefulness and perceived ease of use [27] [28]. The mode defines perceived usefulness as the extent to which users believe the information system is important for helping them complete their job requirements, while perceived ease of use is defined as the extent to which users believe the system is used without effort [27]. The model recognizes that these two factors are influenced by external factors, which can include social, cultural and political factors [27] [29]. As illustrated in Fig. 2 below, the model stipulates that perceived usefulness and ease of use influence attitudes toward the use of technology and that those attitudes influence intention to use the technology, whereas that intention leads to the actual use of technology [27].
Many studies have been conducted to validate the TAM, highlighting the significance of the relationship between perceived usefulness and perceived ease of use on technology acceptance [28] [29]. However, there have also been a high number of studies in which the findings did not fit the TAM model [30].
b. The human-organizational-technology (HOT) fit model
The HOT fit model assumes that the effectiveness of an IS depends on the alignment of human factors, organizational structures and technology, with the purpose of understanding IS success through complex interactions between people, organizations and technology [31]. Human attributes reflect individual characteristics and interactions, such as knowledge, skills, capabilities, motivation, position, tasks, collaboration, communication, competition and supervision; organizational structures encompass internal and external organizational management environments and traits, such as organizational size, culture, policies and strategy, practices and government regulations; and technology relates to IS characteristics and requirements, such as software, hardware, network tools, professional skills, systems quality, information quality, service quality and social presence [31].
Numerous studies have been conducted with hypothetical assumptions based on the fact that when an information system is well aligned with individual attributes, organizational management practices, as well as the technological equipment and tools in place, lead to higher user satisfaction, increased productivity, and improved system performance [32] [33]. Thus, the underlying technology adoption factors highlighted in these studies include (i) training, perception, roles, skills, clarity of system purpose and user involvement as human attributes; (ii) leadership and support, process, participation or user involvement, internal communication and inter-organizational systems as organizational factors; and (iii) ease of use, system usefulness, system flexibility, time efficiency, information accessibility and relevancy as technological factors [32] [33].
However, the literature emphasizes the necessity for constant realignment of HOT components [31]. In fact, the alignment has to be dynamically adapted due to constant technological changes and evolving needs of the organization [31]. The literature also highlights the nuances and complex interactions between HOT components and recommends further research to explore those interactions as well as their implications for emerging technologies and workplace dynamics [31] [34].
c. The Technology-Organization-Environment (TOE) model
The TOE model focuses on technological, organizational and environmental factors for modeling technology adoption and innovation within organizations [35]. Technological factors capture the key characteristics of the technology under consideration, including the complexity of the technology and its compatibility with existing systems [36] [37]. Technological factors cover the internal characteristics and capabilities of an organization, such as the organization's absorptive capacity, leadership and innovation culture [36]. The environmental factors include the elements that are external to the organization but crucial in shaping technology adoption in an organization, such as industry regulations, pressure from competitors and market trends [36]. Studies have shown the relevance of the TOE model, highlighting specific elements that influence technology adoption, such as connectivity, IT capabilities and management support [36] [37].
d. Innovation dissemination theory (IDT)
The IDT theory explains the success of an information system in terms of gradual technology adoption. According to this theory, the extent of technology adoption passes through a diffusion process and communicates over time through members of an organization [38]. In the context of an IS, an innovation is defined as a technology that is perceived as new by its users, even if it has long been on the market, and it is manifested through their knowledge, persuasion and decisions [38]. Thus, the key point of the IDT model is to achieve convergence in understanding and adopting technology through a framework of information sharing where change agents and opinion leaders can significantly influence desired practices [38] [39]. This state of adoption depends on five main factors: perceived attributes of innovation, type of innovation decision, communication channels, nature of the social system, and extent of promotion agents’ efforts [39].
2.4. User satisfaction and individual performance
The measurement of net benefits in terms of individual or organizational impact, as a result of system quality, use and user satisfaction, has been advocated. For instance, D&M has advocated for this measurement, cautioning researchers not only to limit their investigations on use and user satisfaction as measures of IS success but also to extend them toward the benefits realized in terms of performance [14]. Therefore, measuring the impact of user satisfaction on individual work performance is essential and adds value to the literature on IS success.
The impact of IS on individual performance is defined as the extent to which the individual perceives the level of productivity due to the use of IS [40]. While many studies have shown that IS use has a positive impact through increased quantities and quality of productivity, other studies have indicated that it has a negative or neutral impact on individuals’ performance and social life [8] [40] [18] [41].
2.5. Critical analysis and conclusion
The D&M and subsequent models intended to associate IS qualities with user satisfaction or technology adoption in terms of causal relationships between dependent and independent variables. However, the intuitive nature of the relationship is internal rather than external. When a user appreciates positively or expresses difficulties with an aspect of an IS quality or use, such as ease of use or usefulness of the information generated by an IS, this is a means of expressing his satisfaction or dissatisfaction with the IS use or usefulness. Therefore, this study will focus on understanding the reasons for, issues and challenges related to a certain level of satisfaction rather than understanding it in terms of correlation and causality.
Most of the studies conducted have been quantitative and have explored the relationship between user satisfaction and IS quality through a number of dependent and independent variables [15]. However, understanding the challenges and issues associated with user satisfaction might not be limited to parametric models and predefined variables [14] [24]. In addition, there is divergence in how IS quality variables are measured, and critics of their reliability exist [8]. This implies that studies must give the flow to users to express their concerns as measures of IS qualities. Thus, this study will integrate a qualitative component that will further the understanding of the issues of healthcare providers while using the system. Therefore, the D&M model and other models that subsequently emerged were adapted to the conceptual framework of this study, as shown in Fig. 3.
D&M recommended that researchers select a small number of measurements while using their model [14] [15]. However, many of the quantitative studies conducted generally use long questionnaires, and many questions have similarities, which can lead to confusion in distinguishing variables [13] [14] [15] [19] [20] [42]. This study used a concise questionnaire with a single question addressing user satisfaction and another one addressing the perceived impact of user satisfaction on work performance. In this study, the aspects of IS qualities and other issues characterizing user satisfaction and how they affect individual work performance were obtained through qualitative questions. This enables discerning user judgments, avoiding a blurt in expressing their experience and allowing the flow of the expression for the qualitative component of the questionnaire.
2.6. Summary and gaps in the literature
The D&M model has been essential for explaining IS success through user satisfaction and quality characteristics. Following the criticisms of the D&M model, further studies have been conducted through a variety of other models and elaborate other factors that affect IS success, including perceived ease of use and usefulness of IS, trustworthiness, top management support, design of user requirements, training, computer literacy, organizational structure and management style [26] [27] [28] [29] [30]. Despite a multitude of those models, no one is claiming to exhaustively explain all the elements of an IS success. However, qualitative studies can effectively capture a wide range of IS characteristics underlying the status of IS success, taking into account the specificity of organizations, but this approach has yet to be explored. Thus, this study uses a mixed research approach to take advantage of the benefits of both quantitative and qualitative approaches for analyzing the extent of an IS success as well as the underlying issues in the context of a specific hospital.