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Information systems success model

The Information Systems Success Model (ISSM), developed by William H. DeLone and Ephraim R. McLean, is a multidimensional framework designed to evaluate and measure the effectiveness and impact of information systems (IS) within organizations. Originally proposed in 1992, the model synthesizes prior research on IS evaluation by identifying six core, interrelated dimensions of success: system quality (assessing technical performance such as ease of use and reliability), information quality (evaluating output characteristics like accuracy and relevance), use (measuring the extent and manner of system utilization), user satisfaction (capturing users' perceptions of the system), individual impact (examining effects on personal productivity and decision-making), and organizational impact (considering broader contributions to organizational performance). These dimensions form a causal chain, where qualities influence use and satisfaction, which in turn drive impacts, providing a structured approach to conceptualizing IS success as a dependent variable in research. Recognizing shifts in IS landscapes, including the emergence of web-based systems and e-commerce, DeLone and McLean refined the model in 2003 to better accommodate service-oriented aspects and feedback loops.^[1] The updated version retains the foundational dimensions but adds service quality (focusing on support from IS staff and vendors) and replaces individual and organizational impacts with a unified net benefits category to encompass both positive and negative outcomes, while incorporating intention to use alongside actual use to account for voluntary adoption contexts.^[1] This revision emphasizes the model's applicability to diverse IS environments, such as enterprise resource planning systems and online platforms, and highlights reciprocal relationships where net benefits can influence continued use and satisfaction.^[1] Since its inception, the DeLone and McLean ISSM has become one of the most influential and widely adopted frameworks in information systems research, serving as a foundation for empirical studies across sectors like healthcare, education, and government.^[2] The original 1992 publication has amassed over 19,500 citations, underscoring its foundational role in addressing the challenge of defining and measuring IS success.^[3] The 2003 update, with more than 12,000 citations, has further solidified its relevance by adapting to technological advancements and inspiring extensions, such as integrations with technology acceptance models.^[4] Despite critiques regarding contextual limitations and measurement challenges, the model remains a benchmark for IS evaluation, with ongoing validations confirming its robustness in modern digital ecosystems.^[5]

Overview

Definition and Purpose

The Information Systems Success Model, proposed by William H. DeLone and Ephraim R. McLean, is a multidimensional framework for assessing the effectiveness of information systems through a set of interrelated dimensions, moving beyond isolated performance metrics to capture the complexity of IS outcomes.^[6] This approach recognizes IS success as a multifaceted construct that integrates technical, behavioral, and organizational elements into a unified evaluation structure.^[7] The primary purpose of the model is to consolidate the fragmented body of IS evaluation measures—previously scattered across diverse studies—into a cohesive taxonomy that links system attributes directly to user behaviors and broader organizational impacts, thereby enabling more consistent and comparable research findings.^[6] By providing this integrated perspective, the model facilitates the development of a cumulative tradition in IS research, allowing scholars to build upon a shared foundation for measuring success.^[7] Central to the model is a causal chain where foundational qualities influence user engagement and satisfaction, which subsequently drive net benefits, supported by feedback loops that account for reciprocal influences among the dimensions.^[1] These qualities, including information quality, system quality, and service quality, serve as the starting points in this process-oriented view of IS effectiveness.^[8]

Significance in IS Research

The DeLone and McLean Information Systems (IS) Success Model has played a pivotal role in standardizing the measurement of IS success within scholarly research, serving as a foundational benchmark for evaluating and comparing IS performance across diverse contexts such as e-commerce, e-government, and enterprise systems.^[9] Since its introduction in 1992, the model has been adopted in hundreds of empirical studies documented in major databases like Web of Science, with its core papers accumulating over 35,000 citations (as of 2025), enabling consistent theoretical and methodological frameworks that facilitate meta-analyses and cross-study comparisons.^[10]^[9] This standardization has elevated the rigor of IS evaluations by providing a multi-dimensional structure that integrates technical, informational, and behavioral factors, reducing fragmentation in prior ad-hoc approaches.^[3] Beyond IS research, the model has exerted significant influence on adjacent domains, including IT governance, where it supports assessments of enterprise resource planning (ERP) systems to align technology investments with organizational objectives.^[11] In project management, adaptations of the model evaluate the impact of IS on project outcomes, such as in hybrid offshore development environments, by linking system attributes to overall success metrics. Similarly, it informs organizational performance metrics by incorporating net benefits as a key outcome, allowing practitioners to gauge IS contributions to efficiency, decision-making, and strategic goals in settings like business intelligence implementations.^[12] The model's significance also lies in driving the evolution of IS evaluation practices, shifting from narrow, financially oriented measures like return on investment (ROI) to more comprehensive, user-centric assessments that account for satisfaction, usage intentions, and broader impacts.^[13] This holistic perspective, emphasized in the model's causal linkages from quality dimensions to net benefits, has encouraged researchers and organizations to prioritize multifaceted success criteria over simplistic economic proxies.^[7]

Historical Development

Original Model (1992)

The DeLone and McLean Information Systems Success Model was first introduced in 1992 as a framework to evaluate the effectiveness of information systems (IS) by synthesizing and consolidating fragmented prior research on IS success.^[6] Developed by William H. DeLone and Ephraim R. McLean, the model addressed the lack of a clear dependent variable in IS studies by proposing a structured taxonomy of success dimensions derived from an analysis of over 100 empirical and theoretical articles published between 1981 and 1990.^[6] This synthesis aimed to resolve inconsistencies in earlier models, which often treated success measures in isolation, by integrating both process-oriented (e.g., usage) and outcome-oriented (e.g., impacts) indicators into a cohesive structure.^[6] At its core, the original model comprises six key dimensions: system quality, which assesses the technical performance and usability of the IS itself (e.g., reliability, ease of use, and response time); information quality, focusing on the output's attributes like accuracy, timeliness, and relevance; use, measuring the actual utilization of the system; user satisfaction, capturing users' overall contentment with the system; individual impact, evaluating effects on personal productivity, decision-making, and performance; and organizational impact, which considers broader benefits such as cost savings, market share gains, and improved operations.^[6] These dimensions were selected based on their frequent appearance and empirical support in the reviewed literature, with the model emphasizing that IS success is multidimensional rather than unidimensional. The model's causal structure posits interdependencies among these dimensions, forming a process-outcome chain: system quality and information quality directly influence both use and user satisfaction, while use and user satisfaction in turn affect individual impact, which subsequently leads to organizational impact.^[6] This hierarchical arrangement reflects the rationale for consolidation, as it integrates disparate success measures—such as those from Shannon and Weaver's communication model or Ives et al.'s user information satisfaction instrument—into a logical progression that links system attributes to ultimate organizational benefits, thereby providing a testable framework for IS evaluation. By doing so, the model facilitated a more cumulative tradition in IS research, encouraging empirical studies to examine these relationships in various contexts.^[6]

Updated Model (2003)

In response to advancements in information systems (IS) and feedback from empirical research over the preceding decade, William H. DeLone and Ephraim R. McLean revised their original IS success model in a 2003 article published in the Journal of Management Information Systems.^[1] This update addressed limitations in the 1992 framework, particularly its applicability to emerging contexts such as e-commerce and service-oriented systems, where traditional dimensions like system and information quality alone were insufficient to capture user interactions and support dynamics.^[1] The revisions aimed to enhance the model's parsimony and relevance by incorporating new constructs informed by a review of over 100 studies on IS success.^[1] Key modifications included the addition of a service quality dimension to reflect the growing importance of IS support services, such as help desks and user training, which influence user perceptions beyond technical attributes.^[1] The original categories of individual and organizational impacts were consolidated into a single net benefits construct to streamline evaluation of multifaceted outcomes, including productivity gains and decision improvements, while avoiding overlap.^[1] Additionally, "use" was reframed as use/intention to use to better account for voluntary adoption behaviors in modern IS environments, where actual usage may not always precede perceived value.^[1] The updated causal model posits that information quality, system quality, and service quality directly influence both use/intention to use and user satisfaction, which in turn drive net benefits.^[1] A notable inclusion is a feedback loop, wherein realized net benefits reinforce subsequent perceptions of the three quality dimensions, use, and satisfaction, creating a dynamic, iterative process rather than a linear progression.^[1] This structure builds on the foundational dimensions from the 1992 model while adapting to the complexities of contemporary IS deployment.^[1]

Core Dimensions

Information Quality

Information quality represents a core dimension in the DeLone and McLean information systems success model, defined as the extent to which the output information from an information system fulfills the requirements and expectations of its users. This dimension emphasizes the semantic aspects of the information generated by the system, assessing whether it is suitable for decision-making and task performance. Key attributes of information quality include accuracy, which ensures the information is free from errors; timeliness, indicating availability when needed; completeness, covering all necessary elements without omissions; relevance, aligning with user tasks; and conciseness, presenting information without redundancy. These characteristics determine the value of the information as a resource for users, enabling effective utilization in organizational contexts. Within the model, information quality plays a pivotal role by directly impacting user satisfaction and intention to use the system, as high-quality information enhances perceived usefulness and encourages continued engagement. It provides the substantive content that users rely on, thereby supporting positive behavioral and attitudinal outcomes. This dimension interacts briefly with system quality in the model's causal structure, where both contribute to downstream effects like use, though information quality specifically addresses output content rather than processing efficiency. Measurement of information quality typically involves both subjective and objective approaches. Subjective scales, such as those proposed by Seddon (1997), capture perceived information quality through user ratings on items like "the information is accurate" and "the information is timely," often using Likert-type responses to gauge overall satisfaction with output attributes. Objective measures, employed in empirical studies, include quantifiable indicators like error rates in system-generated reports, where lower error percentages signal higher quality. These methods allow researchers to validate the dimension across diverse information systems, prioritizing user-centric and verifiable assessments.^[14] As the "what" of information systems output, information quality distinctly focuses on the intrinsic properties of the data and reports produced, setting it apart from evaluations of system performance or support services, and underscoring its unique contribution to holistic IS success.

System Quality

System quality represents the desirable characteristics of the information system itself, encompassing the technical attributes that enable effective system performance and user interaction. In the original DeLone and McLean model, system quality is defined as the desired characteristics of the information system, focusing on its inherent technical merits rather than the output it produces. This dimension targets the "how" of system delivery, emphasizing the infrastructure and functionality independent of the information content generated.^[13] Key characteristics of system quality include ease of use, reliability, flexibility, availability, response time, and integration capabilities. These attributes ensure the system is user-friendly, dependable, and adaptable to varying needs, such as quick processing and minimal downtime.^[15] For instance, reliability involves low error rates and resistance to failures, while flexibility allows the system to accommodate changes without significant reconfiguration.^[16] In the updated 2003 model, these characteristics were refined to include functionality and portability, maintaining the focus on technical success while integrating feedback from evolving IS research.^[15] Within the information systems success model, system quality plays a pivotal role by directly influencing users' intention to use the system and their overall satisfaction, thereby facilitating effective interaction and adoption. High system quality enables seamless engagement, reducing barriers to usage and enhancing perceived value during interactions. This dimension contributes to broader net benefits by supporting sustained use and positive user experiences across various IS contexts.^[15] Measurement of system quality typically involves assessing usability, reliability, and performance metrics, often integrated with established frameworks like the Technology Acceptance Model (TAM). Perceived ease of use from TAM serves as a common proxy, capturing user perceptions of system navigability through scales that evaluate learning curves and interface intuitiveness.^[13] In studies of enterprise resource planning (ERP) systems, metrics such as response time, availability, and adaptability are quantified via Likert-scale surveys, with average scores indicating performance; for example, one validation study reported reliability scores averaging 3.74 out of 5 and response time satisfaction at 3.87, highlighting variability in ERP implementations across organizations.^[16] Downtime statistics and error rates provide objective measures, complementing subjective usability scores to offer a comprehensive evaluation.^[17]

Service Quality

Service quality, as defined in the DeLone and McLean Information Systems Success Model, pertains to the overall support provided to users through interpersonal interactions and organizational assistance, rather than the technical attributes of the system itself. This dimension draws directly from the SERVQUAL framework, which identifies five key components: tangibles (physical facilities, equipment, and appearance of personnel), reliability (ability to perform the promised service dependably and accurately), responsiveness (willingness to help customers and provide prompt service), assurance (knowledge and courtesy of employees and their ability to inspire trust and confidence), and empathy (caring, individualized attention provided to customers). These elements capture the human-centered aspects of service delivery in information systems environments.^[1] Introduced in the 2003 update to the model, service quality plays a pivotal role by influencing user satisfaction and intention to use, particularly in contexts where users rely on helpdesks, training, or technical support to resolve issues and maximize system benefits. It extends the model's explanatory power to account for post-implementation support dynamics, recognizing that effective IS outcomes depend not only on the system's design but also on the quality of ongoing assistance. This addition addresses a gap in the original framework, emphasizing human and organizational support that fosters trust and engagement in user-system interactions.^[1] Measurement of service quality in IS research typically employs adapted SERVQUAL scales, which use Likert-type items to gauge the gap between user expectations and perceptions of support services. For instance, in helpdesk contexts, surveys assess responsiveness through questions on resolution times and empathy via evaluations of personalized communication, enabling organizations to quantify support effectiveness and its impact on overall IS success. These instruments have been refined for IS-specific applications, ensuring reliability in capturing service-related variances.^[18]^[19]

Causal Relationships and Outcomes

Use and Intention to Use

In the DeLone and McLean Information Systems Success Model, use represents the actual behavioral engagement with the system, measured by frequency, duration, and depth of interaction, while intention to use captures the user's predisposition or willingness to adopt and continue employing the system.^[7] These elements address both voluntary adoption, driven by perceived value, and mandatory contexts, such as required organizational tools.^[7] Within the model, use and intention to use act as intermediary constructs, linking the antecedent quality dimensions—system quality, information quality, and service quality—to downstream outcomes.^[7] The quality factors exert positive influences on these behavioral elements, which subsequently drive broader impacts, with empirical validations confirming their mediational role across diverse IS contexts.^[2] Common measurement approaches for use include self-reported surveys on interaction frequency and duration, supplemented by objective system logs for actual behavior tracking.^[2] Intention to use is typically evaluated using multi-item scales derived from established frameworks like the Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB), which assess attitudinal and normative predictors of future engagement.^[20] A distinctive feature of these constructs emerged in the 2003 model update, where intention to use was explicitly incorporated alongside actual use to bolster predictive accuracy for pre-adoption phases, distinguishing it from the original 1992 framework.^[7] This refinement, further explored in contemporary extensions, emphasizes separating intentions from behaviors to integrate technology acceptance perspectives.^[21]

User Satisfaction

User satisfaction represents the subjective evaluation by users of an information system (IS), encompassing their overall contentment and the extent to which the system meets their expectations.^[2] In the DeLone and McLean IS success model, this dimension captures users' emotional responses and affective appraisals of the IS experience, distinguishing it from more objective measures like system performance.^[22] It is defined as the degree of positive affect or contentment users derive from interacting with the system, often reflecting fulfillment of anticipated benefits.^[7] Within the model, user satisfaction serves as a parallel mediator alongside use, influenced by system quality, information quality, and service quality, while also being affected by actual use of the IS. Positive user satisfaction, in turn, contributes to net benefits at individual and organizational levels and maintains a reciprocal relationship with use, where satisfying experiences reinforce ongoing engagement. This positioning in the causal chain highlights its role in bridging technical attributes of the IS with broader outcomes, as validated in multiple empirical studies.^[23] Measurement of user satisfaction typically employs multi-item Likert scales, with the End-User Computing Satisfaction (EUCS) instrument being a seminal and widely adopted approach.^[24] Developed by Doll and Torkzadeh, the EUCS scale assesses five key components—content, accuracy, format, ease of use, and timeliness—through 12 items rated on a scale from strongly disagree to strongly agree, often applied in post-implementation surveys to gauge user perceptions after system deployment.^[24] Alternative measures include adaptations of the EUCS for specific contexts, ensuring reliability and validity through factor analysis in validation studies.^[25] A distinctive feature of user satisfaction is its emphasis on the emotional dimension of IS interaction, providing insights into affective responses that behavioral metrics may overlook.^[22] Research indicates that user satisfaction often emerges as a stronger predictor of long-term IS adoption and continuance intention compared to initial use patterns, as evidenced in a meta-analysis of the DeLone and McLean model showing a strong average effect size (r = 0.58) to net benefits across 31 studies.^[23] This attitudinal focus makes it particularly valuable for assessing sustained user loyalty in evolving IS environments.^[2]

Net Benefits

Net benefits in the DeLone and McLean information systems success model refer to the combined positive effects of an information system on individuals, such as enhanced productivity and improved decision-making, and on organizations, including cost savings and competitive advantages, offset by any negative impacts.^[26] This dimension consolidates impacts across multiple levels, encompassing individual, organizational, work group, and even societal effects, to capture the overall contribution of the system to stakeholder success.^[26] In the model's causal structure, net benefits serve as the ultimate endpoint, directly resulting from system use (or intention to use) and user satisfaction, while also providing reciprocal influences back to these factors to reinforce ongoing system adoption and satisfaction.^[26] This positioning emphasizes net benefits as the key outcome that validates the effectiveness of preceding dimensions like system quality, information quality, and service quality.^[26] A distinctive feature of net benefits emerged in the 2003 update to the model, where separate constructs for individual impacts and organizational impacts from the original 1992 framework were merged into this single, parsimonious dimension to better reflect the holistic and interdependent nature of system success.^[26] This consolidation addressed limitations in the earlier model by allowing for a more integrated assessment of multi-level outcomes.^[26] Measuring net benefits involves multi-level metrics that span individual performance gains, such as time savings or decision accuracy, and organizational key performance indicators (KPIs), including return on investment (ROI), productivity improvements, and cost reductions.^[26] Approaches often incorporate both quantifiable financial indicators, like profitability and market share gains, and qualitative assessments of broader impacts.^[26] For instance, integrations with the balanced scorecard framework have been proposed to systematically evaluate net benefits across financial, customer, internal process, and learning/growth perspectives tailored to information systems contexts.^[2]

Applications and Extensions

E-commerce and Online Systems

In 2004, DeLone and McLean extended their updated Information Systems Success Model to specifically address the challenges of e-commerce environments, adapting the six core dimensions—system quality, information quality, service quality, use/intention to use, user satisfaction, and net benefits—to the unique demands of online transactions and consumer interactions.^[27] This extension emphasizes website-specific features within system quality, such as ease of navigation, which facilitates intuitive browsing and reduces user frustration during product searches, and security measures, including encryption and authentication protocols, to protect sensitive data and build confidence in digital exchanges. These adaptations recognize the heightened importance of technical reliability in consumer-facing platforms, where seamless functionality directly influences transaction completion rates. Subsequent studies applying the model in e-commerce have incorporated perceived privacy as a critical component, often integrated into system and information quality assessments, to address consumer concerns over data handling and surveillance in online marketplaces. This focus shifts the model toward consumer-centric evaluation, highlighting how privacy assurances contribute to overall trust, which in turn mediates the path to net benefits like repeat purchases and loyalty. For instance, in online shopping contexts, trust emerges as a key success factor, linking quality dimensions to tangible outcomes such as increased customer engagement and reduced cart abandonment. Studies applying this framework have demonstrated that robust privacy features enhance net benefits by fostering long-term consumer relationships in platforms reliant on personal data. Service quality plays a pivotal role in customer retention within e-commerce, as evidenced by research showing its positive influence on user satisfaction and subsequent loyalty behaviors, such as subscription renewals and positive word-of-mouth. For example, responsive customer support and personalized recommendations under service quality have been linked to higher retention rates. Key applications of the model in online shopping underscore how these elements drive net benefits, including economic gains for vendors through sustained revenue streams. Pre-2025 meta-analyses provide empirical support for the model's dimensions in e-commerce, aggregating data from numerous studies to show strong correlations between system, information, and service quality and sales performance metrics. Petter, DeLone, and McLean's 2008 meta-analysis, drawing on over 100 empirical works including e-commerce applications, found that these quality factors explain significant variance in net benefits, such as sales volume and market share growth, with effect sizes indicating robust predictive power at the individual and organizational levels.^[2] Representative examples from platforms like Amazon illustrate this linkage, where enhanced navigation and security have been associated with improved conversion rates and overall platform success in longitudinal studies. The e-commerce adaptation thus builds briefly on the core model's causal structure, reinforcing the interplay among qualities, satisfaction, and benefits in digital commerce.

Emerging Technologies and Recent Adaptations

Recent developments in the DeLone and McLean Information Systems (IS) Success Model have emphasized adaptations for artificial intelligence (AI) integration, particularly in intelligent systems. A 2025 panel at the International Conference on Technology and Organization (ICTO) convened the model's co-creator and experts in AI and digital transformation, concluding that the core six dimensions—system quality, information quality, service quality, use, user satisfaction, and net benefits—remain foundational but require redefined measurements for contemporary contexts.^[28] Panelists highlighted non-linear relationships in success dynamics, reconceptualizing "use" and "user satisfaction" as broader user experience constructs that incorporate enjoyment, well-being, and human-centric design to foster trust in AI-driven systems.^[28] This evolution addresses the shift toward intelligent systems where traditional linear causal paths may not fully capture emergent behaviors, such as adaptive AI interactions.^[28] Adaptations of the model have been applied to social media platforms, where perceived privacy plays a critical moderating role in usage intentions and overall success. A 2025 study in Malaysia extended the model by integrating perceived privacy as a moderator, finding that it significantly strengthens the effects of system quality (β = 0.179, p = 0.017) and service quality (β = 0.196, p = 0.003) on continual usage intentions, while all three quality dimensions positively influence user satisfaction (system: β = 0.241, p = 0.013; service: β = 0.372, p < 0.001; information: β = 0.343, p = 0.003).^[22] User satisfaction, in turn, drives net benefits through sustained usage (β = 0.573, p < 0.001), explaining 60% of variance in continuance.^[22] In business intelligence (BI) and analytics systems, the model measures success by synthesizing measures from 173 studies (2000–2024), emphasizing unique BI&A factors like visualization quality, predictive analytics capabilities, and decision-making performance alongside traditional dimensions such as information accuracy and ease of use.^[29] Recent adaptations include non-financial outcomes like strategic alignment and sustainability impacts, tailoring net benefits to BI&A's focus on organizational utility and ROI.^[29] Key extensions incorporate privacy and ethical dimensions into net benefits, especially for social information systems (SIS). The ICTO 2025 panel broadened net benefits to encompass societal and environmental impacts, implying ethical considerations like equitable AI outcomes and data stewardship as integral to long-term success.^[28] In SIS contexts, such as social media, privacy moderation enhances ethical net benefits by mitigating risks to user trust and well-being, ensuring that benefits outweigh potential harms like data misuse.^[22] Hospital IS evaluations further illustrate these extensions; a 2025 study at Abadan University of Medical Sciences applied the model to assess HIS success, reporting an overall rate of 3.12 (out of 5), with system quality highest (mean: 3.36) and service quality lowest (mean: 2.99), while highlighting privacy and security as key sub-dimensions (mean: 3.47).^[30] The study recommends enhancements in service quality to achieve desirable thresholds (≥3.75), underscoring privacy's role in ethical net benefits for healthcare SIS.^[30] Post-2020 technologies like AI have prompted unique aspects in the model, particularly redefining service quality to include algorithmic transparency. A 2025 study on AI systems found that transparency signaling mitigates negative attitudes toward parent organizations (p < 0.05), especially in high-involvement scenarios like data security, thereby supporting trust and adoption as proxies for service quality in IS success.^[31] This aligns with regulatory contexts like the EU AI Act (2024), emphasizing transparency to enhance net benefits in intelligent systems without directly boosting system-level trust.^[31] Such adaptations ensure the model's relevance to opaque AI processes, prioritizing ethical transparency in service delivery.^[31]

Empirical Evidence and Criticisms

Validation Studies

The foundational empirical support for the DeLone and McLean information systems (IS) success model stems from its original 1992 formulation, which synthesized findings from 180 articles, including conceptual and empirical studies, on IS effectiveness to identify key dimensions and causal linkages, such as the influence of system and information quality on use and user satisfaction. This synthesis drew on diverse quantitative evidence from surveys and usage metrics across early IS implementations, establishing a benchmark taxonomy that has guided subsequent validations. The model's 2003 update incorporated feedback from a decade of post-1992 empirical research, including field studies and surveys that tested and refined the dimensions, adding service quality and net benefits while affirming the original causal paths through aggregated findings from more than 100 articles. This revision was supported by evidence showing consistent positive relationships, such as quality factors driving intention to use and satisfaction in enterprise systems.^[32] Meta-analyses conducted in the 2000s and 2010s further solidified the model's validity by pooling data from hundreds of primary studies; for example, a comprehensive assessment of individual-level success examined correlations across 52 empirical studies, revealing strong average effect sizes (r > 0.30) for paths from information quality to user satisfaction and from use to net benefits, thus confirming the model's interrelationships.^[33] Empirical validations frequently utilize structural equation modeling (SEM) to estimate path strengths, providing quantitative evidence of the model's dynamics; in a study of decision support systems within the Omani banking sector, information quality showed a significant positive correlation (r = 0.426, p < 0.001) to user satisfaction, based on correlation analysis. Similarly, in healthcare contexts like Nigerian hospital information systems, service quality demonstrated a robust path to user satisfaction with β = 0.51 (p < 0.001), alongside system quality influencing use at β = 0.53 (p < 0.001).^[34]^[8] A 2025 literature review aggregating insights from 102 studies over three decades of IS research across sectors underscores the model's enduring validity, identifying information quality and user satisfaction as the strongest predictors of net benefits in weighted analyses, with consistent support for causal paths in both traditional and digital environments. These findings highlight the model's robustness, as tests in finance and healthcare repeatedly validate the core dimensions—system quality, information quality, service quality, use/intention to use, user satisfaction, and net benefits—as reliable indicators of IS success.^[35]

Limitations and Future Directions

The DeLone and McLean Information Systems Success Model has been critiqued for its overemphasis on quantitative measures, such as system usage and efficiency metrics, which often overlook qualitative aspects like user experience and contextual nuances in evaluating success.^[36] This focus limits its applicability in dynamic environments, where intangible factors, such as emotional engagement or cultural influences on satisfaction, are underrepresented.^[37] For instance, satisfaction constructs may exhibit cultural biases, as user perceptions vary across diverse organizational or societal contexts without adequate model adjustments.^[38] Measuring net benefits poses significant challenges, particularly for intangible information systems like social platforms, where societal impacts, addictive designs, and well-being effects are not fully captured by traditional dimensions.^[39] The model's static assumptions fail to account for evolving ecosystems, leading to inconsistencies in application across modern contexts.^[37] In AI-driven and autonomous systems, traditional metrics for "use" and "intention to use" break down, as these systems operate without deliberate human input, rendering behavioral indicators obsolete.^[40] A 2025 panel discussion highlighted these gaps, noting that the model inadequately addresses explainability, bias, and transparency in AI contexts.^[39] Future directions emphasize integrating sustainability and ethics into the model's net benefits dimension to evaluate environmental and societal impacts of information systems.^[39] Longitudinal studies are recommended to capture feedback loops and long-term success, addressing the current lack of post-implementation analysis.^[41] Adaptations for emerging technologies, such as metaverse and VR platforms, suggest hybrid models combining the DeLone and McLean framework with TAM or UTAUT to incorporate factors like perceived usefulness, social needs, and privacy risks.^[38] These enhancements would reconceptualize "use" as user engagement and "satisfaction" as holistic experience, better suiting recent technological gaps.^[39]

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Measuring end user computing satisfaction (EUCS) - ScienceDirect
A new proposed model of EUCS. The first model (Fig. 1) is mainly based on the end-user's computing satisfaction model of Doll and Torkzadeh [23]. It was ...
[28]
The Updated DeLone and McLean Model of Information Systems ...
DeLone and McLean proposed an updated IS Success Model (DeLone and McLean 2003). This chapter gives an overview of the current state of research on the IS ...<|control11|><|separator|>
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The DeLone and McLean Information Systems Success Model
Sep 7, 2025 · The panel discussion revealed both consensus and healthy tensions: panelists agreed the six core dimensions of success remain largely timeless, ...
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Measuring the success of business intelligence and analytics systems
The success or value of BI&A systems is referred to as the positive outcome or the utility and/or the benefits an organization attains from its BI&A investment ...
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Investigating the Success Rate of the Hospital Information System ...
The present study aimed to evaluate the success rate of the HIS in health centers of Abadan University of Medical Sciences based on the DeLone and McLean model.
[32]
AI algorithm transparency, pipelines for trust not prisms - Nature
Jul 23, 2025 · This study explores artificial intelligence (AI) algorithm transparency to mitigate negative attitudes and to enhance trust in AI systems ...
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[PDF] The DeLone and McLean Model of Information Systems Success
The purpose of this paper, therefore, is to update the D&M IS Success Model and evaluate its usefulness in light of the dramatic changes in IS practice, ...
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[PDF] A meta-analytic assessment of the DeLone and McLean IS success ...
Mar 3, 2009 · Their review of the literature resulted in a taxonomy of IS success consisting of six variables: System Quality,. Information Quality, Use, User ...
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[PDF] An Empirical Application of Delone and Mclean Model In Evaluating ...
Therefore it becomes necessary to measure the success of these systems. The main objective of this study is to assess the applicability of DeLone and. McLean ...Missing: rates | Show results with:rates
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Article: DeLone and McLean information systems success model: a ...
Apr 4, 2025 · Article: DeLone and McLean information systems success model: a literature review Journal: International Journal of Business Information Systems ...
[37]
https://doi.org/10.1016/j.ijinfomgt.2020.102139
[38]
https://doi.org/10.3390/su142417037
[39]
https://doi.org/10.17705/1CAIS.05738
[40]
https://doi.org/10.3127/ajis.v28.4611
[41]
https://www.davidpublisher.com/Public/uploads/Contribute/685c9f0d91512.pdf
[42]
[PDF] A Systematic Review of the DeLone & McLean Model in Enterprise ...
technological contexts, (2) analyze the most influential success factors—system quality, information quality, service ... Implementation of Delone and Mclean ...