The core problem the team confronted is one that will be familiar to anyone who has tried to compare studies in this area. Conceptually identical conditions appear under wildly different names: top management support, executive sponsorship and project championing often describe the same underlying reality, yet they are treated as distinct findings. Levels of abstraction vary just as wildly, with one study presenting a single broad construct where another splits it into multiple narrow subfactors. This terminological chaos makes cumulative theory-building nearly impossible, and it also obscures a deeper gap: very few studies have asked practitioners directly whether the factors identified in the literature are actually prioritized and implemented in real projects.

To build their evidence base, the researchers conducted a systematic literature review following PRISMA 2020 reporting principles and Kitchenham-style guidance, searching Web of Science, IEEE Xplore and Scopus as of October 14, 2025. After rigorous two-reviewer screening, which achieved 98.15 percent exact agreement and a Cohen’s kappa of 0.962, 32 primary empirical studies made the final cut. Each study was assessed for methodological quality on twelve criteria, with normalized consensus scores ranging from 66.67 to 100 percent and a mean of 91.93 percent. From these 32 studies, the team extracted 449 verbatim critical success factor entries, which expanded to 457 mappings after splitting compound entries, and were then harmonized through retention, renaming, merging and splitting into 51 generalized candidate factors.

The harmonization process was deliberately conservative, preserving the conceptual meaning of each factor rather than forcing superficial word-matching. For example, the term standard-based BI architecture was generalized to scalable and flexible analytics technical architecture, while synonymous expressions of strategic alignment were merged under clear business vision and strategic plan. Conversely, a compound factor combining business-centric championship and balanced team composition was split into two distinct constructs: business project champion and project team competency. Every one of the 449 original entries was linked to a traceability sheet recording its verbatim wording, extracted label and dimension assignment, allowing future researchers to audit or re-code the entire synthesis. Twenty-three canonical factors survived the confirmation checks, organized into five conceptual dimensions: strategic, organizational, people, project and technical.

The five dimensions capture complementary layers of a socio-technical system. The strategic dimension covers business case and value justification, business project champion, clear business vision and strategic plan, and top management support. The organizational dimension includes adequate resource allocation, data quality management and governance, organizational culture, user-focused change management and vendor support quality. The people dimension encompasses internal expertise and cross-functional collaboration, project team competency, user participation and user training. The project dimension holds clear system requirements, incremental delivery and feature prioritization, project management and structured processes, stakeholder coordination and user-solution alignment. The technical dimension covers analytics system integration and interoperability, effective data management, high system quality, scalable architecture and supporting technology.

Then came a twist that says a great deal about how the field is changing. During pilot testing with 12 experienced practitioners, participants insisted that data security, privacy and compliance deserved recognition as a standalone factor rather than being buried within other constructs. The researchers agreed, bringing the final practitioner-informed taxonomy to 24 factors. That addition proved prescient: in the full survey, data security, privacy and compliance earned one of the highest mean importance ratings of any factor, at 4.3714 on a five-point scale, second only to clear system requirements at 4.3857. A condition absent from the canonical literature core had become, in the eyes of practitioners, nearly indispensable, a signal that regulatory and governance concerns now weigh on analytics work in ways the older literature never anticipated.

The survey itself reached 350 eligible respondents recruited primarily through Prolific, with a small supplement from LinkedIn, collected over ten days in March 2026. Respondents rated each factor’s perceived importance and its level of implementation in their organizations, selected their top five priorities in a forced-ranking format, and answered open-ended questions. Most had direct experience with business intelligence, dashboards and reporting projects, followed by data platform, decision support, big data, predictive modeling and text analytics work. When forced to prioritize, practitioners put clear business vision and strategic plan first, selected by 48 percent of respondents, followed by data quality management and governance at 44.57 percent, top management support at 43.14 percent, data security and compliance at 37.14 percent and project team competency at 36.57 percent.

Perhaps the most practically useful findings came from the importance-implementation gap analysis. Five factors showed statistically significant positive gaps after Holm correction for multiple comparisons: user-solution alignment, business case and value justification, data security, privacy and compliance, effective data management, and clear business vision and strategic plan. In plain terms, practitioners consider these conditions more important than their organizations actually deliver. The effect sizes, however, were modest, with Cohen’s dz values ranging from 0.164 to 0.198, suggesting small but real implementation shortfalls rather than organizational crises. Notably, top management support and data quality governance, though heavily prioritized, did not show statistically significant gaps, illustrating that a factor can be foundational without being substantially under-implemented.

The final analytical stage applied exploratory factor analysis to the importance ratings, and the results complicate the tidy five-dimension picture. The data proved highly suitable for factor analysis, with an overall Kaiser-Meyer-Olkin value of 0.9401 and a significant Bartlett test. Rather than recovering five distinct dimensions, the analysis yielded three correlated empirical factors accounting for 43.22 percent of total variance: strategic governance and delivery capability, blending strategic justification, governance, data foundations, resources, team capability, requirements and delivery discipline; leadership, stakeholder and adoption alignment, covering sponsorship, culture, change management, user involvement and vendor support; and technical infrastructure and capability enablement, which notably absorbed user training alongside integration, architecture and system quality. The inter-factor correlations of 0.553 to 0.601 reinforce the study’s socio-technical framing: practitioners experience these conditions as interdependent capabilities, not isolated silos.

The authors are careful about what their findings do and do not establish. Project outcomes were not directly measured; the study assessed perceptions of importance and implementation, not causal links to success. The sample skewed toward practitioners with fewer than four years of experience and technology-sector projects, and the three-factor structure remains exploratory pending confirmatory factor analysis with independent samples. Still, the practical implications are concrete. Organizations can use the five dimensions as a diagnostic checklist at project initiation, governance reviews and post-implementation evaluation, then combine importance ratings, forced priorities and gap analysis to target improvement efforts. The three empirical factors offer a template for structuring interventions into integrated workstreams spanning governance and delivery, leadership and adoption, and technical enablement. As analytics investments continue to balloon worldwide, this practitioner-informed taxonomy offers something the field has lacked: a common language for asking not just whether a project succeeded, but which conditions were in place to make success possible, and which were quietly missing.

Subject of Research: Critical success factors for analytics and data mining projects, harmonized into a taxonomy and assessed by practitioners

Article Title: Critical success factors for analytics and data mining projects: a harmonized taxonomy and practitioner assessment

Article References: Vindya, K. S., Shetty, S., Prabhu, N. N., & Shetty, N. (2026). Critical success factors for analytics and data mining projects: a harmonized taxonomy and practitioner assessment. International Journal of Data Science and Analytics, 22(1), Article 330. https://doi.org/10.1007/s41060-026-01309-0

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01309-0

Keywords: critical success factors, analytics projects, data mining, business intelligence, project management, taxonomy, practitioner survey, exploratory factor analysis, data governance, data security and compliance, socio-technical systems, systematic literature review

The phenomenon at the center of the study is known as information systems discontinuance, the point at which individuals or organizations stop using a technology they have already adopted. While decades of research have explored why people embrace new systems, far less attention has been paid to the reverse journey. This gap matters commercially as well as scientifically: industry benchmark reports cited by the authors show that mobile app retention rates drop steeply within days of installation, and organizations routinely struggle with costly decisions about decommissioning legacy platforms. Understanding the mechanics of disengagement, the authors contend, is essential for anyone who designs, deploys, or regulates digital technology.

To build their synthesis, Ghelani and Pandey applied the Gioia methodology, a well-established approach to qualitative research that imposes systematic rigor on literature synthesis. The method requires researchers to move from open-coded first-order concepts drawn directly from the source studies, to broader second-order themes, and finally to aggregate dimensions that form a coherent conceptual structure. The 97 studies they reviewed spanned social media platforms such as Facebook, WeChat, Twitter, and Weibo; consumer applications including mobile banking, gamification fitness apps, and short video services; and organizational systems ranging from cloud computing deployments to legacy enterprise software and even mental health chatbots.

The organizing lens for the synthesis is Leavitt’s socio-technical systems framework, a model dating to 1965 that views any organized system as the joint product of four interacting elements: People, Technology, Task, and Structure. The framework has deep roots in information systems research, tracing back to foundational work in the 1970s that explained many system failures as mismatches between social and technical components. By mapping discontinuance research onto these four dimensions, the review reveals patterns that single-context studies could never show on their own.

Within the People dimension, the review identifies a dense cluster of psychological drivers. Overload is among the most heavily documented: information overload, communication overload, and social overload on social networking sites consistently push users toward exhaustion and eventual withdrawal. Technostress, social media fatigue, privacy concerns, cyberbullying, and envy all appear as strains that accumulate until users disengage. The authors also highlight the role of emotions and self-regulation, including guilt, cognitive dissonance, regret, and addiction-related coping, showing that pathological use patterns can paradoxically drive both overuse and the eventual decision to quit. Inhibitors on this dimension include habit, inertia, and satisfaction, which anchor users to incumbent systems even when alternatives beckon.

The Technology dimension captures how the characteristics of systems themselves shape abandonment. Poor system quality, technical failures, and unmet expectations fuel negative disconfirmation, a mismatch between what users anticipated and what they received. Studies of chatbots, wearable self-tracking devices, and mobile shopping apps document how response failures and technostress translate directly into discontinuance intentions. Yet technology can also inhibit departure: switching costs, architectural embeddedness, and the sheer weight of legacy systems keep users locked in. Research on organizational legacy system replacement shows how accumulated technical debt traps firms in outdated platforms, making discontinuance a socio-technical negotiation rather than a simple upgrade choice.

The Task dimension reflects what users are actually trying to accomplish. When a system no longer fits the task, when it delivers irrelevant information, or when competing technologies serve the same purpose better, discontinuance pressure rises. The review documents cases where users intensified their use of a failing system before finally abandoning it, a counterintuitive pattern suggesting that people often test a system’s limits before giving up on it. Task-related drivers interact closely with the fourth dimension, Structure, which encompasses organizational norms, governance arrangements, social networks, and institutional pressures. Critical mass dynamics, for example, determine whether a social platform survives: when enough connections leave, remaining users follow in cascades of network collapse.

Perhaps the review’s most significant conceptual contribution is its treatment of discontinuance as a process with distinct forms. The authors distinguish intermittent discontinuance, in which users take temporary breaks and may return; replacement, in which one system is swapped for another; and permanent discontinuance, in which use ends for good. Which form materializes depends on how the four socio-technical dimensions interact. A dissatisfied user with strong habits and high switching costs may opt for an intermittent pause rather than a permanent exit, while an organization facing obsolescence pressures and institutional mandates may pursue full replacement. Framing these outcomes as points on a continuum of socio-technical interdependence, rather than isolated choices, is the study’s central theoretical advance.

The practical implications reach system designers, organizational leaders, and policymakers alike. For designers, the findings suggest that retention depends not only on polished features but on managing user load, protecting privacy, and aligning the system with genuine task needs. For organizations, the review underscores that replacing or retiring systems requires attention to legacy habits, governance structures, and workforce adaptation, not merely technical migration. For policymakers navigating digital transitions, including the shift away from long-standing public infrastructure, the message is that discontinuance decisions ripple across all four socio-technical dimensions simultaneously. As individuals and institutions continuously reassess their relationships with technology, this synthesis offers a shared map of the terrain, showing that walking away from a system is never just a click of a delete button but the end point of a complex, multidimensional negotiation between people and the socio-technical worlds they inhabit.

Subject of Research: A qualitative review of information systems discontinuance drivers and inhibitors across four socio-technical dimensions

Article Title: A Multi-Dimensional View of IS Discontinuance: Insights Using Socio-Technical Systems

Article References: A Multi-Dimensional View of IS Discontinuance: Insights Using Socio-Technical Systems. (n.d.). https://doi.org/10.1007/s10796-026-10808-1

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10808-1

Keywords: information systems discontinuance, socio-technical systems, post-adoption behavior, social media fatigue, technostress, Gioia methodology, Leavitt framework, system replacement, user abandonment, digital detox, legacy systems, qualitative literature review