نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مدیریت فناوری اطلاعات، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
2 گروه مدیریت، دانشکده مدیریت و حسابداری، دانشگاه حضرت معصومه (س)، قم، ایران
3 گروه خط مشی و اداره امور عمومی، دانشکده مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران
4 گروه مدیریت دولتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Purpose: In recent years, open data has become a central pillar of data governance, recognized as a powerful instrument for promoting transparency, fostering innovation, and driving economic growth. Despite the substantial quantitative expansion of open data initiatives, a notable gap remains between data dissemination and the full realization of its intended benefits. Addressing this gap requires a rigorous assessment of the current landscape and the formulation of a strategic roadmap for progress. In this context, maturity models serve as essential tools for evaluating existing capabilities and guiding improvement efforts. However, the literature in this domain is fragmented, and the absence of a comprehensive theoretical framework that systematically integrates technological, organizational, and environmental dimensions has hindered effective maturity assessment. This research aims to systematically map the existing literature to address this theoretical gap by organizing current knowledge, identifying key maturity dimensions within the Technology–Organization–Environment (TOE) framework, analyzing research gaps, and laying the foundation for the development of more holistic future frameworks.
Methodology: This research employed a Systematic Mapping Study (SMS) approach, following the five-phase procedure outlined by Petersen et al. (2008): defining research questions, conducting a systematic search, study selection, data extraction, and final mapping. The research questions were structured around three main axes: (a) development trends and model applications, (b) methodological approaches to model development, and (c) conceptual and content structures. A systematic search was conducted across 13 reputable databases, including Scopus, Web of Science, IEEE, Springer, and Google Scholar, using Boolean combinations of keywords from “open data” and “maturity model” clusters. To ensure maximum comprehensiveness, no temporal restrictions were applied. Following inclusion/exclusion criteria (English language, focus on maturity models), snowballing, and full-text review, 25 studies were selected for final analysis. A classification scheme was developed to structure data extraction and address the research questions. Finally, qualitative content analysis was used to extract and categorize the data, and the identified maturity dimensions were mapped onto the TOE framework.
Findings: The literature on open data maturity models is predominantly journal- and conference-based, though non-academic technical reports are notably prevalent. Model development exhibits an irregular temporal pattern, with recent surges indicating an unsaturated field. Geographically, knowledge production is concentrated in Europe and North America, revealing a significant gap in context-specific models for other regions. Most models operate at the national level, focusing on open government data, while private-sector and domain-specific applications remain limited. Mixed-methods and qualitative approaches dominate model development. In-depth content analysis identified 11 key maturity dimensions, categorized under the TOE framework: Technological (technology and infrastructure, data management and quality, publication and accessibility); Organizational (governance and strategy, organizational structure and management, capacity building and knowledge, financial and economic); and Environmental (legal and regulatory, participation and engagement, public value and impact, domain diversity). Comparative analysis revealed that although 52% of models address all three TOE dimensions, none comprehensively cover all 11 identified dimensions. Prior models heavily emphasize technical aspects, such as “data management and quality” (72% coverage), while strategic dimensions like “financial and economic” (<20% coverage) are significantly underrepresented.
Conclusion: Open data maturity models have thus far failed to comprehensively encompass the technological, organizational, and environmental dimensions, remaining predominantly focused on technical aspects. Moreover, the geographic concentration of knowledge production in Western countries and the absence of locally adapted models underscore the need to develop frameworks that are responsive to contextual specificities. By presenting a systematic classification and identifying eleven key dimensions, this study provides a theoretical foundation for the design of future models. It recommends that subsequent research adopt mixed-method approaches, apply standardized validation protocols, and integrate theoretical insights with field-based experiences to enhance the relevance and effectiveness of open data maturity assessments.
کلیدواژهها [English]
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