# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 8 Conclusions and Future Work  
This research planned and executed a review of maturity models for IoT in companies or organizations, allowing for the characterization, synthesis, and analysis of the existing knowledge in this domain. For the execution of this study, the methodology proposed by [PERSON] and the PRISMA protocol, which served as a guide for research development, were used. A total of 489 documents were identified in different databases, which were subjected to inclusion and exclusion criteria, resulting in the characterization of 36 primary studies that addressed different aspects of maturity models, such as their internal functioning, the dimensions considered, validation methods, and future work derived from these investigations. The primary result of this research is that the maturity models proposed in the literature have some commonalities in their structures and definitions, such as the use of levels, dimensions, and indicators to represent and measure the maturity or readiness of an organization in terms of IoT capabilities, processes, or outcomes. However, there is great diversity and variability in the number, names, and descriptions of these elements, as well as in the methods and tools used for data collection, analysis, and visualization. In addition, maturity models address various aspects or factors that influence IoT adoption and implementation, encompassing technological, organizational, human, performance, and other specific dimensions, depending on each proposal's context, scope, and purpose. The internal measurement processes of these mechanisms involve data collection, analysis, visualization, and feedback methods tailored to the purpose and design of the model, where quantitative, qualitative, and mixed metrics are used to assess maturity. While these mechanisms effectively measure IoT adoption and implementation in organizations, most do not explicitly support post-measurement improvement because some provide benchmarks, recommendations, and roadmaps. However, practical improvement frameworks are limited and need more. Different methods have been used to validate maturity models, such as case studies, surveys, comparative metrics, or expert reviews. However, more rigorous and detailed validation approaches involving multiple stakeholders are needed to provide empirical evidence of these measurement mechanisms' impact and results in organizations. Critical areas for future refinement of IoT maturity models include additional validation, development of process support tools, expansion of scope and indicators, customization for different contexts, and updating for new IoT-related technologies. Also, some limitations and gaps in the current body of science lack empirical validation and evaluation of the proposed models, the paucity of details on measurement processes and methods, the limited consideration of human and social factors, and the limited use of data to promote improvement and progression along the maturity continuum. Finally, future work derived from this research should focus on developing more comprehensive, holistic, and standardized models that not only address the technological aspects but also integrate the organizational, human, and social dimensions of IoT and the interrelationships and interdependencies between them. It is also crucial to conduct case studies, field tests, and experiments in various organizational environments to assess the practical applicability and effectiveness of current and future maturity models. Furthermore, it is necessary to develop and apply standardized and rigorous methodologies for constructing and validating maturity models, going beyond conceptual descriptions and expert opinions. In terms of data collection and analysis methods, it is essential to sophisticate these processes by using advanced techniques such as machine learning and natural language processing to gain a deeper understanding of organizational data.