# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 6 Discussion of Results  
This study reviewed the state-of-the-art maturity models for IoT in enterprises, addressing four research questions related to these models' definitions, measurements, dimensions, and metrics. The maturity models proposed in the scientific literature usually have some commonalities in their structures and definitions, with some details that differ. In most cases, these mechanisms are multilevel frameworks with dimensions and criteria for assessing an organization's IoT capabilities and progression, each tailored to the specific industry context, domain, and purpose for which they are proposed. Although the structures of the models vary, the key elements include technological, organizational, human, performance, and security dimensions assigned to stages that denote the complexity of IoT integration and performance. In terms of definition, maturity models are defined as multidimensional matrix structures that combine several independent scales and derive a central value based on those dimensions. Regarding publication trends, analysis indicates a growing interest in IoT maturity models, reflecting their importance as essential tools for assessing and optimizing IoT deployments in enterprises. However, primary studies remain relatively low compared to other domains and technologies [24; 83]. Along these lines, the predominance of conceptual proposals rather than empirical validations highlights the need for more studies with real cases to examine the applicability of models in different contexts. Meanwhile, the extension of purely technical models to include organizational and human factors reflects a greater recognition that the success of IoT also depends on socio-technical dynamics beyond the technological infrastructure. In particular, the measurement processes of the proposed models need more explicit detail and focus more on maturity outcomes than on the complex methodologies employed to perform this work. Although several quantitative and qualitative techniques are suggested, standardized parameters and calculations tailored to IoT capabilities still need to be made more explicit, limiting the replicability of measurement and benchmarking initiatives and suggesting the need for more rigorous, transparent, and reproducible measurement techniques. In addition, this study reveals that most maturity models focus on assessment rather than facilitating post-measurement improvement. While recommendations for transitioning between maturity levels are standard, concrete processes for formulating, implementing, and validating improvement strategies using assessment data are scarce, indicating another area requiring further research. An unexpected finding found in this study is the extent of qualitative assessment methods employed in the proposed maturity models, in contrast to the quantitative assessment often associated with maturity frameworks, as well as the reliance on expert analysis and descriptive criteria rather than discrete metrics, suggesting that IoT sophistication involves not only technological enhancement but also managerial and cultural capabilities in organizations, which allows for rethinking how maturity is conceived in the context of rapidly advancing IoT, where constant adaptation is required and speaks to the need for multidimensionality in measurement design. This review contributes to the IoT domain by consolidating the emerging but growing research on enterprise IoT maturity, whereby the structured synthesis of standard models, applications, measurement processes, and improvement frameworks provides a holistic perspective that previous reviews have only superficially addressed by assessing the scientific body of IoT maturity models, identifying persistent gaps and future directions for focused efforts by researchers. Also, analysis of proposed model validation methods indicates the need for standardized empirical approaches that go beyond conceptual description or solicitation of expert opinion, which would improve model reliability as model adoption expands.