# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### 2 What Do the Proposed IoT Maturity Models Consider the Main Dimensions?
This leads to the challenge of addressing the complexity and interconnectedness of human involvement in various technological domains, highlighting the need for comprehensive and adaptive maturity models, and recognizing the gap between the potential and actual use of IoT technology in your environment. What Metrics Are Associated with Assessing the Maturity Levels of the Proposed Maturity Models in the Domain? Assessing the maturity levels of the proposed models requires using metrics adapted to the attributes and objectives to track the expected evolution of specific dimensions or aspects covered by the proposed mechanism. It is crucial to note that according to the literature, metrics can be classified into three distinct types: quantitative; qualitative; and mixed, where each type has its own set of advantages and limitations [26]. Consequently, there is no universally accepted set of metrics, but common approaches use maturity scores assigned from quantitative aggregates or qualitative assessments of organizational capabilities and maturity level descriptors defined within the models themselves to assess progress [51; 67; 71]. The specific choice of metrics and calculations is tailored to the focus, dimensions, and intended uses of each proposed maturity model, ensuring a complete and accurate assessment of the IoT maturity level in the organization. Quantitative metrics are numerical values that measure the performance, efficiency, quality, or effectiveness of IoT technologies, processes, or systems, typically calculated from responses to a questionnaire, data analysis results, or a mathematical formula application. Quantitative metrics help to provide an objective and comparable assessment of maturity levels but may not capture the complexity and diversity of the entire IoT domain [76; 77], such as stakeholder opinions. Some of the most representative examples of quantitative metrics are scores, percentages, frequencies, means, standard deviations, ranks, ANOVA, and Chi-square. On the other hand, qualitative metrics are descriptive measures that assess the characteristics, attributes, or capabilities of IoT technologies, processes, or systems, often derived from the definitions and characteristics of each maturity level, the comparison of the current and desired state of the organization, or the identification of gaps and opportunities for improvement [78]. Qualitative metrics help provide a comprehensive and detailed assessment of maturity levels but may not be easily quantifiable and standardizable [48], often delineated by descriptive characteristics or assessment criteria that denote what an organization can do at each level; in general, these types of metrics are used to a lesser extent in the proposed maturity models for the IoT domain. Qualitative metrics include descriptions, criteria, characteristics, and profiles [51; 63]. Mixed metrics are a combination of quantitative and qualitative metrics that aim to provide a balanced and holistic assessment of maturity levels, often obtained by integrating or mapping quantitative and qualitative metrics or using a hybrid method to assess maturity levels [22; 67]. Mixed metrics may require more effort and resources to implement and validate. In general, scores typically range from 1 to 5, 0 to 4, and 0 to 100, or use labels and then discretize these into numbers received by the model [18]. Examples of mixed metrics are scores and descriptions, percentages and criteria, frequencies, and indicators. In general, the metrics that are associated with assessing the maturity levels of the maturity models encountered vary depending on the purpose and method of the model, where the literature indicates that the most common types of metrics are quantitative and, to a lesser extent, qualitative and mixed metrics that seek to ensure a detailed and nuanced understanding of IoT organizational maturity.