# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### How Are Measurement Data Used to Support Maturity Improvement in Organizations?
In this line, it is essential to indicate the existence of some standard methods and criteria used to assess the validity and reliability of the models, like other scientific fields, in validating their proposals. Among the most prominent are the following methods:  
Expert opinion: This method consists of consulting experts with knowledge and experience in the IoT domain about the proposed model and obtaining their comments and suggestions on the proposal, development, and refinement of the proposal [71], usually applied through interviews, surveys, panels, and working groups to obtain qualitative feedback [58]. Expert opinion can help ensure the model's relevance, specificity, and accuracy and identify its strengths, weaknesses, and areas for improvement [80]. A derivation of this type of validation of maturity models in the domain is the collection of comments and opinions from all stakeholders (not only limited to experts), where qualitative data are collected that reflect the perceptions [47], experiences, and suggestions of those involved in or affected by the maturity model, such as practitioners, experts, researchers, customers, and organizational staff [46]. Comments and opinions can be obtained through interviews, surveys, or observations, providing information on the maturity model's relevance, importance, applicability, and usefulness [73];  
Data collection and analysis: This method consists of distributing a survey or questionnaire to a sample of potential users or stakeholders of the proposed model [61] and collecting and analyzing their responses, helping to measure the perception, satisfaction, and acceptance of the model, as well as to assess the applicability, feasibility, and suitability of the model for different contexts and scenarios [57]. Following this process, statistical methods are used to determine trends that cannot be observed with the naked eye, such as correlation [43], regression [49], factor analysis [62], cluster analysis [58], or structural equation modeling [46]. Data analysis can help quantify and measure the impact and benefits of the model, as well as reveal patterns, trends, and insights from the data;  
Case studies and field testing: This method involves applying the proposed model to a real-world case study or field test [56], conducting an experimental design, determining variables of interest, and observing and analyzing the results [45]. Case studies and field testing can help demonstrate the feasibility and effectiveness of the model, as well as validate and verify its assumptions, hypotheses, and predictions [59]. This form of validation is considered the most reliable according to the scientific literature because it shows in a real scenario the behavior of the proposal, allowing to provide evidence and examples of the benefits and results of the mechanism, such as improvement, enhancement, or innovation of the IoT in the organization with the proposal;  
Metrics or parameters: This approach is used to demonstrate tangible improvements resulting from the application of the maturity model [68], such as increased efficiency, reduced costs, improved quality, reduced lead times, increased revenue and profitability, increased customer retention, and increased competitive advantage [60]. This type of method obtains quantitative data that allows for the comparability of the results of the proposed mechanisms. Later, correlation and regression statistical techniques can validate the relationships between maturity levels and business performance indicators [42];Comparative methods: Used to contrast and highlight the differences and similarities between the proposed maturity model and other existing models or frameworks in the literature or practice, allowing to show the strengths, weaknesses, gaps, and opportunities of the maturity model, as well as to justify and support the novelty and originality of the proposal [18]. The comparison of the results over time and with best practices provides a contextual validation of the proposal.