# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### Answers to the Research Questions Posed
#### 5.2.1 Q1  
How Are Maturity Models Defined in the Context of Enterprise IoT? The maturity models proposed in the scientific literature have some commonalities in their structures and definitions. While some details differ, many are multi-level frameworks with dimensions and criteria for assessing an organization's IoT capabilities and progression, each tailored to the specific industry context, scope, and purpose for which they are proposed. In general, maturity models are defined as multidimensional matrix structures that combine several independent scales and derive a central value based on those dimensions [41]. Common elements in the various maturity models proposed in the literature include using levels or stages to represent an organization or system's maturity or readiness regarding IoT capabilities, processes, outcomes, or challenges in its context [42; 43]. The number and names of the levels vary across maturity models but typically range from three or more levels and follow a progression from low to high maturity. For example, some models use the terms initial, repeatable, defined, managed, and optimized, while others use system monitoring, control, optimization, autonomy, and autonomy [27]. In this line, it is essential to indicate that the proposed empirical evidence uses dimensions, areas, or constructs to capture the different aspects or factors that influence or are influenced by the adoption and implementation of IoT because it allows to establish in a unified way the different aspects that converge in this technology [21; 44]. In this sense, these dimensions, areas, or construct elements vary from one model to another and cover technological, organizational, human, performance, and other sector-specific aspects, generally related to technology, data, organization, culture, communication, strategy, business, performance, security, privacy, compliance, innovation, or other issues relevant to IoT technology [18; 45]. The number and names of the dimensions vary between models but typically range from 3 to 11 and cover both internal and external factors. Some models, for example, use the dimensions of data, infrastructure, IoT integration into business processes, strategy, organization, and culture, while others use the dimensions of technology, organization, human resources, and performance [46]. Some models introduce additional dimensions, such as IoT capabilities, security practices, resilience, or educational objectives, further contributing to the nuanced characterization of maturity and enhancing the specificity of the models [47; 48]. A common element is using indicators, attributes, objectives, criteria, or questions to measure, assess, or evaluate the maturity level of each dimension or area. Indicators can be qualitative, quantitative, objective, or subjective and based on existing standards, frameworks, or methods [49]. The number and names of these vary between models but usually range from 5 to 62 according to the literature and reflect each maturity level's characteristics and requirements. Thus, some models use the indicators of Infrastructure, Standardization, Data Management, Data Analytics, and Strategy, while others use the indicators of Device, Network, Manager, Provider, and User [25]. In general, the levels are characterized as indicators that measure the degree of digitization, standardization, and improvement of IoT in the business context. A notable element observed is the use of hybrid approaches in the domain, where they rely on established industry frameworks to build maturity models tailored to fit the context; examples include the Capability Maturity Model (CMM) [46], the Process Assessment Model (PAM) [50], and Bloom's taxonomy [51], which provide proven means of classifying maturity using concepts such as capability areas, knowledge domains, and competency levels. It should also be noted that although measurement and assessments provide a means of classifying maturity levels, using data to promote improvement directly is rare.