# 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?
#### 5.2.4 Q4  
What Are the Future Work, Possible Improvements, or Directions Derived from the Proposed Maturity Models in the Domain? Exploring future works, possible improvements, and subsequent lines of research derived from the various proposed maturity models is essential to advance in this field because they facilitate refining the executed tasks, avoid redundancies, and align with the right research direction [82]. Punctually, future work or possible improvements derived from the proposed maturity models in the domain are diverse and depend on the context, the area of application, and the specific objectives of each model, as well as the limitations, challenges, and opportunities encountered when implementing the mechanisms, but some studies have provided information on specific aspects; a broader domain overview reveals the following common perspectives of the authors:  
Validation and refinement: Most of the proposed maturity models are based on theoretical foundations, the literature reviews, or expert opinions and lack empirical validation and refinement with real-world data and scenarios [18; 46]. Therefore, a future direction expressed by the authors is to conduct more case studies [59], experiments [57], surveys, or interviews with different types of organizations and professionals to test and verify the applicability, effectiveness, and usefulness of the models, allowing to identify strengths, weaknesses, gaps, and opportunities for improvement [21]. In terms of validation, mention is made of increasing the size and diversity of the sample (organizations) [57], comparing across countries [37], sectors, or industries, and using empirical data, case studies, or benchmarking to improve the generalizability, applicability, and robustness of the models [59]. The above speaks to the need for further standardization in the domain, as there is no universally accepted methodology to validate proposals that speak to the youth of the field at the same time;  
Scaling and integration: Maturity models are often limited in scope, coverage, and granularity [46], leaving out some essential aspects, dimensions, indicators, or criteria that could affect the maturity level or the improvement process [48]. Therefore, a recurrent future direction is to extend and integrate the models to include more factors [61], variables [21], indicators, or criteria that reflect the complexity and diversity of IoT, such as sustainability [66], ethics [49], interoperability [60], and innovation, allowing to contrast the models with other existing proposals or standards in IoT or related fields. Furthermore, other future work should explore the interrelationships [45], dependencies, and trade-offs between the different factors and the weighting, prioritization, and aggregation of the factors into a meaningful and actionable score or maturity level;  
Customization and adaptation: Most of the proposed maturity models are generic and do not consider the needs and specific characteristics of different industries, sectors, regions, or contexts adopting or using IoT solutions [47; 59]. Therefore, a repetitive future direction is to customize [49] and adapt [22] the models according to the particularities and contingencies of each situation, such as the type and size of the organization, industry, market conditions, and regulatory environment, allowing to provide more personalized and relevant recommendations and guidelines for the improvement and transformation of IoT maturity in enterprises. Also, an element that stands out in this section is the exploration of the relationships between model components and organizational outcomes to see the effectiveness of this type of mechanism [58];  
Development and application: Many of the proposed maturity models are conceptual and descriptive and need to provide practical methods and tools to measure and improve IoT maturity [42; 48].