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Functional maturity

Functional maturity

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Functional maturity refers to the stage at which an organization, system, or process demonstrates optimal performance and effectiveness in achieving its intended functions. It encompasses the development and refinement of capabilities, processes, and practices that contribute to sustained operational success and adaptability over time.
lightbulbAbout this topic
Functional maturity refers to the stage at which an organization, system, or process demonstrates optimal performance and effectiveness in achieving its intended functions. It encompasses the development and refinement of capabilities, processes, and practices that contribute to sustained operational success and adaptability over time.

Key research themes

1. How do maturity models assess and guide organizational and process development across different sectors?

This research area focuses on the conceptualization, design, evaluation, and application of maturity models as structured frameworks to assess and progressively improve organizational capabilities, processes, or information systems. Understanding maturity models enables organizations to benchmark their current state, identify developmental gaps, and provide actionable roadmaps for strategic advancement. This theme is crucial because maturity models are widely used in domains such as information systems, healthcare, business processes, digital transformation, and service innovation, and present methodological challenges related to construct validity, model adaptability, and practical implementation.

Maturity Models in Information Systems Research: Literature Search and Analysis
by T B

2016

Key finding: The paper’s systematic review of 76 IS maturity model articles identifies the Capability Maturity Model (CMM) as the dominant foundation, critiques the sparse theoretical grounding on model design and adoption, and highlights... Read more
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Maturity Models for Information Systems - A State of the Art
by Jose Borbinha

2024, Procedia Computer Science

Key finding: This work synthesizes diverse maturity models spanning multiple domains, proposing classification criteria based on maturity levels, quantitative vs. qualitative results, and continuous improvement philosophies. It reveals... Read more
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A Maturity model for hospital information systems
by João Vidal De Carvalho and 
2 more
Álvaro Rocha
Rogier Van de Wetering

2018

Key finding: Developing the Hospital Information Systems Maturity Model (HISMM), this paper operationalizes maturity assessment specifically within healthcare IS, incorporating six stages with key influencing factors. It demonstrates that... Read more
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Information System Maturity Models in Healthcare
by Jorge Gomes

2018, Journal of Medical Systems, 42, 11

Key finding: Identifying critical maturity factors and a broad spectrum of existing healthcare IS maturity models, this study articulates the relationship between IS infrastructure maturity and hospital process orientation. It critically... Read more
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An Evaluation Template for Expert Review of Maturity Models
by Dina Salah

2019

Key finding: This paper provides a comprehensive template for expert evaluation of maturity models’ constructs and instruments, derived from an extensive literature synthesis of design and evaluation principles. The proposed evaluation... Read more
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2. What developmental and cognitive factors underlie the concept of functional maturity in individuals, and how can these be measured?

This theme investigates the developmental psychology and cognitive neuroscience perspectives related to functional maturity, including models of adult and adolescent development and decision-making competence. The focus is on understanding the hierarchical and vertical progression of cognitive and emotional capacities and their relation to maturity, including methodological advances to operationalize and quantify cognitive maturity through latent-variable modeling and developmental stage assessments. Capturing these dimensions is critical for applied fields such as education, healthcare, legal systems, and leadership development.

A Conception of Adult Development
by Jay Mehta

2017

Key finding: This seminal conceptual paper articulates a theoretical framework distinguishing life course, life cycle, and life structure as core constructs in adult development, emphasizing the evolution of the life structure as a... Read more
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Introducing an adolescent cognitive maturity index
by Ashley S VanMeter

2024, Frontiers in Psychology

Key finding: The study develops and validates a Cognitive Maturity Index (CMI) using latent factor models based on inhibitory control, risky decision-making, and emotional processing assessed longitudinally in adolescents. It demonstrates... Read more
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Making the Case for a Developmental Perspective
by Susanne Cook-Greuter

2023

Key finding: This paper introduces the concept of action logics and promotes vertical developmental transformations as critical for meaning-making capacity beyond lateral growth in management contexts. It illuminates the significance of... Read more
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Children’s Developmental (Im)maturity: Aligning Conflicting Decisional Capacity Assessment Approaches in Australia
by Dominique Moritz

2023, Laws

Key finding: Analyzing the Australian legal landscape, this paper underscores conflicting approaches to children's decision-making capacity between health and criminal law. It critically explores developmental maturity's role in legal... Read more
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3. How can functional maturity states be characterized and measured in complex ecological or organizational systems through trophic or capability analyses?

This area explores the identification and classification of functional maturity in multi-dimensional, complex systems—such as coral reef ecosystems or manufacturing organizations—by analyzing structural, topological, or capability-based indicators. It focuses on integrating trophic network analyses, geomorphological data, and capability metrics to map developmental stages or maturity levels. This approach provides actionable understanding to inform sustainability, environmental management, or competitive servitization strategies, demonstrating a novel extension of functional maturity concepts beyond individuals or organizations to broader systemic contexts.

Functional developmental states of the Greater Caribbean coral reefs
by Horacio España

2024, Ecological Indicators

Key finding: Through ecosystem trophic modeling and nonmetric multidimensional scaling, this study identifies a gradient of eight discrete functional developmental states in Caribbean coral reefs, correlating higher maturity with... Read more
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Servitization maturity model: developing distinctive capabilities for successful servitization in manufacturing companies
by keiji ogata

2023, Journal of Manufacturing Technology Management

Key finding: By applying a systematic literature review and case study methodology, this research develops a servitization maturity model comprising 46 capabilities across seven categories, including strategy, organization, and digital... Read more
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All papers in Functional maturity

Functional developmental states of the Greater Caribbean coral reefs
by Horacio España

2024, Ecological Indicators

Trophic networks are the way by which energy is distributed through the ecosystem and out of it. Each ecosystem at a particular functional developmental state (FDS) has attributes that can be measured and understood through the food web.... more
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Functional developmental states of the Greater Caribbean coral reefs
by Fabian Alejandro Rodriguez Zaragoza

2022, Ecological Indicators

Trophic networks are the way by which energy is distributed through the ecosystem and out of it. Each ecosystem at a particular functional developmental state (FDS) has attributes that can be measured and understood through the food web.... more
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Evaluation of the Use of Autonomous Reef Monitoring Structures (ARMS) for Describing the Species Diversity of Two Coral Reefs in the Yucatan Peninsula, Mexico
by Xochitl Vital

2022

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
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Functional developmental states of the Greater Caribbean coral reefs
by Jimmy Arguelles Jimenez and 
3 more
Jesus Ernesto Arias González
Roberto C Hernández-Landa
R. Ulanowicz

2021, Ecological Indicators

Trophic networks are the way by which energy is distributed through the ecosystem and out of it. Each ecosystem at a particular functional developmental state (FDS) has attributes that can be measured and understood through the food web.... more
DA: developmental attribute, Co: contribution (%). Cc: closeness centrality, SS¢ functional diversity, WDDW: weighted degree, NPP: net primary production, O: overhead, DC: developmen capacity, A: ascendency, A/DC: ascendency/development capacity, O/DC: overhead/development capacity. SIMPER with a 95% similarity contribution of the ecosystem developmental attributes for the eight functional developmental states. Table 3
Fig. 3. Attributes of eight functional developmental states identified in the Greater Caribbean coral reefs. A: ascendency, O: overhead, DC: development capacity, NPP: net primary pro- duction, A/DC: ascendency/development capacity, O/DC: overhead/development capacity, SS¢ functional diversity, Cc: closeness centrality, WDDW: weighted degree.
Fig. 4. Partial RDA between topological indices, average trophic levels and functional developmental states of the Greater Caribbean coral reefs (R? adjusted = 0.49; first axis: F-ra- tio = 910.541, p = 0.0001; second axis: F-ratio = 130.58, p = 0.0001; total variance explained by the first two axes 100%). Dark blue arrows indicate topological indices (Degree cen- trality: Dc, Betweenness centrality: Bc, Closeness centrality: Cc, and Weighted degree: DW); sky blue arrows indicate the average trophic level for each module per reef (LTL: low trophic level, ITL: intermediate trophic level, HTL; high trophic level); the code of the reefs corresponds to those in Fig. 1; arrows in gray denote the eight functional developmental states from the less mature (Dev 1) to the more mature (Dev 8).
Abbreviations: A, Ascendency; A/DC, ascendency/development capacity; ANOSIM, Analysis of similarities; Bc, betweenness centrality; Cc, closeness centrality; Dc, development capacity; Dc, degree centrality; DW, weighted degree; FDS, functional developmental states; EwE, Ecopath with Ecosim; NMDS, non-metric multidimensional scaling; NPP, net primary production; O, overhead; O/DC, verhead/development capacity; RDA, redundancy analysis; S,, functional diversity; SIMPER, percentage similarity analysis. * Corresponding authors. E-mail addresses: cayix24@gmail.com (J. Argiielles-Jiménez); fabianrz@cucba.udg.mx (F.A. Rodriguez-Zaragoza); cggandara@hotmail.com (C. Gonzadlez-Gandara); chr.alva@yahoo. com.mx (J.C. Alva-Basurto); earias@cinvestav.mx (J.E. Arias-Gonzalez); rhlanda73@hotmail.com (R. Hernandez-Landa); alfaguilar@gmail.com (A. Aguilar-Perera); yuriokolodkov@ yahoo.com (Y.B. Okolodkov); anisgtz@gmail.com (A. Lilia Gutiérrez-Velazquez); mzetina@ipn.mx (M.J. Zetina-Rején); ulan@umces.edu (R. Ulanowicz); hespana@gmail.com (H. Pérez-Espania)
Ecosystem developmental attributes used to assess functional developmental states of the Greater Caribbean coral reefs. References: Odum (1969) !, Ulanowicz (1986) 2, Wasserman and Faust (1994) 3, Christensen et al. (2008) 4, Argiielles-Jiménez et al. (2020) °.
Fig. 1. Coral reefs studied in the Greater Caribbean. Gulf of Mexico: 1) East Flower Garden Bank (EB), 2) West Flower Garden Bank (WB), 3) Stetson Bank (SB), 4) Blanquilla (Bla), 5 Medio (Med), 6) Lobos (Lob), 7) Tanhuijo (Tah), 8) Enmedio (Enm), 9) Tuxpan (Tux), 10) Palo Seco (PS), 11) Alacranes Reef (Ala); Caribbean Sea: 12) Punta Nizuc (PN), 13) Puert Morelos (PMo), 14) Punta Maroma (PMa), 15) Akumal (Aku), 16) Boca Paila (BP), 17) Yuyum (Yy), 18) Punta Allen (PA), 19) Punta Herrero (PH), 20) Tampalam (Ta), 21) El Placer (EP) 22) Mahahual (Ma), 23) Xahuayxol (Xa), 24) Xcalak (Xc), 25) Cayo Cochinos (CCo), 26) Media Luna (ML), 27) Cayos Miskitos (CMi), and 28) Virgin Islands (VI). See Appendix A fo description of coral reefs. Coral reefs have been studied from different points of view and ap- proaches (e.g. community ensembles, beta diversity, ecosystem func- tioning, effects of climate changes) (Arias-Gonzdalez et al., 2008, 2011; Alba-Basurto and Arias-Gonzalez, 2014; Caceres et al., 2016; Rodriguez-Zaragoza and Arias-Gonzalez, 2015), yet there are still significant gaps in the knowledge and understanding of their functional maturity. Theoretical knowledge to date suggests that ecosystems, as do living beings, go through processes of growth and development increas- ing their maturity (Odum, 1969; Ulanowicz, 1986). These processes can be referred to as functional development states (FDS), and is de- fined here as “discrete states of ecosystem growth that differ in geomor- phological and trophic complexity” and that may or may not be related to the geological age when large adverse effects of natural origin, an- thropogenic or both occur, leading to a reduction in maturity (Chris- tensen and Walters, 2004), i.e. theoretically it originates a functional reversion state. To date, the assignment of a coral reef to a particular FDS can be carried out through comparisons considering the magnitudes of nine developmental attributes (Argtielles-Jiménez et al., 2020); these could be increased with new studies considering new parameters (e.g. physicochemical) based on the theory propose by Odum (1969). The determination of FDS can help in formulating management plans, so that more rational use of resources can be made in coral reef ecosys- tems with different degrees of maturity; e.g. in immature ecosystems it is important to protect intermediate trophic levels, while in mature systems efforts should be focused on preventive actions for species of higher trophic levels (Argiielles-Jiménez et al., 2020).
One-way comparisons between the functional developmental states identified (through NMDS) considering nine ecosystem developmental attributes (A, O, DC, NPP, A/DC, O/DC, Sg Cc and DW). The key Dev represents each developmental stage from 1 to 8. The composition of the groups is shown in Fig. 9. n = number of reefs per group. R values are below the diagonal; adjuste p-values are above.
Modularity is the degree to which the trophic network is struc- tured into subgroups (web compartments), where within each subgroup has intense trophic interactions (Stouffer and Bascompte, 2011; Zetina-Rejon et al., 2015). The modularity of each reef was quan- tified utilizing the hierarchical agglomeration algorithm “fast-greedy" finding com- Ecosystem developmental attributes used (A, O, DC, NPP, A/DC, O/ DC, S, Cc and DW) through the NMDS helped us to distinguish eight groups of FDS for the Greater Caribbean coral reefs, of which five are in the Caribbean Sea (Dev 1, Dev2, Dev 3, Dev 5 and Dev 7) and three in the Gulf of Mexico (Dev 4, Dev, 6 and Dev 8) (Fig. 2). The results of ANOSIM (Table 2) indicated that the developmental states identified can be differentiated.
DA = developmental attributes; Dev = functional developmental states of the Greater Caribbean coral reefs. Indo-Pacific Ocean: BRF, = Bolinao reef flat (Alifio et al., 1990). Pacifi Ocean: O’ahu2, Konaz and French Frigate Shoals (FFS2) from Hawaiian archipelago coral reefs (Weijerman et al., 2013); northwestern Mexican Pacific: Sanctuary of Islands and Islet of Bahia Chamela (SIIBC3), Islas Marietas National Park (IMNP3). and Isla Isabel National Park (IINP4) from Mexican archipelago coral reefs (Hermosillo-Ntifiez et al., 2018). Comparisons of the average and standard deviation (+) of the ecosystem developmental attributes of the Greater Caribbean with studies in the Indo-Pacific and Pacific coral reefs.
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