Seaweed research has witnessed substantial traction in recent years, stimulating interest among researchers, academic bodies, industry players, policymakers, and philanthropic bodies to explore its potential applications for commercialization. Despite the growing body of literature, there is a paucity in investigating the quantitative trend in scientific research trajectory of commercialization of seaweeds and understanding the interconnectivity among the barriers hindering the large-scale commercialization of seaweed.
Methods
For addressing this gap, the current study has conducted a dual approach, integrating comprehensive bibliometric analysis (2000-2025) with Fuzzy Interpretive Structural Modelling (ISM) MICMAC methodology, using VOSViewer, Biblioshiny and RStudio to examine the global seaweed commercialization research. 331 documents were extracted from Scopus (n=230) and Web of Science (n=101) database which summed up to be 258 documents after removing 73 duplicates.
Results
The results reveal a sharp increase in research output in 2025 (n=67) projecting greater scholarly interest in commercialization of seaweed, as a pathway to enhancing marine sustainability outcomes. Further, after identifying conceptual patterns and thematic trends related to seaweed commercialization and blue tourism governance under bibliometric analysis, the Fuzzy ISM-MICMAC analysis was conducted through a combination of 23 academic and industry expert interviews and extensive literature review. Consequently, 7 validated key barriers were identified that are restricting the commercialization of seaweed and water hyacinth across food, biofuel, pharmaceutical, and fertilizer sectors. These key barriers are further segregated into four-levels and four-quadrants based on their dependence and driving power.
Discussion
The novelty of the study lies in it being the first to apply both bibliometric and Fuzzy ISM-MICAMAC analysis on seaweed commercialization, suggesting a potential sequential policy framework to address the barriers hindering commercialization and emphasizing the need for sustained knowledge exchange in this promising domain.
barriersbibliometriccommercializationfuzzy interpretive structural modellingmarine sustainabilityMICMACseaweedThe author(s) declared that financial support was received for this work and/or its publication. The research was fully supported by the PubArt program of the National University of Science and Technology POLITEHNICA Bucharest. The research work is part of Project titled” Mapping the Impact of Blue Carbon Stocks of Seagrass Meadows and Weeds on Economic Prosperity and Livelihood Generation of Chilika Lagoon” funded by Indian Council of Social Science Research. F.No. 10/VVB@2047/2024-3773/ES- B/SCD.section-at-acceptanceMarine Fisheries, Aquaculture and Living ResourcesIntroduction
Seaweeds play a pivotal role in coastal ecosystems and contribute significantly towards marine productivity; nevertheless, their unchecked growth can disturb ecological harmony (Gomez-Zavaglia et al., 2019). Variation in thermal anomalies and marine chemistry resulting from intensified greenhouse effect has induced large-scale shift in biological system of seaweeds. The reproductive, survival, and growth patterns of seaweeds are influenced by climate sensitive environmental variables (Harley et al., 2012), including temperature, salinity, wave heights, pH, water run-off, and carbon-dioxide concentration. When seaweed proliferates excessively, it often competes with native seagrass species for critical resources like sunlight, space, and nutrients. This may result in the deterioration or extinction of seagrass meadows, which are vital for sustaining marine biodiversity and offer critical services including coastal protection, water purification, and carbon sequestration. To maintain the stability and health of these aquatic ecosystems and foster long-term ecological resilience, regulating seaweed overgrowth through sustainable harvesting and repurposing for beneficial applications such as organic fertilizer (Artem et al., 2021), animal feed supplement or livestock diet (Makkar et al., 2016), biofuel production (Soares Dias et al., 2023), and even as a source of bioplastics (Torrejon et al., 2025) and pharmaceutical compounds (Cotas et al., 2024) presents a feasible solution. This method not only reduces environmental strain on native seagrass meadows but also helps to sustainably use resources and fight climate change by including seaweed biomass into circular bioeconomy models.
However, despite the promising potential of seaweed in circular bioeconomy models, several critical barriers continue to hinder its large-scale commercialization such as heavy metal contamination (Filippini et al., 2021), biosorption properties (Sarangi et al., 2025), invasion of water hyacinth in coastal and estuarine areas (Sharma et al., 2016), regulatory frameworks for seaweed-based products (Prasad Behera et al., 2022), creating market entry hurdles (Kotowicz et al., 2024), high production costs stemming from the need for infrastructure (Kite-Powell et al., 2022), skilled labour, and post-harvest processing coupled with limited consumer awareness and acceptance, weak supply chain logistics (Mulyati and Geldermann, 2017)and seasonal variability in yield lead to inconsistent supply, which challenges industrial scalability. Addressing these multifaceted challenges is essential to unlock the full potential of seaweed as a sustainable resource in climate mitigation and ecosystem management strategies (Ansah and Oduro, 2025).
Due to the intersection of ecological significance with multifaceted applications, research on seaweed has fetched global attention leading to voluminous research work on diverse spectrum of seaweed such as marine ecology and biology (Irlandi et al., 2004; Menaa et al., 2020; Stephens et al., 2017; Wernberg et al., 2011), environmental science and coastal management (Chew et al., 2021; Luo et al., 2023), biotechnology and bioengineering (Han et al., 2016; Makkar et al., 2016; Peter et al., 2021; Rajauria et al., 2016), agriculture and aquaculture (Chami and Galli, 2020; Kopittke et al., 2019; Meng et al., 2022; Pei et al., 2024), food and nutritional sciences (Lange et al., 2015; Meng et al., 2022a; Nanri et al., 2017), pharmacology and medicinal chemistry (El-Beltagi et al., 2022; El-Sheekh et al., 2023; Gomez-Zavaglia et al., 2019), and climate science and carbon dynamics (Harley et al., 2012; Prasad Behera et al., 2022). However, in comparison to the extensive scientific literature, relatively limited research has focused on the commercialization of seaweeds and the potential barriers hindering their market integration.
Although voluminous research work has focused on analyzing the (i) quantitative trend of growing scholarly work on commercialization of seaweed through bibliometric analysis (Balestracci et al., 2025; Wang et al., 2024) and (ii) qualitative analysis through recognizing the variable impacts across contexts by systematic literature review (Ansah and Oduro, 2025; Kumar et al., 2024), but barely any study has integrated these strands to develop a comprehensive analytical framework for assessing the sustainability and economic viability of seaweed-based value chains. Further, there is a dearth of literature in analyzing the interconnectivity between the barriers in commercialization of seaweeds. Hence, the current research work contributes towards a comprehensive and up-to-date bibliometric review on commercialization of seaweeds for economic and livelihood opportunities. Followed by exploring the quantitative research trend, this study seeks to find out the barriers in commercializing blue carbon stock for livelihood generation, and how these key barriers are interrelated to each other by using ISM-MICMAC analysis. Upon addressing the gaps in the existing literature, the current research work aims at:
RO1: Analyzing the research landscape on the commercialization of seaweeds for economic and livelihood opportunities.
RO2: Identifying key trends, themes, and gaps in the scientific literature related to the blue economy through the use of a keyword theme map, and author collaboration.
RO3: To map the global research network on blue economy initiatives using bibliometric tools, and collaborations to identify dry research issues and future study paths.
RO4: Ranking the key barriers in commercializing blue carbon stock for livelihood generation.
RO5: Constructing a structural model based on Interpretive Structural Modeling (ISM) and MICMAC Approach to identify and understand the key barriers.
RO6: Investigating the pair-wise interactions between each barrier and categorizing the detected barriers as dependent, linking, autonomous, and driving elements based on their dependence and driving capabilities.
This paper is organized as follows: Section 2 initially provides necessary literature review on key terms and critical barriers paving seaweed commercialization. Section 3 then introduces to the methodology of the study followed by section 4 which highlights results of bibliometric analysis and Section 5 that constructs a structural model based on Fuzzy Interpretive Structural Modelling (ISM) and MICMAC analysis. Section 6 highlights the Conclusion. Finally, Section 7 highlights the limitations of the analysis followed by section 8 that elucidates future research directions followed by recommended policy implications followed in Section 9.
Literature reviewSeaweed
Seaweeds, or macroalgae, refer to numerous species of multicellular photosynthetic organisms that can be classified into three major groups: brown algae (Heterokontophyta), red algae (Rhodophyta), and green algae (Chlorophyta) (Guiry and Guiry, 2026). Some of the seaweeds are acquainted to grow in the coastal location, whereas the other seaweeds are adapted to grow in wave-exposed intertidal areas (Wernberg et al., 2011). However, irrespective of their location, seaweeds play a significant role in marine community and hence, are widely called as ecosystem engineers (Chao Rodriguez et al., 2017). However, due to rampant industrialization resulting in climate change, the excessive growth of these invasive algae has turned to be widespread. Recent studies indicate that environmental stressors such as coastal and marine pollution increasingly affect seaweed cultivation and commercialization (Ansah and Oduro, 2025; Filippini et al., 2021; Han et al., 2016). The presence of emerging contaminants, including microplastics, raises concerns regarding biomass quality, food safety, and regulatory compliance, particularly in markets governed by stringent environmental standards (Ansah and Oduro, 2025; Meng et al., 2022). In addition, anthropogenic pressures on coastal ecosystems may disrupt cultivation operations and undermine supply chain reliability, thereby influencing market acceptance and scalability (El-Sheekh et al., 2023; Filippini et al., 2021). Although, this is not the only reason behind increased proliferation of macroalgae, rather other reasons related to variation in thermal anomalies and marine chemistry has played a significant role. Consequently, the overgrowing seaweed invasion (ranging from 10-40% of marine ecosystem) is hence threatening the marine biodiversity in terms of paving the growth of native species and altering habitat structures (Gomez-Zavaglia et al., 2019). These marine microalgae compete with seagrass for light (Irlandi et al., 2004) and nutrients (Dumay et al., 2002) and complete their nutrition necessary for their growth primarily through photosynthesis. Due to unnoticeable ecological disruption caused during farming operation and the direct benefits the community obtain from the excessive seaweed growth, create a need to understand the ecological effect of seaweed farming on the seagrass and its associated macrofauna community (Lyimo, 2024). This can be evidenced from the work of (Eklöf et al., 2005) and (Lyimo et al., 2008) who reported a significant decline in fish catch on the coasts and increased pressure on the seagrass ecosystem since most seaweed plots are mixed with short seagrasses, like T. hemprichii, which are affecting seagrass functions to ecosystems and, ultimately, causing loss of biodiversity associated with seagrass (Lyimo, 2024). These macroalgae not just pose a threat to marine biodiversity by replacing native species, damaging habitat structure, homogenization of the landscape, but also creates an obstruction in recreational and industrial sector related to aquatic lives. Nevertheless, instead of checking the widespread proliferation of seaweed, measures are taken by aqua scientists to channelize this threat to economic opportunity by generating possible business and application via exploring potential commercial applications and value-added products imbued from seaweeds.
Commercialization and application of seaweeds
The global diversity of algae, including both microalgae and macroalgae, is estimated to comprise over 164, 000 species, of which approximately 9, 800 are classified as seaweeds (El-Beltagi et al., 2022). However, only about 0.17% of these seaweed species have been domesticated for commercial use (Arias-Echeverri et al., 2022). Due to the variety of bioactive compounds in their composition, species of phylum Ochrophyta, class Phaeophyceae, phylum Rhodophyta and Chlorophyta are valuable for the food, cosmetic, pharmaceutical and nutraceutical industries (El-Beltagi et al., 2022). In the current research work, commercialization is conceptualized as a holistic and multi-stage process rather than a narrow act of marketing or sale, aligning with contemporary bioeconomy and value-chain literature. Specifically, it refers to the systematic transformation of seaweed and related aquatic biomass often arising from overgrowth, waste streams, or underutilized resources into value-added products and sustainable livelihood opportunities. This process encompasses biomass harvesting and sourcing, technological processing and extraction, product development and diversification, market entry, and scaling through robust value-chain integration. Importantly, commercialization is framed within a circular bioeconomy perspective, where cascading use of bio-mass, efficient resource utilization, and valorization of processing residues are emphasized to maximize economic value while minimizing environmental impacts. By integrating processing, market access, and scaling with circular value-chain linkages, commercialization enables both economic viability and social benefits, particularly through in-come generation, job creation, and livelihood diversification for coastal and rural communities, thereby linking re-source management, innovation, and sustainability outcomes in a single operational framework. The prevailing body of literature highlights the extensive utilization of seaweeds across multiple domains, which are elaborated upon in the subsequent subsection. The prevailing body of literature highlights the extensive utilization of seaweeds across multiple domains, which are elaborated upon in the subsequent subsection.
Biofuel applications:
It is estimated that demand of renewable fuel such as biofuel will grow exponentially to approximately 40% from 2010 to 2040 (Godvin Sharmila et al., 2021). However, as the fuel sources are briskly and erratically diminishing, scientists believe that seaweed can be used as viable and increasingly researched solution to meet rising biofuel demand. This macroalgae (green, brown, and red marine seaweed) biofuel is found to be extremely compostable and contains no sulfur (Mat Aron et al., 2020). Biofuels produced from seaweed are well-thought-out as 3rd generation offering benefits like fast growth, efficient CO2 absorption, and the ability to be cultivated in non-arable areas, making them a promising solution to the energy crisis (Peter et al., 2021). Biofuel produced from algal substances contain acyl glycerides and fatty acids which can be used for biodiesel and bioethanol production from the extracted oil, thereby replacing fossil fuels usage (Chew et al., 2021).
Food and fodder applications:
Based on the ever-growing population size, it is estimated that the population count will escalate to approximately 11 billion by 2100 which will inevitably drive a substantial surge in food demand. To ensure food security, seaweed can be used as an alternative solution by extending its usefulness as food source for both human being as well as livestock (Rogel-Castillo et al., 2023). This is a sustainable pathway of achieving economic development in advancing economies. The seaweed-based edible products have evidenced to be viable nutritious and sustainable food sources as they are already in use as ingredients for sushi wraps or in salads and soups (Rogel-Castillo et al., 2023). and in processed food such as bread, noodles, cake, cookies, biscuits (Quitral et al., 2022). The nutritional content of seaweed comprises of carbohydrates (dietary fiber), proteins rich in essential amino acids, lipids comprising essential fatty acids (including omega-3 and omega-6), along with a wide range of micronutrients such as vital minerals and vitamins which contribute to enhanced food quality due to their antioxidant and antimicrobial properties (Matos et al., 2024). Hence, based on its nutritional profile, seaweed has been classified as “novel food” according to Regulation (EU) 2015/2283 (Rogel-Castillo et al., 2023) The nutritional composition has been shown to offer a wide range of health benefits, including anti-inflammatory, anti-obesity, anticancer, antioxidant, and antibacterial effects (Quitral et al., 2022). Further, in recent times, livestock farmers are exploring potent strategies for enhancing animal growth and their productivity along with nutritional quality of animal-derived products. In this context, seaweed comprising of green (Chlorophyceae), red (Rhodophyceae), and brown algae (Phaeophyceae) has emerged as a novel, nutritious and safe feed additives as it specifically includes antibiotics, polysaccharides, proteins, antioxidants, acidifiers, vitamins, minerals, amino acids, lipids including polyunsaturated fatty acids, binders, enzymes which can promote the health and productivity of livestock along with providing high-quality animal products. Due to their beneficial properties, such as antibacterial, antifungal, antiviral, antioxidative, anti-inflammatory, etc., seaweeds can not only enhance animal production but also their health (Mahrose and Michalak, 2022).
Seaweed as pharmaceuticals
Seaweeds are considered to be traditional medicinal components that are essential source of nutritional and natural bioactive metabolites exerting several multifunctional pharmaceutical functions (El-Sheekh et al., 2023; Menaa et al., 2020). Given the present circumstances, when the incidence of dietary and lifestyle-related diseases such as Type-2-diabetes, Obesity, Arteriosclerosis, cancer, and cardiovascular complications has increased substantially, regular intake of seaweed as corresponds with significantly lesser incidence of diet-related diseases (Nanri et al., 2017). Besides consuming seaweed in regular dietary, seaweed is gaining importance in using it for pharmaceutical reasons due to its invaluable medical properties. Voluminous research work has evidenced the use of seaweed for medicinal purpose such as (Rosenfeld, 2000)has mentioned the use of seaweed in treatment or prevention of goitre, which is predominantly caused due to the shortage of iodine in the daily diet. Further, seaweed extends medical cure for several non-contagious diseases such as inflammation, colorectal cancer, gastrointestinal inflammation, and aids probiotics, cardiovascular complications, obesity, diabetes, hypertension and other adverse health condition (Rajauria et al., 2016). Additionally, a proof-of-principle clinical trial reported that supplementation with the edible brown seaweed wakame (Undaria pinnatifida) in postmenopausal women reduced urinary uPAR levels (a biomarker associated with unfavorable breast cancer prognosis), suggesting a potential role of Undaria pinnatifida consumption in modulating breast-cancer–related risk pathways (Teas et al., 2012). Combining seaweed extracts such as Fucus vesiculosus, Macrocystis pyrifera and Laminaria japonica along with the zinc, manganese and vitamin B6 can contribute to alleviating osteoarthritis symptoms in a mixed demographic (Myers et al., 2016). Besides its capability to minimize the incidence of chronic and non-communicable diseases, seaweeds can also be relied upon for its antioxidant capacities and bioactive polyphenolic compounds (Stephens et al., 2017) that play a significant role in HIV protection. As seaweed comprises of compounds such as phlorotannins, sulphated polysaccharides, diterpenes and lectins (Nagarajan and Mathaiyan, 2015), it can possibly contribute to preventing HIV transmission and acquisition. However, there are few clinical survey research works that have mentioned about the moderate impact of bioactive compounds present in seaweed on the human body which may last for a short span of time, nevertheless their continuous consumption can significantly contribute to disease prevention and cure (Wang et al., 2019).
Seaweeds as fertilizers
Due to growing industrialization and urbanization, the soil is gradually losing its nutritional supplements and is exposed to land degradation, soil erosion, and agrochemical pollution (Matin and Behera, 2020). This has resulted in loss of soil fertility which has reduced the agricultural land proportion by 10-12% that are now incapable of crop cultivation (Kopittke et al., 2019). In this context, agriculturalists and scientists are suggesting to shift from chemical and fertilizers to seaweed fertilizer, nevertheless organic fertilizers such as vermicompost, animal manure, green manure, humic acid can be used but are limited to comparatively moderate rate of effectiveness and are constrained by issues of limited availability (Han et al., 2016). Hence, overcoming these drawbacks, seaweed fertilizer is gaining substantial reputation as a natural soil conditioner, improving soil structure and water-holding capacity which are considered crucial for enhancing plant growth (Chami and Galli, 2020). Seaweed fertilizers are prepared from extracts of seaweeds that are evidenced to be beneficial for plant growth because (i) they provide multi rich macro elements such as Nitrogen, potassium, calcium, and magnesium along with an array of essential mineral and rare elements such as zinc, bromine, and iodine that are intrinsic to marine organisms (Katakula et al., 2020; Meng et al., 2022), and (ii) it not just enhances the growth of plants in terms of developing roots, flowering and its vigor, but also strengthens the defence mechanism of the plants in extreme climatic conditions such as drought, salinity, and temperature fluctuations (Chen et al., 2023) with the help of plant growth regulators such as auxins, cytokinins, and gibberellins (Battacharyya et al., 2015). Although in comparison to other organic fertilizers, seaweed fertilizers are expensive, still it is the need of the hour considering sustainable growth of the economy. Moreover, their multiple benefits and high profitability have driven market growth, with projections indicating an increase from $1 billion in 2023 to $1.8 billion by 2030, underscoring the growing demand for these fertilizers (Pei et al., 2024).
Barriers in commercialization of Seaweeds
Limited literature has analyzed the barriers in commercialization of seaweed for livelihood generation. The current study has identified the seven prominent barriers that pave the commercialization of seaweeds for livelihood generation such as (i) Heavy Metal Contamination (HMC), (ii) Biosorption Challenges (BSC), (iii) Water Hyacinth Invasion (WHI), (iv) Regulatory Challenges (RC), (v) High Production Cost (HC), (vi) Market Acceptance Issues (MAC), and (vii) Supply Chain Constraints (SCC) which are explained in detail in Table 1.
References to key barriers based on prior literature.
Critical barriers
Meaning
Reference
Heavy Metal Contamination (HMC)
The existence of hazardous metals such as lead, mercury, and cadmium in marine ecosystems is referred to as heavy metal contamination (HMC). Contaminated sea weeds due to heavy metals will not be suitable for human consumption or industrial use. Further, the microplastic and human intervention leading to pollution and anthropogenic impact often co-occurs along with the HMC which can directly lead to seaweed commercialization. The elevated microplastics can curtail the formation of pharmaceuticals and cosmetics thereby reducing growth and polysaccharide content. Fields in HMC areas do not yield good crops making a negative impact on farming sector. Thus, it has a significant negative impact on quality, safety, and marketability, which hinders seaweed-based economic activity.
(Contreras-Porcia et al., 2023; Ribbe et al., 2021; Sarangi et al., 2025; Twigg et al., 2024)
Biosorption Challenges (BSC)
The term “biosorption challenges” (BSC) describes the restrictions on seaweeds’ capacity to absorb and extract contaminants or heavy metals from water. Seaweeds are naturally biosorptive, but effective commercial application of biosorption is challenging due to a number of factors, including species variability, variable environmental conditions, difficulty maintaining ideal biosorption conditions (such as pH, temperature, and salinity), and limited scalability. Further, due to the ever-growing underwater pollution, microplastic contamination has resulted in critical biosorption challenge that puts the safety and quality of the developed products for commercialization at risk. This challenge reduces the feasibility of using seaweeds in industrial waste water treatment.
(Costa et al., 2020; Sarangi et al., 2025; Sebök et al., 2022; Singh et al., 2023; Hashem et al., 2021)
Water Hyacinth Invasion (WHI)
The invasive aquatic plant Eichhornia crassipes, which competes with seaweeds for nutrients, light, and space, is known as “water hyacinth invasion” (WHI). Its dominance in water bodies disturbs various sectors like fishing, tourism and agriculture. The growth of water hyacinth throws off the equilibrium of the ecosystem as a whole. Its existence increases the cost of maintenance of water bodies thus disturbs economy generation. Further, the rampant human intervention results in anthropogenic pollution which comprises of the nutrients run-off from the crops or agricultural land to water, industrial drainage, sewage expulsion, thereby triggering water hyacinth invasion and ultimately challenging seaweed commercialization.
(Adunbi and Ololajulo, 2020; Hashem et al., n.d.; Koh et al., 2024; Majee et al., 2020)
Regulatory Challenges (RC)
The term “Regulatory Challenges” (RC) describes the absence of well-defined regulations, uniform standards, and enabling legal frameworks that control the production, harvesting, and sale of seaweed. These obstacles impede investment, industry expansion, and sustainable resource management. Lack of proper regulations for cultivation delays or obstructs investment. Lack of regulatory frameworks in conducting safety tests is also primary reason making regulatory challenges a barrier in commercialization of sea weed products.
(Bhat et al., 2024; Bhushan et al., 2024; Kotowicz et al., 2024; Mantri et al., 2024)
High Production Cost (HC)
The term “high production cost” (HC) describes the high costs associated with growing, processing, and developing new products from seaweed because of manpower, infrastructure, technology, and post-harvest management. The cost of processing these products increases due to the additional operational and compliance expenditure resulting from filtering and cleaning the anthropogenic wastes such as nutrients run-off from the crops or agricultural land, industrial drainage, sewage expulsion proliferating to freshwater and estuarine systems. These expenses limit the scalability of seaweed-based businesses and lower profit margins. Creating products from sea weeds such as cosmetics, biofuels, fibre, aqua food compost requires controlled environment, chemical treatments for drying and extraction which increases the cost of production and makes it a barrier for economy generation.
(Holdt and Edwards, 2014; Pallaoro et al., 2016; Wu et al., 2022)
Market Acceptance Issues (MAC)
The term “market acceptance issues” (MAC) describes the lack of consumer knowledge, poor demand, and cultural or legal reluctance surrounding seaweed-based products. These problems deter investment in seaweed-driven businesses and impede market expansion. Market acceptance issue comprises of various elements like awareness, brand quality and advertisement, business model sustainability. Even if products are produced due to lack of reachability and acceptance it may fail in the consumer market.
(Jönsson et al., 2020; Knowler et al., 2020; Tran et al., 2020)
Supply Chain Constraints (SCC)
The logistical and infrastructure difficulties associated with harvesting, processing, storing, and transporting seaweed products are referred to as supply chain constraints (SCC). These include disjointed value chains, insufficient cold storage, and poor transportation, all of which have an impact on productivity and market penetration. Anthropogenic pollution and other microplastics hampers the supply chain process by disrupting the reliability of production and distribution systems. Anthropogenic wastes such as nutrients run-off from the crops or agricultural land, industrial drainage, sewage expulsion proliferating to freshwater and estuarine systems obstructs access routes to coastal cultivation sites, delays harvesting operations, and damages farming infrastructure, resulting in irregular and reduced seaweed output. A lack of co-ordination between farmers, processors, logistic providers, marketers and retailers leads to constraints in handling bulk orders, timely production. Thus, making it a barrier in economy generation.
(Ayustaningwarno et al., 2024; Gegg and Wells, 2019; Santibañez-Aguilar et al., 2013)
Source: Compiled by Authors.
Materials and methods
There are majorly three methods used to examine the existing literature such as meta-analysis, bibliometric analysis, and systematic review. However, in the current analysis, the bibliometric analysis is considered to review voluminous existing literature in order to evaluate components such as keywords, publication count, and citation count along with to track recent developments in the concerned subject in various directions. Bibliometric analysis is a quantitative way to keeping track of enormous amounts of published data that may also be used to detect and identify research and collaboration patterns among academics on a specific topic (Dulla et al., 2021). Although both the bibliometric approach and meta-analysis are quantitative methods of analysing current literature, but bibliometric analysis is preferable in this case since the data under consideration is not uniform.
After compiling all the information through bibliometric analysis, the study has adopted the use of experts’ interviews for Fuzzy ISM MICAMAC analysis by establishing the interrelationship among the seven key barriers. Experts were selected through snowball sampling, based on their extensive experience, knowledge, and involvement in seaweed and coastal management research work. To ensure a comprehensive range of insights, we included a diverse mix of academic researchers, industry professionals, policymakers, and technical experts. Semi-structured interviews were conducted to provide flexibility and facilitate in-depth discussions. Each interview commenced with rapport-building, including a brief introduction and an explanation of the interview’s purpose. We emphasized active listening, asked follow-up questions to explore key points in greater detail, and used probing questions to clarify and expand on initial responses. Note-taking and recording were employed to ensure accurate transcription and analysis. Experts were questioned about key barriers hampering seaweed commercialization for livelihood generation and were asked to rank these barriers based on their influence and interdependence. The detailed flow of the ISM MICMAC analysis is projected in Figure 1. Additionally, we explored the interrelationships among the nine key barriers identified in the literature review and analyzed the pairwise relationships among these factors based on expert feedback. Details of these analysis and steps incorporated under them is explained under section 5.
Flow chart of Fuzzy ISM-MICMAC Analysis.
Flowchart illustrating the FUZZY ISM MICMAC methodology, detailing data collection from semi-structured interviews, AFSSIM factorization, reachability matrix, FRM and DFRM processes, level partitioning, conical matrix, diagraph, and FUZZY MICMAC analysis, ending with the ISM model. Reference to specific tables and figures corresponds to analysis stages and results.Data collection
In order to perform bibliometric analysis, the data is obtained from two popularly used databases i.e., Scopus and Web of Science (WoS). Both the databases are known for its widely coverages of research articles in any domain worldwide. Scopus is a wide-ranging citation database with approximately 34, 346 peer-reviewed influential core, interdisciplinary, and multidisciplinary journals and Web of Science (WoS) are the world’s oldest, most widely used, and authoritative database of research publications and citations covering around 34, 000 peer-reviewed journals (Priyadarshini and Dulla, 2022). Additionally, being the oldest, most popular, and reliable database for highly cited research and review articles in the world, Web of Science covers almost 155 million records comprising of peer-reviewed journals, books, proceedings, and 70 million patents (Dash et al., 2024). Based on this note, data search was initiated on November 12, 2025. The choice to use the WoS and Scopus databases for comparison research is grounded in three fundamental considerations. These databases are well acknowledged and often used in bibliometric research (Mongeon and Paul-Hus, 2016). This analysis mitigates any biases associated with using a single database by utilizing both WoS and Scopus (Kraus et al., 2022). This dual-database methodology not only fortifies the analysis’s robustness but also facilitates the identification of similarities across WoS and Scopus, so augmenting the trustworthiness of the conclusions.
Data screening
In the process of data collection, along with seaweed “water hyacinth” is also considered as it is understood that it shares comparable commercialization challenges with seaweed, including high biomass availability, harvesting and logistics barriers, processing inefficiencies, environmental management issues, and limited market integration. Water hyacinths are incredibly prevalent, especially in reservoirs, ponds, lakes, and fish farm ponds. The utilization of water hyacinths is quite broad in scope, but research is still urgently required to prevent the spread of water hyacinths and turn them into valuable goods (Nandiyanto et al., 2023). Although water hyacinth and seaweed are biologically distinct, they are analyzed together due to their shared relevance as sustainable aquatic biomass resources with overlapping commercialization pathways and technological applications.
Figure 1 represents the step-by-step procedure ranging from selection of search string to screening, then checking eligibility of the documents and finally inclusion of the documents from Scopus and Web of Science database. In the first step, by using keywords and Boolean operators as “seaweeds” OR “seaweed” OR “water hyacinth” OR “water hyacinth invasion” AND “food” OR “biofuels” OR “pharmaceuticals” OR “fertilizers” OR “commercialization” AND “barriers” OR “constraints” OR “obstacles”, 302 documents were yielded from Scopus and 104 documents were extracted from WOS database. In the second step, restricting the document type to Articles (N = 64) and Review Articles (N = 37) with English language only, 101 documents were filtered out under WoS database. Similarly, under Scopus database, limiting the document type to Articles (N = 161) and Reviews (N = 73), a total of 232 journal source documents were identified (Figure 2). Finally, by restricting the language to “English, “ a total of 230 documents were retrieved that includes 3 articles in press (early indexed in 2026) and 227 final publications. In the third step, documents were checked exclusively for publication phase covering the period from 2000 to 2025 (3 documents as article in press for 2026 as early indexing). This particular time period is taken into consideration as the period captures the transition of seaweed research from early commercialization-oriented studies in the post-2000 biotechnology phase to its acceleration under the blue economy and sustainability agendas, followed by recent consolidation and diversification across food, biofuel, pharmaceutical, and environmental applications (Bixler and Porse, 2011; Buschmann et al., 2017). Further, the 2026 data included correspond to early indexed publications and preprints made available ahead of print within this calendar year, reflecting the current status in the database rather than completed full-year data. In fourth step, the source files obtained from both the WoS and Scopus databases are merged and it is found that there are 73 duplicates. After the removal of all the duplicates the final 258 files are retrieved and processed for interpretation using R Studio. Only peer-reviewed empirical research papers were included, ensuring quality control and reducing the risk of errors while maintaining the reliability of published work.
Methodological framework for bibliometric analysis.
Flowchart illustrating a systematic review process: identification starts with Scopus (three hundred two) and Web of Science (one hundred four) databases, applies filters for articles, reviews, journals, and English, then limits to publications from two thousand to two thousand twenty-five, yielding two hundred fifty-eight records after removing seventy-three duplicates.Data interpretation
After data extraction and screening, the exported records from both Scopus and WoS database were merged for conducting bibliometric analysis of 1590 documents for investigating on (i) Growth trend analysis, and (ii) Co-occurrence of Keywords, (iii) Co-occurrence of text-data, (iv) Co-occurrence of subject categories. A linear graph showing the growth trend of publications and citations is created using Microsoft Excel. These tools carry the potential to compile bibliographic data from the WoS and Scopus for scholarly dissemination system. Science Mapping provides a clear picture to the relationship between different components through three techniques: (i) collaboration analysis, (ii) co-citation analysis, and (iii) Keyword occurrence analysis. Collaboration analysis will provide information about to what extent researchers have collaborated at national and international level to gather, analyze, and interpret information to make data-driven decisions on a concerned subject. Co-citation analysis is used to detect the subject similarity between two documents by identifying which document has cited other pair of documents. The current analysis makes use of the three software: Biblioshiny (web-based graphical interface of the bibliometrix R-package) for performing the statistical analysis of the mapping, descriptive analysis, network visualization of subject categories, and identification of prominent journals using h-index, g-index, and m-index (Aria and Cuccurullo, 2017) VOSviewer for conducting co-occurrence analysis of author keywords and for generating keyword network visualizations (van Eck and Waltman, 2009), and The Smart Fuzzy ISM tool for conducting Fuzzy ISM-MICMAC analysis (Ahmad, 2024). However, in few sections the results generated from two of the software are compared and hence the most readable map was considered for a comprehensive presentation.
Results and discussionsGrowth trend analysis
Highlighting the annual pattern and trend in Publication Count (PC) and Total Citations (TC) from 2000 to 2025, the graph illustrates the year-wise research output frequency and its impact through total citations related to commercialization of seaweeds for economic and livelihood opportunities (Figure 3). Although the Figure 3 also shows data points for 2026, this occurred because, during data extraction in November 2025, three articles dated 2026 were already early indexed and available online as “in press.” Consequently, these records were captured while extracting data up to the present time. As the dataset does not represent the complete publication year of 2026, the study period has been conservatively reported as up to 2025. Further, it is worth noting that in the tenure of 2000 to 2011 (n ≤5), PC remained relatively low and stable. The reason behind the sluggish growth in PC may be the lack of any compelling necessity of conducting the research on seaweeds because either seaweeds have not posed significant issues or masses have remained unaware about the potential commercialization of seaweeds for economic and livelihood opportunities. However, a gradual upsurge in the PC can be marked from 2019, followed by a consistent rise from 2010 to 2025. However, a sudden change in PC from 2025 can be presumed as temporary anomaly or external influences such as policy changes or funding shifts. Further, it can be marked that there is an inverse association between PC per year and TC explaining the chances of shifting citation behavior of the recent articles that prefer citing recent publications over foundational work.
Growth trend analysis.
Bar and line chart showing PC (red bars, left axis) and TC (blue circles with black line, right axis) from 2000 to 2026. Both metrics are low until 2011, with a rapid increase in subsequent years. PC values rise significantly after 2016, peaking in 2025, while TC values fluctuate with notable spikes in 2012, 2014, and 2021 before dropping sharply after 2024.Prominent sources
The Table 2 encapsulates the data regarding top prominent sources along with their quality indicators such as h-index, g-index, m-index, total citations, number of publications and journal quartiles. Considering the highest count of citations of 459, a reputed journal entitled ‘Frontiers in Marine Science’ has been recognized for publishing only three research articles. In context of h-index, journal naming ‘Food Hydrocolloids’ has gained maximum h-index of eight, thereby referring to publish h number of articles, cited h number of times. This marks the productivity as well as the impact created by the articles published in the journal (Dash et al., 2024). Additionally, in order to map the overall citation performance, g-index of the journals are also assessed (Dulla and Priyadarshini, 2021). Journal naming ‘International Journal of Biological Macromolecules’ has gained maximum g-index of 12 representing highest number of influential paper or blockbuster publication (Priyadarshini and Dulla, 2022). Further, in order to compare and fairly assess younger journals against older ones, m-index is taken into consideration. Interestingly, the same journal naming ‘International Journal of Biological Macromolecules’ has gained maximum m-index of 1.2 representing highest rate of productivity and impact created in relation to its year of first publication. Based on these indexing, the journals are further segregated into quartiles ranging from Q1 to Q4 showcasing relative ranking and impact within the field.
Prominent sources.
Journal
h-index
g-index
m-index
TC
NP
Quartile
FOOD HYDROCOLLOIDS
8
10
0.889
459
10
Q1
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
6
12
1.2
152
12
Q1
JOURNAL OF APPLIED PHYCOLOGY
6
7
0.316
359
7
Q2
AQUACULTURE
5
6
0.278
156
6
Q1
FOODS
5
7
0.714
150
7
Q1
FRONTIERS IN MARINE SCIENCE
3
3
0.5
478
3
Q1
JOURNAL OF CLEANER PRODUCTION
3
3
0.273
315
3
Q1
MARINE DRUGS
3
5
0.25
241
5
Q1
PHYCOLOGY
3
5
0.5
88
5
Q1
ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS
2
3
0.333
73
3
Q1
TC, Total citations; NP, Number of Publications.
Co-occurrences of authors’ keyword
The current study presents the network visualization for co-occurrence of keywords which is framed through bibliometric analysis (VOSViewer software) that has evaluated 943 keywords. Using fractional counting method, a threshold limit of minimum 5 occurrences was set, resulting which 26 met the threshold out of 943 keywords. For these 26 keywords, the total strength of co-occurrence link with other keywords was calculated. The keywords with greatest total link strength are selected for network visualization. Further, these 26 items were divided into 5 clusters: Cluster 1 (Red) with six items naming biofuel production (TLS: 13) biorefinery (TLS: 26), food (TLS: 30), Microalgae (TLS: 18), saccharina-latissima (TLS: 17), seaweeds (TLS: 62), Cluster 2 (Green) with six items naming anaerobic-digestion (TLS: 11) biomass (TLS: 23), circular economy (TLS: 16), Macroalgae (TLS: 33), extraction (TLS: 23), optimization (TLS: 8), Cluster 3 (Blue) with 5 items naming biofuel (TLS: 13) algae (TLS: 19), ecosystem services (TLS: 14), seasonal variation (TLS: 14), seaweeds (TLS: 18), Cluster 4 (Yellow) with 5 items naming aquaculture (TLS: 20), barriers (TLS: 9), cultivation (TLS: 33), future (TLS: 19), sustainability (TLS: 10), and Cluster 5 (Purple) with 4 items naming brown seaweed (TLS: 13) growth (TLS: 8), seaweed extracts (TLS: 4) and water (TLS: 10). It is evidential that keyword “seaweed” is the highest occurred keyword with 31 occurrences and a total link strength of 25. Among these keywords, keywords like “aquaculture”, “extraction”, and “circular economy” are new keywords being used in the research recently published from 2023. The terms that were frequently used in the titles of several manuscripts are displayed in the Figure 4 through a network visualization map.
Network visualization of most frequent keywords.
Network diagram visualizing interconnected keywords related to seaweed research. Central node labeled “seaweed” connects to terms like aquaculture, sustainability, circular economy, cultivation, biofuel, food, and microalgae, with color-coded clusters and variable node sizes illustrating network relationships and topic prominence.Co-occurrence of text data for thematic mapping
The current analysis conducted the thematic evolution of seaweed commercialization through text analysis of titles and abstracts. To conduct the analysis, binary counting method is used with a minimum number of occurrences fixed to10 terms. Out of 3949 terms, only 33 terms met the threshold. Further, a relevant score is calculated and based on this score, the 60 percent of the most relevant terms which is summed up to be 20 in count is visualized in the network visualization figure projected in Figure 5. The 20 are segregated to 3 clusters: Cluster 1 (Red) with nine items naming: addition (occurrence: 14, relevance: 1.02), application (occurrence: 36, relevance: 0.79), attention (occurrence: 14, relevance: 0.49), food (occurrence: 43, relevance: 0.81), interest (occurrence: 15, relevance: 0.62), obstacle (occurrence: 23, relevance: 1.76), pharmaceutical (occurrence: 12, relevance: 0.67), plant (occurrence: 18, relevance: 1.45), seaweed (occurrence: 65, relevance: 1.17); Cluster 2 (Green) with six items naming analysis: constraint (occurrence: 37, relevance: 2.13), opportunity (occurrence: 18, relevance: 0.63), production (occurrence: 51, relevance: 1.02), seaweed cultivation (occurrence: 10, relevance: 0.54), and study (occurrence: 44, relevance: 0.91); Cluster 3 (Red) with five items naming: barrier (occurrence: 41, relevance: 1.42), benefit (occurrence: 21, relevance: 0.77), biofuel (occurrence: 13, relevance: 1.32), cost (occurrence: 26, relevance: 0.69) and paper (occurrence: 12, relevance: 0.80). Here occurrence refers to the frequency of the keyword and relevance can be understood as the intensity of the usefulness of the term for mapping conceptual structure. Out of these terms, highly occurred term is “study” with 44 occurrence and “constraint” is the highly relevant term with an intensity of 2.13 relevance.
Network visualization of text data for thematic mapping.
Network diagram illustrating keyword relationships about seaweed, with nodes such as “seaweed,” “production,” “food,” “barrier,” and “application” distinguished by color and size, and connected by multi-colored lines representing their associations.Co-occurrence network based on subject categories
Co-occurrence analysis of subject categories helps to identify how different research areas are interconnected. When two subject categories frequently appear together in the same publications, it indicates a strong thematic or methodological link between them. The network visualization projected in Figure 6 shows 16 subject categories which are further segregated into 4 clusters based on betweenness, closeness, and PageRank naming: Cluster 1 (Red) with five subjects naming: marine and fresh water biology (betweenness:43.354, closeness:0.043, PageRank:0.137), fisheries (betweenness:0, closeness:0.029, PageRank:0.075), plant sciences (betweenness:12, closeness:0.030, PageRank:0.049), oceanography (betweenness:0, closeness:0.029, PageRank:0.038), agronomy (betweenness:0, closeness:0.022, PageRank:0.026), Cluster 2 (green) with four subjects naming: biotechnology and applied microbiology (betweenness:15.3, closeness:0.036, PageRank:0.054), energy and fuels (betweenness:18.646, closeness:0.036, PageRank:0.074), engineering, chemical (betweenness:0, closeness:0.025, PageRank:0.025), agricultural engineering (betweenness:0, closeness:0.029, PageRank:0.040); Cluster 3 (blue) with five subjects naming: environmental sciences (betweenness:23.483, closeness:0.040, PageRank:0.119), green and sustainable science and technology (betweenness:13.217, closeness:0.037, PageRank:0.098), environmental studies (betweenness:12, closeness:0.032, PageRank:0.076), engineering, environmental (betweenness:0, closeness:0.030, PageRank:0.042), international relations (betweenness:0, closeness:0.023, PageRank:0.021); Cluster 4 (purple) with two subjects naming: chemistry, medicinal (betweenness:0, closeness:1, PageRank:0.062), pharmacology and pharmacy (betweenness:0, closeness:1, PageRank:0.062). In a co-occurrence network, subject categories are represented as nodes, and their co-appearance in the same documents forms links (edges). Centrality measures such as betweenness, closeness, and PageRank indicate the strategic importance of each subject category in the structure of the research field. Here betweenness can be understood as identifying the bridging categories that link distinct research clusters, closeness highlights categories occupying central positions within the network, and PageRank scores indicate the influence of each category based on its connectivity to other important domains.
Network visualization based on subject categories.
Network diagram visualizing research fields with labeled nodes such as marine and freshwater biology, environmental sciences, and environmental studies. Node size varies, and lines show interdisciplinary connections among fields.Interpretive structural modelling
Seaweeds offer immense potential for sustainable economic development through applications in food, bio-fuels, pharmaceuticals and environmental management (T. & M.a., 2023). But there are a number of significant obstacles that prevent this promise from being realized. These obstacles are varied and intricately linked. These difficulties are classified under a number of headings, such as market-driven, technical, regulatory, financial, and environmental limitations. The following seven major obstacles to seaweed-based economic production have been found in this study: Heavy Metal Contamination (HMC), Biosorption Challenges (BSC), Water Hyacinth Invasion (WHI), Regulatory Challenges (RC), High Production Cost (HC), Market Acceptance Issues (MAC), and Supply Chain Constraints (SCC). These factors serve as the main obstacles to the economic growth of seaweed use in the current study. To tackle these issues, a thorough grasp of their interdependencies and influences is necessary. In order to break down complicated systems into smaller, more manageable parts and create a structured, understandable model, Interpretive Structural Modeling (ISM) makes use of the expertise and real-world experience of specialists. This approach makes it possible to impose hierarchy and order among a system’s intricate components (Shanker and Barve, 2021). ISM’s strong analytical powers have led to its widespread adoption in a variety of fields, notably by industry leaders like the National Aeronautics and Space Administration (NASA), to tackle complex problems and produce workable answers (Sindhu et al., 2016). Finding transitive links and interactions between variables within a system has been one of its most successful uses (Öztürk, 2025). Notably, ISM assigns ranks and establishes correlations between the elements using expert judgment rather than any previous historical data about the system being studied.
Structural self interaction matrix:
The ISM methodology’s initial stage entails experts figuring out how two enablers (i and j) interact. The following four symbols are used to represent these relationships:
V: barrier i affects barrier j.
A: barrier j affects barrier i.
X: barrier i and j reciprocates each other.
O: barriers i and j are distinct variables and do not affect each other.
Between January and March 2025, a well-structured questionnaire was disseminated via email to bioscientists engaged in blue economy research, inviting their participation in the study. The survey was accompanied by a cover letter outlining the study’s objectives, relevance, and ethical considerations. Participants were requested to evaluate the contextual relationships among key barriers using the symbolic representations V, A, X, and O. Out of all the questionnaires distributed, 25 valid responses were initially received. However, two were excluded for not meeting the established quality standards, resulting in 23 final responses used for analysis. To interpret the relationships among the identified barriers, a majority voting approach was applied, as the ISM methodology does not prescribe a definitive criterion for symbol-based relationship interpretation. A two-tiered 50% threshold was adopted to ensure consistent and unbiased decision-making. In the first tier, a symbol was accepted if at least 50% of the experts selected it. For further validation, the difference between the first and second highest frequencies was also required to be at least 50% to confirm the dominance of a particular symbol. For instance, if 12 out of 23 experts selected “V” for a specific relationship, it met the first criterion; however, the distribution of the remaining symbols (A, O, X) was also examined to ensure balanced expert opinion. In cases of ambiguity or inconsistency, the responses were revisited and clarified through discussions with the experts. The finalized evaluations were then used to construct the Aggregated Fuzzy SSIM, as presented in Table 3.
AFSSIM matrix of key barriers for economy generation.
The current study uses fuzzy ISM. The notable difference is, in ISM binary numbers indicate whether influence exists or does not exist. Although, in the real environment, it is not that crisp; rather, there exists vagueness in decisions of domain experts. In the fuzzy ISM approach, there are some additional steps that need to be taken care of, such as the identification of a linguistic scale, fuzzy numbers to be used, method of aggregation of fuzzy numbers for multiple decision makers, method of defuzzification, and method of binary conversion that is used to construct the reachability matrix (RM), and the remaining steps may follow from the traditional ISM approach (Ahmad, 2024).
Data collection & expert validation
The current research constituted of a panel of 23 experts over 10 years of experience in Environmental sustainability, Safety Standards and Risk Assessment, Policy Framework and Governance, Regulatory Compliance and Legal Oversight to validate the identified key barriers obstructing the Seaweed commercialization. The expert panel was assembled through a snowball sampling, aiming for a diverse group representing various sectors, locations, and specializations within these fields.
Expert panel composition
The expert panel comprises of 34.7% industry/technical professionals, 21.7% academic and research experts, and 43.4% policy makers with over 10 years of experience in sectors such as marine-based industries, coastal production systems, and ocean-related bioeconomic activities. Geographically, 21.7% of experts were from India, while 26.08%, 30.4%, and 21.7% represented USA, China, Japan respectively. Table 4 refers expert panel composition.
Expert category classification table.
Category
Sub-category/area of expertise
Number of experts
Approximated to 100%
Sector
Industry/Technical experts
8
34.7
Academia/Research
5
21.7
Policy makers
10
43.4
Total
N=23
N=100
Geography
India
5
21.7
USA
6
26.08
China
7
30.4
Japan
5
21.7
Total
N=23
N=100
Sub-Domain Expertise
Environmental Sustainability
7
30.4
Safety Standards and Risk Assessment
6
26.08
Policy Frameworks and Governance
5
21.7
Regulatory Compliance & Legal Oversight
5
21.7
N=23
N=100
Conflict of interest declaration
All participating experts confirmed their independence by providing written consent electronically, stating that they had no financial, consulting, or institutional ties that might bias their evaluations. The data collection process was conducted entirely online, and there was no direct contact between researchers and experts that could influence or bias their responses. Experts were instructed to provide independent assessments based on their professional knowledge and experience, ensuring that their judgments remained impartial and free from external pressure.
Expert Judgement on Linguistic Scale with Fuzzy Numbers
Fuzzy numbers are enclosed in Table 5 number were used fuzzy numbers along with 5 point linguistic scales are tabulated which are used for further analysis.
The Aggregated Fuzzy SSIM is a structured matrix that captures the pairwise influence between barriers, combining expert judgments using fuzzy logic to accommodate uncertainty and subjectivity in decision-making. To systematically analyze the interrelationships among the identified barriers, an Aggregated Fuzzy Structural Self-Interaction Matrix (Fuzzy SSIM) (Table 3) was constructed. Expert judgments were collected using linguistic terms such as (0.0 TO 0.1)”no influence, “ (0.2 TO 0.3)”low influence, “ (0.3 TO 0.4) “medium influence, “ (>0.5)”high influence, “ and “very high influence” to evaluate the contextual relationships between each pair of barriers. These qualitative assessments were then converted into triangular fuzzy numbers to accommodate uncertainty and subjectivity inherent in human judgment. The individual fuzzy matrices obtained from multiple experts were aggregated using a fuzzy averaging method, resulting in a consolidated fuzzy SSIM that reflects the collective opinion. This aggregated matrix was subsequently defuzzified using the centroid method to generate crisp values, and a suitable threshold was applied to convert it into a binary matrix. The final output was used to develop the reachability matrix, laying the foundation for the Interpretive Structural Modeling (ISM) hierarchy.
Fuzzy reachability matrix:
After building and defuzzifying the Aggregated Fuzzy SSIM, the Fuzzy Reachability Matrix was created to identify and organize the interrelationships between the variables in a hierarchical manner (Table 6).
Fuzzy reachability matrix.
Codes
Key barriers
1
2
3
4
5
6
7
Driving power
EB1
HMC
1.0
0.50
0.50
0.50
0.50
0.50
0.50
4
EB2
BSC
0.20
1.00
0.20
0.20
0.20
0.50
0.20
2.5
EB3
WHI
0.20
0.20
1.00
0.20
0.20
0.50
0.50
2.8
EB4
RC
0.20
0.20
0.20
1.00
0.50
0.50
0.20
2.8
EB5
HC
0.10
0.10
0.10
0.10
1.00
0.10
0.10
1.6
EB6
MAC
0.20
0.20
0.20
0.20
0.20
1.00
0.20
2.2
EB7
SCC
0.20
0.20
0.20
0.20
0.20
0.50
1.00
2.5
Dependence power
2.1
2.4
2.4
2.4
2.8
3.6
2.7
Fuzzy Transitivity converged in three iterations.
This matrix is an important step of the Fuzzy ISM process, wherein the defuzzified values are compared with a predetermined threshold to identify the existence or non-existence of any relationship between two given elements. If the defuzzified value is above the threshold, a binary value of ‘1’ is taken to represent a strong influence; otherwise, a ‘0’ is employed. The resulting binary matrix, which is referred to as the reachability matrix, reflects the transitive and direct relationships between variables. This matrix is then utilized to obtain reachability sets, antecedent sets, and the intersection sets for each variable, which assists in identifying their hierarchical levels in the ISM framework. The fuzzy approach provides higher accuracy through vagueness and uncertainty to be allowed in expert opinions, resulting in a more realistic and dependable structural model.
Defuzzied reachability matrix:
The linguistic evaluations and fuzzy numerical data from the Aggregated Fuzzy SSIM are converted into clear binary values (0 or 1) by applying a threshold value that has been specified. According to professional consensus, this matrix depicts the direct impact of one barrier on another. Each fuzzy relational value in this study was defuzzied by comparing it to a predetermined threshold; values that were equal to or over the threshold were given the designation “1, “ signifying influence, while those that were below it were given the designation “0, “ signifying no influence. The defuzzied reachability matrix is a crucial transitional outcome represented in Table 7.
Defuzzied reachability matrix.
Codes
Key barriers
1
2
3
4
5
6
7
Driving power
EB1
HMC
1
1
1
1
1
1
1
7
EB2
BSC
0
1
0
0
0
1
0
2
EB3
WHI
0
0
1
0
0
1
1
3
EB4
RC
0
0
0
1
1
1
0
3
EB5
HC
0
0
0
0
1
0
0
1
EB6
MAC
0
0
0
0
0
1
0
1
EB7
SCC
0
0
0
0
0
1
1
2
Dependence Power
1
2
2
2
3
6
3
x
Fuzzy Transitivity converged in three iterations.
The generated matrix shows how the seven barriers to seaweed-based economic production are directly related to one another. Barriers like EB5 (High Production Cost – HC) and EB6 (Market Acceptance Challenges – MAC) have the lowest driving power of 1, indicating minimal outward influence, whereas EB1 (Heavy Metal Contamination – HMC) was found to have the highest driving power of 7, indicating direct influence over all other barriers. According to the dependence power values, which indicate the number of other barriers influencing a certain element, EB6 is the most influenced (value of 6), indicating that it is a result of multiple upstream problems. The stability and consistency of the hierarchical linkages obtained from this matrix are confirmed by the convergence of fuzzy transitivity within three rounds. This defuzzied reachability matrix helps with the strategic prioritization of interventions by providing the framework for creating the conical matrix and ISM hierarchy.
Fuzzy MICMAC using fuzzy reachability matrix
The Fuzzy MICMAC analysis visualizes (Figure 7) the relationship between seven key barriers (EB1–EB7) to a system, plotting them according to their driving power (influence on other variables) and dependence power (influence received from other variables) (Figure 8). The matrix shows that EB1 (HMC) lies in Quadrant IV, signifying it as an independent variable with high driving power (4.0) and low dependence power (2.1). This indicates EB1 plays a pivotal role in influencing other variables but is itself relatively unaffected, making it a strategic element in the system. EB6 (MAC), on the other hand, falls in Quadrant II as a dependent variable, with a low driving power (2.2) and high dependence (3.6), implying it is influenced by others but does not significantly drive change itself. Variables EB2 (BSC), EB3 (WHI), EB4 (RC), and EB7 (SCC) are grouped around the middle and positioned near Quadrant I, which traditionally houses autonomous variables. However, the exact placement suggests they have moderate values of both driving and dependence power (ranging around 2.4–2.8), indicating they play contextual or transitional roles not central to systemic dynamics but not completely disconnected either. EB5 (HC), with the lowest driving power (1.6) and low dependence (2.8), lies near the bottom of Quadrant I, making it the closest to an autonomous variable, though still somewhat connected. Overall, the graph indicates EB1 as the most influential variable in the system, whereas EB6 is more of a reactive or outcome-based factor, and the remaining variables operate in supporting or intermediary roles. This classification helps prioritize intervention strategies and structural adjustments based on influence dynamics in the system.
Diagraph of key barriers.
Flowchart diagram on a grid background with seven labeled circles, EB1 to EB7, connected by multiple directed arrows indicating various relationships and dependencies among the entities in the system.
FUZZY MICMAC of key barriers for economy generation.
Matrix plot illustration comparing Dependence Power (x-axis) versus Driving Power (y-axis), divided by bold lines into four quadrants labeled as Autonomous, Dependent, Linkage, and Independent Variables, with elements EB1 to EB7 distributed across the quadrants alongside respective labels.Conical matrix
The conical matrix is a reordered version of the terminal reachability matrix that easily categorizes elements (in this instance, barriers) in terms of their driving power and dependence power (Table 8). Driving power is the number of variables (including itself) that an element can drive, and dependence power is how many variables drive an element. In the conical matrix, the columns and rows are swapped so that those with greater driving power and lower dependence are put at the top left (pointing to being most influential or foundational), and those with less driving power and greater dependence at the bottom right (pointing to being impacted by other factors). This format gives a distinct image of the hierarchical and impactful dynamics in the system. For instance, EB1, which has the highest driving power of 7 and the lowest dependence power of 1, is placed at Level 4, which means that it is extremely dependent on overcoming all other obstacles. On the other hand, EB5 and EB6, with a driving power of 1 and greater dependence power, are at Level 1, meaning their status as root enablers that trigger the influence chain. Such a conical configuration thus makes it possible to grasp intuitively the influence-dependence structure, enabling focused decision-making and prioritization in resolving the impediments to economy generation from seaweeds.
Conical matrix.
Codes
5
6
2
4
7
3
1
Driving power
Level
EB5
1
0
0
0
0
0
0
1
1
EB6
0
1
0
0
0
0
0
1
1
EB2
0
1
1
0
0
0
0
2
2
EB4
1
1
0
1
0
0
0
3
2
EB7
0
1
0
0
1
0
0
2
2
EB3
0
1
0
0
1
1
0
3
3
EB1
1
1
1
1
1
1
1
7
4
Dependence power
3
6
2
2
3
2
1
×
×
Level
1
1
2
2
2
3
4
×
×
Level partitioning iterations:
The level partitioning iteration table is a critical component of the Interpretive Structural Modeling (ISM) technique in that it structures the barriers identified into a hierarchical structure based on their interdependencies (Table 9).
Level partitioning iterations.
Codes
Reachability set
Antecedent set
Interaction set
Level
EB1
1
1
1
4
EB2
2
1, 2
2
2
EB3
3
1, 3
3
3
EB4
4
1, 4
4
2
EB5
5
1, 4, 5
5
1
EB6
6
1, 2, 3, 4, 6, 7
6
1
EB7
7
1, 3, 7
7
2
This is done by comparing three sets for every barrier: the reachability set (the elements it can be influenced by), the antecedent set (the elements that can influence it), and the intersection set (elements common to both). A barrier is given a level when its intersection set and reachability set are the same, meaning it does not affect any other unassigned barriers and can be assigned to the topmost level in the current iteration. This process repeats until all barriers are assigned a level. In this study, this methodology uncovered a four-tier hierarchy: EB5 and EB6 were located at the base Level 1, suggesting that they need to be responded to first; EB2, EB4, and EB7 at Level 2; EB3 at Level 3; and EB1 at the highest Level 4, reflecting its high reliance on the solution of the root problems. This systematic analysis assists in giving top priority to interventions to overcome barriers of seaweeds-driven economy generation in an orderly fashion (Figure 9).
Level partitioning of the ISM model.
Flowchart diagram showing four levels labeled Level I to Level IV connected by arrows to blocks labeled EB1 through EB7, grouped within outlined sections, illustrating hierarchical or process relationships.Conclusions
The current study has comprehensively explored the landscape of global research trends in the field of seaweeds commercialization for the livelihood generation. It is observed from the analysis that the trends of publication have significantly increased in the year 2010, that reflects rising scholarly interest and policy relevance in marine bioresources. The thematic and keyword analyses underscore “seaweed farming, “ “aquaculture, “ and “climate change” as central themes, with evolving interest in niche and emerging topics. Overall, the study highlights the strategic importance of collaborative, high-quality research to harness the full potential of seaweed commercialization in fostering sustainable development and blue economy growth.
The current study uses a hybrid methodological approach that combines qualitative ISM modeling with quantitative bibliometric analysis utilizing Smart Fuzzy ISM-MICMAC to investigate the key obstacles to seaweed commercialization. Measures including the quantity of publications, keyword co-occurrence, citation counts, and co-authorship networks were among the many insights into the scholarly environment that were obtained from the bibliometric study, which was carried out using the Biblioshiny platform. This quantitative review found that seaweed-based goods are becoming more and more popular among researchers, particularly when it comes to sustainability, the bio economy, and coastal livelihoods.
In order to determine the primary obstacles preventing the commercialization of seaweed-based goods, a systematic evaluation of the literature was conducted as part of the qualitative phase. Regulatory Challenges (RC), High Production Cost (HC), Water Hyacinth Invasion (WHI), Heavy Metal Contamination (HMC), Biosorption Challenges (BSC), Market Acceptance Challenges (MAC), and Supply Chain Constraints (SCC) were the seven main obstacles that the study discovered. In contrast to earlier research that typically looked at these barriers separately, the current study uses fuzzy ISM-MICMAC modelling to fill the crucial knowledge gap on their interactions.
In order to build the ISM framework, 23 people with subject experience were asked for their expert opinions. Using linguistic scales that recorded different levels of influence (e.g., “no influence” to “very high influence”), an Aggregated Fuzzy Structural Self-Interaction Matrix (Fuzzy SSIM) was obtained. The centroid approach was used to defuzzify these assessments into a crisp matrix after they were transformed into triangular fuzzy values and averaged across respondents. After generating the binary reachability matrix with an appropriate threshold, the driving and dependence powers of each variable were ascertained through additional analysis.
The findings reveal that Heavy Metal Contamination (EB1) has the highest driving power, indicating it is the root cause influencing all other barriers. In contrast, High Production Cost (EB5) and Market Acceptance Challenges (EB6) exhibited the lowest driving power and the highest dependence power, positioning them as outcomes rather than root reasons. The final ISM model is structured across four hierarchical levels: Level 1 includes EB5 (HC) and EB6 (MAC), representing the most dependent barriers, Level 2 contains EB2 (BSC), EB4 (RC), and EB7 (SCC), acting as linkage variables, Level 3 houses EB3 (WHI), a mid-level driver, Level 4, the base of the model, features EB1 (HMC), reflecting its foundational role as a systemic constraint. This hierarchy suggests that addressing root-level environmental and ecological issues like heavy metal contamination and invasive species is vital for reducing costs, improving supply chain efficiency, and enhancing market acceptance. Hence, the ISM-MICMAC framework not only highlights the interconnectedness of barriers but also offers a prioritized roadmap for policy and intervention strategies aimed at enabling sustainable seaweed-based economic growth.
Limitations of the study
While the current study provides a comprehensive analysis of critical success factors and barriers associated with sea-weed commercialization, several limitations constrain the scope and applicability of its findings. First, the study uses a generalized systems approach as its findings are not related to any particular ecological water body or geographic environment. Since seaweed cultivation and commercialization are inevitably influenced by environmental, socioeconomic, and cultural elements unique to a given place, the lack of empirical field data or localized case studies limits the accuracy and applicability of the conclusions drawn. The viability and productivity of seaweed farming are influenced by the considerable variations in coastal ecosystems with respect to salinity, biodiversity, aquatic health, and tidal regimes. As a result, applying these findings consistently across several regions may miss important regional variations. Second, the study ignores the governance and policy frameworks of specific nations or states, which play a crucial role in determining the infrastructure, finance, and regulatory environment surrounding the commercialization of seaweed. The success or failure of seaweed initiatives is greatly influenced by a variety of factors that differ greatly between jurisdictions, including zoning rules, maritime spatial planning, environmental regulations, subsidies, and community engagement programs. Ignoring these aspects of regional governance could lead to simplistic conclusions that don’t inform focused policy initiatives. Third, it can be observed that the total publication has spiked in year 2025 which may be the result of database indexing delays, early online publication practices, and the relatively short citation window associated with recent years. Hence, future studies should be cautious in considering recent publication growth as provisional and should also emphasize the need for longitudinal updates and multi-metric validation. Fourth, the proposed framework is derived from expert-driven structural modeling using the Fuzzy ISM–MICMAC approach and does not involve field-level validation or case-based empirical testing. Consequently, the identified relationships reflect perceived structural influences rather than empirically tested causal effects. While this approach is appropriate for exploratory theory building, future research is encouraged to validate and extend the findings through field studies, case analyses, or empirical modeling techniques such as survey based analysis or longitudinal data. Acknowledging this limitation enhances methodological transparency and appropriately positions the contributions of the present study.
Future research direction
To build on the current understanding and address the multifaceted challenges in seaweed-based product development and commercialization, future research should consider the following directions:
Empirical Validation of ISM-MICMAC Through Field-Level Data.
Although MICMAC analysis and Interpretive Structural Modeling (ISM) are effective methods for mapping the links between barriers and enablers, the majority of the models in use today are derived from secondary literature or expert opinion. Future research should use the ISM-MICMAC framework with field-based, real-time data collected from supply chain players, entrepreneurs, and coastal communities. This empirical validation would improve the model’s resilience, legitimacy, and applicability while assisting practitioners and policymakers in prioritizing intervention methods based on firsthand data. A longitudinal use of this concept may also show how these linkages change over time in response to shifts in the market, policy, and environment.
Cross-Country Comparative Research.
Seaweed economies function in a variety of cultural, legal, and ecological contexts. Comparing seaweed industries in nations like Bangladesh, Indonesia, Vietnam, the Philippines, and India that have thriving or developing seaweed industries can provide important insights into context-sensitive success drivers and obstacles. Through these comparative assessments, best practices in market access, farming methods, regulation, and gender-inclusive value chains could be benchmarked. Knowledge of the ways in which various policy models and socioeconomic settings impact the uptake and expansion of seaweed technologies can help shape regional collaboration and South-South learning platforms for the development of a sustainable blue economy.
Sociocultural Acceptance and Market Psychology of Seaweed-Based Products.
Seaweed is gaining scientific and economic attention, although consumer adoption is still inconsistent and culturally dependent. Future studies should explore the social aspects of seaweed consumption, looking at how tastes, health perceptions, religious beliefs, and dietary customs affect consumer behavior in various markets and geographical areas. This should also involve an evaluation of perception management, branding, and market psychology in industries including personal care, pharmaceuticals, and functional foods. The results of this kind of research would be crucial for creating public awareness campaigns, marketing plans, and legislative incentives that would increase demand and guarantee sustainable use.
Inclusive Policy Design and Participatory Governance.
Without inclusive governance structures that prioritize local knowledge and agency, sustainable seaweed economies cannot prosper. Future research should assess how co-designing policies, in partnership with indigenous communities, cooperatives, Micro, Small, and Medium-Sized Enterprises (MSMEs), and Self-Help Groups (SHGs), might improve the impact, legitimacy, and uptake of seaweed-related interventions. This entails investigating the effects of participatory policy frameworks on equitable value allocation throughout the supply chain, technology transfer, capacity building, and financial access. Such studies can aid in the development of equitable, gender-responsive, and locally based policy models that align with the circular blue economy’s tenets.
Recommended policy framework
To effectively address the profusely overgrowing seaweed invasion and channel its potential toward beneficial commercialization, a sequential monitoring mechanism can be proved to be operationally efficient. The sequential monitoring mechanism comprises of continuously evaluating the objective of the framework, evaluating the relevance and effectiveness of the instruments, reviewing the robustness of the monitoring processes, and examining the efficiency of the implementing bodies. The barrier hierarchy (Figure 9) provides a basis for translating barriers into targeted and measurable policy actions. Foundational environmental barriers such as heavy metal contamination (EB1) and invasive biomass overgrowth (EB3) require strengthened monitoring, source segregation, and contamination control measures, with outcomes assessed through compliance with quality and safety standards. Process-level barriers, including biosorption challenges (EB2), call for technology-intervening policies that support efficient processing and biorefinery solutions, measurable through reductions in treatment costs and improvements in biomass recovery efficiency. Systemic barriers related to regulatory complexity and supply chain constraints (EB4, EB7) necessitate regulatory harmonization, certification, and logistics infrastructure, evaluated via improved supply reliability and reduced time-to-market. Market-facing barriers such as high production costs and limited market acceptance (EB5, EB6) can be addressed through targeted financial incentives, standardization, and consumer awareness initiatives, with effectiveness reflected in enhanced cost efficiency, product acceptance, and market penetration. Together, these aligned interventions enable adaptive policy implementation while advancing efficiency, sustainability, and circular bioeconomy outcomes. Based on the challenging barriers in commercializing the seaweed, the objectives for addressing these challenges are categorized into four key domains (i) The objective of the framework is segregated into (i) advancement in Technology intervening policy for commercializing seaweed, (ii) enhance the economic viability of seaweed value chain, (iii) develop and implement integrated environment and quality monitoring systems, and (iv) enabling policy and regulatory framework for standardizing seaweed products. The suggested approach as projected in Figure 10 is a learning-by-doing mechanism which involves the implementing bodies such as central government, research institutions, agrobusiness funding organizations, international donors, ministries, small and micro industries non-government organizations, and Self-Help Groups (Allen and Garmestani, 2015; Foxcroft and Mcgeoch, 2011) whose intervention can result in innovative solution for commercializing the excessive seaweed invasion. Each body exhibits a targeted role in executing policies, providing skill-based training programmes for coastal communities, funding for agritech startups, certifying seaweed byproducts, providing seaweed clearance for farming license and many more which are clearly exhibited in Figure 10. By following this robust conceptual framework, through collaboration of public-private partnership, with relevant stakeholders, a well-rounded and inclusive approach can be put forwarded into practice to not just control the barriers paving the way in commercializing the seaweed but also identifying sustainable utilization practices that could possibly enhance the effectiveness of future policies. This public-private collaboration can be proved to be advantageous as leverage private sector could provide expertise and resources in one hand and on the other hand government agencies can ensure effect oversight. The timely regular evaluation can enable the policymakers to implement required modifications in order to enhance the outcomes of implementation reforms and also for ensuring long-term viability and pertinence in combating the uncontrolled spreading of invasive seaweed menace and removing barriers of commercializing seaweed for livelihood generation.
Recommended policy framework.
Flowchart graphic outlining a recommended policy framework for commercializing seaweed, organized into four columns: objectives, instruments, monitoring, and implementing. Each column details specific steps on advancing technology, economic viability, monitoring systems, and regulatory frameworks, with corresponding agencies and stakeholders assigned to each component.Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Edited by: Carlos Alberto Zúniga-González, National Autonomous University of Nicaragua, Nicaragua
Reviewed by: Ummu Salma, Diponegoro University, Indonesia
Sachin G. Rathod, Independent Researcher, Nandurbar, India