Developing Sustainable Chef Competency Indicators: An Application of the Fuzzy Delphi Method

Abstract

The hospitality industry plays a pivotal role in advancing the Sustainable Development Goals (SDGs), and the foodservice sector bears specific responsibilities for reducing environmental impacts and embedding sustainability in practice. Meeting these goals requires chefs to pair technical expertise with ecological responsibility. This study establishes a structured competency indicators framework for sustainable chefs by synthesizing principles from sustainable food systems and culinary practice. We applied the Fuzzy Delphi Method (FDM) with dual triangular fuzzy numbers across two expert rounds. In round 1, 47 of 61 indicators met the consensus threshold; 5 new indicators were added based on expert suggestions. Round 2 achieved full convergence, confirming a final set of 38 sustainable chef competency indicators across six domains. The framework clarifies the competencies required of sustainability-oriented chefs and provides a practical basis for curriculum design, workforce training, and sustainability strategies within the hospitality sector.

1. Introduction

Sustainability has become a defining global challenge, encompassing climate change, food and water security, biodiversity loss, and socio-economic inequality [1,2]. In response, international agreements such as the 2015 Paris Climate Accord and the United Nations’ Sustainable Development Goals (SDGs) were established to mitigate environmental risks and promote equitable progress. Despite earlier efforts such as the Millennium Development Goals (MDGs), unresolved issues, including environmental degradation, resource depletion, and food waste, continue to threaten planetary health [3,4,5].

As key stakeholders in the food systems, chefs influence sustainable consumption and production. The hospitality industry, with its significant consumption of energy, water, and raw materials, is both a contributor to and a potential mitigator of environmental impact. Scholars such as Raub and Martin-Rios [6] emphasize that collaboration among governments, civil society, and industries is essential to achieving the SDGs. Food production and consumption systems have been linked to severe ecological damage, including deforestation, soil erosion, biodiversity loss, climate change, and water pollution [7,8]. Given this position, they can act as agents of sustainability through sourcing, preparation, and waste management choices.

The tourism and hospitality sectors are currently at a strategic inflection point that necessitates the rethinking of their operational models. As restaurant operations are major contributors to carbon emissions through on-site energy use and complex supply chains, a holistic and life-cycle approach is needed, one that accounts for ingredient origin, production practices, and disposal methods [9]. Within this context, chefs’ competencies must expand beyond traditional culinary skills to include knowledge and practices that align with ecological principles and sustainable food systems.

Sustainable culinary competencies remain limited in scope. For example, Hsu [10] adapted the Green Restaurant Association indicators to construct a green chef competency model covering resource conservation, pollution prevention, biomimicry, and green investment. Ko and Lu [11] examined professional competencies for reducing food waste based on social cognitive theory, emphasizing knowledge, skills, attitudes, and behavioral intentions [12]. Teng and Chih [13] proposed a sustainable food literacy scale that assesses knowledge, skills, and attitudes related to sustainable dietary behaviors.

While these studies offer important insights into specific sustainability-related competencies in culinary contexts, recent international research has expanded the perspective by emphasizing chefs’ roles as change agents in broader food systems. Filimonau et al. [14] examined how European chefs develop food waste management practices through experiential learning. Bhaskara et al. [15] analyzed environmental habitus and sustainability behavior among chefs in Indonesia. Richardson and Fernqvist [8] explored food democracy through sustainable gastronomy in Sweden, and Pereira et al. [16] highlighted chefs’ integration of indigenous knowledge into sustainable innovation in Latin America.

Despite ongoing efforts, a comprehensive framework for sustainable chef competencies that reflects the interconnected dimensions of food systems remains underdeveloped. In response to this research gap, the present study focuses on developing a robust and consensus-based competency framework for “sustainable chefs.” Drawing on food system theory [17] and the dimensions of sustainable food preparation [18], this study consolidates six domains: Ecosystem Awareness, Environmental Impact, Local Food, Cultural Interaction, Innovation and Governance, and Culinary Skills. A comprehensive set of preliminary indicators was derived from a literature review and expert input, forming the basis for empirical validation through the Fuzzy Delphi Method (FDM).

By constructing and validating a set of sustainable chef competency indicators, this study contributes to both academic and practical domains. For academia, it offers a framework that advances sustainability-oriented competency research in hospitality and culinary studies. For industry and education, it provides concrete guidelines for training and professional development, supporting the cultivation of chefs as sustainability practitioners who drive positive environmental, cultural, and social change.

Accordingly, we develop and validate a sustainability-oriented chef competency framework using the Fuzzy Delphi Method (FDM) with dual triangular fuzzy numbers to synthesize expert judgment for a multidimensional construct.

2. Literature Review

2.1. Sustainability in Food Systems and Culinary Practice

Sustainability in the food domain concerns meeting human needs while preserving ecological integrity, emphasizing responsible use of natural resources and long-term environmental resilience [18]. Elkington’s [19] “triple bottom line” frames this balance across environmental, social, and economic dimensions and remains foundational for sustainable gastronomy and food systems. In hospitality and tourism, this perspective further encompasses cultural preservation, community engagement, and equity as prerequisites for durable sectoral development [20].

Sustainable diets connect ecological responsibility with human health by promoting resource efficiency, reduced environmental impact, and context-appropriate consumption of seasonal, locally produced foods [16,21]. Extending this logic into professional kitchens, sustainable culinary preparation integrates ecological awareness with support for local food systems and safeguarding of cultural heritage while mitigating water use, carbon emissions, and other operational impacts [18].

Food systems comprise interlinked stages including production, processing, distribution, selection, preparation, consumption, and disposal, which are shaped by socio-economic, environmental, and policy factors [17,22]. Food systems play a dual role in the sustainability agenda. On the positive note, they offer critical leverage points for achieving SDGs, particularly in food security (SDG 2), responsible consumption and production (SDG 12), and climate action (SDG 13) [23]. However, current global patterns, for example, high meat and dairy intake, long supply chains, excessive packaging, and food loss and waste, intensify climate change, biodiversity loss, and resource depletion, while persistent food insecurity reveals structural imbalances [24,25]. Land and seascape conversion for agriculture and aquaculture further drives pollution, soil degradation, and diminished biodiversity [26].

Food literacy provides a behavioral and educational pathway for system change. A dual-dimensional model specifies functional, interactive, and critical literacies across the system’s stages from production through disposal, highlighting the need to align technological solutions with informed everyday practices [17]. Within this system, chefs influence ingredient sourcing, menu design, and waste management, shaping demand, biodiversity outcomes, and public health through culinary choices and guest communication [8,12,16]. Consequently, literacy “from origin to plate to bin” becomes essential for decisions that are ecologically responsible, socially conscious, and economically viable.

In sum, transforming food systems requires practitioners who embed sustainability in routine culinary work. Chefs equipped with ecological insight, cultural understanding, and adaptive craft can catalyze change across commercial kitchens, educational institutions, and broader communities.

2.2. Chef Competencies

Within hospitality and tourism, chefs have moved from a narrow production role to a multifaceted practice that shapes nutrition, guest experience, and sustainability outcomes [27]. Classical frameworks group competencies into functional, generic, and core domains covering technical craft, interpersonal capability, and leadership/management. Validation studies further highlight food safety, quality control, management, and innovation as central to performance [28]. Foundational competence theory prioritizes attributes and behaviors that predict effectiveness: McClelland [29] emphasized underlying motives and traits over intelligence scores; Boyatzis [30] articulated role-specific behavioral indicators of effectiveness; and Spencer & Spencer [31] systematized competency clusters linked to superior performance.

Contemporary scholarship extends these models to the realities of modern kitchens. Integrative accounts position chef competencies as a composite of knowledge, skills, and attitudes spanning managerial/leadership capability, technical expertise, strategic capacity, and operational effectiveness, explicitly including food safety, sustainability, and waste management [32]. Yet despite this broadening, sustainability has often been underspecified in traditional taxonomies, creating a gap with current environmental, ethical, and community expectations.

Aligning competency thinking with a food systems view clarifies this gap. The food literacy framework, which is functional, interactive, and critical across production-to-disposal stages, links what chefs know and do to system outcomes [17]. Parallel work in sustainable culinary preparation identifies core dimensions such as ecological awareness, cultural preservation, and environmental accountability as integral to professional practice [18].

Taken together, these developments support a holistic conception of chef competence that integrates craft, governance, ethics, and systems thinking. The next subsection distills this perspective into the six domains that structure sustainability-oriented chef competencies used in this study.

2.3. Sustainable Chefs and Competency Domains

A sustainable chef is an agent of change within the food systems, integrating environmental stewardship, cultural preservation, social equity, and economic responsibility into everyday professional practice [33]. In hospitality and tourism, this expanded role spans ingredient sourcing, menu planning, kitchen operations, waste management, and guest communication, positioning chefs as stakeholders in advancing the SDGs through gastronomy [27,31].

Building on food system literacy and sustainable culinary preparation perspectives, sustainability-oriented competence is multidimensional and connects what chefs know and do to system outcomes across production, distribution, selection, preparation, consumption, and disposal [17,18]. Global and regional initiatives like the Chef’s Manifesto and Taiwan’s Green Dining Pledge frame chefs’ contributions to sustainability as system-level practices embedded in daily kitchen work and dining culture [34,35].

Recent scholarship therefore extends traditional skill sets to include ecological awareness, resource efficiency, and food system thinking [10,11], with practical attention to food waste reduction, energy use, sustainable sourcing, and green kitchen design [9,15]. Competence development is dynamic, shaped by learning environments, social influence, and individual agency [14], and is influenced by psychological factors such as ethical orientation and self-efficacy in shaping intentions and behaviors [12].

Guided by this literature and a food systems lens, the present study structures sustainability-oriented chef competence into six domains that serve as conceptual scaffolding for the empirical work [17,22]:

• Ecosystem awareness: Literacy in biodiversity, planetary boundaries, and production impacts that informs upstream sourcing decisions.
• Environmental impact: Routine behaviors that reduce waste, energy use, and carbon intensity in operations.
• Local food: Procurement practices that prioritize seasonality, provenance, and fair, resilient regional value chains.
• Cultural interaction: Stewardship of foodways and guest education that supports nutrition, safety, and anti-waste norms.
• Innovation and governance: Menu analytics, inventory and cold-chain discipline, surplus valorization, and fair work as organizational enablers of sustainability.
• Culinary skills: Adaptive, safety-first craft that maintains quality while conserving resources and using what is available.

From this perspective, chefs are repositioned as technical experts, ecosystem stewards, cultural intermediaries, and sustainability educators who contribute to environmental, social, and cultural outcomes in their communities [11].

3. Research Method

3.1. Research Procedures and Samples

This study was conducted in four phases, combining theoretical analysis with expert-driven validation to develop sustainable chef competency indicators.

In the first phase, a comprehensive literature review was carried out to identify both theoretical and practical perspectives on sustainable chef competencies. Based on this review, six preliminary competency domains were constructed, each comprising a set of clearly defined constructs.

The second phase involved instrument validation through expert review. A panel of four senior educators, each with over 20 years of experience in culinary and hospitality education, assessed the draft questionnaire. They provided feedback on item clarity, semantic accuracy, and the alignment of items with their respective competency domains.

In the third phase, the finalized questionnaire was distributed to a purposive sample of respondents, including professional chefs, public sector sustainability advocates, and academic scholars. The survey was administered in both paper-based and digital formats. All participants were provided with a research briefing and gave informed consent prior to participation.

The fourth phase focused on data collection and analysis. A total of 10 valid responses were retrieved from 12 distributed questionnaires. Expert ratings were analyzed using the dual triangular fuzzy number (TFN) approach to evaluate the level of consensus. Items that did not meet the consensus threshold were revised and included in a second-round survey sent to the same panel. This iterative process continued until all indicators achieved convergence.

This study employed purposive sampling to recruit experts for the Fuzzy Delphi process, aligning with the study’s objectives. Eligible participants possessed either substantial experience in hospitality education, research, or practice, or demonstrated involvement in sustainable gastronomy, green dining, food education, or related policy initiatives.

Drawing on Klir and Folger [36], a panel size of approximately 10 experts is suitable for generating meaningful consensus within a relatively homogeneous group. Zartha Sossa et al. [37] likewise recommend a sample size of 7 to 10 to enhance the reliability and validity of Delphi outcomes.

Experts selected for this study possessed substantial experience in the hospitality industry and prior engagement with sustainability initiatives, including sustainable diets, green dining, food education, and policy development. This ensured inclusion of forward-looking, interdisciplinary, and practice-based insights. Among the five industry experts, three were from the restaurant sector (executive chefs and eco-restaurant/bakery owners), while two were affiliated with hotels, specializing in sustainability and food and beverage (F&B) operations. The academic panel consisted of two senior faculty members in hospitality and one honorary professor who previously served as a university president in the field of hospitality and tourism. Additionally, two experts from the public and non-profit sectors were leaders in sustainability advocacy and green dining initiatives. A summary of expert profiles is provided in

Table 1. Characteristics of the Expert Panel.

Category	Affiliate	Job Title	Working Years	Education
Academic Expert	University	President and Chair	40 years	Ph.D.
	University	Professor	26 years	Ph.D.
	University	Associate Professor	18 years	Ph.D.
Industry Expert	Kempinski Hotels	GM and Chief Risk Officer	36 years	M.A.
	Bakery & Kitchen	Owner–Chef	22 years	B.A.
	Green Restaurant	Executive Chef	26 years	B.A.
	LDC Hotels & Resorts	Sustainability Consultant	6 years	M.A.
	Dexin Bistro	Head Chef	5 years	B.A.
Public/Non-Profit Expert	Green Harmony Co.	CEO and Secretary General	8 years	M.A.
	Green Dining Guide Assoc	Founder and Chairperson	8 years	M.A

.

All procedures adhered to institutional research ethics guidelines, and informed consent was obtained from all participants.

3.2. FDM and Evaluation Index Development

FDM combines fuzzy set theory with the traditional Delphi technique to address uncertainty and semantic ambiguity in expert judgment. This method utilizes triangular fuzzy numbers (TFNs) to represent expert evaluations across three parameters: the minimum (conservative), most likely (expected), and maximum (optimistic) values [38,39]. Compared to traditional Delphi methods, FDM offers greater analytical flexibility through the use of geometric means and max–min operations [36,40].

In this study, a dual TFN approach was employed to capture both conservative and optimistic expert perspectives, enabling a more refined assessment of consensus and divergence. This approach is particularly well-suited to the multidimensional nature of the sustainable chef competency framework, which encompasses six core domains: ecosystem awareness, environmental impact, local food, cultural interaction, innovation and governance, and culinary skills.

Given this complexity, the application of FDM was considered appropriate. To further refine the analytical rigor and enhance consensus detection, double TFN analysis was implemented.

While FDM enhances empirical rigor and is suitable for studies involving small expert panels, it also shares the limitations of expert-based approaches, as outcomes may be influenced by individual judgment and domain-specific expertise. Nevertheless, FDM remains a suitable and effective method for facilitating a structured, transparent, and consensus-driven process in developing sustainable chef competencies.

The questionnaire of the indicator development process included bilingual verification and expert consensus-building. Initial indicators were translated into Chinese, semantically refined by four scholars specializing in hospitality education, and then back-translated into English to ensure cross-language consistency. Expert meetings were held to align indicators with the six predefined domains, revise wording, and eliminate redundancy based on expert input.

• Ecosystem Awareness focused on chefs’ understanding of biodiversity, responsible sourcing, and natural resource conservation. The original 49 items were refined to 10.
• Environmental Impact emphasized practical abilities such as waste reduction, energy efficiency, and carbon footprint mitigation. This domain was reduced from 15 to 8 items.
• Local Food included indicators related to seasonal sourcing, low-carbon ingredients, and support for local producers. After revision, 9 items were retained.
• Cultural Interaction addressed chefs’ sensitivity to culinary heritage and cross-cultural integration. This domain was streamlined from 6 to 5 items.
• Innovation and Governance reflected chefs’ competencies in process optimization, sustainable leadership, and decision-making. Expert feedback expanded the original 17 items to 19.
• Culinary Skills defined core technical abilities, including ingredient handling, cooking techniques, food safety, and sustainable meal preparation. This domain retained 10 items.

After the expert validation process and iterative revisions, the final Fuzzy Delphi questionnaire was confirmed. The resulting competency framework consisted of 61 indicators across six domains: 10 indicators for ecosystem awareness, 8 for environmental impact, 9 for local food, 5 for cultural interaction, 19 for innovation and governance, and 10 for culinary skills. These validated indicators form the core structure of the Sustainable Chef Competency Framework developed in this study (see

Table A1. FDM Expert Questionnaire: Competency Indicators for Sustainable Chefs.

Ecosystem Awareness (EA): Dimension (Code): Indicator	Source
EA1 Know how to control eco-friendly food from the source. EA2 Have good food characteristics knowledge for kitchen staff. EA3 Have the ability to quantitatively use ingredients and raw materials. EA4 Choose suitable eco-friendly raw materials and reliable providers. EA5 Know the procedures for using eco-friendly ingredients to avoid repeated thawing.	[11]
EA6 Avoid using genetically modified ingredients to maintain ecological balance. EA7 Find information about food production, such as the “animal welfare” certification for meat and eggs.	[14]
EA8 Prioritize plant-based ingredients to reduce reliance on livestock farming. EA9 Choose nutritious food that is accessible and affordable for everyone.	[31]
EA10 Understand that “sustainable food” benefits personal health, ecology, and the environment.	[13]
Environmental Impact (EI): Dimension (Code): Indicator	Source
EI1 Use kitchen tools effectively to avoid ingredient waste. EI2 Prevent food spoilage caused by environmental contamination. EI3 Understand food sterilization methods to reduce contamination. EI4 Understand regulations related to food and surplus management.	[11]
EI5 Understand how different food transportation methods impact the environment and society. EI6 Prioritize low-carbon local ingredients to reduce food miles.	[14]
EI7 Apply the 3R principles (reduce, reuse, recycle) to lower carbon footprint.	[32]
EI8 Understand how dietary choices affect environmental sustainability.	[13]
Local Food (LF): Dimension (Code): Indicator	Source
LF1 Understand the seasons and prices of various ingredients.	[11]
LF2 Check for agricultural food certification labels (organic, pesticide-free, etc.). LF3 Check the country of origin of ingredients. LF4 Prioritize local ingredients to reduce food miles.	[14]
LF5 Choose producers who practice animal-friendly farming LF6 Invest in improving the livelihoods of farmers, upstream suppliers, and employees. LF7 Purchase local seasonal ingredients and make use of native varieties.	[31]
LF8 Support local farmers and fishers by sourcing their sustainable products. LF9 Provide more plant-based products to reduce meat consumption and lower environmental impact.	[32]
Cultural Interaction (CI): Dimension (Code): Indicator	Source
CI1 Personal morals tell me not to waste food. CI2 Use education to convey the idea of reducing food waste.	[11]
CI3 Analyze the characteristics and impacts of different food cultures.	[14]
CI4 Promote cooking education on food safety, healthy diets, and nutrition.	[31]
CI5 Understand the meaning of “animal welfare”.	[13]
Innovation and Governance (IG): Dimension (Code): Indicator	Source
IG1 Include sustainable dining concepts such as portion control, health, and environmental protection in menu design. IG2 Use ingredient characteristics to design innovative dishes. IG3 Accurately calculate ingredient purchase quantities. IG4 Follow ingredient storage standards and procedures. IG5 Know the correct packaging or sub-packaging sizes for ingredients. IG6 Efficiently plan refrigeration and freezer storage space. IG7 Establish monitoring systems to optimize ingredient inventory management. IG8 Classify ingredient storage based on characteristics to extend shelf life. IG9 Store ingredients at different times and in separate areas after cleaning to avoid cross-contamination. IG10 Proactively learn to use new tools for ingredient processing. IG11 Make full and careful use of each ingredient to maximize its value. IG12 Use data analysis to optimize menu design and reduce ingredient waste. IG13 Incorporate food surplus education or concepts into daily kitchen operations. IG14 Innovatively use surplus ingredients to create new dishes and increase resource value. IG15 Observe kitchen waste patterns to identify menu shortcomings and make timely adjustments. IG16 Dispose of kitchen waste according to waste classification guidelines.	[11]
IG17 Choose ingredients based on nutrition labels.	[14]
IG18 Select reliable ingredient suppliers with a commitment to sustainability.	[31]
IG19 Provide employees with fair wages and a reasonable working environment.	[32]
Culinary Skills (CSs): Code/Indicator	Source
CS1 Ability to follow cooking and energy-saving principles. CS2 Be familiar with ingredient characteristics and appropriate cooking methods to reduce waste. CS3 Flexibly adjust menus using seasonal alternative ingredients to reduce waste.	[11]
CS4 Cook and store food carefully to avoid foodborne illness. CS5 Assess food hygiene by observing preparation and cooking processes. CS6 Prepare nutritionally balanced meals. CS7 Judge ingredient quality based on taste, freshness, and other factors.	[14]
CS8 Flexibly adjust ingredients and menus. CS9 Adapt recipes based on available ingredients CS10 Improvise meals using available ingredients, including leftovers.	[13]

).

3.3. Fuzzy Delphi Computation and Consensus Evaluation

3.3.1. Expert Questionnaire and Fuzzification

The expert questionnaire employed a five-point Likert scale from “very unimportant” to “very important.” To capture more nuanced evaluations, each item included a fuzzy scale ranging from 0 to 10. Experts provided three values: the minimum (L) for the most conservative view, the most likely score (M) as the expected evaluation, and the maximum (U) for the most optimistic view. These triplets were converted into triangular fuzzy numbers based on the fuzzy arithmetic model by Kaufmann and Gupta [38]. For example, an expert response of (L = 3, M = 5, U = 7) reflects both uncertainty and central tendency in perception.

3.3.2. Fuzzy Number Computation

Upon completion of data collection, expert responses were aggregated into dual triangular fuzzy sets for each competency indicator i. Two sets were constructed:

• Conservative fuzzy set:

$$C^i=\left(C_L^i,C_M^i,C_U^i\right)$$

(1)

where

$$C_L^i=\min \left(L_1,L_2,\dots ,L_n\right)$$

(2)

$$C_M^i={\left({\prod }_{k=1}^n{L}_k\right)}^{1/n}\left(geometricmean\right)$$

(3)

$$C_U^i=\max \left(L_1,L_2,\dots ,L_n\right)$$

(4)

• Optimistic fuzzy set:

$$O^i=\left(O_L^i,O_M^i,O_U^i\right)$$

(5)

where each component is calculated using the same method applied to the upper-bound values U values.

This dual-fuzzy representation reflects the full range of expert perspectives, from cautious to optimistic, offering a more precise basis for consensus evaluation in the Fuzzy Delphi process.

3.3.3. Consensus Evaluation Criteria

To assess expert consensus, this study applied the gray zone analysis method proposed by Gil-Lafuente et al. [41]. The degree of agreement was determined by analyzing the overlap between the conservative and optimistic fuzzy sets for each indicator $i$, using the following indices:

• Gray Zone Index $Z^i$: Measures the extent of overlap between fuzzy sets.

  $$Z^i=C_U^i-O_L^i$$

  (6)
• Convergence Index $M^i$: Indicates the degree of convergence between central (mean) values.

  $$M^i=O_M^i-C_M^i$$

  (7)

The following decision rules were applied:

• Type 1: No Gray Zone (Full Consensus)

If the two fuzzy numbers do not overlap, i.e.,

$$C_U^i\le O_L^i$$

(8)

then full consensus is achieved. The importance score for the indicator is calculated as:

$$G_i={\displaystyle \frac{C_i^M+O_i^M}{2}}$$

(9)

2.

Type 2: Gray Zone with Minor Divergence

$${C_U^i>O_L^i}_i\mathrm{and}{Z}_i<M_i$$

(10)

then minor divergence is accepted. The importance score is calculated using the fuzzy intersection formula

$$G_i={\displaystyle \frac{\left(C_U^i\cdot O_M^i\right)-\left(O_L^i\cdot C_M^i\right)}{\left(O_M^i-O_L^i\right)+\left(C_U^i-C_M^i\right)}}$$

(11)

3.

Type 3: Gray Zone with Major Disagreement

If the gray zone is wider than the convergence index, i.e.,

$$Z_i\ge M_i$$

(12)

the item is considered non-consensual and should be revised or included in the next Delphi round.

3.3.4. Indicator Validation and Thresholding

Once the importance scores $G_i$ were calculated for all indicators, a threshold score S was established to identify high-consensus items. This threshold was either predefined based on theoretical expectations or computed as the arithmetic mean of all $G_i$ values [42]. Indicators scoring below S were excluded to maintain the clarity, relevance, and parsimony of the competency framework. Those meeting or exceeding the threshold were retained in the final model. Items lacking convergence were reworded and reintroduced in subsequent Delphi iterations until consensus was achieved.

The dual triangular fuzzy computation process enhanced the robustness of expert-based validation by systematically integrating both consensus strength and uncertainty, thereby supporting the development of a reliable and context-sensitive competency model for sustainable chefs.

4. Results and Discussion

4.1. First-Round Delphi Results

The Fuzzy Delphi Method was employed to refine and validate the sustainable chef competency framework. In the first round, 61 indicators across six dimensions were assessed by 10 experts, yielding an effective response rate of 83.3%. The dual triangular fuzzy number approach was applied, using both the convergence index ($M^i$) and the gray zone value ($Z^i$) to evaluate the level of expert agreement.

Indicators were classified based on the dual triangular fuzzy model, where those meeting the criteria for convergence (Type 1 or Type 2) and exceeding the average consensus score ($G_i$) within their respective dimensions were retained for the next phase

Results from the first round revealed that 47 indicators reached consensus, while 14 did not (see

Table 2. FDM Round 1 Evaluation Results.

Indicator	$\mathbf{C}{\mathbf{M}}_{\mathbf{i}}$ (Min)	$\mathbf{O}{\mathbf{M}}_{\mathbf{i}}$ (Max)	Zi	Mi	Converged	Type *	Gi
EA1	7.89	9.46	1	1.57	Yes	2	8.570
EA2	7.89	9.46	1	1.57	Yes	2	8.570
EA3	7.15	8.73	3	1.58	No	3	7.789
EA4	7.98	9.52	1	1.54	Yes	2	8.598
EA5	8.10	9.62	1	1.53	Yes	2	8.642
EA6	7.89	9.46	1	1.57	Yes	2	8.570
EA7	6.46	8.11	3	1.65	No	3	7.363
EA8	5.73	7.51	3	1.78	No	3	6.948
EA9	8.27	9.78	1	1.50	Yes	2	8.710
EA10	8.33	9.90	0	1.56	Yes	1	9.114
EI1	8.33	9.90	0	1.56	Yes	1	9.114
EI2	7.60	9.29	3	1.69	No	3	8.106
EI3	7.65	9.16	1	1.51	Yes	2	8.462
EI4	7.81	9.34	2	1.54	No	3	8.324
EI5	7.81	9.34	2	1.54	No	3	8.324
EI6	7.97	9.49	2	1.52	No	3	8.416
EI7	7.31	8.82	3	1.51	No	3	7.876
EI8	7.89	9.41	1	1.51	Yes	2	8.560
LF1	8.02	9.47	1	1.45	Yes	2	8.599
LF2	8.49	10.00	−1	1.51	Yes	1	9.243
LF3	8.22	9.62	1	1.40	Yes	2	8.676
LF4	8.27	9.78	1	1.50	Yes	2	8.710
LF5	7.98	9.52	1	1.54	Yes	2	8.598
LF6	8.49	10.00	−1	1.51	Yes	1	9.243
LF7	8.33	9.84	0.5	1.51	Yes	2	8.834
LF8	5.50	7.46	2	1.96	No	3	6.737
LF9	7.70	9.31	1	1.61	Yes	2	8.502
CI1	6.26	7.90	2	1.64	No	3	7.044
CI2	8.16	9.76	1	1.59	Yes	2	8.678
CI3	7.41	8.81	1	1.40	Yes	2	8.339
CI4	8.33	9.90	0	1.56	Yes	1	9.114
CI5	7.89	9.46	1	1.57	Yes	2	8.570
IG1	7.10	8.67	3	1.56	No	3	7.752
IG2	7.57	9.05	1	1.48	Yes	2	8.424
IG3	7.93	9.41	1	1.48	Yes	2	8.569
IG4	7.99	9.47	1	1.48	Yes	2	8.592
IG5	7.62	9.14	3	1.52	No	3	8.084
IG6	8.10	9.62	1	1.53	Yes	2	8.642
IG7	7.30	8.67	2.5	1.37	No	3	7.902
IG8	7.89	9.41	1	1.51	Yes	2	8.560
IG9	7.93	9.41	1	1.48	Yes	2	8.569
IG10	7.79	9.26	1	1.47	Yes	2	8.510
IG11	8.22	9.68	0.5	1.46	Yes	2	8.801
IG12	7.30	8.72	2.5	1.42	No	3	7.916
IG13	8.22	9.68	0.5	1.46	Yes	2	8.801
IG14	8.22	9.74	0.5	1.51	Yes	2	8.807
IG15	8.37	9.84	0.5	1.47	Yes	2	8.840
IG16	8.07	9.58	0.5	1.51	Yes	2	8.769
IG17	8.37	9.84	0.5	1.47	Yes	2	8.840
IG18	7.69	9.05	1	1.37	Yes	2	8.445
IG19	7.93	9.37	1	1.44	Yes	2	8.560
CS1	8.49	10.00	−1	1.51	Yes	1	9.243
CS2	8.22	9.62	1	1.40	Yes	2	8.676
CS3	8.49	10.00	−1	1.51	Yes	1	9.243
CS4	8.27	9.78	1	1.50	Yes	2	8.710
CS5	8.27	9.78	1	1.50	Yes	2	8.710
CS6	8.27	9.78	1	1.50	Yes	2	8.710
CS7	7.91	9.26	1	1.35	Yes	2	8.536
CS8	8.07	9.47	1	1.39	Yes	2	8.613
CS9	8.13	9.62	1	1.50	Yes	2	8.650
CS10	8.27	9.78	1	1.50	Yes	2	8.710

* Note: Type 1: ${\mathrm{Z}}_{\mathrm{i}}\le 0$; Type 2: ${\mathrm{Z}}_{\mathrm{i}}>0$; Type 3: $\mathrm{C}{\mathrm{U}}_{\mathrm{i}}>\mathrm{O}{\mathrm{L}}_{\mathrm{i}}$ and ${\mathrm{Z}}_{\mathrm{i}}\ge {\mathrm{M}}_{\mathrm{i}}$.

). Based on expert feedback, five new indicators were added: one in Ecosystem Awareness, one in Environmental Impact (EI9), two in Cultural Interaction (CI6; CI7), and one in Culinary Skills (CS11). A total of 19 indicators were therefore carried forward for re-evaluation in the second round.

4.2. Second-Round Delphi Results

In the second round, experts re-evaluated both retained and newly added indicators. Convergence was again assessed using the criteria of $M^i$ and $Z^i$, with the average consensus score $G_i$ within each dimension serving as the threshold for retention.

The final outcomes were as follows:

• Ecosystem Awareness (EA1–EA11): Eight indicators were retained (EA1, EA2, EA4, EA5, EA6, EA8, EA9, EA10), while EA3, EA7, and EA11 were excluded due to lower consensus.
• Environmental Impact (EI1–EI9): Five indicators were retained (EI1, EI2, EI4, EI6, EI8), while EI3, EI5, EI7, and EI9 were removed.
• Local Food (LF1–LF9): Six indicators were retained (LF2, LF3, LF4, LF5, LF6, LF7), while LF1, LF8, and LF9 were excluded.
• Cultural Interaction (CI1–CI7): Four indicators were retained (CI2, CI4, CI5, CI7), while CI1, CI3, and CI6 were excluded.
• Innovation and Governance (IG1–IG19): Nine indicators were retained (IG6, IG11, IG13, IG14, IG15, IG16, IG17, IG18, IG19); the remaining indicators were excluded.
• Culinary skills (CS1–CS11): Six indicators were retained (CS1, CS3, CS4, CS5, CS6, CS10), while CS2, CS7, CS8, CS9, and CS11 were excluded.

Through this iterative process, the initial 61 indicators were refined to a final set of 38 high-consensus indicators. This refinement enhances the framework’s parsimony and ensures that the retained competencies reflect strong expert agreement and alignment with the core objectives of sustainable chef competency development (see

Table 3. FDM Round 2 Evaluation Results.

Indicator	$\mathbf{C}{\mathbf{M}}_{\mathbf{i}}$ (Min)	$\mathbf{O}{\mathbf{M}}_{\mathbf{i}}$ (Max)	Zi	Mi	Converged	Type *	Gi	Threshold/Decision
EA1	8.14	9.46	1	1.32	Yes	2	8.630	8.59	Retain
EA2	8.14	9.46	1	1.32	Yes	2	8.630	Retain
EA3	7.54	9.46	1	1.92	Yes	2	8.501	Remove
EA4	7.98	9.71	0.5	1.73	Yes	2	8.771	Retain
EA5	8.37	9.82	0.5	1.45	Yes	2	8.838	Retain
EA6	8.14	9.46	1	1.32	Yes	2	8.630	Retain
EA7	6.30	8.15	0	1.85	Yes	1	7.221	Remove
EA8	7.77	9.46	0	1.70	Yes	1	8.615	Retain
EA9	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
EA10	8.33	10.00	−1	1.67	Yes	1	9.166	Retain
EA11	7.53	9.31	1	1.78	Yes	2	8.471	Remove
EI1	8.33	10.00	−1	1.67	Yes	1	9.166	8.62	Retain
EI2	8.16	9.76	1	1.59	Yes	2	8.678	Retain
EI3	7.65	9.16	1	1.51	Yes	2	8.462	Remove
EI4	8.09	9.88	0	1.79	Yes	1	8.987	Retain
EI5	7.51	9.35	1	1.84	Yes	2	8.476	Remove
EI6	7.56	9.59	0.5	2.04	Yes	2	8.716	Retain
EI7	7.43	9.12	0	1.69	Yes	1	8.272	Remove
EI8	8.14	9.41	1	1.27	Yes	2	8.621	Retain
EI9	6.45	10.00	−2	3.55	Yes	1	8.225	Remove
LF1	8.02	9.47	1	1.45	Yes	2	8.599	8.67	Remove
LF2	8.49	10.00	−1	1.51	Yes	1	9.243	Retain
LF3	8.22	9.82	0.5	1.60	Yes	2	8.815	Retain
LF4	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
LF5	7.98	9.71	0.5	1.73	Yes	2	8.771	Retain
LF6	8.49	10.00	−1	1.51	Yes	1	9.243	Retain
LF7	8.33	10.00	−1	1.67	Yes	1	9.166	Retain
LF8	5.60	7.92	0	2.32	Yes	1	6.762	Remove
LF9	7.92	9.31	1	1.40	Yes	2	8.547	Remove
CI1	7.13	8.90	1	1.77	Yes	2	8.326	8.48	Remove
CI2	8.49	10.00	−1	1.51	Yes	1	9.243	Retain
CI3	7.25	8.81	0	1.56	Yes	1	8.034	Remove
CI4	8.33	10.00	−1	1.67	Yes	1	8.666	Retain
CI5	8.14	9.46	1	1.32	Yes	2	8.630	Retain
CI6	6.86	8.72	0	1.85	Yes	1	7.792	Remove
CI7	7.87	9.71	0.5	1.84	Yes	2	8.758	Retain
IG1	7.06	8.92	0	1.85	Yes	1	7.990	8.70	Remove
IG2	7.57	9.05	1	1.48	Yes	2	8.424	Remove
IG3	7.93	9.41	1	1.48	Yes	2	8.569	Remove
IG4	8.25	9.47	1	1.22	Yes	2	8.661	Remove
IG5	7.99	9.56	1	1.58	Yes	2	8.607	Remove
IG6	8.37	9.82	0.5	1.45	Yes	2	8.838	Retain
IG7	7.58	9.59	−0.5	2.01	Yes	1	8.589	Remove
IG8	8.14	9.41	1	1.27	Yes	2	8.621	Remove
IG9	7.93	9.41	1	1.48	Yes	2	8.569	Remove
IG10	7.79	9.26	1	1.47	Yes	2	8.510	Remove
IG11	8.22	9.68	0.5	1.46	Yes	2	8.801	Retain
IG12	7.16	9.01	0	1.85	Yes	1	8.085	Remove
IG13	8.22	9.68	0.5	1.46	Yes	2	8.801	Retain
IG14	8.22	9.88	0	1.66	Yes	1	9.053	Retain
IG15	8.54	10.00	−1	1.46	Yes	1	9.270	Retain
IG16	8.07	10.00	−1	1.93	Yes	1	9.036	Retain
IG17	8.54	10.00	−1	1.46	Yes	1	9.270	Retain
IG18	7.69	10.00	−1	2.31	Yes	1	8.843	Retain
IG19	7.93	10.00	−1	2.07	Yes	1	8.964	Retain
CS1	8.49	10.00	−1	1.51	Yes	1	9.243	8.96	Retain
CS2	8.22	9.82	0.5	1.60	Yes	2	8.815	Remove
CS3	8.49	10.00	−1	1.51	Yes	1	9.243	Retain
CS4	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
CS5	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
CS6	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
CS7	7.91	9.26	1	1.35	Yes	2	8.536	Remove
CS8	8.07	9.47	1	1.39	Yes	2	8.613	Remove
CS9	8.13	9.82	0.5	1.70	Yes	2	8.801	Remove
CS10	8.43	10.00	−1	1.57	Yes	1	9.215	Retain
CS11	7.73	9.56	1	1.83	Yes	2	8.552	Remove

* Note: Type 1: ${\mathrm{Z}}_{\mathrm{i}}\le 0$; Type 2: ${\mathrm{Z}}_{\mathrm{i}}>0$; Type 3: $\mathrm{C}{\mathrm{U}}_{\mathrm{i}}>\mathrm{O}{\mathrm{L}}_{\mathrm{i}}$ and ${\mathrm{Z}}_{\mathrm{i}}\ge {\mathrm{M}}_{\mathrm{i}}.$

).

4.3. Importance Ranking of Sustainable Chef Competency Indicators

Following the final selection of indicators, importance ranking was conducted based on the Gi values derived from the Fuzzy Delphi results. All six dimensions achieved scores above 8.0, indicating strong expert consensus regarding their relevance to sustainable culinary practice.

At the dimension level, the rankings were as follows:

• Culinary Skills (Gi = 8.96): Highest-ranked, affirming that technical proficiency remains foundational to sustainable culinary practice.
• Innovation and Governance (Gi = 8.70): Emphasizes the chef’s role in driving sustainable innovation and decision-making.
• Local Food (Gi = 8.67): Highlights the importance of supporting regional food systems and sourcing practices.
• Environmental Impact (Gi = 8.62): Reflects the need for awareness and mitigation of environmental externalities.
• Ecosystem Awareness (Gi = 8.59): Underlines the role of chefs in resource conservation and ecological responsibility.
• Cultural Interaction (Gi = 8.48): Emphasizes cultural literacy and heritage preservation within sustainability discourse.

At the indicator level, the retained competencies for each domain are listed below:

• Culinary skills: CS1, CS3, CS4, CS5, CS6, CS10.
• Innovation and Governance: IG15, IG17, IG14, IG16, IG19, IG18, IG6, IG11, IG13.
• Local Food: LF2, LF6, LF4, LF7, LF3, LF5.
• Environmental Impact: EI1, EI4, EI6, EI2, EI8.
• Ecosystem Awareness: EA9, EA10, EA5, EA4, EA1, EA2, EA6, EA8.
• Cultural Interaction: CI2, CI7, CI4, CI5.

Within the Culinary Skills domain, CS1 (ability to follow cooking and energy-saving principles) ranked highest, followed by CS3 (flexibly adjust menus using seasonal alternative ingredients to reduce waste) and CS4 (cook and store food carefully to avoid foodborne illness). In Innovation and Governance, IG15 (observe kitchen waste patterns to identify menu shortcomings and make timely adjustments) and IG17 (choose ingredients based on nutrition labels) were rated as most important. For Local Food, LF2 (check for agricultural food certification labels) received the highest consensus. In the Environmental Impact domain, EI1 (use kitchen tools effectively to avoid ingredient waste) emerged as the leading indicator. Ecosystem Awareness was led by EA9 (choose nutritious food that is accessible and affordable for everyone), while Cultural Interaction was topped by CI2 (use education to convey the idea of reducing food waste).

The resulting 38 high-consensus indicators constitute a comprehensive and prioritized competency framework. This hierarchy offers actionable guidance for culinary education, professional training, and sustainability-oriented policy development, ensuring that chef competencies are grounded in both technical proficiency and contextual relevance (see Figure 1). Further details of the competency indicators for sustainable chefs are provided in Appendix A.2,

Table A2. The Competency Indicators for Sustainable Chefs.

Culinary Skills (CSs): Dimension (Code): Indicator

CS1 Ability to follow cooking and energy-saving principles. CS3 Flexibly adjust menus using seasonal alternative ingredients to reduce waste. CS4 Cook and store food carefully to avoid foodborne illness. CS5 Assess food hygiene by observing preparation and cooking processes. CS6 Prepare nutritionally balanced meals. CS10 Improvise meals using available ingredients, including leftovers.

Innovation and Governance (IG): Dimension (Code): Indicator

IG6 Efficiently plan refrigeration and freezer storage space. IG11 Make full and careful use of each ingredient to maximize its value. IG13 Incorporate food surplus education or concepts into daily kitchen operations. IG14 Innovatively use surplus ingredients to create new dishes and increase resource value. IG15 Observe kitchen waste patterns to identify menu shortcomings and make timely adjustments. IG16 Dispose of kitchen waste according to waste classification guidelines. IG17 Choose ingredients based on nutrition labels. IG18 Select reliable ingredient suppliers with a commitment to sustainability. IG19 Provide employees with fair wages and a reasonable working environment.

Local Food (LF): Dimension (Code): Indicator

LF2 Check for agricultural food certification labels (organic, pesticide-free, etc.). LF3 Check the country of origin of ingredients. LF4 Prioritize local ingredients to reduce food miles. LF5 Choose producers who practice animal-friendly farming. LF6 Invest in improving the livelihoods of farmers, upstream suppliers, and employees. LF7 Purchase local seasonal ingredients and make use of native varieties.

Environmental Impact (EI): Dimension (Code): Indicator

EI1 Use kitchen tools effectively to avoid ingredient waste. EI2 Prevent food spoilage caused by environmental contamination. EI4 Understand regulations related to food and surplus management. EI6 Prioritize low-carbon local ingredients to reduce food miles. EI8 Understand how dietary choices affect environmental sustainability.

Ecosystem Awareness (EA): Dimension (Code): Indicator

EA1 Know how to control eco-friendly food from the source. EA2 Have good food characteristics knowledge for kitchen staff. EA4 Choose suitable eco-friendly raw materials and reliable providers. EA5 Know the procedures for using eco-friendly ingredients to avoid repeated thawing. EA6 Avoid using genetically modified ingredients to maintain ecological balance. EA8 Prioritize plant-based ingredients to reduce reliance on livestock farming. EA9 Choose nutritious food that is accessible and affordable for everyone EA10 Understand that “sustainable food” benefits personal health, ecology, and the environment.

Cultural Interaction (CI): Dimension (Code): Indicator

CI2 Use education to convey the idea of reducing food waste. CI4 Promote cooking education on food safety, healthy diets, and nutrition. CI5 Understand the meaning of “animal welfare”. CI7 Respect the local food culture and reflect it through dish design

.

4.4. Interpreting the Findings Relative to Prior Research

The consensus pattern confirms and extends prior work across all six domains while clarifying why some items fell below the threshold.

• Ecosystem awareness: Experts prioritized upstream control (origin checks, production methods, plant-forward choices), which is consistent with studies positioning chefs as system actors who shape biodiversity and dietary impacts through procurement and menu design [8,9]. This alignment supports the view that ecological literacy translates into sourcing decisions before ingredients enter the kitchen [32].
• Environmental impact: High rankings for 3R routines, low-carbon purchasing and energy-aware workflows mirror findings that chef-led operational behaviors drive measurable reductions in waste and emissions in professional kitchens [11,12]. The result reinforces arguments that day-to-day practices—not only equipment upgrades—are the main levers for decarbonization in foodservice settings [34].
• Local food: Convergence on seasonality, certifications, origin verification, native varieties, and livelihoods support reframes “local” from simple proximity to value chain quality and justice, echoing sustainable gastronomy research and sector guidance [13,17]. This strengthens the rationale for procurement policies that embed kitchens within resilient regional supply networks [35].
• Cultural interaction: Items emphasizing education, such as food safety, nutrition, and anti-waste norms, are congruent with the K→A→P (Knowledge, Attitude, and Practice) logic linking literacy to behavior in hospitality contexts [15,18]. This supports the role of chefs as cultural intermediaries who transmit sustainability norms through menus and guest communication, rather than treating communication as peripheral to craft [32].
• Innovation and governance: The salience of menu analytics, inventory and cold-chain discipline, surplus valorization, and fair work practices integrates creativity with controls, aligning with contemporary competency models that connect performance, safety, and sustainability outcomes [14]. The pattern suggests that governance mechanisms such as SOPs, data capture, and incentives are integral to making “green intent” reliable in service.
• Culinary skills: Endorsed items emphasize adaptive, nutrition-aware and safety-first craftsmanship, such as using what is available and improvisation with leftovers, that embeds sustainability within core technique, consistent with recent extensions of chef competency frameworks [18].

Certain indicators lost prominence, and the explanation could be items that were narrowly procedural, duplicative, or less actionable in daily service, which attracted weaker consensus. This divergence indicates expert preference for high-leverage, frequently enacted practices that are teachable, assessable, and linked to operational KPIs (waste, CO₂e, margin), rather than one-off or highly specific controls [11,12,14,34].

5. Conclusions and Suggestions

5.1. Conclusions

This study developed a validated and consensus-based competency framework for sustainable chefs by applying the FDM to synthesize expert knowledge from academia, industry, and policy sectors. Anchored in food systems theory and sustainability-oriented culinary practices, the framework identifies six key domains: ecosystem awareness, environmental impact, local food, cultural interaction, innovation and governance, and culinary skills. A total of 38 refined indicators were confirmed through expert consensus.

Theoretically, this study extends traditional competency models by embedding sustainability principles derived from food systems thinking [17] and sustainable culinary preparation [18], addressing earlier critiques that conventional frameworks often overlook ecological and ethical dimensions [28,32]. It draws on the competency-based theory of performance [30,31], emphasizing the integration of knowledge, skills, and attitudes within professional contexts. The findings also reinforce McClelland’s view [29] that observable, behavior-based indicators are more reliable predictors of performance than cognitive traits or static qualifications.

In addition to its theoretical relevance, this research contributes to the broader development of sustainability theory by operationalizing systems thinking within the context of food systems. Chefs are positioned not only as technical practitioners but also as influential actors capable of shaping sustainable consumption patterns, reducing environmental impact, and fostering socio-cultural resilience within communities. By aligning culinary competencies with the strategic goals of sustainable food systems, the framework supports the practical realization of Sustainable Development Goals (SDGs) such as Zero Hunger (SDG 2), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13) [43,44]. This competency-based approach provides a transferable model for advancing food system transformation and sustainability across the hospitality and tourism sector.

These findings position chefs as system-embedded practitioners and connect competency development to measurable operational outcomes such as waste, carbon, and menu engineering, thereby bridging prior sustainability studies with a consensus-based, teachable indicator set.

5.2. Suggestions

The validated competency framework offers a structured reference for designing curricula, enhancing workforce training, and supporting strategic planning across various levels of the foodservice system.

• For industry practitioners

The framework emphasizes the integration of sustainability into staff training, particularly in areas such as ingredient sourcing, waste reduction, energy efficiency, and employee education. Establishing partnerships with local agricultural and fishery producers can help strengthen regional supply chains and reduce environmental impacts. Additionally, developing clear performance metrics related to sustainability, including ethical procurement, resource efficiency, and community engagement, can support continuous operational improvement.

• For educational Institutions

Culinary schools and hospitality programs are encouraged to align course content with the competency framework. This alignment ensures that students acquire both technical expertise and critical thinking abilities relevant to sustainable practice. The adoption of experiential learning strategies, such as kitchen audits, simulation exercises, and farm visits, can further reinforce students’ understanding of sustainability through direct engagement with real-world challenges.

• For government and policymakers

The framework may inform the creation of national or regional sustainability standards within the foodservice industry. Complementary public initiatives, including consumer education campaigns, eco-labeling systems, and incentive programs, can reinforce sustainability values and support alignment between industry practices and consumer behavior.

5.3. Limitations

Although this study applied a rigorous methodological process using the Fuzzy Delphi Method, certain limitations must be acknowledged. The expert panel was relatively small and regionally concentrated, which may affect the generalizability of the findings. Furthermore, the framework reflects the views of institutional experts and may not fully represent the perspectives of frontline practitioners, students, or consumers.

Future studies are encouraged to expand the validation process by involving a broader and more diverse group of stakeholders, including culinary educators, hospitality managers, and foodservice workers from various countries and cultural contexts. Such efforts would enhance the global applicability of the competency framework and support the advancement of sustainable culinary transformation.