Notes on Sustainability Policies in Italian Michelin-Starred Restaurants
Introduction
- Research investigates sustainability in Michelin-starred restaurants in Italy.
- Focuses on how external sustainability drivers and customer demands influence internal sustainability policies and associated costs.
- Survey data from over 70 Italian Michelin-starred restaurants analyzed.
- 35% of variance in internal sustainability policies adoption is explained by external drives related to sustainability and customers' demands.
- Italian food service is considered iconic, making results relevant worldwide.
Background
- Food is economically and culturally significant in Italy.
- Restaurants are vital gathering places and economic assets.
- The Italian restaurant market is the second largest in Europe.
- Sustainability is increasingly important in the food sector, exemplified by the Michelin Green Star.
- Michelin Green Star recognizes restaurants with high ethical and environmental standards.
Literature Review
- Sustainability, defined by the Brundtland Commission, involves meeting present needs without compromising future generations.
- Sustainability ensures people can survive, satisfy needs, and prosper.
- The 17 Sustainable Development Goals (SDGs) provide a framework for sustainable development.
- Social sustainability includes practices that create healthy, fair, and inclusive communities.
- There is a need for more data on the long-term impacts and challenges of sustainable practices in restaurants and customer perceptions.
Hypotheses
- H1: External sustainability drivers positively influence customer demands for sustainability.
- H2: External sustainability drivers positively influence the adoption of internal sustainability policies.
- H3: Customer demands for sustainability positively influence the adoption of internal sustainability policies.
- H4: The adoption of internal sustainability policies positively influences costs derived from sustainability policies.
Methods
- Quantitative research based on 18-item questionnaires distributed to restaurant managers and chefs from Michelin-recognized Italian restaurants.
- 71 respondents completed the survey.
- Data analyzed using structural equation modeling based on partial least squares (PLS-SEM) with SmartPLS 4.0.
- Model includes constructs for external drivers, customer demands, internal policies, and associated costs.
Findings
- External drivers positively influence customer demands (H1 supported).
- External drivers positively influence internal sustainability policies (H2 supported).
- Customer demands positively influence internal sustainability policies (H3 supported).
- Internal sustainability policies positively influence costs (H4 supported).
Discussion
- Customer interest and response to sustainability imperatives at a societal scale influence stakeholders' expectations and explicit demands regarding sustainability practices.
- Michelin-starred restaurants acknowledge the need to approach sustainability as essential for strategic development and competitiveness.
- Adopting sustainability policies leads to higher internal company costs and potentially higher final costs for customers.
Conclusions
- 35% of the variance in Internal Sustainability Policies adoption is explained by external drives related to sustainability and customers' demands.
Implications
- Food service industry cannot ignore sustainability pressures.
- Michelin-starred restaurants should invest in sustainability-focused policies for long-term performance.
- Implementation of social sustainability practices can positively influence employee well-being.
- Supporting local economies and communities is crucial.
Limitations
- Relies on a convenience sample of 71 key informants from Italian Michelin-starred restaurants.
- The conceptual model leaves aside general or specific factors.
- Relies on self-reported measures, implying a high level of subjectivity.
Equations
- The inverse square root method by Kock and Hadaya (2018): used to appraise the sample size adequacy
- inverse square root method: Suitable to detect path coefficients between 0.21 and 0.30 with a significance level of 5% and statistical power of 80%.