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 0.210.21 and 0.300.30 with a significance level of 5%5\% and statistical power of 80%80\%.