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This paper empirically addresses the effect of coercive, normative and mimetic pressures on sustainability results, focussing on the three dimensions of the triple bottom line approach: environmental, economic and social. The mediating role of compliance, analyser or proactive corporate strategies towards sustainability is also considered.

The hypotheses developed in this study were tested using data from a sample of private companies from two industries: manufacture of chemicals and chemical products, and manufacture of basic metals.

The results confirm the role played by institutional pressures for sustainability in explaining the involvement of organisations in economic, social and environmental aspects. The mediating effect of corporate strategy is also confirmed, although only for environmental aspects.

Research into sustainability development is evolving rapidly; however, few studies have explored its diffusion amongst organisations from a triple bottom line perspective by considering the role of different current external pressures, the corporate strategy and the diverse results.

本研究擬對強制壓力、規範壓力和模仿壓力對可持續性成果的影響進行實證研究。研究的焦點放在三重底線法的三個層面上,即是環境層面、經濟層面和社會層面。研究人員亦探討尋求可持續性的承諾、分析儀和積極主動的公司戰略的中介作用。

研究人員測試其建立的各項假設; 使用的數據取自兩個企業的私人公司的樣本,它們是製造化學品和化學產品的企業,以及製造基本金屬的企業。

研究結果確認了尋求可持續性所帶來的制度壓力,在解說企業於經濟、社會和環境三方面的參與上所扮演的角色。研究結果亦確認了公司戰略的中介作用,唯這只見於環境的層面上。

探討可持續性發展的學術研究發展迅速,唯當中較少從三個基本的角度去探討可持續性發展在組織內的傳播; 本研究考慮了目前各種外來壓力、公司戰略和不同的結果所扮演的角色,以彌補這研究差距。

This paper proposes a framework for the implementation of sustainability practices by individual companies and corresponding supply chains (SCs), considering the various SC tiers, the three dimensions of sustainability and the following critical areas: Governance, Product and Process, Customers and Suppliers Management, and Stakeholder focus.

Using several case studies of small- and medium-size companies, a qualitative methodology is used to develop the proposed framework.

The analysis of these case studies makes it possible to identify the sustainability practices that are the most important for practitioners, thereby reducing the number of sustainability practices specified in the literature and giving some insights regarding which should be used in addressing critical areas. A set of guidelines and actions is also suggested for managers to continue the path of sustainability.

Further studies in other cultural and national realities and industrial contexts are desirable to assess the proposed framework's suitability and the results obtained.

The results of this study show that the practices implemented are highly dependent on the resources that the company can mobilize and, on the choices, and commitment to sustainability on the part of top management.

The authors propose a framework to help individual companies and corresponding SCs to implement sustainability practices considering the various dimensions of sustainability, critical areas and SC's various partners.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.