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Cuenca, a World Heritage City, faces urban expansion as residents move to the outskirts, leaving the historic center abandoned and deteriorating. The challenge now is to relocate these spaces into sustainable and cohesive nodes. This research aims to identify cultural facility oversupply in the city center and understand the required usage for heritage buildings to promote a habitable, sustainable and cohesive historic center.

The study consisted of two phases. Firstly, a georeferenced spatial analysis and monthly usage frequency of each facility is proposed. Secondly, interviews explored the criteria for designating heritage buildings as cultural facilities. Additionally, a survey assessed urban habitability in three historic center parishes, measuring aspects like coverage, satisfaction and security from residents' perspectives.

The underutilization of cultural facilities demonstrates both inefficient heritage management and a lack of resident interest in cultural activities and neighborhood decision-making. Thus, ensuring collective ownership of heritage assets becomes crucial. Additionally, the municipality's approach to heritage must be reconsidered. While implementing a cultural program may seem faster and cheaper, the long-term cost-benefit of maintaining a cohesive historical center outweighs that of a dispersed city.

This paper calls for a fundamental reimagining of the concept of built heritage, emphasizing the need for a more inclusive and integrated approach that goes beyond museum and tourism-driven strategies. This perspective recognizes the importance of social, cultural and environmental sustainability in revitalizing the historic center, considering the broader context of the city and its diverse inhabitants.

Used clothes supply chains are becoming increasingly complex, fragmented and less transparent due to rising volumes of discarded clothes and its dispersed reverse logistics operations across the Global North (GN) and Global South (GS). While it has a promising impact on circular economy and international trade growth, increasing exports of used clothes and overflowing landfills raise some negative concerns on its overall sustainability. This paper addresses the dichotomy that exists in terms of interpreting the sustainability credentials of used clothes supply chains.

A systematic literature review was carried out and 55 articles were examined to identify the triple bottom line (TBL) sustainability impacts of used clothes supply chains. TBL sustainability issues were identified, reflected through the lens of natural resource-based view and interpreted in the form of propositions.

The paper pinpoints seven TBL sustainability concerns and prescribes three sets of strategic resources required in glocal used clothes supply chains for mitigating these. These are (1) slowing the supply chain by tackling poor quality, overproduction and oversupply issues, (2) improving logistics/supply chain infrastructure and ecosystem collaboration and (2) embedding transparent environmental, social and governance (ESG) measures taken by both value chain actors and regulatory bodies, for embracing system-level sustainable development.

This is one of the first studies to analyse TBL sustainability of glocal north–south used clothes supply chains. The study is unique in terms of its scope and contribution to the sustainable supply chain literature.

This study aims to examine the dimensions of organisational failure in the Brazilian sugarcane and ethanol refineries, as reported in judicial recovery plans.

This paper follows a qualitative, inductive approach that uses content analysis to examine the details of recovery plans. Besides, a cause-and-effect relationship diagram is proposed, making it possible to identify the interconnections between the identified variables.

There is evidence that organisational failures are not a linear outcome. Organisational failures are complex and occur because of several factors, often interdependent and operating at different levels.

Organisational failures basically have three interrelated levels: the macro-level (external environment), the meso-level (organisational environment) and the micro-level (associated with the decision-maker). The relationship between these levels is not trivial and involves coordinated research efforts.

Organisations must consider all types of failure levels when developing business reorganisation plans. Reorganisation plans are more than a formal document to achieve judicial recovery, as they should incorporate strategic factors.

Organisational failures are regularity in organisations’ day-to-day. Understanding failure’s sources is vital to design firms’ strategies and public policies.

The study of organisational failure involves the analysis of complex and multidimensional phenomena. Judicial recovery plans are the means for companies to get a second chance. To that end, this paper addresses the sources of organisational failures through the lens of judicial plans.

This paper aims to establish the determinants of production in the Spanish Designation of Origin (DO) area for Cava wine and forecasts sales to establish vineyard area variations that maintain market equilibrium.

By applying a vector autoregressive (VAR) model, the authors forecast demand and the consequent requirements for base wine production.

The results show that Cava sales determine the base wine supply. After forecasting demand and the consequent requirements for base wine, the authors’ results show that, to avoid oversupply, the vineyard area for Cava wine should not be increased.

The paper develops a simple and effective method for DOs affected by the current European wine plantation regulations to forecast from a supply and demand perspective and their surface needs in response to market changes.

This study contributes to the literature because, to the best of the authors’ knowledge, no other study has investigated the determinants of Cava supply and demand or defines a model to assess the effects of changes in growing areas. The model is applicable to other European protected designations of origin wines and would help policymakers to accurately establish vine planting authorizations.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit.

The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model.

By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).