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This research seeks to provide methodological bases for the identification, documentation and critical reflection of good practices of architectural design in built heritage. These are applied explicitly to the School of Porto architects, which express a high sense of pedagogy and community practice in this field. The methodological approach defines the selection criteria for a georeferenced inventory and the procedures for in-depth analysis of adaptive reuse strategies.

The works included in the inventory were selected according to geographical, chronological, typological, qualitative and quantitative criteria. The cases chosen for in-depth analysis have been studied along four thematic axes to dissect all the intervention processes (previous state, design/construction and final state). This approach is supported by a cross-analysis of different sources (oral, written, graphic) and using drawing as a fundamental research tool.

The research has collected and disseminated up to 150 works by 44 architects, providing a comprehensive portrait of heritage intervention by the School of Porto over the past decades. The selection of 22 buildings for in-depth documentation reveals a particular sensibility toward the cultural values through a case-by-case approach based in deep knowledge of the preexisting context and the introduction of contemporary additions in continuity and harmonious relation with the environmental and sociocultural context.

This work provides a novel methodology suitable for further extension and adaptation to other case studies, as a first contribution to a more comprehensive “Atlas of Architectural Design in Built Heritage” with European case studies. The research aims to introduce new and deeper perspectives on reference works that may constitute pedagogy for the future practice of architects within contextual, inclusive and sustainable approaches.

Building on contingency theory, this paper aims to investigate the extent to which the “4Ps international adaptation strategy” and internationalization intensity shape the servitization–profitability relationship.

The authors use primary (survey) and secondary (archival) data to perform multiple regression analysis.

The results indicate a positive relationship between servitization and profitability, and international intensity strengthens this association. The effects, however, are not consistent across the 4Ps – the price international adaptation strategy strengthens the positive relationship between servitization and profitability, while product and place international adaptation strategies weaken that relationship.

The findings have implications for the role of international intensity and the 4Ps in the marketing servitization context.

The study provides guidance for small firms in realizing higher performance by leveraging the 4Ps in the servitization context. Counter to expectations, placement and product lead to lower performance with increasing servitization, whereas price strengthens this relationship. The study adds to the international industrial management and marketing literature, providing evidence that contingency factors such as international marketing mix adaptation/standardization strategies moderate the servitization–profitability relationship.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

The main goal of this paper is to identify the attributes of consumer experience in Michelin-starred restaurants and to estimate their effects on restaurant ratings.

A sample of 70,233 online reviews of 224 Spanish Michelin-starred restaurants were analysed with the latent Dirichlet allocation algorithm. A sentiment analysis and a logistic regression analysis were also employed to estimate the effect of attributes on restaurant ratings.

Customer attention, food quality, decor and ambience and value for money are frequently used to define restaurant experience. However, it is shown in this study that the experience in a Michelin-starred restaurant goes beyond the evaluation of those four attributes. Furthermore, the effect of the factors that were identified on customer satisfaction differed depending on the restaurant ratings.

The findings are linked to the context of Spanish Michelin-starred restaurants. It is also assumed in this study that online reviews are based on truthful opinions.

Restaurant managers should primarily focus on customer attention and food quality to achieve customer satisfaction. In addition, those restaurants with an error-free service and a highly appreciated wine list among diners are more likely to achieve the culinary excellence that deserves a 5-star rating on TripAdvisor.

The attributes of the restaurant experience are frequently identified in literature reviews. Research based on text-mining analyses of customer reviews to discover a posteriori the factors that define a restaurant experience is scarce, and particularly difficult to find in the context of Michelin-starred restaurants.