Purpose To analyze the literature on models for evaluating innovation performance in industrial clusters. Design/methodology/approach A systematic literature review was conducted, supported by bibliometric analysis. Studies were retrieved from Web of Science databases and analyzed both quantitatively and qualitatively, using Microsoft Excel and VOSviewer software. Findings This topic continues to attract interest from the scientific community, but there is still a gap in the literature regarding specific models for assessing the innovation performance of industrial clusters. These models can encompass essential dimensions such as networks, knowledge dynamics, innovative capabilities, and business performance. In addition, they incorporated the innovation indicators identified in this study. Originality/value This study provides a holistic understanding of scientific production to evaluate the innovation performance of industrial clusters. Consequently, in addition to filling the gaps in the literature, this study proposes alternatives to overcome the challenges related to these evaluations.

Purpose This study aims to assess the role of institutional pressure on innovation practices in the port sector. Design/methodology/approach This is a qualitative, exploratory, descriptive approach using remote interviews and asynchronous messaging with professionals from the Brazilian port sector. Findings Coercive pressures are highlighted by regulatory agencies that standardize and supervise port activities. Mimetic pressures lead to the adoption of competitive practices such as benchmarking and technical visits. On the other hand, normative pressures are related to ethical codes and social responsibilities, which influence innovation practises. These pressures have both direct and indirect effects on innovation in this sector. Research limitations/implications This study provides evidence of the influence of institutional pressures on innovation-related strategic decisions in the port sector. Practical implications This study reveals the importance of adequate public policies, including inter-port cooperation and organizational culture, to favor innovation. Originality/value This study addresses the gap in professionals’ comprehension of the institutional pressures affecting innovation in the port sector, thus providing a pioneering approach to the factors involved in this process.

Purpose This study analyzes the sales behavior of a Brazilian fashion retailer before, during, and after the COVID-19 pandemic, aiming to generate short-term forecasts using machine learning models. The pandemic’s impact on the retail sector created a need for accurate sales forecasting. Design/methodology/approach Sales behavior was analyzed using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Neural Network AutoRegressive (NNAR) models. Performance was tested during the Full-Price and Off-Price stages, considering eight clothing collections launched before and during the pandemic. Forecast accuracy was evaluated using the Root Mean Square Error (RMSE), Symmetric Mean Absolute Percentage Error (SMAPE), and Total Absolute Percentage Error (TAPE). Findings Sales before and after COVID-19 showed low volume and variability during the full-price period and high income volatility during the off-price stage. Collection 4, launched in February 2020, displayed stable sales with reduced promotional impact. NNAR slightly outperformed SARIMA, highlighting the importance of nonlinear models in capturing retail sales volatility. Sales showed greater variability before and after restrictions, particularly during discounts, which resulted in higher prediction errors. Originality/value This study helps fashion retailers to choose suitable models for forecasting sales during the full- and off-price stages, considering specific environmental conditions. It also provides insights into retail dynamics during disruptions.

Purpose This study proposes a theoretical model to evaluate the direct and mediating relationships between business intelligence resources and digital capabilities and between strategic capabilities and small and medium-sized enterprises (SME) innovation across five Latin American countries. Design/methodology/approach Partial least squares structural equation modeling (PLS-SEM) was used to evaluate a general model with an entire sample of 480 SMEs from five Latin American countries for 2022. Findings The main findings show that business intelligence resources and digital capabilities positively affect SMEs’ innovation and mediate the relationship between strategic capabilities and innovation in the entire sample of Latin American SMEs. Research limitations/implications Business intelligence resources and digital capabilities simultaneously promote innovation among SMEs in Latin American countries. Therefore, the development of public policies and organizational strategies should focus on enhancing the acquisition of business intelligence resources and developing digital capabilities. Institutional and market differences may explain the variations between countries. Originality/value This study contributes to the analysis of the relationship between resources and capabilities within the digital economy context of Latin American SMEs and in five specific countries. Specifically, it examines the direct and mediating effects of business intelligence resources and digital capabilities on the link between SMEs’ strategic capabilities and innovation.

Purpose This study investigates the impact of fundamental factors (returns, liquidity, and volatility) and investor sentiment, captured through indirect (ARMS index) and direct (Google Trends search volumes of positive and negative terms) measures across the Expansion and Contraction phases and three time horizons, on the risk of stock market crashes in Brazil. Design/methodology/approach The analysis applies the CMAX (current index level relative to the historical maximum) method for crisis identification and the local bull-bear indicator to classify economic phases over short-, medium-, and long-term horizons. Probit models were then estimated to assess how these factors affect the likelihood of crisis occurrence. Findings The results reveal that declining returns, reduced liquidity, and heightened volatility increase the probability of crisis. Investor sentiment, marked by optimism during expansions and pessimism during contractions, significantly predicts crisis risk, particularly over shorter horizons, although its effect diminishes as market fundamentals regain their influence. Notably, Google Trends exhibited strong predictive power, outperforming the ARMS index. Practical implications These findings provide timely insights for investors, analysts, and policymakers. Real-time sentiment monitoring via Google Trends supports early detection of market instability, aiding portfolio management, stress testing, and policy actions. The study also informs macroprudential regulation by incorporating behavioral indicators into systemic risk frameworks. Originality/value This study is among the first to examine direct and indirect investor sentiment across economic cycles in an emerging market, offering a robust framework to anticipate crises while highlighting the societal benefits of early detection and the importance of financial literacy in curbing emotional market behavior.

Purpose The article aims to clarify whether the Smart City concept can be universally adopted or whether it is reconfigured based on the conditions of a given reality, shaping a specific understanding of the Smart City. Design/methodology/approach Through bibliographic analysis and contextual studies of two distinct realities – Shanghai and São Paulo – different political structures and market economies are identified, which highlight distinct institutional factors that, in turn, can lead to different understandings of Smart City. Findings The research findings warn against the adoption of absolute concepts of Smart City and allow for effective comparisons between different realities, considering the institutional conditions of each location, leading to different understandings of Smart City: e-autocracy for Shanghai and e-democracy for São Paulo. Research limitations/implications Due to the proposal to compare such different realities, one of which is Chinese, there is a language barrier and a scarcity of Chinese studies published in English, which makes it difficult to fully understand the Chinese reality. Practical implications The article enables more coherent comparisons between different realities, avoiding the adoption of a universal parameter and considering the ambition of each locus to format a smart city. Originality/value This article shows that the understanding of smart city is distinct and influenced by the political structure and institutional factors in force in each reality, pointing out two concepts of a smart city: e-autocracy and e-democracy.

Purpose This research aims to explore the role of digital transformation in the tourism sector, identifying both opportunities and challenges associated with advanced technologies. Design/methodology/approach A qualitative research approach was employed, utilizing primary data collected through semi-structured interviews with industry professionals. Findings The study finds that artificial intelligence enhances personalized travel recommendations and customer service through chatbots and virtual assistants. The Internet of Things, virtual reality, blockchain and robotics improve efficiency and customer experience. However, challenges such as high implementation costs, data privacy concerns and the need for continuous technological updates persist. Additionally, the digital divide remains a significant barrier for smaller businesses and emerging markets. Practical implications The findings suggest that while digital transformation presents significant opportunities for enhancing services and operations, addressing associated challenges is critical for sustainable growth. Industry stakeholders must adopt a strategic approach to technology implementation to ensure inclusive benefits and enhanced competitiveness. Originality/value This study provides valuable insights into the impact of digital technologies on the tourism sector, offering practical recommendations for leveraging digital transformation to improve efficiency and customer satisfaction.