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This study aims to examine the moderating role of generational identity in the relationship between user-generated content (UGC) and tourist destination image. It seeks to unveil the differential impact of UGC on the cognitive and affective dimensions of tourist destination image across generational cohorts: Generation Z, Millennials and Generation X.

This study has an experimental design. It used online surveys to gather data from 317 participants recruited through social media channels. Participants were presented with both positive and negative UGC, and their responses were measured using cognitive and affective scales.

This study reveals that UGC significantly affects both the cognitive and affective dimensions of the destination image. However, the impact on the cognitive aspect is moderated by generational differences, with distinct variations observed across Generation Z, Millennials and Generation X. No significant generational moderation was found for the affective dimension, indicating a universal influence of UGC on emotional perceptions of destinations.

The findings suggest that marketers should tailor their communication strategies to the generational characteristics of their target audience, especially concerning the cognitive aspects of destination marketing. However, for affective appeals, a uniform strategy could be applied across generations, simplifying the approach for emotional marketing content.

This research contributes to the extant literature by highlighting the importance of generational segmentation in the strategic marketing of tourism destinations. It extends the discourse to generational analysis, providing a more granular understanding of the interactions between tourists and UGC.

This study aims to identify how the sustainability of Brazilian Federal Institutes of Education, Science and Technology is implemented.

The research is descriptive and quantitative, and questionnaires were used to collect data, which were applied to the campuses and deans of the 38 IFs. The sample selection resulted from a sampling calculation for a finite population, which considered the five Brazilian regions, reaching 248 respondents. To analyze the collected data, descriptive statistics were used to demonstrate the mean, standard deviation (σ) and coefficient of variation of each of the seven axes of the questionnaire and also of the axes and regions jointly; and the sustainability indicator was defined.

A majority of the analyzed FIs were considered to be medium or highly sustainable. The classifications achieved by the FIs demonstrate that sustainability practices are reasonably present in a significant number of campi and Principals’ Offices.

This study contributes by creating a data collection instrument to measure the sustainability practice, not only in educational institutions but also in any type of institution; and the sustainability indicators created, which make it possible to classify organs and entities in accordance with sustainable initiatives implemented by them.

This study’s authors can highlight the mapping of sustainability implementation in the Brazilian Federal Institutes analyzed, which, owing to the sample used, allows for the verification of similarities and differences in these practices between the five Brazilian regions, in addition to making comparisons possible.

Digital sustainable maturity (DSM) is an important factor for gaining a competitive advantage. However, the literature on this topic is scarce. This study was designed to understand and formalize the concept of DSM and investigate models for measuring it in the context of supply chains.

To this end, a systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) tool, whose steps and evaluation criteria resulted in a portfolio of 87 articles. These were analyzed based on pre-established criteria from the literature on scope, design and application.

The results revealed an emerging field, but with the domain of DSM still underexplored. No formal concept of DSM was found in the literature. Thus, this paper presents the first definition in the field, which can be understood as the organization’s dynamic capability to leverage its digital assets to promote sustainable performance in economic, environmental and social dimensions. Regarding the analyzed models, 72 maturity models were identified considering both domains. However, only nine of them provided a model for evaluating DSM. Overall, the models presented varied architectures, which made it impossible to determine a consistent approach for measuring maturity in this domain.

This study is original as it systematically integrated digital and sustainable constructs to measure maturity. Furthermore, it formalizes the concept of DSM, which was not found in the existing literature.

Artificial intelligence (AI) has been touted as one of the viable solutions to address urban mobility issues. Despite a growing body of research on AI across various sectors, its use in the mobility sector remains underexplored. This study addresses this limitation by investigating AI applications and identifying the AI material properties and use cases that offer mobility-specific affordances.

Although AI applications in mobility are growing, academic research on the subject has yet to catch up. Therefore, we follow a systematic review and analysis of practitioner literature. We conducted a comprehensive search for relevant documents through Advanced Google and OECD databases and identified 173 sources. We selected 40 sources published between 2015 and 2022 and analysed the corpus of evidence through abductive qualitative analysis technique.

The analysis reveals that mobility organisations are implementing various AI technologies and systems such as cameras, sensors, IoT, computer vision, natural language processing, robotic process automation, machine learning, deep learning and neural networks. These technologies offer material properties for sensing mobility objects and events, comprehending mobility data, automating mobility activities and learning from mobility data. By exploiting these material properties, mobility organisations are integrating urban mobility management, personalising and automating urban mobility, enabling the smartification of infrastructure and asset management, developing better urban transport planning and management, and enabling automatic driving.

The study contributes a mid-range theory of the affordances of AI for mobility (AI4M) at the infrastructure, operation and service levels. This contribution extends the existing understanding of AI and offers an interconnected perspective of AI affordances for further research. For practitioners, the study provides insights on how to explore AI in alignment with organisational goals to collectively transform urban mobility to be affordable, efficient and sustainable.