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Smart tourism is a destination management approach that requires the buy-in of a myriad of stakeholders. Its many audiences and complexity demand the creation of meaningful brands to effectively position and communicate smart tourism initiatives. The purpose of this paper is to explore how smart tourism branding strategies have been implemented to communicate relevant values, benefits and attributes to industry stakeholders through institutional websites.

Based on a semiotic analysis of two smart tourism-related sites (destinosinteligentes.es and smarttourismcapital.eu), the research interprets the brand-related visual and verbal signs.

The findings highlight how brand elements embedded in websites communicate a brand identity and facilitate particular interpretations of smart tourism. Both brands use similar signs to promote a techtopian vision of smart destinations but employ different strategies to motivate stakeholder buy-in.

Smart tourism is currently largely embedded in overall smart city initiatives and finding tourism-specific examples online is difficult. However, the two selected websites reflect the brands of multiple destinations and permit a detailed analysis of meaning making. Future research can focus on how brand-related signs are perceived by different stakeholders.

Identifying the strategies and shortcomings of current smart tourism brands informs future smart tourism branding efforts and effective communication with smart tourism stakeholders.

Semiotics is a relevant but underutilized method to understand how smart tourism initiatives conceptualize “smartness.”

This study focuses on establishing relations with some important but underestimated elements of knowledge dynamics and firm orientations to characterize organizational circular economy activities through eco-innovation (EIN). The advent of the circular economy (CE) in this post-pandemic era has brought unpredictable sustainable challenges for the manufacturing industries. This research paper aims to bring more clarity to the extant literature on the relationship between environmental innovation (EI) and CE.

In this study, a systematic literature review methodology was used to research the determinants of EI in the knowledge environment that drives the implementation of a CE.

This paper proposes a framework that articulates organizational learning and orientation dynamics and offers a new set of internal knowledge resources for a corporate CE. It is found that change toward CE requires connection with EI. However, successful CE growth largely depends on leveraging knowledge resources and orientation dynamics (stakeholder orientation, sustainability orientation, organization learning orientation and entrepreneurial orientation). CE techniques are still in their early phases of adoption and their implementation is still in its development. Circular knowledge economy (CKE) has the potential to be a useful alternative to achieving thriving CE to achieve sustainability in local and global businesses operations.

This study helps companies to understand the organizational learning and different orientation dynamics for achieving CE principles. The research findings imply that EI is critical in establishing a sustainable transition toward CE through organizational learning and orientation dynamics and has garnered significant attention from academics, public policymakers and practitioners. The proposed framework can guide managers to develop sustainable policies related to the CE. This research recognizes that firm-level CKE is important in shaping how knowledge resources relate to CE within transition management literature.

This paper abridges the knowledge gap in identifying key drivers and presents the current eminence, challenges and prognostications of sustainable EI parameters in the changing climate of CE. This study builds a framework that combines insights from different viewpoints and disciplines and extends one’s understanding of the relationship between EI and CE. From a theoretical perspective, this study explains the knowledge management complexity links between EI and CE. It builds a theoretical bridge between EI and CE to illustrate how firms transition toward CE following the recommendations. Thus, researchers should continue to support their research with appropriate theories that have the potential to explain EI and CE relationship phenomena, with a particular emphasis on some promising but underutilized theories such as organizational learning, dynamic capabilities and stakeholder theories.

The purpose of this paper is to propose physically based varying fidelity surrogates to be used in structural design optimization of space trusses. The main aim is to demonstrate its efficiency in reducing the number of high fidelity (HF) runs in the optimization process.

In this work, surrogate models are built for space truss structures. This study uses functional as well as physical surrogates. In the latter, a grid analogy of the space truss is used thereby reducing drastically the analysis cost. Global and local approaches are considered. The latter will require a globalization scheme (sequential approximate optimization (SAO)) to ensure convergence.

Physically based surrogates were proposed. Classical techniques, namely Taylor series and kriging, are also implemented for comparison purposes. A parameter study in kriging is necessary to select the best kriging model to be used as surrogate. A test case was considered for optimization and several surrogates were built. The CPU time is reduced when compared with the HF solution, for all surrogate‐based optimization performed. The best result was achieved combining the proposed physical model with additive corrections in a SAO strategy in which C1 continuity was imposed at each trust region center. Some guidance for other engineering applications was given.

This is the first time that physical‐based surrogates for optimum design of space truss systems are used in the SAO framework. Physical surrogates typically exhibit better generalization properties than other surrogates forms, produce faster solutions, and do not suffer from dimensionality curse when used in approximate optimization strategies.