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This study aims to identify the main challenges to achieving humanitarian logistics in the context of United Nations peace missions in sub-Saharan Africa and to present suggestions for overcoming the logistical gaps encountered.

The methodological approach of the work focuses on the comparative case study of the United Nations Mission in South Sudan, the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic and The United Nations Organisation Stabilisation Mission in the Democratic Republic of Congo from 2014 to 2021. The approach combined a systematic literature review with the authors’ empirical experience as participant observers in each mission, combining theory and practice.

As a result, six common challenges were identified for carrying out humanitarian logistics in the three peace missions. Each challenge revealed a logistical gap for which an appropriate solution was suggested based on the best practices found in the case study of each mission.

This paper presents limitations when addressing the logistical analysis based on only three countries under the UN mission as a case study, as well as conceiving that certain flaws in the system, in the observed period, are already in the process of correction with the adoption of the 2016–2021 strategy by the UN Global Logistic Cluster. The authors suggest that further studies can be carried out by expanding the number of cases or using countries where other bodies (AU, NATO or EU) work.

To the best of the authors’ knowledge, this study is the first comparative case study of humanitarian logistics on the three principal missions of the UN conducted by academics and practitioners.

In response to multiple disruptions, the purchasing supply management (PSM) function in construction supply chain management (CSCM) has gained prominence due to stakeholder pressures, dynamic market conditions and the need to adhere to complex sustainability, safety and health regulations and standards. However, there is a noticeable absence of empirical research on measuring and mitigating PSM vulnerabilities, especially considering the distinct challenges faced by large engineer-to-order project-oriented manufacturers. To address these issues, the purpose of this study is to develop and test a novel method to assist companies in construction supply chains in assessing and managing risks associated with sustainable procuring and sourcing materials.

Grounded in the literature gaps on construction PSM and a real case supply chain, the research uses the design science research (DSR) approach to develop an integrated method for assessing PSM strategies in this sector. The method integrates three essential purchasing dimensions: supply risks, profit impact and sustainability risks of materials, supported by nine subdimensions. Empirical validation took place within a multinational European construction company based in Denmark.

Findings from the supplier–buyer relationships confirmed that the developed method allows for the identification of the key components that significantly impact supplier–buyer relationships, profitability and sustainability. The research further suggests that construction supply chain managers and purchasing practitioners can use the proposed method to evaluate PSM, thus enabling them to make more informed decisions.

Through the utilization of the proposed artifact, construction companies can take a more proactive approach to address PSM uncertainties, thereby enhancing their competitiveness in dynamic construction supply chains.

The research contributes to bridging the theory and practice, providing valuable assistance for construction companies assessing and managing the PSM and supply risks within global construction value chains. This paper provides original insights for the CSCM, aiding in adopting competitive PSM strategies to improve overall supply chain performance.

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.

The purpose of this study is to empirically investigate the role of dynamic capabilities, specifically the sequence of sensing, seizing, and transforming capabilities, in highly uncertain, emerging technology environments. Focusing on the extended reality industry, the study aims to understand the antecedents to these dynamic capabilities, their sequential nature, and their subsequent impact on innovation and company performance.

Based on a survey of 130 German companies in the extended reality sector, we built a structural equation model that explores the relationship between dynamic capabilities, their antecedents, and their effect on innovation and company performance.

The analysis suggests that sensing capabilities positively influence seizing and transforming capabilities, while seizing directly contributes to transforming. Transforming capabilities are linked to improved innovation performance, which in turn boosts company performance. Organizational ambidexterity, market orientation, and technology orientation are found to be crucial antecedents, accounting for 33.1% of the variance in sensing capabilities.

This research illuminates the interdependence of dynamic capabilities in highly uncertain business environments, such as emerging technology markets. It contributes original insights by elucidating the sequential nature of dynamic capabilities and identifying their vital antecedents. It also enlarges the understanding of how dynamic capabilities impact firms’ innovation performance.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

This study aims to compare the influence of origin types (country-of-origin – COO versus geographic indication – GI) and wine worlds (new versus old wine country – represented by Brazil and Italia, respectively) on the attitude of low-involvement Generation Z potential consumers from Brazil toward wine ads.

The authors conducted a two (COO vs GI) by two (New vs Old World) within-subject experimental design with 98 Brazilians from Generation Z. Latin square was used to systematically counterbalances the sequence in which participants were exposed to ads.

The results reveal that potential consumers’ attitudes are more positive when a COO is used in wine advertisements than when a GI is used. Besides, the consumers’ conative response is more positive when an Old World country (Italy) is used in wine ads than when New World country (Brazil) is used.

This study expands existing literature by showing that, in wine ads, COO information elicits more positive responses than GI. It also suggests a preference among potential consumers for Old over New World wines in purchase, recommendation and consumption decisions.

The wine advertisements for the market segment investigated should highlight the COO instead of geographical indication (GI). Besides, the authors point out the importance of promoting the concept of GI among low-involvement Generation Z potential consumers.

To the best of the authors’ knowledge, this is the first study to assess potential consumers’ attitudes toward wine ads, comparing COO and GI and determining which geographical cue elicits more positive consumer attitudes: origin types (COO vs GI) or wine worlds (Old vs New).

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Considering the restricted and enclosed nature of prisons and closed environments and the prolonged and close contact between individuals, COVID-19 is more likely to have a higher incidence in these settings. This study aims to assess the prevalence of COVID-19 among prisoners.

Papers published in English from 2019 to July 7, 2023, were identified using relevant keywords such as prevalence, COVID-19 and prisoner in the following databases: PubMed/MEDLINE, Scopus and Google Scholar. For the meta-analysis of the prevalence, Cochrane’s Q statistics were calculated. A random effect model was used due to the heterogeneity in COVID-19 prevalence across included studies in the meta-analysis. All analyses were performed in STATA-13.

The pooled data presented a COVID-19 prevalence of 20% [95%CI: 0.13, 0.26] and 24% [95%CI: 0.07, 0.41], respectively, in studies that used PCR and antibody tests. Furthermore, two study designs, cross-sectional and cohort, were used. The results of the meta-analysis showed studies with cross-sectional and cohort designs reported 20% [95%CI: 0.11, 0.29] and 25% [95%CI: 0.13, 0.38], respectively.

Through more meticulous planning, it is feasible to reduce the number of individuals in prison cells, thereby preventing the further spread of COVID-19.

Albeit gradual and uneven, the emergence of social and environmental reporting by publicly held corporations has been a major development in the last few decades. This paper aims to explore patterns of the emergence of these disclosures. Using an institutional theory lens, this paper considers mimetic, normative and coercive possibilities.

US publicly traded company data from 2013 to 2019 is used to test the hypotheses. Mimetic forces are proxied with corporate board interlock frequency. Normative ones use the extent of gender diversity on corporate boards. Measures of business climate and industry regulatory sensitivity proxy coercive potentiality.

Studied in isolation, each of the three forces through which organizations pursue the heightened legitimacy of enhanced environmental and social disclosures has credibility. The strongest support exists for mimetic and normative mechanisms, perhaps because the US government has been reluctant to make these expanded disclosures mandatory.

In the world of voluntary action, more attention to diffusion is needed. For these purposes, better proxies will be needed to study change. Social and environmental information should be separated for individual analysis.

At least in the USA, companies are attentive to what other companies are doing. There is something to be said for the ethical dimension of corporate transparency.

Governmental action in this area has not been effective, at current levels. Corporate leadership is essential. Critical information is shared about disclosure by board members.

Although institutional theory makes several appearances in this area, to the best of the authors’ knowledge, the current study is the first empirical archival study to examine the three forces simultaneously, providing evidence as to the relative magnitude of each institutional force on environmental and social disclosures. Should these disclosures not be mandated by government, this study shows pathways for enhanced disclosures to continue to spread.

The adoption of emerging technologies is critical to enhance construction industry performance. Previous studies have shown that the Nigerian construction industry (NCI) is slow to adopt digital technologies and faces performance issues. As a result, this study aims to investigate and model the barriers to adopting digital twin (DT) technology in the NCI with the view to provide stakeholders with adequate information on the multifaceted nature of DT barriers and provide strategies to improve DT adoption.

The study adopts a qualitative and quantitative approach to achieve the overall aim of the study. The qualitative approach included a scoping review used to identify barriers to DT adoption from the literature. A five-point Likert scale questionnaire was administered to 246 construction professionals in the NCI. This was followed by critical analysis using mean ranking and standard deviation, Kruskal–Wallis, factor analysis and partial least squares structural equation modelling (PLS-SEM).

The exploratory factor analysis revealed four categories of barriers to DT adoption in the NCI: “technological and investment”, “data management and government”, “project and human resources” and “digital transformation”. The PLS-SEM results revealed the causal relationships of four barriers categories and their concomitant effects on DT adoption in the NCI. The top three barrier categories that require critical attention in order of significance are: technological and investment (ß = 0.655), data management and government (ß = 0.313) and project and human resources (ß = 0.194). Digital transformation (ß = −0.046) has the least significance. Overall, all the barriers’ categories were accepted at a significance level of p < 0.05.

The practical implications include guiding policymakers and practitioners in making informed decisions to address the identified barriers to DT adoption in the NCI. The findings may also be applicable to other developing countries in Africa and beyond. By implementing effective policies and stakeholder guidelines, the NCI can advance technologically and enhance its competitiveness to execute advanced construction projects.

This research contributes to the construction industry by shedding light on the barriers to DT adoption and their intricate interconnections within the NCI context. It is also the first study in NCI context to present the level of DT awareness and explore the concomitant effects of the barriers.