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Using Schein’s Descriptive Index (SDI), this paper aims to first examine gender role stereotypes and requisite managerial characteristics among Irish business students over a 10-year period. Then, the paper investigates whether there have been changes in gender role stereotypes during this period and subsequently unpack the reasons behind any changes recorded.

In total, 1,124 students from the same business student population rated men, women and managers in general, using SDI. Data was collected first during the academic year 2008–2009 and again in 2018–2019 to determine stability or change in gender role stereotypes and requisite managerial characteristics. Intraclass correlation coefficients scores were computed to determine the relationship between gender and requisite managerial characteristics and identify differences and similarities between the two samples. To explore the content of gender stereotypes, an examination of the specific descriptive items was conducted by performing a factorial analysis using Duncan’s Multiple Range Test. Finally, the authors adapted the scales developed by Duehr and Bono (2006) to determine whether broad gender stereotypic characteristics with respect to communal and agentic, attributed to men, women and managers, differ by sample.

The overall findings indicate changes in the extent of gender role stereotyping of the managerial role among the male cohorts studied. The subsequent analysis of the descriptive items identified that the change among the male cohort is due to the levels of agency they perceive women to now possess.

The authors contribute to the literature on both gendered and managerial stereotypes by showing changes in the pro-male stereotype of the managerial role and contribute to the existing debate on a shift towards a more androgynous view of leadership.

These findings help understand the content of gender role stereotypes that recent graduates bring with them to their first job post-graduation. The observed changes in the level of agency ascribed to women by their male counterparts could prove to be an important step forward for women’s advancement to managerial positions.

The findings indicate that both male and female cohorts in Sample 2 perceived men and women in general to possess the same levels of communal and agentic traits as their managerial counterparts.

This paper aims to present a comprehensive theoretical framework that seeks to explore the impact of cultural, legal and social factors within the external environment on the relationship between women on corporate boards and firm performance. By investigating these boundary conditions, the paper aims to shed light on how these pressures influence the aforementioned relationship.

To build the sample of companies, the authors selected companies listed on the stock exchanges of countries that represent a diverse range of institutional contexts. These contexts encompass countries with individualistic cultures, collectivist cultures, environments with mandatory gender quotas, environments without gender quotas, contexts with substantial progress toward gender equality and contexts with limited progress in achieving gender equality. To test the hypotheses, the authors used linear regression analysis as a primary analytical approach. Furthermore, they used the propensity score matching technique to address potential issues of reverse causality and unobserved heterogeneity.

The findings indicate that the positive influence of a critical mass of women on corporate boards on firm performance is contingent upon the institutional context. Specifically, the authors observed that this relationship is strengthened in institutional contexts characterized by an individualistic culture, whereas it is not as pronounced in collectivist cultural contexts. Furthermore, this research provides compelling evidence that the presence of a critical mass of women on boards leads to enhanced firm performance in institutional settings where gender quotas are not binding, as opposed to settings where such quotas are enforced. Lastly, the results demonstrate that the presence of a critical mass of women on boards is associated with improved firm performance in institutional settings characterized by low progress in achieving gender equality. However, the authors did not observe the same effect in institutional contexts that have made significant strides toward gender equality.

This research offers a unique perspective by investigating the relationship between women’s presence on corporate boards and firm performance across different institutional contexts. In this investigation, the authors recognize that gender diversity on corporate boards is not a one-size-fits-all solution and that its effects can be shaped by the unique institutional contexts in which companies operate.

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

The purpose of this study is to contribute to the debate on the impact of organizational culture and national culture on green supply chain management (GSCM) adoption by empirically testing the developed framework, and ultimately pave the way toward potential areas for future research.

Using survey data from a sample of Moroccan manufacturing firms, 130 responses were collected and analyzed using SPSS 25 and Smart PLS v 3.3.3 software. The paper used a convenience sample, as it is required by the quantitative method, which legitimate making generalization under certain conditions.

The research results indicated that the national culture does not influence the GSCM implementation. The results contradict a number of prior works. As for the second direct effect measured postulated that organizational culture has a direct and significant impact on the GSCM. The results indicate that adhocracy culture, clan culture and hierarchical culture have a positive impact on the implementation of GSCM initiatives. To assess the impact of ownership type on GSCM, we underlined the difference between local and foreign firms. In fact, as argued, the foreign firms are more implementing GSCM initiatives than local firms do. Based on the arguments advanced on prior literature, the firm size does, as expected, exert significant control over the adoption of GSCM initiatives.

The paper here is a starting point to understand how environmental sustainability and culture are interlinked; further research might contribute to this topic by empirically testing the model in similar or different contexts, using different cultural frameworks.

The practical implications for the paper are related to the necessity of adopting adequate organizational culture to build responsible behaviors for GSCM adoption by Moroccan firms. Recognizing the powerful role of organizational culture as a crucial factor responsible for GSCM’s success beyond the well-defined corporate strategies, including market presence and technological advantages, etc.

This paper contributes to the establishment of codependent links between sociology and management fields as it helps to update the social theories present in the operations management area.

To the best of the author’s knowledge, few works have pursued to review and bridge cultural theories with the GSCM implementation.

A debate on whether new ventures should be supported with public funding is taking place. Adopting a position on this discussion requires rigorous assessments of implemented programs. However, the few existing efforts have mostly focused on regional cases in developed countries. To fill this gap, this paper aims to measure the effects of a regional acceleration program in a developing country (Medellin, Colombia).

The economic notion of capabilities is used to frame the analysis of firm characteristics and productivity, which are hypothesized to be heterogeneous within the program. To test these relationships, propensity score matching is used in a sample of 60 treatment and 16,994 control firms.

This paper finds that treated firms had higher revenue than propensity score-matched controls on average, confirming a positive impact on growth measures. However, such financial growth is mostly observed in service firms rather than other economic sectors.

Further evaluations, with a longer period and using more outcome variables, are suggested in the context of similar publicly funded programs in developing countries.

These findings tip the balance in favor of the literature suggesting supportive programs for high-growth firms as opposed to everyday entrepreneurship. This is an insight, especially under the context of an emerging economy, which has scarce funding to support entrepreneurship.

As women’s position in the economy and society is often explained by cultural factors, this study aims to verify whether the observed changes in female empowerment in the region of Central and East European (CEE) countries of the European Union (EU) are associated with masculinity as a cultural trait.

The authors apply the k-means clustering method to group CEE countries into clusters with similar levels of female empowerment in two time points – 2013 and 2019. Next, the authors examine the clusters and cross-reference them with the national culture’s masculinity to explore the interrelations between female empowerment and cultural traits in the CEE countries and their development in time.

The analyses reveal that female empowerment is not uniform or stable across the CEE countries. The masculinity level is not strongly related to women’s position in these countries, and changes in female empowerment are not closely linked to masculinity.

Despite the tumultuous history of women’s empowerment in the CEE countries, the issues related to gender equality and cultural traits pertaining to the region are relatively understudied in the literature. By focusing on the CEE region, the authors fill the gap in examining the independencies between female empowerment and cultural masculinity.

This study focuses on the triadic multilevel psychic distance (MPD) between the firm, target market and bridge-maker and its consequences for firm internationalization. Specifically, it spotlights the triadic psychic distance between firms, the levels of psychic distance in the target market (country and business) and the bridge-maker. Therefore, this study examines the triadic MPD among these three entities and its impact on firm internationalization.

This study uses qualitative and case study research approaches. It is based on 8 case companies and 24 internationalization cases. Secondary data were collected, and interviews with bridge-makers and industry experts were conducted.

The study found that MPD appeared in the triad. The MPD between firms and markets is related to country-specific differences and business difficulties. The MPD between the firm and the bridge-maker is based on the latter’s lack of knowledge vis-à-vis bridging the firm’s MPD. Finally, the MPD between bridge-makers and the market is based on the former’s lack of knowledge of the home country’s business difficulties.

This is the first study to develop and adopt a triadic multilevel psychic distance conceptualization that provides evidence for and sheds light on the triadic MPD and its effect on firm internationalization. This study identifies the reasons behind triadic MPD in connection to firm internationalization. Notably, firm internationalization is interdependent on the triadic MPD setting between the firm, bridge-maker and target market. It has theoretical value and contributes to the recent advancement in the understanding of MPD in international marketing literature.

As populations are ageing and the global average life expectancy is rising, the provision of care for older people is an increasingly salient issue. This paper aims to focus on family-provided care for older immigrants, examining how older immigrants and care providers experience and construct family caregiving.

Based on interviews with care recipients, family care providers, municipal staff and representatives for migrant organisations in Sweden, this study presents a typology of family caregiving for older immigrants.

The authors found three caregiving types, namely, solely family-provided care and a combination of family care and public care (predominantly one or the other). The decision to select family-provided or publicly-funded care depends on personal and institutional factors.

The paper makes three empirical contributions to the literature on care provision for older immigrants. Firstly, this study provides insights into the structural and personal factors that shape care-giving arrangements for older immigrants. Secondly, this study examines the perspectives of care recipients and care providers on family-provided care. Care expectations differ between both groups and sometimes result in intergenerational disagreement. Thirdly, in terms of institutional support, this study finds that the Swedish state’s notion of individual needs does not match the needs of immigrant elderly and their caregivers. The paper places the care types in a broader discussion about eldercare provision in the Swedish welfare state, which has experienced a decline in publicly funded care services and an increase in family caregiving in the past 30 years. In addition, it addresses questions of dignified ageing from a minority perspective.

The purpose of this study is to examine how the tourism economy affects local food availability, access, utilization and stability in dessert-prone agricultural heritage sites. Specifically, the study aims to explore the relationship between the tourism industry and local agricultural practices and how this connection influences food security in the Siwa Oasis, located in the Western Desert of Egypt.

The study employs a qualitative exploratory research design using in-depth interviews and focus groups to investigate the impact of the tourism economy on food security and identify potential benefits and limitations for food security in the region.

The research reveals that the tourism economy in Siwa Oasis has only a marginal contribution to food security. The study highlights a lack of a strong connection between the tourism industry and local agricultural practices within the heritage site. As a result, the potential benefits and synergies that could be achieved between tourism and agriculture have not been fully realized, leading to a limited impact on food stability.

This study primarily relies on qualitative data from Siwa Oasis, Egypt, which may limit the generalizability of findings beyond this specific context. Additionally, while the study provides valuable insights into the complex relationship between tourism and food security, it does not quantitatively measure the magnitude of tourism's impact. Future research could incorporate quantitative methods for a more comprehensive understanding of this relationship in diverse desert-prone regions. Finally, the study highlights the need for more integrated approaches to enhance food security through tourism, but the specific strategies and policy recommendations require further investigation and adaptation to local contexts.

This study underscores the need for tourism development strategies that prioritize food security in desert-prone areas like Siwa Oasis. Policymakers and stakeholders should promote sustainable tourism practices that enhance local agriculture, create diversified income sources and foster equitable benefits for communities. Moreover, recognizing the seasonal nature of tourism, interventions to address food shortages during off-peak periods are crucial. Efforts should also focus on skill development and gender-inclusive opportunities within the tourism sector to ensure broader community participation. Additionally, collaborations between tourism and agriculture should be encouraged to optimize food availability and stability while preserving cultural food traditions.

This study adds original insights by examining the specific impact of the tourism economy on food security in dessert-prone agricultural heritage sites. The study's originality lies in its exploration of the untapped potential for synergy between the tourism and agricultural sectors and the implications for local food security. This research contributes to understanding how tourism can improve food security in specific contexts and provides valuable insights into sustainable development in heritage sites.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.