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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.

This paper explores a five-year case example of two educators engaged in practice-based professional development (PBPD) for the Self-Regulated Strategy Development (SRSD) model in a middle school. It examines the transformative effects and challenges of improving writing instruction, the activities involved and alternative PBPD delivery methods. Highlighting a collaborative effort between an institute of higher education (IHE), a middle school and ThinkSRSD, a PBPD for SRSD developer, the example underscores the long-term benefits and innovative insights into engaging with PBPD for SRSD over multiple years.

The case involves analyzing survey data collected over five years. These surveys, which included specific SRSD-related queries and open-ended questions, were instrumental in assessing the evolution of the educators’ perceptions regarding SRSD and their engagement with PBPD. Additionally, the paper details PBPD activities as documented in a research journal, providing a comprehensive account of the developmental process.

Through a cross-institutional partnership, two middle school general educators participated in PBPD for SRSD for 30 h across five years. Their engagement with PBPD progressed from initial introduction and implementation to facilitating PBPD for SRSD among peers and at the national level. Over time, the most consistently enacted SRSD action was “memorize it,” while actions such as “discuss it,” “support it” and “independent performance” showed greater variability. Both educators consistently praised SRSD and sought continued PBPD engagement over the five years.

Our case example is the first five-year analysis of PBPD for SRSD among general middle school educators, highlighting the benefits and challenges of adopting evidence-based writing instruction. Our example emphasizes the need for continuous and focused professional development in areas crucial for student success, including self-regulation, prewriting strategies and techniques for fostering independent performance. Moreover, the two middle school educators’ critical feedback is invaluable for refining PBPD for SRSD. This work also enriches professional development schools (PDS) literature by offering effective strategies to support middle school teachers in developing a vibrant writing community, a cornerstone for student advancement in writing.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

This research aims to explore how social media influences the academic performance of university students in Bangladesh and examines the benefits and drawbacks of its usage.

We distributed a well-structured questionnaire among students enrolled in various programs at different universities in Bangladesh to collect data. We analyzed the data using factor analysis and regression models to uncover the impact of social media on academic performance.

Our research findings demonstrate that social media plays a crucial role in facilitating communication, information sharing and content development among university students in Bangladesh. Excessive reliance on social media can lead to dependence and hinder innovation, as students tend to excessively rely on readily available resources.

We relied on self-reported data from a limited sample size, which may affect the generalizability of our findings.

This study highlights the need to promote responsible use of social media among university students in Bangladesh to enhance their academic performance. We recommend implementing effective policy measures to control and manage undesired usage patterns, foster an intellectually equipped student body and contribute to the development of a knowledgeable and successful Bangladesh.

This research makes a significant contribution by examining the influence of social media on academic performance among university students in Bangladesh. It proposes practical policy measures to address the drawbacks associated with excessive reliance on social media, thereby contributing to decision-making and intervention strategies for promoting responsible usage.

This study applies the modified brand avoidance model to examine factors that influence sustainable fashion avoidance behaviour among millennial shoppers in South Africa.

A positivistic approach and a web-based online survey were employed to collect cross-sectional data from 423 millennial fashion shoppers. Standard multiple regression analysis was used to test proposed hypotheses.

Unmet expectations, materialism and symbolic incongruence emerged as major predictors of millennials' intention to avoid sustainable fashion. Sustainable fashion avoidance intention was found to have a positive effect on sustainable fashion avoidance behaviour.

This study relied on self-reported data collected from millennial shoppers. Future studies may improve the generalizability of this study's results by conducting a comparative study with other cohorts such as baby boomers and Generation X who espouse different shopping values. Future studies may benefit from the use of longitudinal data in order to understand how millennial shoppers relate to sustainable fashion as it evolves.

The results of this study suggest the importance of developing value propositions that align sustainable fashion with cultural, personality and symbolic cues valued by millennial shoppers. Consumer education on the benefits of sustainable fashion is recommended as a long-term behavioural change strategy.

The purchase behaviour of sustainable fashion should be encouraged as it enhances environmental sustainability including safeguarding the livelihoods of future generations.

This study contributes to literature on sustainable fashion avoidance behaviour. This is one of the pioneering studies to empirically examine the influence of unmet expectations, symbolic incongruence and ideological incompatibility in the context of an emerging market, such as South Africa.

Although significant research has examined the concept of transformational leadership, few studies have explored the indirect impact of transformational leadership on individual and organizational outcomes within the context of crisis. Accordingly, this study aims to advance our understanding of the indirect impact of transformational leadership on school performance and principals' work alienation within the context of the COVID-19 pandemic. In doing so, this study contributes to this developing stream of literature by hypothesizing the indirect effect of two relational resources, namely employee trust and relational coordination, which mediate the relationship between transformational leadership, school performance and principals' work alienation.

This study draws on a unique sample of 634 principals from Irish primary schools navigating the COVID-19 crisis. Structural equation modeling (SEM) was performed using Mplus 8.3 to test the hypothesized model.

Mixed findings emerged concerning the mediating process of relational resources and their impact on transformational leadership, school performance and principals' work-alienation. In particular, support is found for the critical role of principals whose transformational leadership style can help increase school performance. However, evidence suggests that employee trust does not mediate the relationship between transformational leadership and principals' work alienation.

This study provides several practical insights for education professionals, policymakers and HRM practitioners across each phase of the crisis management cycle. Firstly, regarding the pre-crisis stage, educational institutions should invest in targeted leadership development programs that prioritize relationship-building and effective communication among stakeholders. Second, during crises, the study emphasizes the role of relational resources in mediating the impact of leadership on school performance. Moreover, the study illustrates the importance of proactively cultivating strong connections with stakeholders, fostering timely, problem-solving-based communication. Finally, in the post-crisis phase, collaboration with government stakeholders is recommended to inform recovery policies.

This study makes several contributions to the literature on leadership and crisis management. First, this study adds new insights suggesting how principals as leaders influence school performance during crisis. Second, by adopting a relational perspective, this study suggests two types of relational resources (i.e. employee trust and relational coordination), as the mediators between transformational leadership, school performance and principals' work alienation. Third, this study moves the existing research on leadership during crisis forward by focusing on the functional effectiveness of leadership while focusing on the principals' work alienation during the pandemic.

The aim of this research is to investigate the association between perceived overqualification and leisure crafting in the context of immigrants. Drawing on the cross-cultural adaptation theory, the study tests the moderating role that acculturation plays in this relationship.

Data were collected from a total of 226 immigrants living in Northern Cyprus. In the initial survey, data were collected on perceived overqualification and acculturation, which was followed by the measurement of leisure crafting. Data analysis was performed using structural equation modeling.

Perceived overqualification asserts a significantly positive impact on leisure crafting. Assuming that acculturation plays a moderating role, the research shows that the positive effect that perceived overqualification has on leisure crafting is increased in cases where positive acculturation is elevated as opposed to reduced.

The study results were based on self-reported surveys and data were limited to overqualified immigrant groups in Northern Cyprus.

The study provides significant practical implications for management teams. They can design managerial interventions to increase the acculturation of immigrants, which may in turn reduce the perceived overqualification and increase the positive impact of leisure crafting. Also, the government needs to implement policies targeted at immigrants in order to help them rapidly adapt to the host society.

This research will be a pioneering attempt to explore the positive relationship between perceived overqualification and leisure crafting. The results suggest actions that can be taken to promote leisure crafting behaviors through the use of acculturation to enhance organizational commitment, belongingness to the host society, and well-being in overqualified immigrants.

This study aimed to collect data on the perception of top executive women in the Aruban hotel sector regarding implicit gender bias.

A qualitative study on the metacognitive processes of awareness, evaluation and autocorrection was utilized. Through purposive sampling women in the top two leadership levels in Aruba Hotel and Tourism Association (AHATA) member-hotels were selected.

Results showed that a third of the top female executives experienced implicit gender bias career barriers. Different types of bias were identified such as: judgments regarding pregnancy, unequal pay and obstructions by the male general manager. How the women dealt with this bias depended on the type of bias and their personality. The identification of bias and its effects on the career trajectory were also influenced by characteristics of the work setting such as the size of the hotel and functional area.

The research limitations include the chosen scope, the impediment of the generalizability of the findings due to the nature of the study, self-perceived data and possible researcher and respondent bias.

This study added to the existing body of leadership development literature with a focus on the effects of implicit bias on female leadership advancement. Some specific theoretical concepts that were combined in this study are organizational leadership, metacognition and the unconscious mind. The important role of personality was also confirmed in this study however one element that stood out in the current study was the effect of resilience in overcoming perceived barriers and attaining personal career goals. Suggestions and directions for future research are provided.

Despite the fact that gender bias was not observed in an explicit form, participants advised to be aware of the existence and effects of the implicit form and to seek education and guidance from female mentors and to remain goal oriented when confronted with this bias. Since female under-representation in senior leadership positions in other economic sectors is not observed this advice serves as a significant practical implication for the development of female leadership in this important sector in Aruba.

The study's objective is to estimate the association of specific perceived employer-provided benefits on employees' intention to leave in different age cohorts during coronavirus disease 2019 (COVID-19). Informed by the psychological theories of ageing, the authors propose three age-cohort-specific hypotheses in three motivational domains: security and health benefits, flexible work arrangement and education-related benefits.

The authors use a large survey of employees in Estonia (n = 7,209) conducted in 2020 and test the association of specific benefits and their interactions with age on employees' intention to leave.

The results show that older cohorts are generally less prone to leave their jobs. Benefits that employers could use during the COVID-19 crisis generally had negative associations with the intention to leave, but age-specific differences were negligible; only the perceived provision of flexible work arrangements reduced the younger cohort's intention to leave relatively more.

This study is one of the few that allows us to make inferences regarding the benefits preferences amongst the working population during an unprecedented health crisis.

This paper describes an exploratory, international research collaboration that seeks to gain a deeper understanding of the development and experiences of peer leaders in higher education across different international contexts, namely the USA, Canada (CAN), Australasia (Australia and New Zealand) (ANZ), the United Kingdom (UK) and South Africa (SA).

Data are summarized and compared across each of the participating countries, providing a more global context and depth of perspective on peer leadership (PL) in higher education than is currently available in the literature.

The findings highlight some apparent differences between countries in relation to student engagement in peer leader roles and the ways in which PL is supported by higher education institutions, as well as some similarities across the different international contexts, particularly in the way peer leaders view the benefits of their involvement in PL.

These insights provide a valuable addition to the literature on PL and practical information to higher education institutions for supporting student leadership development and involvement.