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This study examined the influence of university corporate social responsibility (University CSR) on university corporate brand legitimacy through the lens of university brand trust.

The study utilized a cross-sectional research design with a quantitative approach to gather data from a sample of 398 university students. The collected data were analyzed using structural equation modeling.

The findings of the study suggest that University CSR has a significant influence on the legitimacy of a university's corporate brand. Moreover, the study identified the mediating role of university brand trust in the proposed relationship.

The study was conducted in the context of higher education in Tanzania. As a result, the generalizability of the findings to other contexts that significantly differ from Tanzania, a developing country, may be limited.

The study recommends that the management of higher education institutions in developing countries should include CSR practices in the strategic plans of universities. Additionally, faculty members should be empowered to play a significant role as initiators and implementers of CSR programs.

This study is one of the few attempts to examine the interplay between university CSR, corporate brand trust and university corporate brand legitimacy. The study contributes to the state of knowledge in the education sector by highlighting the role of university CSR in building social acceptance, which is a crucial pillar in empowering universities to play a role in social and economic development.

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.

Elucidating on the concrete outline of conservation of resource theory, this study aims to explore the links between work-family conflict and workplace thriving. In particular, this study has integrated depersonalization as a mediator and tested the moderated mediation effects of intrinsic motivation in work-family conflict and depersonalization relationships.

Data were collected using a sample of 357 doctors working across various public hospitals in India over two waves (T1 and T2) and was tested using AMOS and Process Macros.

Exploration reveals that work-family conflict is negatively associated with workplace thriving. The mediating role of depersonalization in between work-family conflict and workplace thriving was established. Moreover, the moderating role of intrinsic motivation in work-family conflict and workplace thriving via depersonalization was also established.

The present study makes a theoretical addition to the literature by investigating nuances through which work-family conflict relationships and thriving at the workplace can be affected. To date, such a relationship has not been established. The study also extends the role of depersonalization as an underlying mechanism between work-family conflict and workplace thriving, making an imperative contribution. This study also tested the moderating role of intrinsic motivation. Overall, these relationships are novel and have been seldom reported.