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In the study of expatriation and expatriate adaptation, there are limited studies that focus on issues faced by expatriates working in foreign countries with very distinct cultures. This study aims to explore this idea through the experiences of western expatriates working in the Kingdom of Saudi Arabia (KSA). Two research questions were posed to examine the cross-cultural issues and challenges faced by expatriates in the KSA, as well as the role of cross-cultural training in expatriate adjustment.

The study was guided by an interpretivism paradigm through a qualitative method by using a semi-structured in-depth interview approach. Interviews were conducted among 12 expatriates from the USA and UK who are currently working in KSA.

A coding technique and theoretical thematic analysis was conducted to analyze the data. The results of this study highlighted three key themes that had a considerable influence on expatriates’ adjustment, in particular: culture shock, lack of pre-departure training and the demand for an extensive cross-cultural training.

It is acknowledged that the existence of sub-cultures within the KSA would expose the respondents to varying cultural values within the community. Thus, future studies within a similar context should consider the influence of intra-cultural variations.

The findings of the study emphasized on the importance understanding the cultural gap between home and host country and the individual cultural awareness of the expatriate. It calls attention to the need for a tailored and extensive pre-departure, cross-cultural training and a collaborative effort between employees’ and managers to improve expatriates’ motivation and retention.

Some firms excel at positively presenting their employer brand (talk), while others excel at effectively implementing human resource management (HRM) practices for the benefit of the employees (walk). Which approach is more effective? Focusing specifically on small and medium-sized hotels (SMHs), this study explores the relation of employer branding (EB) and HRM practices with organizational performance (OP).

Stratified sampling was used to identify 34 top management figures (owners, CEOs, and top HR managers) from SMHs across Greece. These individuals agreed to participate in in-depth, semi-structured, one-on-one interviews, focusing on their hotels’ HRM, EB, and organizational performance. The interviews were subjected to content analysis, further coupled with graphical exploration of the relations between the concepts under study.

The findings reveal a noteworthy pattern: high-performing SMHs tend to prioritize EB, particularly leveraging social media channels. This prioritization is further reinforced by the implementation of HRM practices, including extensive training and rewards. Clustering SMHs into four different levels based on their application of EB and HRM practices and the effect of these practices on OP, enables us to extend this study and gain valuable insights into the interplay of these factors.

This study highlights the need for practitioners to invest in HRM practices, especially in training and rewards, while giving due attention to EB, despite the potential resource limitations SMHs often face. Importantly, when basic levels of HRM are combined with high levels of EB, OP seems to be maximized.

Both HRM and EB deal with the employer – employee interaction, that’s why EB in most companies is the responsibility of the HRM department. Surprisingly, academic research has treated them as distinct fields, in isolation, ignoring their combined effects. This paper is the first to conceptualize EB as communication (“talk”) and HRM as practice (“walk”) and to thus propose that a complementarity relationship between these two dynamics may facilitate OP. Additionally, this study is the first to combine content analysis with a quantitative exploration to gain more holistic and valuable insights on the topic.

Drawing from the Job Demands-Resources (JD-R) framework and Conservation of Resources (COR) theory, the authors’ study examines the impact of high commitment HR management (HCHRM) practices and psychological capital (PsyCap) on job autonomy and job demands in predicting burnout in frontline food service employees.

A moderated mediation model was developed and tested on 257 Australian workers employed in the food service industry. Hypotheses were tested using structural equation modeling.

There was support for the mediation effect of HCHRM on burnout, via two sequential mediators: job autonomy and job demands. PsyCap was found to buffer (moderation) the effect of job demands on burnout. Frontline employees also perceived HCHRM to be a “negative signal” that was implemented for the good of management.

The authors are aware of the potential of common method variance due to the cross-sectional research design. Future research should adopt a longitudinal research design or collect data from several sources of informants. As the authors did not find support for the optimistic perspective hypothesis, despite its theoretical and empirical relevance under JD-R and COR perspectives, they call for further research exploring the link between HRM, job design and psychological conditions in promoting employee wellbeing.

Burnout is one of the most common and critical health issues faced by frontline food service employees. Food service organizations have to strategize their management practices to reduce employees' experience with burnout by implementing high commitment enhancing HR practices and developing employees' PsyCap.

This study provided a better understanding of how (macro) HCHRM practices as an organizational resource reduce burnout of frontline food service employees via two (micro) mediators: job autonomy and job demands. PsyCap is an important personal resource that lessens burnout, consistent with the COR theory. These findings contribute to the literature on strategic HRM and its relationship to employee wellbeing.

This paper aims to contribute to research on management training and development by exploring the impact of extensive training labeled as drill on coping with critical situations. More specifically, it inquires into conditions and supplements for drill to move from mere adaptation to exaptation, relating to the transfer of drilled procedures to serve novel requirements, in events involving different types of surprise.

The paper adopts an interpretive research approach. Data were collected through semi-structured interviews with members of the Austrian Military on cases of resilient field action in manifold situations of surprise.

The paper reveals that two different kinds of drill lead to properties that are essential for recovery from shock during critical events: the pure drill and the preaptative drill. Pure drill enables automatized action in situations when time or emotional pressure is too high for reflection or consideration of different options. Preaptative drill, pertaining to drill enhanced with background knowledge, leads to adaption or even exaptation of automatized action through reflection.

The present paper is the first to show the potential impact of drill on the ability to deal with specific kinds of surprise. It suggests that incorporating explanatory background knowledge about why and how rules and learned behaviors that were created into training programs can be of vital importance for dealing with surprise successfully.

The economic crisis of 2008 has caused a significant increase in the number of unemployed in Spain and a decrease in investments in active training policies. In this context, it is even more necessary to demand improvements in the degree of efficiency of the training programs aimed at unemployed people.

This paper presents the opinions of a group of experts in labor intermediation on the need to include transversal competences in the training contents of employment courses aimed at the tourism sector to improve the degree of employment of the unemployed.

All the experts consulted have indicated the need to enrich the subjects of the training courses by incorporating workshops that favor the implementation of certain transversal competences such as team work, management in stress situations, problem-solving, willingness to learn, self-initiative, verbal communication and mastery of social networks.

The main limitations are given by the small number of experts in the field. However, their long career and participation in employment programs make their opinions valuable.

The main practical implication is in the fact that the proposed suggestions about modifications in the contents of the training courses for employment in the tourism sector are perfectly applicable, and according to the expert’s opinions, they would improve the degree of labor insertion of the participants.

The improvement of the degree of employability of the unemployed who participate in the training actions and a greater adaptation to the specific characteristics of the jobs offered by the entrepreneurs of the sector.

The adaptation of the formative contents of the courses focused on the unemployed can make possible two desirable effects. On the one hand, the improvement of the quality of the tourist resources and on the other hand, increase the degree of employability of the unemployed, and in this way improve the efficiency of training programs.

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.

Previous literature has identified human resources as a key source of competitive advantage in organizations of all sizes. However, Small and Medium-sized Enterprises (SMEs) face difficulty in comprehensively implementing all recommended Human Resource Management (HRM) functions. In this study, we shed light on the field of HRM in SMEs by focusing on the context of Greek Small and Medium-sized Hotels (SMHs), which represent a dominant private sector employer across the country.

Using a fuzzy-set qualitative comparative analysis (fsQCA) and 34 in-depth interviews with SMHs' owners/managers, we explore the HRM conditions leading to high levels of performance, while taking into consideration the influence of internal key determinants.

We uncover three alternative successful HRM strategies that maximize business performance, namely the Compensation-based performers, the HRM developers and the HRM investors. Each strategy fits discreet organizational characteristics related to company size, ownership type and organizational structure.

To the best of the authors' knowledge this is among the first empirical studies that examine different and equifinal performance-enhancing configurations of HRM practices in SMHs.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.