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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.

The purpose of this paper is to examine FaceReader as a tool to compare the olfactory preferences of two selected countries. This paper examines the olfactory preferences of customers in the bakery department of a grocery store in the Slovak and the Spanish market.

The aim of this study is to examine subconscious/unconscious preferences in the selection of aromas suitable for the bakery department in the Slovak and the Spanish market. In this case, it is not a classical qualitative sensory testing of the perception of fragrances. The aim is to identify the associations of scents related to the selected sales department through images of the selected aromas. A special platform is used to obtain subconscious/unconscious feedback, which allows online collection of implicit feedback using the software FaceReader 7.

The authors noticed the different moods of the two groups of respondents when they answered the question about what they associate with the smell of bakery products. The Spanish respondents were slightly pleasantly disposed, while the Slovak respondents were slightly unpleasantly disposed. The smell of bakery products evoked more memories and emotions in the Spanish respondents than in the Slovak respondents, which can be explained by the higher pleasant mood. The main contribution of this work lies in the new opportunities to obtain feedback that can be used in marketing research and that rely not only on explicit but also implicit data. The extension of the methodological apparatus to implicit feedback presupposes some form of control of the data collected by the questionnaire. The use of biometric tools can represent an efficient alternative in terms of time and money to the use of neuroimaging tools in the selection/research of aromas for specific stores/departments.

It must be noted that the sample is small, and adequate conclusions cannot be made about entire population. Based on empirical findings and pandemic-related limitations, the authors plan to conduct similar research with real aroma samples and with even larger sample of tested respondents, considering weather, season, olfactory sensitivity (anosmia, hyposmia and normosmia) and participant fatigue (beginning and end of the week).

Today, marketers are facing the greatest challenge of how to attract consumers’ attention. Every individual has a different perception of the shopping environment based on his own experience, beliefs and attitudes. This is why new marketing techniques and approaches are becoming increasingly popular in the marketing environment.

El objetivo de esta investigación es examinar FaceReader como una herramienta para comparar las preferencias olfativas entre dos países. Concretamente, examinamos las preferencias olfativas de los clientes en el departamento de panadería de un supermercado en el mercado eslovaco y español.

El objetivo de este estudio es examinar las preferencias subconscientes/inconscientes en la selección de aromas adecuados para el departamento de panadería en el mercado eslovaco y español. En este caso, no se trata de una prueba sensorial cualitativa clásica de la percepción de fragancias. El objetivo es identificar las asociaciones de olores relacionados con el departamento de ventas seleccionado a través de imágenes de los aromas seleccionados. Se utiliza una plataforma especial para obtener comentarios subconscientes/inconscientes, que permite la recopilación en línea de comentarios implícitos utilizando el software FaceReader 7.

Observamos diferentes estados de ánimo de los dos grupos de encuestados cuando respondieron a la pregunta sobre qué asociaban con el olor de los productos de panadería. Los encuestados españoles estaban ligeramente más predispuestos hacia aromas más agradables, mientras que los encuestados eslovacos estaban ligeramente más predispuestos hacia aromas menos agradables. El olor de los productos de panadería evocó más recuerdos y emociones en los encuestados españoles que en los eslovacos, lo que puede explicarse por el estado de ánimo. La principal contribución de este trabajo radica en las nuevas oportunidades para obtener comentarios que pueden ser utilizados en investigaciones de marketing y que no solo se basan en datos explícitos, sino también implícitos. La ampliación del aparato metodológico para obtener comentarios implícitos presupone algún tipo de control de los datos recopilados mediante el cuestionario. El uso de herramientas biométricas puede representar una alternativa eficiente en términos de tiempo y dinero al uso de herramientas de neuroimagen en la selección/investigación de aromas para tiendas/departamentos específicos.

Debe tenerse en cuenta que la muestra utilizada es pequeña y no se pueden extrapolar conclusiones para toda la población. Basándonos en los resultados empíricos y con las limitaciones relacionadas con la pandemia, planeamos realizar una investigación similar con muestras de aroma reales y con una muestra aún más grande de encuestados, considerando el clima, la temporada, la sensibilidad olfativa (anosmia, hiposmia, normosmia) y la fatiga de los participantes (inicio y fin de semana).

Hoy en día, los profesionales del marketing se enfrentan al gran desafío de cómo atraer la atención de los consumidores. Cada individuo tiene una percepción diferente del entorno de compra basada en su propia experiencia, creencias y actitudes. Es por eso que las nuevas técnicas y enfoques de marketing se están volviendo cada vez más populares en el entorno del marketing.

本文旨在探讨FaceReader在比较斯洛伐克和西班牙两个国家的顾客嗅觉偏好方面的效用。我们以斯洛伐克和西班牙市场一家食品杂货店的面点部门顾客为研究对象, 考察其嗅觉偏好。

本研究的目标是探讨在斯洛伐克和西班牙市场选择适合面点部门的香气时潜在的/无意识的偏好。与传统的定性感官测试不同, 我们旨在通过选定香气的图像识别与选定销售部门相关的气味的联想, 并通过FaceReader 7软件在线收集隐性反馈。

我们观察到两组受访者在回答关于面点产品气味联想时的心境差异。西班牙受访者略带愉悦, 而斯洛伐克受访者略带不悦。西班牙受访者对面点产品的气味引起的记忆和情感更为丰富, 这可能是由更高愉悦心境所解释的。该研究的主要贡献在于提供了在营销研究中利用反馈的新机会, 该反馈不仅依赖于明确的数据, 还依赖于隐性数据。将方法学工具扩展到隐性反馈的前提是以某种形式对问卷收集的数据进行控制。在为特定商店/部门选择/研究香气方面, 相对于使用神经影像工具在时间和金钱方面的花费, 生物测定工具的使用可以作为高效替代。

由于本研究的样本量较小, 因此不能对整个人口做出充分的结论。基于经验发现和受到大流行病限制, 我们计划进行类似研究, 使用真实的香气样本, 并考虑更大规模的受试者样本, 同时考虑到天气、季节、嗅觉敏感度（嗅觉缺失、嗅觉减退、正常嗅觉）和参与者疲劳程度（周初和周末）对受试者的影响。

当今, 营销人员面临着吸引消费者注意的最大挑战。每个个体根据其自身经验、信仰和态度对购物环境有着不同的感知。因此, 在营销环境中, 新的营销技术和方法变得越来越受欢迎。

This paper explores the collaborative dynamics and dimensions within a virtual multi-cultural and interdisciplinary workplace. The study focusses on the use of online communication technologies to enhance social inclusion and networking within academia.

This study uses an autoethnographic approach to draw on the personal experiences of a team of four scholars, including three early-career researchers and a senior scholar. Their reflections on their academic positionality and the institutional constraints reveal both the strengths and vulnerabilities of collaborating in a virtual workplace.

The findings offer insights into the complexities of navigating social dynamics, such as delegating responsibilities, organising meetings across various time zones and encouraging continuous collaboration, inclusivity and effective communication during an extensive timeline. As a result, their experiences revealed that a virtual workplace culture with similar and different attributes to a “normal” workplace emerged.

The paper demonstrates how to create an effective and inclusive virtual workplace by exemplifying best practices in academia and providing practical guidance for individuals and institutions based on honest, co-produced autoethnographic reflections of the authors’ lived experiences.

Discussions on tourism development address the urgent need to reduce the negative impacts of tourism on tourist destinations. Despite decades of trying to find potential ways to foster sustainability, however, current tourism development is still mainly driven by political interests and growth agendas. In spite of concepts intending to improve sustainable tourism development, negative dynamics, such as over-tourism and the exploitation of nature and local communities, dominate the current reality of tourism. This article focuses on the concept of degrowth as a potential solution for rethinking tourism policy and practices to ensure greater sustainability. Its aim is to explore the gap between these policies and the academic theories on instigating sustainable change, and the actual reality of the tourism industry, which is primarily driven by economic motivations such as growth.

To explore this dichotomy, this paper investigates the values of tourism lifestyle entrepreneurs. Small businesses are the most dominant group in the industry in terms of numbers. I contend that researching their viewpoint on current developmental trends could lead to valuable insights into how to tackle this gap between theory and reality. This paper also explores how the degrowth paradigm may promote sustainability in tourism, as well as the potential role that tourism lifestyle entrepreneurs could play in this development. The discussion is illustrated by a case study based on interviews with tourism entrepreneurs in Iceland.

The findings indicate that various tourism stakeholders have different approaches to growth, with many tourism lifestyle entrepreneurs tending to embrace degrowth practices by acting according to their value base, albeit sometimes unconsciously. This focus on aspects other than growth could potentially encourage tourism lifestyle entrepreneurs to contribute to sustainable development.

The examples discussed in this paper are locally limited and cannot be generalized due to the small size of the interviewed sample group. The scalability of individual entrepreneurs’ impact is limited due to their small size.

The actions and values applied by these tourism lifestyle entrepreneurs demonstrate how degrowth can be manifest on a small scale: growth is only embraced up to a certain limit, so it oes not exceed social and environmental capacities; from that point on, community well-being plays the key role. This study demonstrates the untapped knowledge tourism lifestyle entrepreneurs could provide to rethinking the tourism industry.

This study demonstrates the importance of shedding more light on ethical issues and values beyond growth in both academic and political discussions. Addressing tourism lifestyle entrepreneurs as smaller-scale actors of tourism degrowth could be a meaningful starting point for holistically rethinking tourism and give them a voice.

This research emphasizes untapped knowledge by acknowledging entrepreneurs and their potential for rethinking tourism development, concluding with recommendations for practice and policy.

In this article, we reflect on how smart technology is transforming service research discourses about service innovation and value co-creation. We adopt the concept of technology smartness’ to refer to the ability of technology to sense, adapt and learn from interactions. Accordingly, we seek to address how smart technologies (i.e. cognitive and distributed technology) can be powerful resources, capable of innovating in relation to actors’ agency, the structure of the service ecosystem and value co-creation practices.

This conceptual article integrates evidence from the existing theories with illustrative examples to advance research on service innovation and value co-creation.

Through the performative utterances of new tech words, such as onlife and materiality, this article identifies the emergence of innovative forms of agency and structure. Onlife agency entails automated, relational and performative forms, which provide for new decision-making capabilities and expanded opportunities to co-create value. Phygital materiality pertains to new structural features, comprised of new resources and contexts that have distinctive intelligence, autonomy and performativity. The dialectic between onlife agency and phygital materiality (structure) lies in the agencement of smart tech–enabled value co-creation practices based on the notion of becoming that involves not only resources but also actors and contexts.

This paper proposes a novel conceptual framework that advances a tech-based ecology for service ecosystems, in which value co-creation is enacted by the smartness of technology, which emerges through systemic and performative intra-actions between actors (onlife agency), resources and contexts (phygital materiality and structure).

This paper is a case study of student response systems used in large-class teaching. It considers the benefits, including the engagement of students and academic gains such as reduced administration. The constraints and impacts in classroom teaching are noted, drawing upon the experience of two teachers with their learning captured as a means of dissemination of practice to support other teachers who may be considering adopting and later adapting such practices (Gribble and Beckmann, 2023).

An autoethnographical account was undertaken using an action-learning approach as a sense-making exercise. These accounts enabled a depth of insight beyond the anecdotal evidence experienced by an individual teacher alone.

The findings show that while student response systems have constraints, these can be addressed by putting pedagogical concerns in front of any technology deployment, reaping benefits for students and teachers. Once engaged in using the system, students become more willing to enter further discussions. However, the limitations of both systems indicate that there may be a need for multiple systems to be available based on the pedagogical needs of the class.

The exploration of student response systems and outcomes of positive engagement by students in classroom settings provides insight to those wishing to explore such systems for use in large-class teaching settings.

This work extends discussions surrounding interactivity using student response systems. Additionally, practical insights from the users into their experiences with their students in using such systems provide alternatives for engagement in delivering large-class learning at scale.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

The literature currently offers only fragmentary insights into the research on the relationship between employee emotions and human resource management (HRM). Therefore, further research is essential to bridge this knowledge gap. Our study aims to identify the mediating effects of positive employee emotions and exhaustion in the relationship between HRM and employee engagement.

Drawing on the literature review findings, a conceptual model was formulated to illustrate the relationship between HRM, employee emotions and engagement. A confirmatory analysis was conducted using structural equation modelling (SEM CFA) on a sample of 1,000 employees to validate the proposed model. The data were collected in 2021, with a particular emphasis on exploring the indirect influence of HRM on engagement through positive employee emotions and exhaustion.

The quantitative research aimed to test a model depicting the relationship between HRM and employee emotions. The findings indicate the robust effect of HRM on positive employee emotions and exhaustion. The authors observed significant variation in the level of impact depending on the size of the organisation (stronger in large firms) and the sector (stronger in the public sector).

The study bridges the gap in our understanding of the link between HRM and employee emotions. It would be advisable to further explore the specific impact of individual HRM practices on both positive and negative employee emotions. It is worth extending the scope of future research to explore components of the investigated constructs as well as mediators and moderators of the relationship between HRM and employee emotions.

Following the imperative for human-centric digital innovation brought by the paradigm of Industry 5.0, the article aims to integrate the dispersed and multi-disciplinary literature on individual risks for workers to define, explain and predict individual risks related to Industry 4.0 technologies.

The paper follows the question, “What is the current knowledge and evidence base concerning risks related to Industry 4.0 technologies, and how can this inform digital innovation management in the manufacturing sector through the lens of the Industry 5.0 paradigm?” and uses the method of systematic literature review to identify and discuss potential risks for individuals associated with digital innovation. N = 51 contributions met the inclusion criteria.

The literature review indicates dominant trends and significant gaps in understanding risks from a human-centric perspective. The paper identifies individual risks, their interplay with different technologies and their antecedents at the social, organizational and individual levels. Despite this, the paper shows how the literature concentrates in studying risks on only a limited number of categories and/or concepts. Moreover, there is a lack of consensus in the theoretical and conceptual frameworks. The paper concludes by illustrating an initial understanding of digital innovation via a human-centered perspective on psychological risks.

Findings yield practical implications. In investing in the adoption, generation or recombination of new digital technologies in organizations, the paper recommends managers ensure to prevent risks at the individual level. Accordingly, the study’s findings can be used as a common starting point for extending the repertoire of managerial practices and interventions and realizing human-centric innovation.

Following the paradigm of Industry 5.0, the paper offers a holistic view of risks that incorporates the central role of the worker as crucial to the success of digital innovation. This human-centric perspective serves to inform the managerial field about important factors in risk management that can result in more effective targeted interventions in risk mitigation approaches. Lastly, it can serve to reinterpret digital innovation management and propose future avenues of research on risk.

This paper introduces a methodology to identify, analyse and represent heritage site attributes, emphasizing their impact on value, authenticity, integrity and management, with a case study on Ghadames, Libya. Inscribed in 1986 and moved to the In-Danger List in 2016 due to conflict, this work seeks to update the site's attributes and values for improved management.

This methodology, focusing on Ghadames, leverages recent heritage management advancements to monitor conflict-induced changes, aiming to enhance decision-making through a detailed analysis of the site's natural and cultural attributes.

Our findings highlight the need for systematic and holistic assessments of heritage site attributes and values, crucial for managing sites of both local and global significance. This approach is a key to understanding their identity, guiding interpretation, management and preserving cultural significance.

Developed for Ghadames, the methodology requires adaptation for other sites, underscoring the importance of identifying core tangible and intangible attributes that define a site's uniqueness.

Our developed methodology offers a replicable framework that can be modified by local heritage professionals to map attributes and assess the direct and indirect impact of conflict on heritage sites.

The detailed assessment provides a foundation for crafting informed policies and effective management strategies. It specifically targets minimizing the adverse effects of conflict on heritage sites' attributes. This effort is instrumental in preparing the necessary documentation to support the delisting of these sites from the UNESCO World Heritage Site In-Danger List, promoting their preservation and recovery.