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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 objective of this study is to propose a model for the implementation of a technological platform for participants to develop solutions to problems related to the Fourth Industrial Revolution (4IR) megatrends, and taking advantage of artificial intelligence (AI) to develop their complex thinking through co-creation work.

The development of the model is based on a combination of participatory action research and user-centered design (UCD) methodologies, seeking to ensure that the platform is user-oriented and based on the experiences of the authors. The model itself is structured around the active and transformational learning (ATL) framework.

This study highlights the importance of addressing 4IR megatrends in education to prepare students for a technology-driven world. The proposed model, based on ATL and supported by AI, integrates essential competencies for tackling challenges and generating innovative solutions. The integration of AI into the platform fosters personalized learning, collaboration and reflection and enhances creativity by offering new insights and tools, whereas UCD ensures alignment with user needs and expectations.

This research presents an innovative educational model that combines ATL with AI to foster complex thinking and co-creation of solutions to problems related to 4IR megatrends. Integrating ATL ensures engagement with real-world problems and critical thinking while AI provides personalized content, tutoring, data analysis and creative support. The collaborative platform encourages diverse perspectives and collective intelligence, benefiting other researchers to better conceive learner-centered platforms promoting 21st-century skills and co-creation.

In the Netherlands, thesis assessment quality is a growing concern for the national accreditation organization due to increasing student numbers and supervisor workload. However, the accreditation framework lacks guidance on how to meet quality standards. This study aims to address these issues by sharing our experience, identifying problems and proposing guidelines for quality assurance for a thesis assessment system.

This study has two parts. The first part is a narrative literature review conducted to derive guidelines for thesis assessment based on observations made at four Dutch universities. The second part is a case study conducted in one bachelor’s psychology-related program, where the assessment practitioners and the vice program director analyzed the assessment documents based on the guidelines developed from the literature review.

The findings of this study include a list of guidelines based on the four standards. The case study results showed that the program meets most of the guidelines, as it has a comprehensive set of thesis learning outcomes, peer coaching for novice supervisors, clear and complete assessment information and procedures for both examiners and students, and a concise assessment form.

This study is original in that it demonstrates how to holistically ensure the quality of thesis assessments by considering the context of the program and paying more attention to validity (e.g. program curriculum and assessment design), transparency (e.g. integrating assessment into the supervision process) and the assessment expertise of teaching staff.

Integrated marketing communications (IMC) is possibly “the richest and most accessible service-learning experience” in the marketing curriculum (Petkus, 2000, p. 68). Yet, despite this recognition, scholars and practitioners continue to lament the pronounced theory-practice gap between how IMC is taught and the practice of it in industry (Schultz and Patti, 2009; Kerr and Kelly, 2017). This research embeds IMC practice within a classroom setting and subsequently explores student marketers' perceptions of their skill development through experiential client-based learning.

An in-depth qualitative study demonstrates the value of integrating experiential learning within an IMC course, captured through students' reflective practice.

Evidence suggests that experiential, client-based projects are suitable for fostering key practice-based skills in the classroom through students “experiencing” IMC at work. However, this is not always easy. In fact, building key skills such as leadership, motivation, communications, organisation and teamwork presents various challenges for students, whilst students appear unaware of other pertinent skills (e.g. persuasion, critical thinking) gained through exposure to “real-world” IMC tasks.

Instructors adopting experiential learning in the marketing classroom have an opportunity to actively design tasks to embed key workplace skills to bridge the theory-practice gap. Client-based projects offer fertile ground for students to experience marketing in action whilst ultimately bolstering their confidence in their workplace skills.

This research contributes to the marketing education literature and acknowledges the importance of embedding key workplace skills into the contemporary marketing curriculum. An overview of challenges and solutions for instructors seeking to adopt experiential learning via client-based projects in the IMC classroom is presented within this research.

New Space activities offer benefits for human progress and life beyond the Earth. However, there is a risk that the New Space Economy may develop according to an anthropocentric mindset favouring human progress and survival at the expense of all other species and the environment. This mindset raises concerns over the social and environmental impacts of space activities and the accountability of space actors. This research article explores the accountability of space actors by presenting a pluralistic accountability framework to understand, inspire and change accountability in the New Space Economy. This study also identifies future research opportunities.

This paper is a reflective and normative essay. The arguments are developed using contemporary multidisciplinary academic literature, publicly available evidence and examples. Further, the authors use Dillard and Vinnari's accountability framework to examine a pluralistic accountability system for space businesses.

The New Space Economy requires public and private entities to embrace hybrid and pluralistic accountability for their social and environmental impacts. A new way of seeing the relationship between human life, the Earth and celestial space is needed. Accounting language is used to mirror and mobilise broader forms of responsibility in those involved in space.

This paper responds to the AAAJ's special issue call for examining how accountability can be ensured in the New Space Age. The space activities businesses conduct, and the anthropocentric view inspiring their race toward space is concerning. Hence, the authors advocate the need for rethinking accountability between humans and nature. The paper contributes to fostering the debate on social and environmental accounting and the accountability of space actors in the New Space Economy. To this end, the authors use a pluralistic accountability framework to help understand how the New Space Economy can face the risks emanating from its anthropocentric mindset.

This paper aims to analyze the adoption of social media platforms by tourists in Pakistan. Based on an adaptation of the technology acceptance model (TAM), this study assesses the factors that lead users to adopt these platforms.

A survey was administered to a convenience sample of 399 travelers who use social media in Pakistan. A Confirmatory factor analysis was conducted using AMOS to evaluate convergent and discriminant validity as well as composite reliability. Structural equation modeling was applied to examine the causal relationship among all proposed constructs.

The findings reveal that the perceived usefulness (PU) and perceived ease of use (PEoU) of a social media platform positively impact the behavioral intention of its users. The proposed constructs of compatibility, enjoyment, user expertise and e-trust all demonstrated their crucial roles in the adoption of a social media platform for tourism-related activities by enhancing the platform's PEoU and usefulness.

This research validates the relationship between PEoU and PU of a social media platform in the hospitality industry. Interestingly, this study has expanded TAM by validating the addition of four more constructs, (1) compatibility, (2) enjoyment, (3) e-trust, and (4) expertise, to add worth to this model regarding the understanding of social media usage in this specific industry. The findings are valuable both for managers and policymakers in the tourism sector in Pakistan, as the latter can utilize the results to entice a larger segment of social media users to the tourism industry.

This paper aims to examine the key challenges experienced and lessons learned when organizations undergo large-scale agile transformations and seeks to answer the question of how incumbent firms achieve agility at scale.

Building on a case study of a multinational corporation seeking to scale up agility, the authors combined 36 semistructured interviews with secondary data from the organization to analyze its transformation since the early planning period.

The results show how incumbent firms develop and successfully integrate agility-enhancing capabilities to sense, seize and transform in times of digital transformation and rapid change. The findings highlight how agility can be established initially at the divisional level, namely with a key accelerator in the form of a center of competence, and later prepared to be scaled up across the organization. Moreover, the authors abstract and organize the findings according to the dynamic capabilities framework and offer propositions of how companies can achieve organizational agility by scaling up agility from a divisional to an organizational level.

Along with in-depth insights into agile transformations, this article provides practitioners with guidance for developing agility-enhancing capabilities within incumbent organizations and creating, scaling and managing agility across them.

Examining the case of a multinational corporation's exceptional, pioneering effort to scale agility, this article addresses the strategic importance of agility and explains how organizational agility can serve incumbent firms in industries characterized by uncertainty and intense competition.

Contemporary entrepreneurial ecosystem models and frameworks advocate that culture is a criterion for entrepreneurial intention and central to entrepreneurship discourse. However, there is limited research from resource-constrained economies, such as sub-Saharan Africa and at a sub-national level. Responding to calls for bottom-up perspectives hinged on local context and heterogeneous nature, this paper aims to provide an in-depth understanding from multiple perspectives about the effect that culture and entrepreneurial intention have on the entrepreneurship process and performance in Nelson Mandela Bay, South Africa.

A mixed-method research design followed a sequential independent process consisting of two phases. Phase 1 included the dissemination of questionnaires to economically active participants, and 300 responses were statistically analysed. In Phase 2, 15 semi-structured interviews with influential economic development agents were conducted.

The results indicated that social legitimacy towards entrepreneurship existed and self-employment was viewed positively. However, self-employment endeavours were mainly necessity driven, and the systemic low levels of innovation, poor business competitiveness and the inability to scale were highlighted. The findings indicated that individuals venturing into business had a culture of being dependant on the government, lacking a risk appetite, fearing failure, with disparate groups suffering from a poor legacy of entrepreneurship.

Despite research done on the role of culture and entrepreneurial intention on entrepreneurial ecosystems, there are few case studies showing their influence at a sub-national level. This study responds to calls for studies on a sub-national level by exploring the influence that culture and entrepreneurial intention have on entrepreneurship in a resource-constrained metropole.

Combining two organizational change theories, life cycle and organizational development, this study examines how strategic change cycle has been adopted and implemented across three different organizations, a public organization, an NGO and an intergovernmental organization toward achieving their goals.

This study triangulates three different qualitative research methods: open-ended semi-structured interviews conducted with UN Women Egypt's director, text analysis of the three organizations' websites and the discourse analysis of the Tri-County Foundation's leaders.

Strategic change cycle has been differently formulated, adopted and implemented by the three organizations based on their goals, resources and contexts. While Office Board of Investment adopted a comprehensive reactive change, Tri-County Foundation followed a partial proactive transformation and UN Women Egypt developed a partial reactive strategy. Henceforth, public organizations and nonprofit organizations can develop different strategies of change in function of needs, resources, goals and context.

This study advances a theoretical framework on organizational change by integrating two theories, life cycle and organizational development, presenting four patterns of change: comprehensive reactive, comprehensive proactive, partial reactive and partial proactive.

The purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems.

A qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis.

The findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes.

This paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.