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Disruptive events caused by cyber incidents, such as supply chain (SC) cyber incidents, can affect firms’ SC operations on a large scale, causing disruptions in material, information and financial flows and impacting the availability, integrity and confidentiality of SC assets. While SC resilience (SCRES) research has received much attention in recent years, the purpose of this study is to investigate specific capabilities for building SCRES to cyber risks. Based on a nuanced understanding of SC cyber risk characteristics, this study explores how to build SC cyber resilience (SCCR) using the perspective of dynamic capability (DC) theory.

Based on 79 in-depth interviews, this qualitative study examines 28 firms representing 4 SCs in Central Europe. The researchers interpret data from semistructured interviews and secondary data using the DC perspective, which covers sensing, seizing and transforming.

The authors identify SCRES capabilities, in general, and SCCR-specific capabilities that form the basis for the realignment of DCs for addressing cyber risks in SCs. The authors argue that SCRES capabilities should, in general, be combined with specific capabilities for SCCR to deal with SC cyber risks. Based on these findings, 10 propositions for future research are provided.

Practitioners should collaborate specifically to address cyber threats and risks in SCs, integrate new SC partners and use new approaches. Furthermore, this study shows that cyber risks need to be treated differently from traditional SC risks.

This empirical study enriches the SC management literature by examining SCRES to cyber risks through the insightful lens of DCs. It identifies DCs for building SCCR, makes several managerial contributions and is among the few that apply the DC approach to address specific SC risks.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

This aims to explore the relationship between the agile methods barriers in large-scale contexts and the benefits for business, team and product and process, exploring the organizational readiness (OR) mediating role.

We propose a theoretical model through survey-based research, applying partial least square structural equation modelling.

We confirmed that OR mediating effect on the relationship between agile methods barriers and team benefits. We operationalized OR in a broader context that embeds the strategic alignment of large-scale agile implementation, considering variables such as organizational structure and culture.

The data are cross-sectional rather than longitudinal, which limits temporal interpretations of the associations between agile methods and organizational issues.

The findings offer a way forward for organizations already using or planning to implement agile management to understand the pathway towards achieving the expected benefits. Our study also unveils the importance of looking at OR when implementing such a complex change in management from traditional to large-scale contexts.

Our results show the significant and positive influence of agile method on all three benefit variables (team, business, product and processes). Furthermore, we identified the significant and positive mediating role of OR on the relationship between agile method and team benefits.

This study aims to explore aesthetic atmospheres and their affordances in urban squares to advance knowledge on the research and design of attractive living environments.

Descriptions of pleasant and unpleasant experiences of urban squares were collected using qualitative questionnaires with open-ended questions. The theoretical framework and the lens of aesthetic affordances were applied to pinpoint and understand the connections between the place attributes and experiences.

This study found four distinct aesthetic atmospheres formed by perceived synergies of both the material and immaterial aspects of the environment. It was also found that the atmospheres may shift. A model that shows the aesthetic atmospheres and their potential affordances as layered and emerging is presented.

Everyday aesthetics considered as affordances open new research perspectives for the understanding of what generates attractive living environments – or not.

Aesthetics affordances may provide the design professionals and alike means on how to design places that engender specific aesthetic atmosphere.

Gathering and discussing commonplace aesthetic experiences in everyday life may enhance democratic participation in place development among people with different levels of design expertise.

This study combines theories of place with a novel concept of aesthetic affordances to identify distinct aesthetic atmospheres. A holistic overview structure of how the various constituents of aesthetic atmospheres relate to each other provides new ways of studying and understanding urban aesthetic atmospheres.

This paper aimed to develop a participatory methodology to analyze the disaster risk creation in coastal cities, based on an approach that combines social, urban, environmental and disaster risk elements.

The methodology uses some aspects of three theoretical approaches in a complementary way: i) the Pressure and Release (PAR) framework for the identification of dynamic pressures that contribute to disaster risk creation; ii) the application of Drivers, Pressure, State, Impact, Response (DPSIR) framework to analyze environmental dimensions; and iii) urban analysis, applying the Strengths, Weaknesses, Opportunities and Threats (SWOT) tool to classify urban processes. The methodology combined the use of satellite remote sensing data to analyze the urban sprawl and citizen science methods to collect social and environmental data, using the case study of the watershed of the Juqueriquerê River in the coastal city of Caraguatatuba, Brazil. The pilot project was part of a local university extension project of the undergraduate course on Architecture and Urban Planning and also engaged residents and city hall representatives.

The satellite remote sense data analysis indicated a continuous urban sprawl between 1985 and 2020, especially in the south of the Juqueriquerê watershed, reducing urban drainage and increasing the extension and water depth of urban flooding and riverine floods. Using citizen science methods, undergraduates identified settlements with limited economic resources to elevate houses and a lack of infrastructure to promote a resilient coastal city. After identifying the dynamic pressures that contribute to disaster risk creation and the weaknesses and strengths of a resilient city, undergraduate students proposed urban planning interventions and gray and green infrastructure projects to mitigate disaster risks.

The paper identifies urban sprawl in disaster-prone areas as one of the risk factors contributing to disaster. It also comprehensively analyzes differences between different zones in the Juqueriqere River, which will be useful for policy-making.

The method presented an interdisciplinary approach that used satellite remote sensing data and citizen science techniques to analyze disaster risks in coastal cities. The multidimensional approach used to evaluate risks is useful and can be replicated in other similar studies to gain a more comprehensive understanding of disaster risks.

Industry 4.0 broadly implies the digital transformation of industrial works. In India’s industrial arena, the textile industry is extremely important in the non-farm sector, both regarding value addition and employment generation. This chapter attempts to think about new avenues of research while integrating different streams of literature. For example – literature on innovation, literature on the industrial ecosystem, literature on industry 4.0, and consequences for the Indian economy – all such streams of literature have been considered synoptically to think of a new research program. The focus of this research program is to explore pathways of synergizing these different literatures and thinking about how to integrate and apply innovations for the betterment of the unorganized manufacturing sector in India. The unorganized manufacturing sector is a vast area in India, so here, we focus on some specific sections of the textile sector which is the handloom weaving industry. How have changes in techniques happened within the handloom weaving sector so far? What are the possible ways of applying these new technologies in altering the products and processes within the textile sector? What can the government do in this regard? These are the research questions that need attention in today’s context, and we have not found serious works in this direction in the context of the Indian economy; hence, we are investigating these issues in this chapter.

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 COVID-19 pandemic showed that preestablished contingency plans and resilience practices were insufficient to cope with long-term and global disruptions. Companies thus struggled to develop capabilities that ensure their survivability during similar crises. Building on the adaptation-based view (ABV) of supply chain resilience, this study aims to offer an in-depth perspective on survivability in supply chains (SCs).

The paper empirically tests related relationships between adaptation capabilities and practices that ensure operational continuity. Responses from 252 organisations were collected and analysed using partial least squares structural equation modelling.

The results empirically support the ABV’s theoretical propositions and assess the possibilities of intertwining, digitalisation, a circular economy and maturity for the survivability of SCs.

The derived insights are attractive for managers and researchers to foster supply chain survivability and contribute to the increasing efforts of middle-range theorising in logistics and supply chain management research.

To the best of the authors’ knowledge, this study is one of the first studies to define factors enhancing the survivability of SCs through the lens of the ABV.

The service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time.

This study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics.

The paper identifies three types of service ecosystem dynamics—behavioral patterns of service ecosystems—and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern).

The typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it.

The paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.