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The aim of this article is to obtain a stable tensegrity structure by using the minimum knowledge of the structure.

Three methods have been formulated based on the eigen value decomposition (EVD) and singular value decomposition theorems. These two theorems are being implemented on the matrices, which are computed from the minimal data of the structure. The required minimum data for the structure is the dimension of the structure, the connectivity matrix of the structure and the initial force density matrix computed from the type of elements. The stability of the structure is analyzed based on the rank deficiency of the force density matrix and equilibrium matrix.

The main purpose of this article is to use the defined methods to find (1) the nodal coordinates of the structure, (2) the final force density values of the structure, (3) single self-stress from multiple self-stresses and (4) the stable structure.

By using the defined approaches, one can understand the difference of each method, which includes, (1) the selection of eigenvalues, (2) the selection of nodal coordinates from the first decomposition theorem, (3) the selection of mechanism mode and force density values further and (4) the solution of single feasible self-stress from multiple self-stresses.

The rapid growth in cruise shipping coupled with increasing public awareness of climate change has led to increasing concerns about the impact cruise shipping poses on the environment, especially regarding air emissions. This study analyses the cruise shipping network of ports in and around the emission control areas (ECAs) to understand the structural properties of the network and ports.

A complex network approach was used to analyse the network data of 239 voyages serviced by 14 international cruise lines, visiting 127 ports across 44 countries in the Caribbean Sea.

It is found that the network has a small-world property with a short average path length and a high clustering coefficient. The regulations affect connections among ports, in which most ports in ECAs have lower connections than ports outside ECAs. A few ports in ECAs play important key roles, but many ports outside ECAs play a more important role in the network because the regulations are barriers for cruise ships entering the ports.

The findings of this study have drawn useful guidelines for cruise lines and port authorities to improve their operations. Constrictive recommendations are suggested to policymakers for designing reasonable regulations to attract more cruise shipping to travel in ECAs.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

Like every other sector, educational institutions have also been suffering immensely due to COVID-19 pandemic. Many educational institutions are now adopting digital classroom services. However, an online platform with the need for appropriate technology and infrastructure from the students’ perspective poses a severe challenge to developing countries like Bangladesh. The paper aims to figure out the relevant factors that affect the extent of student satisfaction with digital classroom services at the school and tertiary levels.

It is a quantitative study of 450 students from Bangladesh who encountered online classes during the pandemic of COVID-19. An equal number of students from all levels, including schools, colleges and tertiary stages, participated in the survey. Exploratory and confirmatory factor analyses are used to interpret the data. Structural equation modeling using AMOS graphic software is incorporated to test the study’s hypothesis.

Among all the four determinants of student satisfaction during this critical era, all levels look satisfied with the three underlying influences: technological, convenience and resource-related factors. However, school-level students found the digital classroom services abrasive with Internet connectivity and technical structures during online classes and exams.

A comprehensive study can assess the difference between private and public university students in this regard. In addition, the impact of gender and/or location (rural/urban area) can be assessed by using the same model of the study.

Having the experience of the students’ satisfaction level during this pandemic, the government, educational institutions and other stakeholders can take away the findings of the results to have a better plan for Internet-based education at every level.

The study is unique to see the readiness of developing nations such as Bangladesh to focus on the sudden uncertainty like a pandemic in introducing the digital education platform. The study can add value to achieving the country’s sustainable development goal of becoming a digitally enabled regional education hub.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

Effective leadership is critical in driving innovation and success in organisations, particularly in today’s rapidly changing environment. However, achieving effective leadership at all levels of the organisation can be challenging. This chapter argues that understanding how the brain functions is essential for innovation leaders to achieve positive results and higher rates of success in their projects. By analysing relevant research on neuroscientific functioning patterns and developing interventions based on these foundations, this chapter establishes that the brain’s self-organising ability and cognitive processing systems offer valuable insights for effective innovation leadership. Based on neuroscientific evidence this chapter concludes that effective innovation leadership should focus on inviting others to engaged co-creation, rather than directing others to perform specific tasks as if they were ‘a prolonged arm’. Additionally, effective innovation leadership integrates insights from information processing in the brain by providing behavioural-oriented impulses that activate the brain, enabling individuals to maintain focus, restore motivation or emotional stability, enhance mood and confidence, and increase cognitive flexibility. Evidence-based interventions range from structured breaks to powernapping and walking. The importance of self-leadership is stressed throughout the chapter. By deriving solutions from an understanding of how the brain functions, interventions that may have been known for a long time can become evidence-based and optimised for use in organisations. Future research could explore the intersection of neuro- and behavioural science with leadership to further innovate organisational principles.

This study is focused on achieving operational resilience through the practices of kaizen across the operational structure. The research is based on a case study of a commercial livestock farm in Nigeria. The study views the practice of kaizen from the perspective of the commitment of the members of the case study organisation who are directly involved with the operations of the organisation.

The study applies a qualitative approach to explore the topic, engaging members of the case study organisation in the research to gather relevant data on the implementation of kaizen practices and the drive to attain resilience in the case study organisation. Semi-structured personal interviews and workshops were used for data collection. The study adopts systems theory to explore the topic, identifying and engaging relevant stakeholders.

Parts of the findings relating to kaizen are the issues with the livestock production process, aggressive leadership and the issue of livestock mortality in the case study organisation. These were discussed based on extant literature. The study affirms the importance of organisational members' commitment and adequate leadership support to achieve sustainable kaizen practices. The study highlights the need to align kaizen practices with relevant organisational practices, such as reward systems and contextual requirements for its implementation in an operational process. The study suggests that further study can focus on the dynamics of the legal system on the implementation of kaizen, especially from a developing economic background like Nigeria, where this study was conducted.

The study projects learning about the spatial factors that can affect the practices of kaizen in critical sectors like livestock management.

This paper brings together the literature on theories of complexity adaptive systems (CAS), develops an analytical framework, applies this framework to the development of tourism destinations and critically reflects on the use of this perspective.

This paper elaborates on a CAS perspective on destination development, to further develop complexity thinking in tourism studies. This approach enables to identify policy avenues geared towards improving destination governance and contributing to sustainable tourism development.

Theories of CAS offer an analytical lens to better understand destination development, drawing explicit attention to (1) the levels of the individual, (emergent) structures, the structure-agency interface and the system level, (2) the steps related to the process of adaptation that is critical for systems to survive and thrive in times of change and (3) the undervalued importance of considering the factor of time.

Applying CAS theories help to address a range of (policy) avenues to improve destination governance, contributing to a shift in focus from reactively fixing problems to proactively addressing the structural issue of adaptive capacity building. It shows that managing tourism destination as complex systems involves a set of conditions that are critical as well as difficult to meet in tourism practice.