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Sustainable management requires companies to build up new knowledge to acquire the competencies needed for action. This chapter aims to deliver knowledge about sustainability and knowledge for sustainability. Firstly, we systematically analyse the sustainability literature in the social sciences through a bibliographic analysis and topic modelling using VOSviewer and Mallet software. We outline research directions, themes and critical contributions for each research cluster identified. Additionally, we categorise over 30 definitions of sustainability identified by Meuer, Koelbel, and Hoffmann (2020). Secondly, we enumerate knowledge types needed for effective sustainability transitions of organisations. We trace typologies of sustainable business models and their distinct evaluations of sustainability. In this chapter, we argue that integrating the triad of social, ecological and economic goals is central for sustainability attempts as well as long-term thinking. Therefore, our research offers a comprehensive overview of sustainability in the social sciences supporting researchers and practitioners to navigate this miscellaneous and scattered field. Accordingly, our study is precious to young scholars researching sustainability who want to use the term in an informed and meaningful way.

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.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

The purpose of this paper, applying concepts in the plant-based food sector, is a focus on the competitive rival trap for startup firms, with their initial advantage for under-served market segments, only to be overtaken by scale, speed, and brands of incumbent brand firms. As a case study of industry transformation, the food production sector illustrates how organizational innovation brings new forms of rivalry, from the farm gate to the kitchen plate. As a result, startups face a rivalry trap, if unable to scale quickly, as incumbents reframe their strategic response with startup acquisitions, corporate incubators or alliance partnerships consumer demand.

This paper outlines the features of precision agriculture, a new paradigm for agriculture and food production, requiring new competences and skillsets in the protein revolution, including issues like virus, bacteria and the molecular structure of food groups, animal breeding and veterinary medicine. Plant-based foods is used as a case study for startups and the rivalry trap.

The emergence of plant-based foods is a case study of market opportunity and creative destruction, where the potential market varies from $25bn to $72bn, and growing faster in the dairy sector. However, food incumbents bring new strategic responses and a rivalry trap, where startups must gain scale quickly in capabilities, talent and marketing prowess, often exploiting demand in market niches unimpeded by incumbent rivals.

Startups in biological sciences face massive challenges to increase scale and scope, even with unique intellectual property.

Startup firms need multidisciplinary management teams with a global outlook.

Plant-based foods form part of the protein revolution but face challenges of scale, cost competitiveness and taste, despite advantages for climate mitigation.

The impact of technological and science applications has blurred the traditional concept of industry boundary, with huge variations in the intangible knowledge component in their core activities and capabilities. Underlying variations imply that not all industries have similar supply demand conditions, with variations in input costs, capital intensity and innovation needs, with strategic implications for the rivalry trap.

Most new media research focuses on behavior as a measure of engagement, while the psychological state of being occupied with its content has received little attention. This study examined news engagement beyond pure action observation by exploring young people’s psychological experiences with the news.

The study carried out a digital native’s survey on 212 people (18–28 years). The focus of the survey was on understanding individuals’ engagement with online news using affective and cognitive components. The authors compared the influence of each type of engagement on youth consumption of and attitudes toward online news.

The results of the hierarchical regression analysis showed that affective engagement can be a stronger predictor of online news consumption than cognitive engagement. While affective engagement significantly predicts positive attitudes toward online news, cognitive engagement had no significant effect.

These findings suggest that “engaging the heart” is more influential than “engaging the mind” in drawing young people to the news in today’s information environment. The study thus contributes to the understanding of the cognitive and emotional focus on news content and their importance in shaping young people’s expectations of online news. The findings from this study could have broader implications for future trends in online news consumption.