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The paper investigates the relationship between accounting and promises in the context of digital change.

Relying on emergent literature on accounting and promises, a qualitative field study has been conducted covering 57 interviews with municipal directors, digitalization strategists, administration managers and CFOs in a Swedish region consisting of 13 municipalities.

The paper provides insights into how municipalities draw on accounting in attempts to reconstruct promissory narratives of the digital. By highlighting two contrasting cases, we show how this can involve practices of either restoration or transformation. Likewise, we find that attempts to restore promises can sometimes have unanticipated effects, in our case a transformation of the promise instead.

We introduce a “promise” lens to the literature on accounting and digital change and empirically describe how accounting is implicated in shaping promises in the context of public sector digital change.

This study aims to test Singer’s suggestion that ‘over the next 20 years meat could follow smoking into disrepute” using the findings of the recent literature on meat consumption, education and smoking and data from consumers in Switzerland in 1990–2017.

We hypothesise that meat consumption in developed countries has increasingly shifted to people with less education, as has been observed for smoking in previous studies. Using trend analysis by regressions, we describe the consumption dynamics of nine sorts of meat in Switzerland and estimate meat consumption trends for populations with and without university education separately.

Our results partly confirm the hypothesis. Less educated households consume more non-fish meat per person than households with at least one member educating or having finished education at university. For most categories of meat, the relative decline in consumption has been significantly higher for households in which at least one person holds a university education.

Our study contributes to the studies on sociology of meat eating and suggests paying more attention to risks related to meat consumption and to awareness of the population about these risks.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0335

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.

Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets.

Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively.

Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.

The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.