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This study aims to help develop “business principles for stakeholder capitalism” in two steps. First, the study defines internal logic of three theories of capitalism and two variants within each theory. Second, it examines approaches to integration into modern democratic capitalism. Treating the three theories as substitutes identifies relative strengths and weaknesses; complementarity and partial overlap approaches to integration study the institutional settings within which stakeholder capitalism operates. Empirical outcomes reflect competition between market and stakeholder businesses for participants, with institutional conditions determining the scope of collective action.

The approach aligns three typologies in a unique conceptual arrangement defining the three theories of capitalism: forms of capitalism, potential failures of each form and associated types of goods. The first method examines the internal logic of each theory of capitalism. The second draws on traditional narrative review of references documenting each theory of capitalism and variants together with modern Marxist anti-capitalism.

Three typologies align uniquely with the theories of capitalism, each having two variants. Both variants of stakeholder capitalism are compatible with compassionate capitalism, constitutional government or polycentric governance but not with self-interest capitalism, dictatorship or Marxism. A theory of modern democratic capitalism allocates roles for private, club and social goods with empirically variable mixes occurring across countries. Competition among different types of enterprises provides an empirical test for comparative advantages of stakeholder capitalism. Future research should consider approaches for testing the proposed conceptual scheme in practice concerning capacity to deal with grand challenges, wicked problems and black swan events.

Research approach is limited to logical examination of theories and literature documentation without direct empirical confirmation. The study does not address practical implications for managers and public officials or social implications concerning private incentives, stakeholder cooperation or collective action.

Originality lies in shifting terms of debate about stakeholder capitalism from advocacy of substitute theories to understanding of its relationship to market capitalism and collective action capitalism. Value lies in explaining desirability of theoretical integration of three types of capitalism into a comprehensive framework for modern democratic capitalism.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

The COVID-19 pandemic is not a foodborne infectious disease, but it has dramatically impacted food safety practices worldwide due to its potential for transmission through contaminated surfaces and food. Accordingly, the Omicron variant seems to have affected food-related activities and behaviours and disturbed food supply networks since its appearance in November 2021. Hence, this paper aims to assess how the Omicron variant impacted food safety knowledge, attitudes and practices amongst adult consumers in five countries: Bosnia and Herzegovina, North Macedonia, Serbia, Montenegro and Russia.

The study is based on an online survey. The questionnaire was developed and revised based on previous research on the impact of previous COVID-19 waves on food-related activities in several countries. The questionnaire was distributed through the SurveyMonkey platform from January 15 to February 25, 2022. It consisted of 29 multiple-choice and one-option questions organised into three sections. A total of 6,483 valid responses were received. Statistical Package for Social Sciences (SPSS) version 25.0 was used to analyse the survey results.

According to the survey findings, food safety practices evolved during the Omicron wave in the studied countries. Firstly, less than half of the sample used a face mask whilst purchasing food. Secondly, regarding food safety knowledge, the survey results suggest that there is still a lack of knowledge in the studied countries. Thirdly, the survey indicates a lack of knowledge amongst the respondents regarding food safety attitudes. For instance, more than a third of the sample (34.4%) are unsure whether the COVID-19 virus can be transmitted through food. These results are surprising and alarming, especially considering that our sample has a higher education than the population of the studied countries.

The main limitation of this research is the sample bias. Survey participants were randomly chosen, enrolled voluntarily and not rewarded. As a result, the questionnaire was self-administered and completed exclusively by people motivated by an interest in the topic. Consequently, our survey does not represent the general population of the studied countries. People with a high degree of education and women, for example, were overrepresented in our sample.

This study is unique in that it is the first to gather information and analyse people’s perceptions of the effects of the Omicron variant on food safety. As a result, the findings of this survey offer a solid basis for future investigations into the impact of the pandemic on food safety in the Balkan region and Russia. This study can help further understand the changes during the COVID-19 pandemic. It provides crucial insights that can be used to guide future decision-making and policy development regarding improving food safety practices. This and other future studies will be a foundation for organisational and government readiness for future shocks, crises and pandemics. The effects of the present Ukrainian conflict on agricultural systems and supply chains throughout the globe (e.g. increased food prices) show that this is timely, urgent and highly required.

We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns.

We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022.

This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns.

This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

A novel method for modelling permanent magnets is investigated based on numerical approximations with rational functions. This study aims to introduce the AAA algorithm and other recently developed, cutting-edge mathematical tools, which provide outstandingly fast and accurate numerical computation of potentials and vector fields.

First, the AAA algorithm is briefly introduced along with its main variants and other advanced mathematical tools involved in the modelling. Then, the analysis of a circular Halbach array with a one-pole pair is carried out by means of the AAA-least squares method, focusing on vector potential and flux density in the bore and validating results by means of classic finite element software. Finally, the investigation is completed by a finite difference analysis.

AAA methods for field analysis prove to be strikingly fast and accurate. Results are in excellent agreement with those provided by the finite element model, and the very good agreement with those from finite differences suggests future improvements. They are also easy programming; the MATLAB code is less than 200 lines. This indicates they can provide an effective tool for rapid analysis.

AAA methods in magnetostatics are novel, but their extension to analogous physical problems seems straightforward. Being a meshless method, it is unlikely that local non-linearities can be considered. An aspect of particular interest, left for future research, is the capability of handling inhomogeneous domains, i.e. solving general interface problems.

The authors use cutting-edge mathematical tools for the modelling of complex physical objects in magnetostatics.

This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.

The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.

The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.

This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This study examines the effects of corporate governance on market returns during the first four waves of the COVID-19 crisis.

Event study and linear regression methods were applied on a sample of 293 US firms.

The results show that differences in abnormal returns are more significant during the second wave of COVID-19 and the two following waves. Moreover, estimations show that good corporate governance alleviated the effect of COVID-19 during the second wave and the two following waves. However, corporate governance did not affect abnormal returns during the first wave. Furthermore, evidence highlights that the effect of corporate governance is more observed in the industries most affected by COVID-19 than in the least affected industries.

Many studies have attempted to investigate the effect of corporate governance on stock returns during the first wave of the pandemic. However, to the authors' knowledge, this is the first study that focuses on different waves that occurred during 2020 and 2021.