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In this work, the authors are interested in the notion of vector valued and set valued Pettis integrable pramarts. The notion of pramart is more general than that of martingale. Every martingale is a pramart, but the converse is not generally true.

In this work, the authors present several properties and convergence theorems for Pettis integrable pramarts with convex weakly compact values in a separable Banach space.

The existence of the conditional expectation of Pettis integrable mutifunctions indexed by bounded stopping times is provided. The authors prove the almost sure convergence in Mosco and linear topologies of Pettis integrable pramarts with values in (cwk(E)) the family of convex weakly compact subsets of a separable Banach space.

The purpose of the present paper is to present new properties and various new convergence results for convex weakly compact valued Pettis integrable pramarts in Banach space.

In this paper, the authors revisit the computation of closed-form expressions of the topological indicator function for a one step imaging algorithm of two- and three-dimensional sound-soft (Dirichlet condition), sound-hard (Neumann condition) and isotropic inclusions (transmission conditions) in the free space.

From the addition theorem for translated harmonics, explicit expressions of the scattered waves by infinitesimal circular (and spherical) holes subject to an incident plane wave or a compactly supported distribution of point sources are available. Then the authors derive the first-order term in the asymptotic expansion of the Dirichlet and Neumann traces and their surface derivatives on the boundary of the singular medium perturbation.

As the shape gradient of shape functionals are expressed in terms of boundary integrals involving the boundary traces of the state and the associated adjoint field, then the topological gradient formulae follow readily.

The authors exhibit singular perturbation asymptotics that can be reused in the derivation of the topological gradient function that generates initial guesses in the iterated numerical solution of any shape optimization problem or imaging problems relying on time-harmonic acoustic wave propagation.

This paper aims to compare different static history-independent hysteresis models (mathematical-, behavioural- and physical-based ones) and a history-dependent hysteresis model in terms of parameter identification effort and accuracy.

The discussed models were tested for distorted-excitation waveforms to explore their predictions of complex magnetization curves. Static hysteresis models were evaluated by comparing the calculated and measured major and minor static hysteresis loops.

The analysis shows that the resulting accuracy of the different hysteresis models is strongly dependent on the excitation waveform, i.e. smooth excitations, distorted flux waveforms, transients or steady-state regimes. Obtained results show significant differences between predictions of discussed static hysteresis models.

The general aim was to identify the models on a very basic and limited set of measured data, i.e. if possible using only the measured major static loop of the material. The quasi-static major hysteresis loop was measured at B_max = 1.5 T.

The presented analysis allows selection of the most-suited hysteresis model for the sought-for application and appraisal of the individual limitations.

The presented analysis shows differences in intrinsic mechanisms to predict magnetization curves of the majority of the well-known static hysteresis models. The results are essential when selecting the most-suited hysteresis model for a specific application.

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.

The purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process (AHP) and interval grey number (IGN) to solve the clustering evaluation problem with IGNs.

First, the centre-point triangular whitenisation weight function with real numbers is built, and then by using interval mean function, the whitenisation weight function is extended to IGNs. The weights of evaluation indexes are determined by AHP. Finally, this model is used to evaluate the flight safety of a Chinese airline. The results indicate that the model is effective and reasonable.

When IGN meets certain conditions, the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative. It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.

The traditional grey clustering model is extended to the field of IGN. It can make full use of all the information of the IGN, so the result of the evaluation is more objective and reasonable, which provides supports for solving practical problems.

The interest in corporate social responsibility (CSR) has become burgeoning in the construction industry as firms are under constant pressure from socially conscious stakeholders to demonstrate their efforts to address various CSR issues. This study aims to unveil the key practices and impact factors (KPIFs) of CSR implementation in construction firms and the interrelationships among different key impact factors toward attaining CSR practices.

Mobilizing the integrated institutional, stakeholder and self-determination theories, a theoretical framework was first developed to elaborate the potential inter-relationships among the key impact factors toward CSR implementation. Data were collected from extra-grade contractors through an online questionnaire survey and was then analyzed by the partial least square structural equation modeling method.

The results show that construction firms' CSR practices could be classified into eight distinct key dimensions, e.g. shareholders' interests, government commitment and environment preservation. It is found that three groups of key impact factors – external institutional factors (especially coercive-normative factors), intrinsic factors (especially strategic business direction and organizational culture) and identified factors (i.e. the perceived importance of CSR practices) – have statistically significant positive impacts on most key dimensions of CSR practices.

The research findings have implications for top management to better understand CSR implementation, thereby helping them secure legitimacy to survive and advance in the competitive construction businesses.

The findings contribute to the theoretical body of knowledge in CSR by modeling and empirically demonstrating the influence mechanism of CSR implementation in construction within an integrated model.

The ability of construction firms to become more environmentally conscious and socially responsible for their business activities has been touted as the key driver for improved individual firms' competitiveness. This study explores the key dimensions of corporate social responsibility (CSR) practices and their impact factors among construction firms.

Through the institutional, stakeholders and self-determination theories, this study proposed a conceptual framework of CSR implementation. For its validation, data were collected from 90 top-tier construction firms using an online survey and analyzed via a two-pronged factor analysis method.

The empirical results demonstrate that the CSR practices of construction firms include eight key dimensions, e.g. shareholders' interests, government commitment and CSR institutional arrangement. The three key groups of impact factor of CSR implementation are (1) identified factors (i.e. contractors' perceived importance of CSR practices); (2) external institutional factors (i.e. coercive and normative factors and mimetic factors); and (3) intrinsic factors (i.e. strategic business direction, resource and capability and organizational culture).

The research findings inform the practitioners about how to enact, manage and improve firms' socially responsible goals so as to fulfill their key stakeholders' requirements and expectations and thus enhance their legitimacy in construction businesses.

This study contributes to CSR knowledge by identifying and empirically demonstrating valid measurements of the key dimensions of practices and impact factors toward CSR implementation by construction firms.

Because no international accounting policy exists to mandate human capital (HC) information must be reported on financial reports, the association between workforce HC and firm performance/efficiency is not well-established. South Korea is a rare example with high HC reporting quality, as well as relatively high national productivity. On the other hand, in some developed countries (such as the UK), HC reporting quality and productivity is low. Moreover, there is an increasing propensity to offer employees non-standard contracts. Thus, because of a divergence in HC reporting quality internationally, the South Korean sample can provide valuable insights to countries with weak HC reporting quality about the association between contract quality and firm performance/efficiency.

Using a sample of Korean listed firms (2010–2015), pooled Ordinary Least Squares (OLS) regression analysis is conducted to show whether firms that offer employees higher levels of permanent, relative to temporary contacts, demonstrate higher firm performance/efficiency.

Firms that provide employees with increasing permanent (temporary) contracts experience higher (lower) performance/efficiency.

This research is limited due to sample selection. However, the sample represents the population of all firms that report contract type information in South Korea, a market with highly robust HC information reporting.

Because of data unavailability, a positive association firm-level performance/efficiency and permanent employment can only be made in a handful of countries. The study has policy implications and extends the non-financial reporting literature by addressing HC reporting limitations that exist in the mainstream accounting framework. Based on relative operational efficiency/performance, the study offers practical insights to management about the importance of staff retainment. Moreover, the authors also offer an anthropocentric perspective by inferring how low HC reporting quality can have a negative impact on society in Industry 4.0.

This paper examines the effects of limiting the number of loans a bank can issue, reflecting a policy recently implemented by the US Federal Reserve.

This paper does so in a streamlined model of the banking sector.

This paper finds that a binding limit on loans can enhance welfare by motivating the bank to reduce the number of socially unproductive loans it makes. However, the limit can sometimes reduce welfare by inducing a reduction in the number of socially productive loans the bank issues, the quality of the bank’s loan portfolio, and/or the accuracy with which the bank screens loan opportunities.

The research demonstrates that limits on the loans a bank issues can have subtle and unintended consequences. Consequently, careful thought is warranted before such limits are imposed.

To our knowledge, the existing literature does not provide guidance on the merits of such loan restrictions.

The integration of System Dynamics with LP‐ and Input‐Output models is realized by means of simple examples. Aspects of programming techniques as well as the conceptual meaning of this model‐method integration are discussed. By the combination of the stated methods with System Dynamics models an improved flexibility of the System Dynamics method is obtained. The optimization of a Simulation model is represented by the integration of a System Dynamics model into a higher level feedback loop structure. The optimization algorithm, the Razor Search procedure, modifies the parameters of the control vector in order to generate the optimal behaviour of the state vector of the model in relation to an objective function.