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The purpose of this work is to apply a recently proposed constitutive model for mechanically induced martensitic transformations to the prediction of transformation loci. Additionally, this study aims to elucidate if a stress-assisted criterion can account for transformations in the so-called strain-induced regime.

The model is derived by generalising the stress-based criterion of Patel and Cohen (1953), relying on lattice information obtained using the Phenomenological Theory of Martensite Crystallography. Transformation multipliers (cf. plastic multipliers) are introduced, from which the martensite volume fraction evolution ensues. The associated transformation functions provide a variant selection mechanism. Austenite plasticity follows a classical single crystal formulation, to account for transformations in the strain-induced regime. The resulting model is incorporated into a fully implicit RVE-based computational homogenisation finite element code.

Results show good agreement with experimental data for a meta-stable austenitic stainless steel. In particular, the transformation locus is well reproduced, even in a material with considerable slip plasticity at the martensite onset, corroborating the hypothesis that an energy-based criterion can account for transformations in both stress-assisted and strain-induced regimes.

A recently developed constitutive model for mechanically induced martensitic transformations is further assessed and validated. Its formulation is fundamentally based on a physical metallurgical mechanism and derived in a thermodynamically consistent way, inheriting a consistent mechanical dissipation. This model draws on a reduced number of phenomenological elements and is a step towards the fully predictive modelling of materials that exhibit such phenomena.

The purpose of this paper is to predict the yield locus of porous ductile materials, evaluate the impact of void geometry and compare the computational results with existing analytical models.

A computational homogenization strategy for the definition of the elasto-plastic transition is proposed. Representative volume elements (RVEs) containing single-centred ellipsoidal voids are analysed using three-dimensional finite element models under the geometrically non-linear hypothesis of finite strains. Yield curves are obtained by means of systematic analysis of RVEs considering different kinematical models: linear boundary displacements (upper bound), boundary displacement fluctuation periodicity and uniform boundary traction (lower bound).

The influence of void geometry is captured and the reduction in the material strength is observed. Analytical models usually overestimate the impact of void geometry on the yield locus.

This paper proposes an alternative criterion for porous ductile materials and assesses the accuracy of analytical models through the simulation of three-dimensional finite element models under geometrically non-linear hypothesis.

The purpose of this paper is to analyse the advertising productivity of a collective brand strategy vs a non-collective brand strategy, as well as the moderating role of company characteristics (age of the company, individual brand reputation and degree of competition that the company faces). The main hypothesis is that a collective brand has a positive influence on the advertising productivity of its member companies, as it is a collective reputation indicator in experience goods.

The methodology is based on the application of regression models with panel data of companies in a Spanish experience goods industry between 2004 and 2012. The empirical analysis is made in the Spanish winery sector, given the proliferation in the wine market of public collective brands (i.e. protected designation of origin labels).

The results show that a company associated with a collective brand has greater advertising productivity than a non-associated company. Advertising productivity is also higher for brands with better individual reputations associated with a collective brand. Moreover, the relative effect of a collective brand on advertising productivity is higher when the company competes in a market with a higher level of competition.

The literature has paid little attention to the relationship between collective brand strategy and the advertising productivity of member companies. This study considers that the advertising productivity of companies in collective brands could be explained by the effects derived from the collective brand reputation.

Introduction: In a world characterised by volatility, uncertainty, complexity, and ambiguity, change is the only constant. Over the years, human resource management (HRM) has evolved from conventional functions of hiring and firing to being a strategic partner in organisations. Similarly, there has been a paradigm shift in the landscape of artificial intelligence (AI) from being a mere searching tool to the design and development of intelligent robots. Over the years, AI has emerged into a collection of powerful technologies re-inventing different functional areas, including HRM. The application of AI in HRM is perceived as an optimistic opportunity since it ought to bring maximum value at minimum cost. AI focuses on building tools that exhibit human-level intelligence and discernment in making decisions.

Purpose: The purpose of this chapter is to draw deeper insights into the relevance of AI in different functional areas of HRM. Integrating AI into HRM functions such as talent acquisition, training and development, performance management, employee engagement, and the like can help leverage efficiency and create an engaging employee experience. In the wake of Industry 4.0, where digitalisation has become imperative, this chapter explores the integration of AI into specific HR functions for a synergistic competitive advantage in companies. The purpose of this chapter is to signify the integration of AI into four vital functions of HRM, namely talent acquisition, training and development, performance management, and employee engagement. The objective is to chart how companies integrate various AI tools in four specific HRM functions to enhance efficiency. Also, the companies willing to implement AI in their HR functions can refer to the case studies used as exemplars in the chapter.

Methodology: This conceptual chapter is based on the secondary sources, which also build upon case studies of different companies that have implemented AI-enabled solutions and integrated them into different HRM functions and processes per needs. This chapter utilises the conceptual framework of both AI and HRM functions to give deeper insight into the challenges and implementation of technology-enabled solutions.

Findings: AI is used in HRM functions to automate repetitive and operational tasks to shift the focus to more strategic aspects. Despite many advantages of AI and machine learning, very few companies are using it, and companies may integrate technology-enabled solutions based on the size and nature of business.

Given the growing interest in social movements as policy agenda setters, this paper investigates the contexts within which movement groups and actors work with political elites to promote their common goals for policy change. In asking how and why so-called outsiders gain access to elites and to the policymaking process, I address several contemporary theoretical and empirical concerns associated with policy change as a social movement goal. I examine the claim that movements use a multipronged, long-term strategy by working with and targeting policymakers and political institutions on the one hand, while shaping public preferences – hearts and minds – on the other; that these efforts are not mutually exclusive. In addition, I look at how social movement organizations and actors are critical in expanding issue conflict outside narrow policy networks, often encouraged to do so by political elites with similar policy objectives. And, I discuss actors’ mobility in transitioning from institutional activists to movement and organizational leaders, and even to protesters, and vice versa. The interchangeability of roles among actors promoting social change in strategic action fields points to the porous and fluid boundaries between state and nonstate actors and organizations.

Several industries including banking are booming because of COVID-19. However, it is still unknown whether this growth is momentary or permanent in nature. Hence, this study aims to identify the role of health-related concerns and trust as stimuli on M-payment loyalty.

Data were collected through Google Forms from 431 participants. Subjects were M-payment users. The hypothesized model was tested using structural equational modeling.

Results of the study indicate that perceived severity and trust act as stimuli for M-payment loyalty. Further, trust not only influences loyalty directly but also through intimacy. Additionally, no linear relationship was found between perceived usefulness and M-payment loyalty.

This work is an early attempt to consider health-related concerns and trust as stimuli to predict M-payment loyalty. Further, this study focused on three new constructs, namely perceived severity, perceived susceptibility and intimacy, that are underexplored in digital banking literature.

The existing consensus reaching mechanisms ignore the influence of social triangle structure on the decision-makers’ (DMs') weights, and the consensus reaching process (CRP) fails to fully reflect the DMs' subjectivity and can be time consuming and costly. To solve these issues, a novel CRP for multi-criteria group decision-making (MCGDM) problems with intuitionistic grey linguistic numbers (IGLNs) is proposed in this paper.

First, a weight calculation method is proposed by analysing the triangle structure of DMs' social network and scale of adjacent nodes. Then, a consensus degree index based on three-level polygon area is defined and applied to identify the inconsistent DMs. Finally, the feedback mechanism based on particle swarm optimisation (PSO) algorithm under grey linguistic environment is developed, where subjective trust relationships in social network is utilised to determine the adjustment coefficient.

The advantages of the proposed method are highlighted by two practical applications of the evaluation of tunnel construction method and the selection of a hotel for the centralised isolation. Comparision analysis and numerical simulation are performed to reveal the effectiveness and applicability of the method.

The proposed model can not only reflect the effect of triangle structure in social network on DMs' weights, but also reduce the time and cost of decision-making.

The main contribution of this paper is to propose a new MCGDM model based on intuitionistic grey linguistic numbers, which can handle the problem of inconsistency of information more effectively.