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The recent increase in economic inequalities in many countries heightened the debates about policy preferences on income distribution. Attitudes toward inequality vary greatly across countries and numerous explanations are offered to clarify the factors leading to support for redistribution. The purpose of this paper is to examine the link between subjective social class and redistributive demands by jointly considering the individual and national factors. The author argues that subjective measures of social positions can be highly explanatory for preferences about redistribution policies.

The author uses data from 48 countries gathered by World Values Survey and empirically tests the impact of self-positioning into classes by multilevel ordered logit model. Several model specifications and estimation strategies have been employed to obtain consistent estimates and to check for the robustness of the results.

The findings show that, in addition to objective factors, subjective class status is highly explanatory for redistributive preferences across countries. The author also exhibits that there is interaction between self-ranking of social status and national context. The author’s estimations from the multilevel models verify that subjective social class has greater explanatory power in more equal societies. This is in contrast to the previous studies that establish a positive link between inequality and redistribution.

The paper contributes to the literature by introducing subjective social class as a determinant. Self-ranked positions can be very relieving about policy preferences given the information these categorizations encompass about individuals’ perceptions about their and others’ place in the society.

The purpose of this paper is to attempt to look at the link between labor market risks and social insurance demands by taking occupational unemployment rates, and specificity of skills into account.

Occupational unemployment rate is treated as an estimate of labor market risk in addition to human capital investment. Then, the variations in Germany and the USA – with diverse labor markets and a considerable difference in terms of social insurance support – are examined.

The results suggest that occupational unemployment rate is explanatory for the demands for social insurance along with income.

Conclusions reached in the paper aim to contribute to the understanding of the political support for social insurance and hence provide tools for the design of such insurance mechanisms.

Contrary to the widespread association between the type of human capital and social insurance preferences in the literature, the paper argues that the cross‐country variations can be explained by occupational unemployment rates.

Digital transformation (DT) harnessing the potential of emerging technology creates opportunities and challenges for organizations worldwide. Senior executives view DT as a key initiative for future competitiveness, a view shared by academic researchers. What may challenge the organization is that the vision may be present while preparedness may be lacking. Organizational preparedness depends on managers and employees charged with implementing DT and their perceptions on preparedness are often not aligned with senior executives.

In this research, the authors explore the perceptions of managers and employees on DT preparedness in an organization by gathering data from 579 participants. This study uses an innovative approach to qualitative data analysis using interactive topic modeling.

Findings in this qualitative study provide valuable insights on the perceptions of these individuals and helps understand (a) how they view DT preparedness and (b) may behave in this context. In general DT is well understood, however managers are not keen to change work processes to take advantage of the new digital tools and there appears that generational gap is a barrier to successful DT.

Senior executives play a central role communicating the DT vision necessary to inspire managers and employees. As organizations continue to invest large sums of money to explore value creation for customers and stakeholders by leveraging digital technologies, the information systems (IS) discipline can take the lead by asking the question, what can be done to improve the understanding of DT implementation in an organization?

This study aims to investigate the impact of I4.0 technologies and their interoperability on supply chains (SCs) performance and how the integration of such technologies and their interoperability can create pathways for SCs resilience post-COVID-19. This is of paramount importance in the context of COVID-19 as the investigation around I4.0 technologies may provide relevant insights on how SCs may better respond to unexpected situations like the current pandemic with the use of digital technologies.

A survey research method was designed based on some constructs extracted from the literature regarding the main disruptive technologies, interoperability, elements of supply chains processes (SCPs) performance such as integration, collaboration, transparency, efficiency, responsiveness and profitability. The data were collected from March to July 2020 from different regions of the world when the peak of the first wave of the pandemic had occurred. The survey resulted in 115 valid responses. The study used a combination of descriptive, correlation and multiple regression methods to analyse the data.

The study indicates that disruptive technologies significantly impact SCPs performance (integration, collaboration, responsiveness and transparency) and their resilience. The findings did not support the notion that these technologies improve the efficiency of SCs, a significant contrast to the existing literature. Our findings also refute the existing understanding that interoperability moderates the impact of disruptive technologies on SCPs performance and enhancing the resilience of SCs. However, the findings show that the integration of I4.0 technologies and their interoperability has a positive impact on SCPs profitability.

The findings strongly advocate that this integration plays an important role in improving SC performance, and a future pathway of SC resiliency post-COVID-19. Considering that the I4.0 trend will impact SCs in the coming years, this study brings a relevant contribution to researchers and practitioners.

This study makes a unique contribution by investigating a novel causal relationship between the main elements (I4.0 technologies, interoperability, processes performance and strategic outcomes) related to the SC in this new context.

This paper aims to offer an extensive empirical case study analysis by investigating housing affordability in Turkey as a whole, and in Istanbul, Ankara and Izmir over the period of 2006 and 2017 and its sub-periods.

This paper develops a theoretically informed model to assess affordability using complementary methodologies in quantitative analysis. This study seeks to help outline the nature of the problem in aggregate level and in the cities; it also seeks to offer lessons about how to address measurement and modelling challenges in emergent market contexts by constructing aggregate-/city-level housing cost-to-income (HCI) ratio, adjusted HCI (AHCI) ratio, housing affordability index (HAI) and effective HAI sensitive to multiple calculation methodologies and alternative data set involving income distribution and poverty tranches.

HCI, AHCI, HAI and EHAI models generally suggest the parallel results: housing is not affordable in Turkey and in Istanbul, Ankara and Izmir except for the highest income groups. The evidence implies that besides macroeconomic instabilities, distorted interest rates and short average mortgage maturity, poverty and unequal income/wealth distributions are the main reasons of the Turkish housing affordability crisis specifically heightened in metropolitan areas such as in Istanbul.

The evidence provides an insight on housing affordability problems in Turkey. However, small sample size and short observation period create a limit for generalisation of the findings. Further analysis would be required to illustrate how housing affordability changes in different cities of Turkey in a longer period.

By using empirical approaches, this paper helps to understand how serious housing affordability problems of Turkey in aggregate and urban levels. This evidence helps to explain declining ownership ratio in low-income groups and in urban areas. Reliable explanations on existing housing crisis of Turkey also help to develop affordable housing policies.

Declining housing affordability and homeownership ratio may translate as the rising housing inequality and insecurity among Turkish households. Moreover, better affordability values of higher income groups suggest that existing inequality, economic/social segmentation, and hence social tension between high and low income groups, may further increase. In this respect, the authors suggest socially important policies such as reducing income/wealth inequalities and increasing affordable housing supply.

This study offers a detailed empirical case study analysis that can be used as an exemplar of how to overcome data constraints in other evolving housing market contexts. This study sets out an approach overcoming the challenges of measurement. This study also combines existing methodological approaches with the modified variables to provide a more realistic aggregate-/urban-level housing affordability picture. The authors calculated some parts of housing affordability ratio and index series using discretionary income, minimum wage and effective minimum wage to show the variations of different measurement approaches. Some constructed series are also sensitive to income distribution and poverty thresholds. Collectively, this empirical approach, developed by using emerging market data, provides a contribution to the literature.