“He’s Not Good at Sensing That Look That Says,I’m Drowning Here!” Academic Mothers’ Perceptions of Spousal Support

Abstract

Research consistently shows that, regardless of increased parental involvement in child-rearing over the last few decades, the ‘lion’s share’ of childcare and housework is still undertaken by women rather than men. Academia is known to have no fixed working hours as such. As a result, the pressure on academic mothers tends to be inflated, and this has repercussions on women’s promotional paths, publishing outcomes and general professional and personal well-being. The positive effect of working women having spousal support is well documented in many employment areas. This paper looks at perceived spousal support which academic mothers, living and working in the United Arab Emirates (UAE), report. While some of the women did report their spouses to be supportive, this was often theoretical rather than practical. The ways in which this spousal support situation has affected the academic mothers’ lives is discussed.     

Social Networks,Social Cohesion,and Later-Life Health

Our study contributes to the literature acknowledging the joint role of social networks and social cohesion in shaping individual’s health, focusing on the older population aged 50 and over. Exploiting rich ego-centered social network data from the Survey of Health, Ageing and Retirement in Europe and following the conceptual model of social integration and health proposed by Berkman et al. (Soc Sci Med 51:843–857. doi:10.1016/S0277-9536(00)00065-4, 2000), we estimate multilevel models of self-reported and observer-measured later-life health outcomes. These models simultaneously account for (a) characteristics of 39,551 respondents’ personal social networks and (b) a measure of social cohesion—namely, participation in social organizations—across 57 Continental European regions, clustered in 14 countries. We find significant associations between individuals’ health and various social network characteristics (size, support, quality) as well as social cohesion. Moreover, cross-level interaction effects suggest that the social-network-health nexus is contextually bound. We conclude with a discussion of limitations and perspectives for future research.     

Multinational enterprises' Entry Mode Strategies and Uncertainty: A Review and Extension

The issue of uncertainty has been one of the most important topics in the research on multinational enterprises' foreign market entry modes. This paper provides a conceptual review of the literature on the role of uncertainty in entry‐mode strategies. It is found that transaction cost theory has focused primarily on entry modes as a way of minimizing behavioural uncertainty, while research using the more recent real options approach has distinguished between endogenous and exogenous types of uncertainty and provided evidence that managing uncertainty through appropriate entry modes may lead to competitive advantage. By synthesizing the relevant literature, this paper provides some new perspectives and avenues for future research on uncertainty and entry‐mode choices by considering the impact of firm learning, prior experience and host market attractiveness.     

Life satisfaction linked to different independence-from-parents conditions in Italian emerging adults

ABSTRACT

The transition toward adulthood in Italy lasts longer than in other countries with respect to living with the family of origin, starting work, and gaining economic independence. This study aimed to analyse the transition to adulthood in Italian youths by exploring these developmental tasks. The first aim was to analyse gender and age differences in these developmental tasks. Given that these tasks are not necessarily achieved at the same time, the second aim was to establish if it might be possible to identify different clusters of individuals according to their residential, economic and employment conditions. The third aim was to explore life satisfaction in different clusters. 191 Italian emerging adults (65 males) aged 20–30 years (M?=?24.40) completed the Satisfaction with Life Scale and a specific questionnaire assessing residential independence, economic autonomy, and job stability. Results showed no gender differences in the three developmental tasks. However, older participants reported a higher level of independence. Cluster analysis identified three different groups: ‘totally dependent’, ‘partially independent’ and ‘totally independent’ from parental family, differently composed in terms of partnering. These groups do not differ in life satisfaction levels. Results are discussed focusing on different independence-from-parents conditions that characterize Italian emerging adults.     

Effect of choice complexity on design efficiency in conjoint choice experiments

Conjoint choice experiments have become a powerful tool to explore individual preferences. The consistency of respondents' choices depends on the choice complexity. For example, it is easier to make a choice between two alternatives with few attributes than between five alternatives with several attributes. In the latter case it will be much harder to choose the preferred alternative which is reflected in a higher response error. Several authors have dealt with this choice complexity in the estimation stage but very little attention has been paid to set up designs that take this complexity into account. The core issue of this paper is to find out whether it is worthwhile to take this complexity into account in the design stage. We construct efficient semi-Bayesian D-optimal designs for the heteroscedastic conditional logit model which is used to model the across respondent variability that occurs due to the choice complexity. The degree of complexity is measured by the entropy, as suggested by Swait and Adamowicz (2001). The proposed designs are compared with a semi-Bayesian D-optimal design constructed without taking the complexity into account. The simulation study shows that it is much better to take the choice complexity into account when constructing conjoint choice experiments.     

Relative Distribution Methods

We present an outline of relative distribution methods, with an application to recent changes in the U.S. wage distribution. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, and inference. The relative distribution is similar to a density ratio. It is technically defined as the random variable obtained by transforming a variable from a comparison group by the cumulative distribution function (CDF) of that variable for a reference group. This transformation produces a set of observations, the relative data, that represent the rank of the original comparison value in terms of the reference group's CDF. The density and CDF of the relative data can therefore be used to fully represent and analyze distributional differences. Analysis can move beyond comparisons of means and variances to tap the detailed information inherent in distributions. The analytic framework is general and flexible, as the relative density is decomposable into the effect of location and shape differences, and into effects that represent both compositional changes in covariates, and changes in the covariate-outcome variable relationship.     

sCARy! Risk Perceptions in Autonomous Driving: The Influence of Experience on Perceived Benefits and Barriers

The increasing development of autonomous vehicles (AVs) influences the future of transportation. Beyond the potential benefits in terms of safety, efficiency, and comfort, also potential risks of novel driving technologies need to be addressed. In this article, we explore risk perceptions toward connected and autonomous driving in comparison to conventional driving. In order to gain a deeper understanding of individual risk perceptions, we adopted a two‐step empirical procedure. First, focus groups ( $N=17$) were carried out to identify relevant risk factors for autonomous and connected driving. Further, a questionnaire was developed, which was answered by 516 German participants. In the questionnaire, three driving technologies (connected, autonomous, conventional) were evaluated via semantic differential (rating scale to identify connotative meaning of technologies). Second, participants rated perceived risk levels (for data, traffic environment, vehicle, and passenger) and perceived benefits and barriers of connected/autonomous driving. Since previous experience with automated functions of driver assistance systems can have an impact on the evaluation, three experience groups have been formed. The effect of experience on benefits and barrier perceptions was also analyzed. Risk perceptions were significantly smaller for conventional driving compared to connected/autonomous driving. With increasing experience, risk perception decreases for novel driving technologies with one exception: the perceived risk in handling data is not influenced by experience. The findings contribute to an understanding of risk perception in autonomous driving, which helps to foster a successful implementation of AVs on the market and to develop public information strategies.     

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.     

Youth interaction with online strangers: experiences and reactions to unknown people on the Internet

Previous research on youths’ experiences with online strangers has mostly utilized quantitative designs which may result in the omission of important parts of the youths’ experiences and perceptions. In this qualitative study, we adopt a child-centred approach and focus on the children’s and adolescents’ experiences with unknown people from the Internet. This is one of the most pronounced parental concerns, despite the fact that an online presence is now normative for most children. We use the data from focus-groups and individual interviews in nine European countries (N?=?368) conducted with youths between the ages of 9 and 16 years. Participants were asked about their perceptions of potentially negative or problematic situations while using the Internet and digital technology. Using thematic analysis, we classified youths’ experiences and reactions related to interacting with online strangers in four themes: (1) non-verbal interaction; (2) initiation of contact; (3) communication; and (4) face-to-face meeting. In all four levels of interactions, children and adolescents reported a variety of positive and negative experiences and reactions. We interpret our findings with a co-construction model which perceives children and adolescents as active participants in the online world. We conclude that children are successfully making active decisions to initiate, continue, and stop interactions with unknown people from the Internet. These findings broaden the current perspectives of youths’ meetings with unknown people online.