An employee-focused human resource management perspective for the management of global virtual teams

Abstract

The number of global virtual teams (GVTs) has increased in recent years due to globalization of business, improved information and communication technology, and higher innovation needs. Practitioners expect GVTs to be creative, innovative, and high-performing. However, GVT members suffer from interpersonal problems, stress, and misunderstandings based on cultural differences. It is therefore important that the HRM function intervenes in the functioning of GVTs. HRM and GVT research has mostly focused on instrumental practices to improve performance, but globalization and the dynamic business environment require international organizations to adopt new HRM perspectives. To advance research on GVTs and HRM, we draw on the job demands-resources model and research on quality of work life to introduce an employee-focused HRM perspective for the management of GVTs. This new perspective focuses explicitly on improving employee wellbeing in GVTs. We develop a novel theoretical framework that provides HR practitioners and leaders with several employee-focused management tools such as flexible work practices, international training and development opportunities, and fair pay and procedures. These tools are likely to help GVT members to cope with job demands and to improve their wellbeing, and are particularly useful for GVTs due to their interpersonal problems, stressful environment, and cultural differences.     

Emotionally Based Strategic Communications in the Process of Societal Deradicalization

Pervasiveness of extreme negative emotions, especially anger, hatred and humiliation, as well as negative appraisal style, has a significant impact on the process of societal radicalization. Dominance of such emotions, and the corresponding appraisal style, very often threaten societal security. Emotionally Based Strategic Communications (EBSC), proposed in this article, can be used as a communications strategy for mitigating negative and promoting positive emotions within societal groups exposed to radicalization processes. Essentially, EBSC as mechanisms of positive emotional regulation strategy are based on reshaping the (re)appraisal style of radicalized groups. Grounded in the appraisal theories of emotions, EBSC are entirely non-coercive, and applicable to a wide variety of groups. Such communications strategies are also extendible to Internet-based social media networks, opening new possibilities in deradicalization processes using sentiment analysis, cognitive computing, botnets and other ICT-based methods and techniques.     

Analysing the fulfilment of service recovery paradox in retailing

The literature dealing with the Service Recovery Paradox (SRP) is vast, but some results are confusing and contradictory. In addition to this, scarce attention has been paid to service recovery in the context of retailing. This paper aims at verifying the compliance of the SRP in the context of retailing. Considering a sample of consumers that experienced a failure in the service delivered by a retailer and are very satisfied with the solution provided by the store employee, we test first the fulfilment of the SRP in the context of retailing. Secondly, we test the existence of significant differences in the customer satisfaction levels with the store before and after experiencing the problem considering several factors potentially influencing the impact of service recovery on customer satisfaction, therefore providing an encompassing analysis of the SRP and its main influencing variables, including some potential factors not previously explored. While we do not find evidence of the fulfilment of the SRP in retailing, differences in customer satisfaction before and after service recovery are highly related with consumer sociodemographic characteristics and shopping experience. Findings raise some practical implications.     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

The role of satisfaction in labor diaspora dynamics: An analysis of BREXIT effects

In diaspora research, people's international mobility is often understood as a response to pull-push forces on an economic macro-level or as part of diasporic waves. However, labor diaspora formations are also influenced by micro-level (i.e., individual perceptions) drivers related to work per se, such as satisfaction. This explorative qualitative study takes a novel angle and focuses on the role of Portuguese nurses’ satisfaction with the evaluation of the Brexit scenario and its effects on this labor diaspora's mobility and stability. The findings illustrate that satisfaction, although affected by uncertainty, mainly relates to perceived working conditions and legislation and the possibilities to communicate with locals and other diasporans. This paper provides suggestions for future research and contributes to the development of theory on staying and human stickiness, explaining the dynamics of exit and entry in (re-)migration decision making.     

A large-scale field experiment on occupational gender segregation and hiring discrimination

We analyze the relationship between occupational gender composition and gender discrimination in recruitment and investigate whether there is hiring discrimination against men in female-dominated occupations. We do this with a large-scale field experiment where we submitted more than 12,000 job applications for 12 occupations in Australia, varying the gender of the applicants. Men received around 50% more callbacks than women in male-dominated occupations, while they received over 40% fewer callbacks in female-dominated occupations.     

How Wealthy are the Rich?

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and use German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from 1 to 9 trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale- or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale- or type-dependence, on one hand, and statistical biases, on the other hand. Yet both economic theory and mathematical formalism indicate that sampling and reporting biases are more plausible explanations for the observed data than scale- or type-dependence.