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Immigrant integration has become a primary political concern for leaders in Germany and the United States. The information systems (IS) community has begun to research how information and communications technologies can assist immigrants and refugees, such as by examining how countries can facilitate social-inclusion processes. Migrants face the challenge of joining closed communities that cannot integrate or fear doing so. We conducted a panel discussion at the 2019 Americas Conference on Information Systems (AMCIS) in Cancun, Mexico, to introduce multiple viewpoints on immigration. In particular, the panel discussed how technology can both support and prevent immigrants from succeeding in their quest. We conducted the panel to stimulate a thoughtful and dynamic discussion on best practices and recommendations to enhance the discipline's impact on alleviating the challenges that occur for immigrants in their host countries. In this panel report, we introduce the topic of using ICT to help immigrants integrate and identify differences between North/Central America and Europe. We also discuss how immigrants (particularly refugees) use ICT to connect with others, feel that they belong, and maintain their identity. We also uncover the dark and bright sides of how governments use ICT to deter illegal immigration. Finally, we present recommendations for researchers and practitioners on how to best use ICT to assist with immigration.
Job satisfaction is a major driver of an individual’s subjective well-being and thus affects public health, societal prosperity, and organisations, as dissatisfied employees are less productive and more likely to change jobs. However, changing jobs does not necessarily lead to higher job satisfaction in the long run. Previous studies have shown, instead, that changing jobs only increases job satisfaction for a short period of time before it gradually falls back to similar levels as before. This phenomenon is known as the ’honeymoon–hangover’ pattern. In our study, we identify an important new moderator of the relation between job change and job satisfaction: the job–education match of job changes. Based on relative deprivation theory, we argue that job changes from being overeducated in a job lowers the likelihood of negative comparisons and thus increases the honeymoon period, lessens the hangover period, and increases long-term job satisfaction. We use data from the Socio-Economic Panel ranging from 1994–2018 and focus specifically on individual periods of employees before and after job changes (n = 134,404). Our results confirm that a change to a job that requires a matched education has a stronger and longer-lasting effect on job satisfaction, and that this effect is slightly lower for respondents born abroad.
Background: Following the rapid increase of asylum seekers arriving in the European Union in 2015/16, policymakers have invested heavily in improving their foresight and forecasting capabilities. A common method to elicit expert predictions are Delphi surveys. This approach has attracted concern in the literature, given the high uncertainty in experts’ predictions. However, there exists limited guidance on specific design choices for future-related Delphi surveys.
Objective: We test whether or not small adjustments to the Delphi survey can increase certainty (i.e., reduce variation) in expert predictions on immigration to the EU in 2030.
Methods: Based on a two-round Delphi survey with 178 migration experts, we compare variation and subjective confidence in expert predictions and assess whether additional context information (type of migration flow, sociopolitical context) promotes convergence among experts (i.e., less variation) and confidence in their own estimates.
Results: We find that additional context information does not reduce variation and does not increase confidence in expert predictions on migration.
Conclusions: The results reaffirm recent concerns regarding the limited scope for reducing uncertainty by manipulating the survey setup. Persistent uncertainty may be a result of the complexity of migration processes and limited agreement among migration experts regarding key drivers.
Contribution: We caution policymakers and academics on the use of Delphi surveys for eliciting expert predictions on immigration, even when conducted based on a large pool of experts and using specific scenarios. The potential of alternative approaches such as prediction markets should be further explored.