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There is a dearth of literature examining the impact of the COVID-19 pandemic on older people with dual diagnosis referred to mental health services. The purpose of this study was to compare dual diagnosis before and after lockdown in people aged between 55 and 74 with alcohol use.

Data were collected for people referred to mental health services using an anonymised database of de-identified records to identify people with both substance use disorder alone, or accompanied by co-existing mental disorders.

In total, 366 older people were assessed with the Alcohol Use Disorders Identification Test (AUDIT), 185 before and 181 after lockdown. People with dual diagnosis were more likely to be referred than those without, after compared to before lockdown (13 and 6%, respectively, p < 0.05). People with any substance use disorder with and without dual diagnosis showed an even greater likelihood of referral after, compared with before, lockdown (61 and 34%, respectively, p < 0.0001). Opioid use more than once a month was more likely to be reported after, compared with before, lockdown (66 and 36%, respectively, p < 0.005).

The finding of a higher likelihood of opioid use after compared with before lockdown during the COVID-19 pandemic warrants further exploration. There is also further scope for further studies that involve older non-drinkers.

A greater likelihood of both dual diagnosis and substance use disorder alone after, compared with before lockdown has implications for both mental health and addiction service provision during a pandemic.

In a recent quasi-experimental study, the effects of a large German public startup support measure entitled “EXIST – Business Startup Grant” (EGS) on a variety of outcomes were determined, but without examining which factors are responsible for these program effects. The present study investigates the contribution of several factors to the success of the program in promoting product development and business planning.

By means of a two-wave panel design and fixed-effects panel regressions, evidence is generated that provides unique insights into the effect mechanisms of a publicly funded startup grant. The data for the study come from the program monitoring of the startup support measure.

Several factors were identified that significantly drive the effects of the program on the product development and business planning stages, namely the program-induced improvement of the skills of the startup team, intensification of cooperation with pilot customers/users, increase in the degree of networking and advice/support from third parties and the effort put into business plan preparation.

Startup support programs are a crucial aspect of technology and innovation policies, which are often evaluated in order to find out whether they generate effects. Assessing whether a program is effective or not, however, does not usually allow specific recommendations on how to improve the measure to be developed. Further information on the mechanisms of intervention is needed for this purpose. The present study takes up on this idea and provides this information for a specific type of public startup support measure.

Governments and energy operators are often confronted with opposition to the construction of new high-voltage transmission lines. Besides other factors, a potential determinant of public opposition and acceptance that has gained increasing attention is the fairness of the planning and approval procedure as perceived by the citizens. The purpose of this study is to develop and validate a scale for measuring perceived procedural fairness (PPF).

The authors developed the ten-item “perceived procedural fairness scale (PPFS)” and assessed its quality by means of item response theory. By using a Rasch rating scale model, the authors tested whether the instrument met the requirements of this kind of measurement model. For conducting their research, the authors used data from two telephone surveys in Germany that were collected in areas that are affected by grid expansion.

The findings suggest that the scale can be considered a reliable and internal valid instrument for measuring citizens’ PPF.

At the moment, there is no psychometrically rigorously evaluated scale available for measuring PPF in the context of power grid expansion. Therefore, the study contributes to filling this gap and provides a valuable tool for researchers and practitioners concerned with further investigating citizens’ PPF and its relationships with other relevant constructs in the field.

Online flight booking websites compare airfares, convenience and other consumer relevant attributes. Environmental concerns are typically not addressed, even though aviation is the most emission-intensive mode of transportation. This article demonstrates the potential for digital nudges to facilitate more environmentally friendly decision-making on online flight booking websites.

The authors used the digital nudging design process to implement two nudging interventions in an experimental setting on a fictitious flight booking website. The two nudging interventions are (1) an informational nudge, presented as an emission label, and (2) an understanding mapping nudge, presented as an emission converter.

This article finds that both digital nudges are useful interventions in online choice environments; however, emission labels more effectively encourage sustainable booking behavior.

The contributions of this article are twofold. In contribution to research, this article builds on existing research in sustainability contexts and successfully evaluates the effectiveness of anchoring and understanding mapping heuristics to influence sustainable decision-making in virtual environments. Furthermore, in contribution to practice, this article contributes knowledge to nudge design and provides hands on examples for designers or website operators on how to put nudge designs to practice in virtual choice environments. Additionally, this article contributes relevant considerations in a high-impact research field with growing importance given the global climate crisis.

This paper aims to introduce a new large‐scale panel study (“PASS”) for research on the labour market, the welfare state and poverty that combines a sample of 6,000 recipient households with an equally large sample of the general population.

The authors show how research goals and the specific population of the survey are accounted for in a tailored survey design.

The authors point the reader to new research potential created by the new study. The new potential is mainly derived from the sampling design (large recipient sample combined with a population sample), the direct measurement of poverty by a deprivation index, the detailed measurement of the migratory background, additional information (like attitudes, search intensity) for models of recipiency dynamics, and the linkage of the survey data with administrative data.

The data set described fills a major gap in the data‐infrastructure available for labour‐market research. From a methodological point of view it presents an innovative sampling design.

In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

This chapter introduces the readers to the theme of the book. The chapter starts by outlining the socio-economic importance of gazelles and by discussing definitional challenges of what gazelles or high-growth firms are. The chapter then points to the academic relevance and to unresolved issues in international gazelle research. The chapter closes with an outline of the three blocks and eight chapters of the book.

Despite the mounting evidence of gazelles’ higher economic contribution, there is no consensus on whether and how to support gazelles. In this chapter, we therefore discuss varied gazelle policy views as well as the inherent challenges of supporting gazelles. Here, we draw on relevant academic literature as well as on international policy examples. Additionally, we analyze Germany’s gazelle policy stance by triangulating information collected from the literature, practical policy examples and interviews, we carried out with German support agencies and policymakers. Though Germany’s general policies and framework conditions create necessary conditions for gazelles to emerge, they are insufficient to guarantee their prevalence. Moreover, Germany’s policy approach of encouraging new firm formation and self-employment and its focus on the financial support for small as well as technology or innovation-oriented firms is not consistent with the state-of-the-art knowledge about gazelles. Using the case of Germany, we argue that general entrepreneurship policies and high-growth entrepreneurship (gazelle) policies are distinct approaches having divergent and sometimes conflicting objectives and support mechanisms, which need to be synchronized to be effective.

The purpose of this article is to analyze the supply (technology, education, labour, unemployment and real estate development) and demand (fiscal revenue and resident income) factors that influence regional entrepreneurial activity in China. Entrepreneurship develops at a rapid pace in China with significant differences among the country’s regions.

Statistics of 31 Chinese provinces from 2005 to 2010 were collected, and an econometric model of the panel data was established.

Empirical results show that technology and employment positively impact on regional entrepreneurial activity. A subsequent analysis comparing data from 2005-2008 to 2009-2010 showed that different variables on regional entrepreneurship weaken during a period of financial crisis, with technology remaining as the only significant variable across all models. Finally, the study summarizes China’s entrepreneurial activity as primarily supply-driven.

This study is limited by the data sources and index design, which may not fully capture all influences on regional entrepreneurship to determine whether an inflection point or other interaction mechanisms exist.

The study demonstrates a differential emphasis on the impact of economic supply factors in a developing economy to positively affect entrepreneurial activities and sustained economic growth at the regional level. Conversely, it can be inferred that increased government spending during an economic crisis positively influences regional entrepreneurial activities.

The study contributes toward the development of a theoretical framework that emphasizes the relationship between entrepreneurial activities and its regional supply and demand factors. The overall model and findings highlight technology’s importance on the development of innovation clusters that spur industrial agglomeration.