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When humans encounter attitude objects (e.g., other people, objects, or constructs), they evaluate them. Often, these evaluations are based on attitudes. Whereas most research focuses on univalent (i.e., only positive or only negative) attitude formation, little research exists on ambivalent (i.e., simultaneously positive and negative) attitude formation. Following a general introduction into ambivalence, I present three original manuscripts investigating ambivalent attitude formation. The first manuscript addresses ambivalent attitude formation from previously univalent attitudes. The results indicate that responding to a univalent attitude object incongruently leads to ambivalence measured via mouse tracking but not ambivalence measured via self-report. The second manuscript addresses whether the same number of positive and negative statements presented block-wise in an impression formation task leads to ambivalence. The third manuscript also used an impression formation task and addresses the question of whether randomly presenting the same number of positive and negative statements leads to ambivalence. Additionally, the effect of block size of the same valent statements is investigated. The results of the last two manuscripts indicate that presenting all statements of one valence and then all statements of the opposite valence leads to ambivalence measured via self-report and mouse tracking. Finally, I discuss implications for attitude theory and research as well as future research directions.
Data fusions are becoming increasingly relevant in official statistics. The aim of a data fusion is to combine two or more data sources using statistical methods in order to be able to analyse different characteristics that were not jointly observed in one data source. Record linkage of official data sources using unique identifiers is often not possible due to methodological and legal restrictions. Appropriate data fusion methods are therefore of central importance in order to use the diverse data sources of official statistics more effectively and to be able to jointly analyse different characteristics. However, the literature lacks comprehensive evaluations of which fusion approaches provide promising results for which data constellations. Therefore, the central aim of this thesis is to evaluate a concrete plethora of possible fusion algorithms, which includes classical imputation approaches as well as statistical and machine learning methods, in selected data constellations.
To specify and identify these data contexts, data and imputation-related scenario types of a data fusion are introduced: Explicit scenarios, implicit scenarios and imputation scenarios. From these three scenario types, fusion scenarios that are particularly relevant for official statistics are selected as the basis for the simulations and evaluations. The explicit scenarios are the fulfilment or violation of the Conditional Independence Assumption (CIA) and varying sample sizes of the data to be matched. Both aspects are likely to have a direct, that is, explicit, effect on the performance of different fusion methods. The summed sample size of the data sources to be fused and the scale level of the variable to be imputed are considered as implicit scenarios. Both aspects suggest or exclude the applicability of certain fusion methods due to the nature of the data. The univariate or simultaneous, multivariate imputation solution and the imputation of artificially generated or previously observed values in the case of metric characteristics serve as imputation scenarios.
With regard to the concrete plethora of possible fusion algorithms, three classical imputation approaches are considered: Distance Hot Deck (DHD), the Regression Model (RM) and Predictive Mean Matching (PMM). With Decision Trees (DT) and Random Forest (RF), two prominent tree-based methods from the field of statistical learning are discussed in the context of data fusion. However, such prediction methods aim to predict individual values as accurately as possible, which can clash with the primary objective of data fusion, namely the reproduction of joint distributions. In addition, DT and RF only comprise univariate imputation solutions and, in the case of metric variables, artificially generated values are imputed instead of real observed values. Therefore, Predictive Value Matching (PVM) is introduced as a new, statistical learning-based nearest neighbour method, which could overcome the distributional disadvantages of DT and RF, offers a univariate and multivariate imputation solution and, in addition, imputes real and previously observed values for metric characteristics. All prediction methods can form the basis of the new PVM approach. In this thesis, PVM based on Decision Trees (PVM-DT) and Random Forest (PVM-RF) is considered.
The underlying fusion methods are investigated in comprehensive simulations and evaluations. The evaluation of the various data fusion techniques focusses on the selected fusion scenarios. The basis for this is formed by two concrete and current use cases of data fusion in official statistics, the fusion of EU-SILC and the Household Budget Survey on the one hand and of the Tax Statistics and the Microcensus on the other. Both use cases show significant differences with regard to different fusion scenarios and thus serve the purpose of covering a variety of data constellations. Simulation designs are developed from both use cases, whereby the explicit scenarios in particular are incorporated into the simulations.
The results show that PVM-RF in particular is a promising and universal fusion approach under compliance with the CIA. This is because PVM-RF provides satisfactory results for both categorical and metric variables to be imputed and also offers a univariate and multivariate imputation solution, regardless of the scale level. PMM also represents an adequate fusion method, but only in relation to metric characteristics. The results also imply that the application of statistical learning methods is both an opportunity and a risk. In the case of CIA violation, potential correlation-related exaggeration effects of DT and RF, and in some cases also of RM, can be useful. In contrast, the other methods induce poor results if the CIA is violated. However, if the CIA is fulfilled, there is a risk that the prediction methods RM, DT and RF will overestimate correlations. The size ratios of the studies to be fused in turn have a rather minor influence on the performance of fusion methods. This is an important indication that the larger dataset does not necessarily have to serve as a donor study, as was previously the case.
The results of the simulations and evaluations provide concrete implications as to which data fusion methods should be used and considered under the selected data and imputation constellations. Science in general and official statistics in particular benefit from these implications. This is because they provide important indications for future data fusion projects in order to assess which specific data fusion method could provide adequate results along the data constellations analysed in this thesis. Furthermore, with PVM this thesis offers a promising methodological innovation for future data fusions and for imputation problems in general.
Social entrepreneurship is a successful activity to solve social problems and economic
challenges. Social entrepreneurship uses for-profit industry techniques and tools to build
financially sound businesses that provide nonprofit services. Social entrepreneurial activities
also lead to the achievement of sustainable development goals. However, due to the complex,
hybrid nature of the business, social entrepreneurial activities are typically supported by macrolevel
determinants. To expand our knowledge of how beneficial macro-level determinants can
be, this work examines empirical evidence about the impact of macro-level determinants on
social entrepreneurship. Another aim of this dissertation is to examine the impact at the micro
level, as the growth ambitions of social and commercial entrepreneurs differ. At the beginning,
the introductory section is explained in Chapter 1, which contains the motivation for the
research, the research question, and the structure of the work.
There is an ongoing debate about the origin and definition of social entrepreneurship.
Therefore, the numerous phenomena of social entrepreneurship are examined theoretically in
the previous literature. To determine the common consensus on the topic, Chapter 2 presents
the theoretical foundations and definition of social entrepreneurship. The literature shows that
a variety of determinants at the micro and macro levels are essential for the emergence of social
entrepreneurship as a distinctive business model (Hartog & Hoogendoorn, 2011; Stephan et
al., 2015; Hoogendoorn, 2016). It is impossible to create a society based on a social mission without the support of micro and macro-level-level determinants. This work examines the
determinants and consequences of social entrepreneurship from different methodological
perspectives. The theoretical foundations of the micro- and macro-level determinants
influencing social entrepreneurial activities were discussed in Chapter 3
The purpose of reproducibility in research is to confirm previously published results
(Hubbard et al., 1998; Aguinis & Solarino, 2019). However, due to the lack of data, lack of
transparency of methodology, reluctance to publish, and lack of interest from researchers, there
is a lack of promoting replication of the existing research study (Baker, 2016; Hedges &
Schauer, 2019a). Promoting replication studies has been regularly emphasized in the business
and management literature (Kerr et al., 2016; Camerer et al., 2016). However, studies that
provide replicability of the reported results are considered rare in previous research (Burman
et al., 2010; Ryan & Tipu, 2022). Based on the research of Köhler and Cortina (2019), an
empirical study on this topic is carried out in Chapter 4 of this work.
Given this focus, researchers have published a large body of research on the impact of microand
macro-level determinants on social inclusion, although it is still unclear whether these
studies accurately reflect reality. It is important to provide conceptual underpinnings to the
field through a reassessment of published results (Bettis et al., 2016). The results of their
research make it abundantly clear that the macro determinants support social entrepreneurship.
In keeping with the more narrative approach, which is a crucial concern and requires attention,
Chapter 5 considered the reproducibility of previous results, particularly on the topic of social
entrepreneurship. We replicated the results of Stephan et al. (2015) to establish the trend of
reproducibility and validate the specific conclusions they drew. The literal and constructive
replication in the dissertation inspired us to explore technical replication research on social
entrepreneurship. Chapter 6 evaluates the fundamental characteristics that have proven to be key factors in the
growth of social ventures. The current debate reviews and references literature that has
specifically focused on the development of social entrepreneurship. An empirical analysis of
factors directly related to the ambitious growth of social entrepreneurship is also carried out.
Numerous social entrepreneurial groups have been studied concerning this association. Chapter
6 compares the growth ambitions of social and traditional (commercial) entrepreneurship as
consequences at the micro level. This study examined many characteristics of social and
commercial entrepreneurs' growth ambitions. Scholars have claimed to some extent that the
growth of social entrepreneurship differs from commercial entrepreneurial activities due to
objectivity differences (Lumpkin et al., 2013; Garrido-Skurkowicz et al., 2022). Qualitative
research has been used in studies to support the evidence on related topics, including Gupta et
al (2020) emphasized that research needs to focus on specific concepts of social
entrepreneurship for the field to advance. Therefore, this study provides a quantitative,
analysis-based assessment of facts and data. For this purpose, a data set from the Global
Entrepreneurship Monitor (GEM) 2015 was used, which examined 12,695 entrepreneurs from
38 countries. Furthermore, this work conducted a regression analysis to evaluate the influence
of various social and commercial characteristics of entrepreneurship on economic growth in
developing countries. Chapter 7 briefly explains future directions and practical/theoretical
implications.