Campaign dynamics of cognitive accessibility of political judgments: measuring the impact of campaigns and campaign events using response latencies in two German rolling cross section studies

Do campaigns matter? Based on two rolling cross-section computer-assisted telephone surveys conducted in the run-up to the 2009 and 2013 German Federal Election, we test whether we can detect campaign effects on the accessibility of voters’ judgments: how do response latencies of political judgments evolve over the course of campaigns? The study uses response latencies, i.e. the standardized time it takes respondents to answer a survey question, as a proxy measurement of cognitive accessibility of political judgments. If campaigns do help voters to make up their minds, we should be able to observe changes at the implicit level of response latencies. Do people answer questions about their voting behavior and political attitudes faster as Election Day comes closer? Our results suggest that attitudes towards candidates and voting intentions become more cognitively accessible during campaigns whereas the accessibility of party identification is conditional on the contextual features of campaigns. In addition we find specific short-term effects of TV debates.     

Country-level Accounting Enforcement and IPO Underpricing

Using a sample of up to 2,503 initial public offerings (IPOs) in 32 countries from 2011–2017, we predict and find that higher levels of country-level accounting enforcement are associated with lower levels of IPO underpricing. IPOs in countries with a relatively low accounting enforcement score (second quintile) exhibit a mean underpricing of 19%, whereas the mean underpricing amounts to just 9% in countries with a relatively high score (fourth quintile). The results remain qualitatively the same when we employ a multi-level model or a difference-in-difference design. In countries that substantially strengthened their accounting enforcement in the 2003–2009 period, the level of IPO underpricing decreased significantly. We show that accounting enforcement matters for the cost of going public.     

Forschung und Innovation: Wo steht Deutschland?

Aufwendungen für Forschung und Entwicklung werden im Hinblick auf die internationale Wettbewerbsf?higkeit eines Wirtschafts- und Technologiestandorts als besonders bedeutsam eingesch?tzt. Wie l?sst sich der Zusammenhang zwischen Forschung und wirtschaftlicher Entwicklung empirisch nachweisen? Welchen Rang nimmt Deutschland im internationalen Vergleich der Forschungs- und Innovationsaktivit?ten ein? Welche Handlungsoptionen ergeben sich für die Wirtschaftspolitik?     

Auditor Reputation Under Different Negligence Regimes

Do auditor reputation effects evolve the same way under precise negligence as under vague negligence? Or are there differences? We assume that investors update their beliefs on unobservable auditor quality when an auditor discloses an inaccurate report. We call this a reputation effect. A necessary condition for reputation effects to occur is that, ex ante, investors expect ‘good’ auditors to take more care than ‘bad’ auditors such that ‘good’ auditors are less likely to issue an inaccurate report. Consistent with empirical evidence, we assume that wealthier (‘good’) auditors tend to take more care than less wealthy (‘bad’) auditors. We find that under vague negligence, reputation effects will occur, inducing both types of auditor to increase the level of care taken. A ‘good’ auditor is likely to exert excessive care. Then, even in the absence of auditor risk aversion, a (properly defined) liability cap is necessary to induce efficient incentives. A contractual liability cap is preferable to a legally fixed liability cap. Under precise negligence, a ‘good’ auditor will exert the standard of due care. However, a ‘bad’ auditor will also do so if sufficiently wealthy. Consequently, ex ante, investors do not expect different levels of care to be taken or reputation effects to occur. A liability cap is not desirable. This paper highlights the importance of non‐legal sanctions in auditor liability. Finally, it links the ‘reputation’ and ‘deep pocket’ hypotheses, both of which have attempted separately in the past to explain the positive correlation between auditor size and auditor quality.     

Siemens: Maßhalten beim Moralisieren

Ohne Zusammenfassung Prof. Dr. Jochen Zimmermann ist Professor für Betriebswirtschaftslehre an der Universit?t Bremen     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.