publications

2024

1. The application of AI in digital HRM. An experiment on human decision-making in personel selection

   Christine Malin, Jürgen Fleiß, Isabella Seeber, Bettina Kubicek, and 2 more authors

   Business Process Management Journal, 2024

   Abs

   Purpose How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality. Design/methodology/approach This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality. Findings Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e., homogeneous) as when certain applicants received more search views than others (i.e., heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

2. Reciprocity in migration policy and labor market integration: A lab experiment

   Kerstin Mitterbacher, Jürgen Fleiß, and Stefan Palan

   Economic Analysis and Policy, 2024

   Abs

   We experimentally study policy variations to examine economic migrants’ willingness to relocate to, and take up work in, a destination country, and, in turn, destination country citizens’ willingness to allow economic migrants to relocate to and pursue formal work in their country. We focus on economic migrants coming from less developed countries and citizens of more developed destination countries and find clear evidence for a reciprocal relationship between the individuals in these roles. The labor market participation of economic migrants co-moves with destination countries’ openness to welcoming them. However, open migration policy without the threat of restrictions leads to lower migrant labor market participation than when the threat of restrictive policies looms. Yet, while the existence of such a threat encourages migrants to work, the actual implementation of restrictive policies reduces migrants’ willingness to work. We conclude that thoughtful and balanced migration policies that consider the reciprocal relationship between migrants and citizens are crucial to support mutually beneficial co-existence in society.

3. Take the aTrain. Introducing an interface for the Accessible Transcription of Interviews

   Armin Haberl, Jürgen Fleiß, Dominik Kowald, and Stefan Thalmann

   Journal of Behavioral and Experimental Finance, 2024

   Abs

   Research in behavioral and experimental finance becomes more multifaceted and the analysis of data from speech interactions more important. This raises the need for technical support for researchers using qualitative data generated from speech interactions. aTrain serves this need and is an open-source, offline transcription tool with a graphical interface for audio data in multiple languages. It requires no programming skills, runs on most computers, operates without internet, and ensures data is not uploaded to external servers. aTrain combines OpenAI’s Whisper transcription models with speaker recognition and provides output that integrates with MAXQDA and ATLAS.ti. Available on the Microsoft Store for easy installation, its source code is also accessible on GitHub. aTrain, designed for speed on local computers, transcribes audio files at 2-3 times the audio duration on mobile CPUs using the highest-accuracy Whisper transcription models. With an entry-level graphics card, this speed improves to 30% of the audio duration.

4. Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents

   Jürgen Fleiß, Elisabeth Bäck, and Stefan Thalmann

   SIGMIS Database, 2024

   Abs

   The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users’ perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a "one-size-fits-all approach." Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user’s characteristics and preferences.

5. Data Anonymization as Instrument to manage Knowledge Risks in Supply Chains

   Johannes Paul Zeiringer, Jürgen Fleiß, and Stefan Thalmann

   Proceedings of the 57th Hawaii International Conference on System Sciences , 2024

   Abs

   In times of interconnected and digitalized supply chains (SCs), managing knowledge risks is challenging. As sharing data is associated to the risk of unintentional disclosure of competitive knowledge, SC partners must balance knowledge sharing and protection. However, knowledge risks can inhibit knowledge sharing and therefore harm the SC management as well as desired innovation. To address this problem, data anonymization can be a solution. Further, decision support how to use the data anonymization on data sets seems necessary. For this, an already developed data anonymization tool was used as basis for a vignette study with 1.000 participants, to investigate the effect of a decision support, in form of a tradeoff visualization, on knowledge sharing. The results showed that having an anonymization tool in place does increase knowledge sharing if also decision support is provided. This helps in making an individual decision easy and transparent, and, despite a high perception of risk, there is willingness to share data and it is also considered to be beneficial.

6. Computational Antitrust and the Future of Competition Law Enforcement

   Viktoria H.S.E. Robertson, and Jürgen Fleiß

   GRUR International, 2024

   Abs

   Digitalisation has had a profound impact on the economy and, as such, also on EU competition law. While digitalisation has significantly challenged the cornerstones of competition analysis in substantive terms, it enabled the novel field of computational antitrust to provide competition authorities with a set of new data-driven tools that can support the enforcement of competition rules in an unprecedented way. While still in their infancy, these tools may well represent the future of competition law enforcement. In the following, we briefly revisit the far-reaching changes that digitalisation has led to in substantive competition law. We then turn to how digitalisation may now support the enforcement of competition rules and what prerequisites must be fulfilled for a successful application of computational antitrust tools.

7. The application of AI in digital HRM–an experiment on human decision-making in personnel selection

   Christine Dagmar Malin, Jürgen Fleiß, Isabella Seeber, Bettina Kubicek, and 2 more authors

   Business Process Management Journal, 2024

   Abs

   Purpose How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality. Design/methodology/approach This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality. Findings Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality. Originality/value Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

2023

1. Detecting resale price maintenance for competition law purposes: Proof-of-concept study using web scraped data

   Jan Amthauer, Jürgen Fleiß, Franziska Guggi, and Viktoria H.S.E. Robertson

   Computer Law & Security Review, 2023

   Abs

   Computational antitrust tools can support competition authorities in the detection of antitrust infringements. However, these tools require the availability of suitable data sets in order to produce reliable results. The present proof-of-concept study focuses on the understudied area of resale price maintenance, that is, the fixing of retail prices between manufacturers and retailers. By applying web scraping to price data for washing machines in Austria from a publicly accessibly price comparison website, we compiled a comprehensive data set for a period of nearly three months. Visualised with the help of interactive dashboards, this data was then analysed using various benchmarks in order to determine whether individual washing machine manufacturers and their retailers may be engaging in resale price maintenance. We conclude that the availability of data is a strong driver for research into and the application of computational antitrust tools. If market data were publicly accessible and provided in a more structured format, researchers and competition enforcers could develop ever more refined computational antitrust applications and screens that would, ultimately, help safeguard competition in markets.

2. In the AI of the Beholder—A Qualitative Study of HR Professionals’ Beliefs about AI-Based Chatbots and Decision Support in Candidate Pre-Selection

   Christine Malin, Cordula Kupfer, Jürgen Fleiß, Bettina Kubicek, and 1 more author

   Administrative Sciences, 2023

   Abs

   Despite the high potential of artificial intelligence (AI), its actual adoption in recruiting is low. Explanations for this discrepancy are scarce. Hence, this paper presents an exploratory interview study investigating HR professionals’ beliefs about AI to examine their impact on use cases and barriers and to identify the reasons that lead to the non-adoption of AI in recruiting. Semi-structured interviews were conducted with 25 HR professionals from 21 companies. The results revealed that HR professionals’ beliefs about AI could be categorised along two dimensions: (1) the scope of AI and (2) the definition of instruction. “Scope of Al” describes the perceived technical capabilities of AI and determines the use cases that HR professionals imagine. In contrast, the “definition of instruction” describes the perceived effort to enable an AI to take on a task and determines how HR professionals perceive barriers to Al. Our findings suggest that HR professionals’ beliefs are based on vague knowledge about AI, leading to non-adoption. Drawing on our findings, we discuss theoretical implications for the existing literature on HR and algorithm aversion and practical implications for managers, employees, and policymakers.

3. Ready or not? A systematic review of case studies using data-driven approaches to detect real-world antitrust violations

   Jan Amthauer, Jürgen Fleiß, Franziska Guggi, and Viktoria H.S.E. Robertson

   Computer Law & Security Review, 2023

   Abs

   Cartels and other anti-competitive behaviour by companies have a tremendously negative impact on the economy and, ultimately, on consumers. To detect such anti-competitive behaviour, competition authorities need reliable tools. Recently, new data-driven approaches have started to emerge in the area of computational antitrust that can complement already established tools, such as leniency programs. Our systematic review of case studies shows how data-driven approaches can be used to detect real-world antitrust violations. Relying on statistical analysis or machine learning, ever more sophisticated methods have been developed and applied to real-world scenarios to identify whether an antitrust infringement has taken place. Our review suggests that the approaches already applied in case studies have become more complex and more sophisticated over time, and may also be transferrable to further types of cases. While computational tools may not yet be ready to take over antitrust enforcement, they are ready to be employed more fully.

4. Check the box! How to deal with automation bias in AI-based personnel selection

   Cordula Kupfer, Rita Prassl, Jürgen Fleiß, Christine Malin, and 2 more authors

   Frontiers in Psychology, 2023

   Abs

   Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system’s recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.

2022

1. Validation of AI-based Information Systems for Sensitive Use Cases: Using an XAI Approach in Pharmaceutical Engineering.

   Anna Polzer, Jürgen Fleiß, Thomas Ebner, Philipp Kainz, and 2 more authors

   Proceedings of the 55th Hawaii International Conference on System Sciences, 2022

   Abs

   Artificial Intelligence (AI) is adopted in many businesses. However, adoption lacks behind for use cases with regulatory or compliance requirements, as validation and auditing of AI is still unresolved. AI’s opaqueness (i.e., "black box") makes the validation challenging for auditors. Explainable AI (XAI) is the proposed technical countermeasure that can support validation and auditing of AI. We developed an XAI based validation approach for AI in sensitive use cases that facilitates the understanding of the system’s behaviour. We conducted a case study in pharmaceutical manufacturing where strict regulatory requirements are present. The validation approach and an XAI prototype were developed through multiple workshops and was then tested and evaluated with interviews. Our approach proved suitable to collect the required evidence for a software validation, but requires additional efforts compared to a traditional software validation. AI validation is an iterative process and clear regulations and guidelines are needed.

2. Caution or Trust in AI? How to design XAI in sensitive Use Cases?

   Anika Kloker, Jürgen Fleiß, Christoph Koeth, Thomas Kloiber, and 2 more authors

   AMCIS 2022 Proceedings, 2022

   Abs Video

   Artificial Intelligence (AI) becomes increasingly common, but adoption in sensitive use cases lacks due to the black-box character of AI hindering auditing and trust-building. Explainable AI (XAI) promises to make AI transparent, allowing for auditing and increasing user trust. However, in sensitive use cases maximizing trust is not the goal, rather to balance caution and trust to find the level of appropriate trust. Studies on user perception of XAI in professional contexts and especially for sensitive use cases are scarce. We present the results of a case study involving domain-experts as users of a prototype XAI-based IS for decision support in the quality assurance in pharmaceutical manufacturing. We find that for this sensitive use case, simply delivering an explanation falls short if it does not match the beliefs of experts on what information is critical for a certain decision to be reached. Unsuitable explanations override all other quality criteria. Suitable explanations can, together with other quality criteria, lead to a suitable balance of trust and caution in the system. Based on our case study we discuss design options in this regard.

2021

1. On the Stability of Social Preferences in Inter-Group Conflict: A Lab-in-the-Field Panel Study

   Robert Böhm, Jürgen Fleiß, and Robert Rybnicek

   Journal of Conflict Resolution, 2021

   Abs

   Despite the omnipresence of inter-group conflicts, little is known about the heterogeneity and stability of individuals’ social preferences toward in-group and out-group members. To identify the prevalence and stability of social preferences in inter-group conflict, we gather quota-representative, incentivized data from a lab-in-the-field study during the heated 2016 Austrian presidential election. We assess social preferences toward in-group and out-group members one week before, one week after, and three months after the election. We find considerable heterogeneity in individuals’ group-(in)dependent social preferences. Utilizing various econometric strategies, we find largely stable social preferences over the course of conflict. Yet, there is some indication of variation, particularly when the conflict becomes less salient. Variation is larger in social preferences toward in-group members and among specific preference types. We discuss the theoretical implications of our findings and outline potential avenues for future research.

2. The Development of Prosociality: Evidence for a Negative Association between Age and Prosocial Value Orientation from a Representative Sample in Austria

   Alexander Ehlert, Robert Böhm, Jürgen Fleiß, Heiko Rauhut, and 2 more authors

   Games, 2021

   Abs

   While the ontogeny of prosociality during infancy, childhood, and adolescence has received substantial attention over the last decades, little is known about how prosocial preferences develop beyond emerging adulthood. Recent evidence suggests that the previously observed positive association between age and prosocial preferences is less robust than assumed. This study reports results on the association between social preferences, age, gender, and education from an Austrian representative sample (N = 777, aged 16–94 years) in which incentivized social value orientations (SVO) were measured along with various other sociodemographic characteristics. The analyses confirm that men are less prosocial than women, however, mainly during emerging adulthood (16–25 years). At the same time, the decline of prosociality is stronger among women leading to a convergence of prosociality between men and women as they age. Overall, we find that a prosocial value orientation is negatively correlated with people’s age. We suspect that the susceptibility of peoples’ social preferences to the preferences of others in their social environment is a critical factor unifying these different observations in the development of prosociality. We hypothesize that the opposite associations between age and SVO observed in two previous studies using unincentivized measures of social preferences are explained in parts by an age-related change in social desirability, measurement inaccuracy (continuous vs. categorical), and cross-cultural differences promoting competitive preferences among emerging adults in Japan. Moreover, we find that political orientations towards right-wing populists are consistently associated with less prosocial preferences, while education seems to be positively associated with prosociality. Overall, our study highlights the importance of conducting representative studies using incentivized measurements across cultures.

2019

1. To claim or not to claim: Anonymity, symmetric externalities and honesty

   Christian Schitter, Jürgen Fleiß, and Stefan Palan

   Journal of Economic Psychology, 2019

   Abs

   In many situations, economic actors submit claims for money which are unverifiable or hard to verify. Examples include claims for a tax return or an insurance payout. This paper investigates what role anonymity and externalities play for the decision of whether to be (dis)honest when making such claims. First, does honest claiming increase when anonymity is removed and unverified claims are made public? We present experimental evidence to this effect. Second, does honest reporting increase when it is public knowledge that claims affect others’ payoffs and claimants’ payoffs are symmetrically affected by others’ claims? We find no such effect. Making claims public and having symmetric externalities together increases honesty, but this effect is driven solely by the reduction in anonymity.

2. Social and environmental preferences: measuring how people make tradeoffs among themselves, others, and collective goods

   Jürgen Fleiß , Kurt A. Ackermann, Eva Fleiß, Ryan O. Murphy, and 1 more author

   Central European Journal of Operations Research, 2019

   Abs

   Social preferences like social value orientation are considered a promising solution to social dilemmas, such as mitigating anthropogenic climate change. However, evidence on the relationship between social preferences and environmental concerns is mixed, possibly because these constructs have commonly been measured by distinct methods that do not facilitate direct comparisons. We address this gap by introducing an incentivized preference-based measurement approach, extending a subject’s concerns for the wellbeing of others to a subject’s willingness to support environmental and humanitarian endeavors, based on a simple social preferences utility function. In this measurement approach, subjects make resource allocation choices with real consequences and the design ensures comparability of different revealed preferences (i.e., people’s willingness to make tradeoffs between themselves and others via donations to NGOs supporting different environmental and social causes). We then use this measurement method in an exploratory fashion to consistently assess preferences for environmental and humanitarian concerns in a laboratory experiment. We find that social and environmental value orientations are robustly interrelated, and further that people are generally more willing to pay to benefit people in need, compared to abstract environmental causes. We conclude that interventions to nudge people towards proenvironmental behavior will have a greater impact if human suffering resulting from global climate change is made more salient.

2016

1. Reciprocity as an Individual Difference

   Kurt A. Ackermann, Jürgen Fleiß, and Ryan O. Murphy

   Journal of Conflict Resolution, 2016

   Publisher: SAGE Publications Inc

   Abs

   There is accumulating evidence that decision makers (DMs) are sensitive to the distribution of resources among themselves and others, beyond what is expected from the predictions of narrow self-interest. These social preferences are typically conceptualized as being static and existing independently of information about the other people influenced by a DM’s allocation choices. In this article, we consider the reactivity of a DM’s social preferences in response to information about the intentions or past behavior of the person to be affected by the DM’s allocation choices (i.e., how do social preferences change in relation to the other’s type). This article offers a conceptual framework for characterizing the link between distributive preferences and reciprocity, and reports on experiments in which these two constructs are disentangled and the relation between the two is characterized.

2015

1. Merit norms in the ultimatum game: an experimental study of the effect of merit on individual behavior and aggregate outcomes

   Jürgen Fleiß

   Central European Journal of Operations Research, 2015

   Abs

   The paper reports the results of an ultimatum game experiment designed to test the effects of meritocratic norms on individual behavior and aggregate outcomes. In one treatment the roles of proposer and responder were assigned randomly. In the other treatment the roles were earned in a general knowledge quiz. The results show that proposers offer significantly less when they have earned their roles and responders have a significantly lower acceptance threshold. Rejection rates are lower for offers lower than the equal split when positions are allocated based on merit: Proposers earn significantly more in this setting. Responders suffer some loss in this treatment. This leads to an increase in overall inequality of payoffs measured by the Gini index when positions are allocated based on merit.

2014

1. Once Nice, Always Nice? Results on Factors Influencing Nice Behavior from an Iterated Prisoner’s Dilemma Experiment

   Jürgen Fleiß, and Ulrike Leopold‐Wildburger

   Systems Research and Behavioral Science, 2014

   _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sres.2194

   Abs

   The reliability of human behavior in situations where cooperation is beneficial for all but is hindered by individual incentives not to cooperate is a central research question in economics and the social sciences. Little is known about how the interaction results of a subject with one partner may affect this subject’s behavior when subsequently matched with a new partner. We extend this knowledge by studying the development of cooperation in a repeated prisoner’s dilemma experiment. After several rounds, each subject is matched with a new partner. We analyse whether the interaction outcomes with the first partner lead subjects to change their behavior when they interact with the second partner. We focus on niceness as introduced by Axelrod and find statistically significant effects. Mutual cooperation with the first partner is positively correlated with the continuation of a nice strategy, whereas mutual defection leads subjects to give up their nice strategy. Copyright © 2013 John Wiley & Sons, Ltd.

2013

1. Of Coordinators and Dictators: A Public Goods Experiment

   Jürgen Fleiß, and Stefan Palan

   Games, 2013

   Number: 4

   Abs

   We experimentally investigate whether human subjects are willing to give up individual freedom in return for the benefits of improved coordination. We conduct a modified iterated public goods game in which subjects in each period first decide which of two groups to join. One group employs a voluntary contribution mechanism, the other group an allocator contribution mechanism. The setup of the allocator mechanism differs between two treatments. In the coordinator treatment, the randomly selected allocator can set a uniform contribution for all group members, including herself. In the dictator treatment, the allocator can choose different contributions for herself and all other group members. We find that subjects willingly submit to authority in both treatments, even when competing with a voluntary contribution mechanism. The allocator groups achieve high contribution levels in both treatments.

2010

1. Paul Lazarsfelds typologische Methode und die Grounded Theory. Generierung und Qualität von Typologien

   Jürgen Fleiß

   Österreichische Zeitschrift für Soziologie, 2010

   Abs

   Der Beitrag beschäftigt sich mit Verbindungspunkten zwischen Paul Lazarsfelds typologischer Methode und der Grounded Theory. Es wird die Frage gestellt, wie man in der qualitativen Sozialforschung möglichst „gute“ Typologien konstruieren kann. Formale Kriterien sind dabei eine notwendige, aber keine hinreichende Bedingung für eine gute Typologie; auch inhaltliche Qualität wird gefordert. Es wird gezeigt, dass eine Anknüpfung an die Grounded Theory zur Lösung dieser Frage beitragen kann, indem Merkmale zur Konstruktion von Typologien in einem ähnlichen Prozess generiert werden, wie ihn die Grounded Theory zur Konstruktion von Theorien verwendet. Umgekehrt kann Lazarsfelds Konzept des Merkmalsraumes das „theoretical sampling“, welches die Grounded Theory fordert, unterstützen und Hilfestellung bei der Systematisierung des Auswahlprozesses der zu untersuchenden Fälle leisten. Abschließend werden die behandelten Punkte an einem empirischen Beispiel erläutert.

2009

1. Nationalstolz zwischen Patriotismus und Nationalismus?

   Jürgen Fleiß, Franz Höllinger, and Helmut Kuzmics

   Berliner Journal für Soziologie, 2009

   Abs

   In der politischen Soziologie wird heute vielfach zwischen einem positiven und einem negativen Nationalstolz (Patriotismus und Nationalismus) unterschieden. Der vorliegende Beitrag untersucht, inwieweit sich diese beiden theoretischen Konstrukte anhand der Umfragedaten des International Social Survey Programme 2003 empirisch nachweisen lassen. In einem ersten Schritt wird die Kriteriums- und Konstruktvalidität der Skalen „Nationalismus“ und „Patriotismus“ mit den üblichen statistischen Methoden geprüft. Sodann werden die Ergebnisse einer in Österreich durchgeführten Probing-Studie präsentiert, in der die Befragten nach dem Ausfüllen des ISSP-Fragebogens gebeten wurden, ihre Antworten zu begründen. Abschließend wird versucht, mit einer literatursoziologischen Analyse zusätzliche Aspekte herauszuarbeiten, die beim konventionellen soziologischen Zugang zum Thema meist unbeachtet bleiben. Angesichts der Inkonsistenzen, die sich sowohl in den quantitativen als auch in den qualitativen Analysen, vor allem aber im Vergleich der beiden Analyseebenen zeigen, stellt sich die Frage, ob die polarisierende Gegenüberstellung von Nationalismus und Patriotismus theoretisch sinnvoll und die üblichen Messungen dieser Konstrukte valide sind.