Predicting the causal agent in verbally described social interactions

Implicit causality in interpersonal verbs (i.e., causal assumptions about the initiator of a social interaction) has been extensively investigated, especially in English and German language (cf. Rudolph & Försterling, 1997). The present study is the first to investigate verb causality in Danish language using a student sample (N = 96) while simultaneously examining consensus (i.e., to what extent others besides the grammatical subject treat the object like this) and distinctiveness (i.e., to what extent solely the object person is treated by the subject like this) as predictors of causal attribution to subject or object. A strong verb causality effect in Danish language emerged. Consensus proved to be a better predictor than distinctiveness for causal attribution

The 100 most eminent psychologists of the 20th century on the internet – Do internet page counts provide latent indicators of scientific eminence?

Recently, Haggbloom et al. (2002) established a rank-ordered list of the 100 most eminent psychologists of the 20th century (though only the first 99 are actually reported by the authors) meticulously measured by several quantitative and qualitative indicators. We aimed at replicating this listing by simply using page counts obtained from three major internet search engines using different search queries with a five times repeated measurement. The resulting highly reliable indicators of internet frequency were consistently positively associated with the existing ranking and this correlation reached significance when the field of research was included in the query as an operator. We conclude that frequency data obtained by this method can be considered a simple and valid indicator of scientific impact and discuss additional applications of this method

Dimensions of perceived product quality – understanding the consumer’s view

Objectives. We identify core product quality components from the consumers’ perspective and construct a psychometrically sound instrument for their assessment within different purchasing contexts. Methods. Study 1 (N = 34) used a qualitative approach to reveal core components of perceived product quality in the context of apparel evaluations. Study 2 (N = 305) was designed to construct and quantitatively validate the quality scale in a retail store setting. In Study 3 (N = 180) using a second purchasing context the scale’s dimensionality was cross-validated in a mail-order context. Results. Six components of product quality emerged (material, workmanship, design, care, color, fit) and explained approximately 75% of the variance in consumers’ product evaluations. Their dimensional structure was validated using confirmatory factor analysis. Differences concerning the quality dimensions’ relative importance were found for the two purchasing situations. Conclusion. The quality scale proved to be reliable and valid in two important purchasing contexts

Gender roles and implicit causality

Numerous studies investigated the phenomenon of implicit verb causality (cf. Rudolph & Försterling, 1997). This research revealed the robust finding that different types of interpersonal verbs lead to systematic causal attributions to one of the interacting persons. However, few studies addressed the interaction between verb causality and context variables. The present cross-cultural study investigates implicit gender roles in action and state verbs comparing two samples from Germany and China. Results show that the German sample perceived actions to be caused by men whereas states were causally attributed to women. However, our Chinese sample perceived men and women rather equally accountable

On predictability of rare events leveraging social media: a machine learning perspective

Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8% marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the games, allowing for informed bets. We discuss the rationale behind our approach, describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games, and real-time Twitter data collected by monitoring the conversations about all soccer matches of four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure

Walk this Way! Incentive Structures of Different Token Designs for Blockchain-Based Applications

Cryptoeconomics is an emerging research area in the field of blockchain technology aiming at understanding token design mechanisms intended to incentivize certain behaviors. Whereas several blockchain ecosystems have been emerging in recent years, little is known about incentive design in blockchain protocols other than Bitcoin. To address this gap, we use agent-based modeling (ABM) to simulate the effects of different token designs on usage in the context of prediction markets. We find that network tokens (i.e., tokens providing services within a system) provide the largest incentive for individuals to join and become long-term active users. Moreover, we find that investment tokens (i.e., tokens used to passively invest in the issuing entity) provide the smallest incentive compared to network tokens and cryptocurrencies (i.e., means of payment in a blockchain ecosystem). We advance the literature by testing the boundary conditions of different token designs for blockchain-based ecosystems using a novel ABM approach

Is content king? Job seekers’ engagement with social media employer branding content

Resumen de la ponencia[EN] Increasing digitization and the emergence of social media have radically changed the recruitment landscape adding interactive digital platforms to traditional means of employer communication. Removing barriers of distance and timing, social media enable firms to continue their efforts of promoting their employment brand online. However, social media employer communication and employer brand building remains woefully understudied. Our study addresses this gap by investigating how firms use social media to promote their employer brand. We analyze employer branding communication in a sample of N = 216,828 human resources (HR) related Tweets from N = 166 Fortune 500 companies. Using supervised machine learning we classify the Tweet content according to its informational and inspirational nature, identifying five categories of employer branding social media communication on Twitter.Moser, K.; Tumasjan, A.; Welpe, I. (2016). Is content king? Job seekers’ engagement with social media employer branding content. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 124-124. https://doi.org/10.4995/CARMA2016.2016.3103OCS12412

Disrupting Industries with Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures

The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the emerging blockchain business-application landscape by analyzing its industry, venture capital funding, and regional distribution. By matching four venture databases on blockchain-based startups we create a unique database to analyze the technology from a diffusion of innovation theoretical perspective. First, our results show that blockchain startups are present across all industry segments and are most prominently represented in the Finance & Insurance and Information & Communication industries. A fine-grained analysis of financial services yields increasing novel applications in existing service offerings. Second, we find that mainly Finance & Insurance and Information & Communication industries are funded by venture capital, but that blockchain startups are present across all industries. Third, our regional distribution analysis of the emerging ventures identifies two leading geographical blockchain clusters (i.e., the US and UK)

Empirical competence-testing: A psychometric examination of the German version of the Emotional Competence Inventory

The “Emotional Competence Inventory“ (ECI 2.0) by Goleman and Boyatzis assesses emotional intelligence (EI) in organizational context by means of 72 items in 4 clusters (self-awareness, self- management, social awareness, social skills) which at large consist of 18 competencies. Our study examines the psychometric properties of the first German translation of this instrument in two different surveys (N = 236). If all items are included in reliability analysis the ECI is reliable (Cronbach’s Alpha = .90), whereas the reliability of the four sub dimensions is much smaller (Alpha = .62 - .81). For 43 items the corrected item-total correlation with its own scale is higher than correlations with the other three clusters. Convergent validity was examined by using another EI instrument (Wong & Law, 2002). We found a significant correlation between the two instruments (r = .41). The German version of the ECI seems to be quite useful, although the high reliability is achieved by a large number of items. Possibilities of improvement are discussed