Andranik Tumasjan

Bio: Andranik Tumasjan is an academic researcher from University of Mainz. The author has contributed to research in topics: Employer branding & Blockchain. The author has an hindex of 20, co-authored 62 publications receiving 4479 citations. Previous affiliations of Andranik Tumasjan include Ludwig Maximilian University of Munich & Technische Universität München.

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

Abstract: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician Our results show that Twitter is indeed used extensively for political deliberation We find that the mere number of messages mentioning a party reflects the election result Moreover, joint mentions of two parties are in line with real world political ties and coalitions An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research

Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape

Abstract: This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.

Tweets and Trades: The Information Content of Stock Microblogs

Abstract: Microblogging forums (e.g., Twitter) have become a vibrant online platform for exchanging stock-related information. Using methods from computational linguistics, we analyse roughly 250,000 stock-related messages (so-called tweets) on a daily basis. We find an association between tweet sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility. In contrast to previous related research, we also analyse the mechanism leading to an efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice.

In the Eye of the Beholder: How Regulatory Focus and Self-Efficacy Interact in Influencing Opportunity Recognition

Abstract: Although there is evidence that regulatory focus is associated with opportunity exploitation, there is a lack of research examining its role at the early stages of opportunity recognition. The present study makes two major contributions to address this gap. First, we demonstrate that entrepreneurs' promotion focus is positively related to opportunity recognition, whereas prevention focus is not significantly related to opportunity recognition. Second, integrating two theories of self-regulation - regulatory focus theory and self-efficacy theory - our findings reveal that a high promotion focus compensates for entrepreneurs’ low levels of creative and entrepreneurial self-efficacy in opportunity recognition. Our study extends extant cognitive theories of opportunity recognition.

Employer branding: A brand equity-based literature review and research agenda

Abstract: Over the past two decades, scholarly interest in employer branding has strongly increased. Simultaneously, however, employer branding research has developed into a fragmented field with heterogeneous interpretations of the employer branding concept and its scope, which has impeded further theoretical and empirical advancement. To strengthen the foundation for future work, this paper takes a brand equity perspective to review the extant literature and create an integrative model of employer branding. Using an analytical approach, the authors identify 187 articles, which they integrate along different employer brand dimensions and branding strategies: (i) conceptual; (ii) employer knowledge dimensions; (iii) employer branding activities and strategies. On the basis of this review, the authors develop an employer branding value chain model and derive future research avenues as well as practical implications.

Sentiment Analysis and Opinion Mining

Abstract: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text

Abstract: The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.

Understanding digital transformation: A review and a research agenda

Abstract: Extant literature has increased our understanding of specific aspects of digital transformation, however we lack a comprehensive portrait of its nature and implications. Through a review of 282 works, we inductively build a framework of digital transformation articulated across eight building blocks. Our framework foregrounds digital transformation as a process where digital technologies create disruptions triggering strategic responses from organizations that seek to alter their value creation paths while managing the structural changes and organizational barriers that affect the positive and negative outcomes of this process. Building on this framework, we elaborate a research agenda that proposes [1] examining the role of dynamic capabilities, and [2] accounting for ethical issues as important avenues for future strategic IS research on digital transformation.

On making causal claims : A review and recommendations

Abstract: Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.