A COMPARISON OF UNDERWRITER REPUTATION MEASUREMENT METHODS IN EXPLAINING IPO STOCK PERFORMANCE

This study objective compares the underwriter reputation, measured by a different method, in explaining Initial Public Offering (IPO) performance. The reputation is measured based on underwriter IPO frequency and deal value. The underwriter's reputation is then ranked and categorized into quartiles. We use cross-section regression methods to test the effect of different underwriter reputation measurement methods on IPO performance. The dependent variable is short-term and long-term IPO performance. The independent variable is four underwriter reputation categories represented by three-level dummy variables. We found that only underwriter reputation measured by IPO frequency can explain IPO performance. The findings suggest IPO frequency help underwriter understand the market condition and value IPO more accurately. Firms that want to reduce the cost of IPO underpricing should choose underwriters with a higher IPO frequency.

Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content

In the field of software engineering, practitioners’ share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors’ mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves [Formula: see text] accuracy in binary classification in SO design patterns tag and [Formula: see text] accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system’s predicted reputation labels match the quality of provided answers.

VALIDATION OF REPUTATION MEASUREMENT METHODS IN THE POST-SOVIET TERRITORY

Different approaches to definition of “reputation” concept and its presence in different disciplines’ discourse result in development of large number of reputation measurement approaches. The closest approaches to the sociological understanding of reputation are the RQ (CRQ) and RepTrak™ ones. These approaches were developed by western authors and have not been validated in the post-Soviet territory yet. The paper reviewed the verification of RepTrak™ methodology’s reliability and validity, and analyzed the terminological differences between the types of validity used by the authors of the methodology and their semantic counterparts, traditional for sociology. It is noted in the article that the developers of the methodology did not offer a unified model of corporate reputation; validation was carried out separately for two reputation constructs. The first construct confirms the connection between the emotional component of reputation and articulated willingness to act, the second — between the emotional and cognitive components of reputation. Using empirical data collected in Ukraine, Russia and Kazakhstan, the authors of the article reproduced the procedures and approaches used by the methodology developers for confirmation of the methodology’s reliability and validity. To this end, authors of the article used confirmatory factor analysis and built structural models that fully correspond to the models used by the developers of the model. The authors of the article compare the parameters of the models and their criteria of fitting to the empirical data. As a result of a comparison done for each reputational construct a conclusion about the possibility of using the emotional reputation index in all three countries was done. It was concluded also that it is possible to use a complete original methodology for reputation research in Ukraine and Russia and it is required to modify it for Kazakhstan.

Social listening: a potential game changer in reputation management How big data analysis can contribute to understanding stakeholders' views on organisations

PurposeThe purpose of this paper is to investigate to what extend an automated, algorithm-based analysis of online conversations of stakeholders in social media and other Internet media can be used for reputation management.Design/methodology/approachExamination of the reputation of the 5,000 companies with the largest number of employees in Germany based on communication with these companies in 350m online sources on the German-speaking Internet within one year. The method is grounded on an adapted reputation model based on Fombrun.FindingsThe central result of the study is the identification of the ideal balance between the different dimensions leading to the best overall reputation. The resulting correlation matrix with the respective correlation coefficients (according to Pearson) thus forms the basis for the optimal reputation architecture.Research limitations/implicationsThe discovered “optimal reputation architecture” refers to a German context. Future studies should investigate in how far the adapted model and the “optimal reputation architecture” also work for other cultures. It can be assumed that there may be differences as different dimensions, for example, sustainability, may have a different importance in other cultural contexts. Apart from the question if the “optimal reputation architecture” is also valid for other cultural contexts, the concept has to be validated for German companies as well as it is just based on the two described studies.Practical implicationsThe method used shows that social listening can deliver valuable results for research in the field of reputation management as it expands the possibilities to investigate reputation on a large scale. The approach shows in how far scientific research can be expanded beyond classic content analysis as the number of items which can be analysed exceeds that of classic analytical approaches by far. Explicit and implicit experiences, which are the drivers of reputation, can be systematically recorded and analysed using social listening, thus delivering valuable insights in how stakeholders perceive the performance of a company in different dimensions.Social implicationsMeasuring the reputation on the basis of social listening is very important for practical applications in companies, because the data is available digitally and can deliver up-to-date reputation values almost in real time – so that the communication can be aligned very quickly with current events. This makes it easier to implement and control the interaction between companies and their environment in the digital space.Originality/valueThe classic approach in reputation management is traditional market research. It is relatively expensive and takes a relatively long time to produce results. Reputation management based on social listening digitises reputation measurement, lowers costs and delivers results in a very timely manner. It might be the future of reputation measurement. This is relevant not only for practical purposes but also for scientific approaches.

Opinions that matter: the hybridization of opinion and reputation measurement in social media listening software

Since the 2000s, numerous start-ups and agencies have argued for the necessity of analyzing social media data to ‘know what people think’, as they are deemed to provide access to spontaneous expression of thoughts, tastes, and representations. How do these actors, and the various types of knowledge and technology they draw upon, change the way we know and act upon people’s opinions? This article offers insight on these understudied actors, by describing the emergence in France of a market for measuring online opinion. It shows two distinct trajectories of innovation, and the key role played by the early clients of these companies and by the demand for tools for online reputation management in the shaping of these instruments, and the definition of epistemic value. Both approaches of online opinion break with the classical egalitarian conception of public opinion. They instead conceive opinion as a mediated and collective process in which not all opinions have an equal value.