What Drives the Helpfulness of Online Product Reviews? From Stars to Facts and Emotions

For consumers, online product reviews have become an important source for product-related information. Furthermore, they represent a beneficial addition to online retailers’ websites. Due to the increasing amount of available product reviews, identifying the most helpful product reviews represents an important task in order to reduce information overload. Therefore, the factors influencing review helpfulness have to be identified. Thus, in order to explain Review helpfulness, we build upon and extend review diagnosticity theory with concepts from marketing research and propose a research model that includes product quality, review sentiment and review uncertainty. Based on a sample of amazon.com product reviews, we evaluate our research model and find that statements about product quality positively influence review helpfulness. Furthermore, we identify that sentiment as well as uncertainty expressed in product reviews have an impact on review helpfulness. Finally, we confirm that the product category has a moderating effect on these relationships

”Status Effect” in User-Generated Content: Evidence from Online Service Reviews

This paper provides first empirical evidence on the impact of reviewer status on the objectivity of his contributions in online communities. While previous research indicates that user-generated online reviews guide consumer decision making, little is known about drivers of the actual review generation process. By drawing on Functional Role Theory, we derive four research hypotheses covering the general research question of factors influencing the objectivity of service reviews. Utilizing a data sample covering 413,077 reviews posted over 12 years on www.TripAdvisor.com, we evaluate our research model. Our findings indicate that with increased user status, review objectivity increases. Thus, we contribute to theory by generalizing the so-called Popularity Effect to a multi-dimensional Status Effect , which is more widely applicable (e.g. settings without users-follow-users relationships). Furthermore, our results enable practitioners to find their most valuable content-producers

How to Identify Tomorrow\u27s Most Active Social Commerce Contributors? Inviting Starlets to the Reviewer Hall of Fame

Social commerce contributors share their experiences of products and services, which is appreciated by consumers and online retailers. Since such user generated content is especially valuable for online retailers, they incentivize the most active contributors to provide further product reviews. Our paper aims to explore the question of which user characteristics can be used to identify contributors of valuable contents. This is especially relevant for newly registered users who have not extensively contributed yet. Drawing upon the literature on social information processing, signaling and communication theory, we explore how individual user characteristics published in the personal user profiles are associated with the actual contribution activity. Therefore, we analyze more than 30,000 user profiles from amazon.com. We find that information disclosure, emotiveness and problem-orientation are related to the contribution activity. Consequently, our results advance the understanding of who are the most active contributors and provide new implications for theory and practice

Employee Empowerment with Computer Based Learning: An Empirical Investigation

Enterprises are confronted with frequent changes in their business environment which require quick responses. Thereby, highly skilled and flexible employees play a major role since they are able to respond promptly. To enhance competencies and flexibility, the concept of employee empowerment has been proposed. In this respect, the workforce is given an increased level of autonomy and offered support during their decision-making processes. It is evident that technology can contribute within this context. However, the role of computer based learning with regard to the support of decision-making activities and the acquisition of competencies, especially in combination with increased employee autonomy, has been neglected until now. On the basis of an empirical case study, we find that the usage of computer based learning within employee empowerment initiatives fosters the acquisition of competencies and increases employee flexibility. Additionally, enhanced employee autonomy is found to have a positive moderating effect on both relationships

Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations

We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud. Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features. We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers. We perform a naive evaluation that assesses the classifiers’ performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures. The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection. We discuss implications for supervisory authorities, industry, and individual users

The Impact of IT-Based Trading on Securities Markets

The emergence of IT-based trading activities like algorithmic trading or high-frequency trading alters the traditional trading environment within financial markets. Thus, the question arises whether this technological arms race positively affects market quality or represents a risk related to market integrity. Within this study, we evaluate the order-to-trade-ratio for measuring overall IT-based trading activity. Furthermore, in a longitudinal study, we assess the impact of the order-to-trade-ratio on market quality. We find strong indications that price uncertainty has decreased with an increased order-to-trade-ratio and therefore has a positive impact on financial markets. However, the mere upgrade of the trading systems does not relate into increased market liquidity

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system

Pump and dump market manipulations: still a risk for investors?

DURING PUMP AND DUMP MARKET MANIPULATIONS, DECEIVERS ADVERTISE STOCKS BY PUBLISHING VERY POSITIVE NEWS TO PROFIT FROM AN INCREASED PRICE LEVEL. MARKET SURVEILLANCE AUTHORITIES HAVE TAKEN SEVERAL COUNTERMEASURES AGAINST SUCH FRAUDULENT STOCK RECOMMENDATIONS, BUT SIMULTANEOUSLY, DECEIVERS HAVE CONSTANTLY UPDATED THEIR TACTICS. THE RESEARCH INVESTIGATES WHETHER SUCH MANIPULATIONS STILL POSE A RISK FOR INVESTORS AND IF YES, WHICH CHARACTERISTICS DRIVE THEIR SUCCESS