Verena Dorner

The Recipe for the Perfect Review

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.

A configuration-based recommender system for supporting e-commerce decisions

Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers’ decision processes in e-commerce shopping tasks.

Designing a robo-advisor for risk-averse, low-budget consumers

Banks have reacted much more enthusiastically to the FinTech revolution than many of their customers. Robo-advisory, automated web-based investment advisory, in particular promises many advantages for both banks and customers - but consumer adoption has been slow so far. Recent studies suggest that this might be due to a mix of low trust in banks, high expectations of transparency and general inability or unwillingness to engage with investment questions. Research in decision support and guidance shows customers’ willingness to interact with a decision support tool depends greatly on its usability. We identify requirements for robo-advisory, derive design principles and evaluate them in two iterations with a real robo-advisor in a controlled laboratory study. The evaluation results confirm the validity of our identified design principles.

The Psychophysiology of Flow: A Systematic Review of Peripheral Nervous System Features

As information systems (IS) are increasingly able to induce highly engaging and interactive experiences, the phenomenon of flow is considered a promising vehicle to understand IS user behavior and to ultimately inform the design of flow-fostering IS. However, despite growing interest of researchers in the phenomenon, knowledge about how to continuously assess flow during IS usage is limited. Hereby, recent developments in NeuroIS and psychophysiology propose novel possibilities to overcome this limitation. This article presents the results of a systematic literature review (SLR) on peripheral nervous system indicators of flow. The findings revealed that currently four major approaches exist towards physiological measurement. Propositions for simple and unobtrusive measurement in IS research are derived in conclusion.

Decision Inertia and Arousal: Using NeuroIS to Analyze Bio-Physiological Correlates of Decision Inertia in a Dual-Choice Paradigm

Decision inertia is a cognitive process describing the reluctance to incorporate new information in choices, manifesting in the tendency to repeat previous choices regardless of the consequences. In this work, we discuss recent research in decision inertia, and show that inter-individual differences in arousal may play an important role for understanding decision inertia. We derive a NeuroIS framework for the operationalization of decision inertia, and discuss our conceptualization with a view towards a general theory of decision inertia.

Overcoming Innovation Resistance beyond Status Quo Bias - A Decision Support System Approach (Research-in-Progress)

When innovative products and services are launched to the market, many consumers initially resist adopting them, even if the innovation is likely to enhance their life quality. Explanations for this behavior can also be found in specific personality traits and in general pitfalls of human decisionmaking. We believe that decision support systems (DSS) can help alleviate such innovation resistance. We propose a DSS design that addresses innovation resistance to complex innovations on an individual’s cognitive level. An experimental study will be conducted to test the influence of different DSS modifications on the perception and selection of complex innovations. We aim to identify levers for reducing innovation resistance and to derive DSS design implications.

A practical guide for human lab experiments in information systems research: A tutorial with Brownie

Purpose Human lab experiments have become an established method in information systems research for investigating user behavior, perception and even neurophysiology. The purpose of this paper is to facilitate experimental research by providing a practical guide on how to implement and conduct lab experiments in the freely available experimental platform Brownie. Design/methodology/approach Laying the groundwork of the tutorial, the paper first provides a brief overview of common design considerations for lab experiments and a generic session framework. Building on the use case of the widely used trust game, the paper then covers the different stages involved in running an experimental session and maps the conceptual elements of the study design to the implementation of the experimental software. Findings The paper generates findings on how computerized lab experiments can be designed and implemented. Furthermore, it maps out the design considerations an experimenter may take into account when implementing an experiment and organizing it along a session structure (e.g. participant instructions, individual and group interaction, state and trait questionnaires). Originality/value The paper reduces barriers for researchers to engage in experiment implementation and replication by providing a step-by-step tutorial for the design and implementation of human lab experiments.

Impact of Cognitive Workload and Emotional Arousal on Performance in Cooperative and Competitive Interactions

We examine whether changes in the social environment (competitive or cooperative), affect the relationship between the internal state (cognitive workload and emotional arousal), and the performance in a given task. In a controlled experimental game setting, participants played cooperatively and competitively with different partners. EEG activity and heart rate changes measured cognitive workload and emotional arousal respectively. Cognitive workload was associated negatively with performance in the competitive but not the cooperative mode. By contrast, arousal was associated negatively with performance in the cooperative mode but not the competitive mode.

Designing Live Biofeedback for Groups to Support Emotion Management in Digital Collaboration

Digital collaboration of individuals has increased in diverse areas such as gaming, learning and product innovation. Across scenarios, adequate intra- and interpersonal emotion management is increasingly acknowledged to be beneficial to cognitive and affective interaction outcomes. Unfortunately, individuals differ notably in their emotion management abilities. Additionally, many types of computer mediated collaboration lack the richness of affective cues traditionally found in face-to-face interaction. We envision psychophysiology-based emotion feedback as an automated tool to improve emotion management, and therefore group performance and satisfaction. The presented prototype presents a first iteration of this idea, centered around information on emotional arousal derived from peripheral nervous system measures.

Das Rezept für die perfekte Rezension

ZusammenfassungInternethändler bieten ihren Kunden vermehrt die Möglichkeit, Online-Rezensionen zu erstellen. Diese reduzieren die Suchkosten anderer Kunden und erhöhen deren Verweildauer im E-Shop. Mittlerweile sind jedoch so viele Rezensionen verfügbar, dass das Auffinden von Produktinformationen und die Einschätzung der Produktqualität schwierig geworden sind. Abhilfe sollte die Bewertung der Nützlichkeit der Rezensionen durch Leser schaffen. Dieser Mechanismus hat jedoch zwei kritische Schwachstellen. Zum einen bleiben viele Rezensionen unbewertet, sodass sie bei einer Sortierung nach der Nützlichkeit herausfallen. Zum anderen gibt es keine Anhaltspunkte für Rezensenten, wie eine nützliche Rezension aussehen sollte. Zur Ableitung von Einflussfaktoren auf die Nützlichkeit von Produktrezensionen wird das Modell von Wang und Strong zur kontextabhängigen Beurteilung von Datenqualität adaptiert. Eine empirische Analyse von 27.104 Kundenrezensionen auf Amazon.com über sechs Produktkategorien zeigt, dass die Nützlichkeit einer Rezension nicht nur von ihren eigenen Attributen abhängt, sondern auch von kontextuellen Faktoren, die sich aus der Gesamtheit aller verfügbaren Rezensionen ergeben. Rezensionen für Erfahrungs- und Suchgüter unterscheiden sich systematisch voneinander. Das vorgeschlagene Modell erlaubt die Berechnung vorläufiger Nützlichkeitswerte für unbewertete Rezensionen und bildet die Basis für einen Kundenleitfaden zur Erstellung nützlicherer Rezensionen.AbstractOnline product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.