DigitalAngewandte WirtschaftswissenschaftenTC Grafenau
Michael Scholz, V. Dorner
The Recipe for the Perfect Review?
Business & Information Systems Engineering, vol. 5, no. 3, pp. 141-151
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.
We analyze determinants of review helpfulness in online retailing based on Wang and Strong’s (1996) data quality framework. Helpful reviews consist of 9 % of adjectives, display high product feature entropy, and present opinions that differ from previous reviews for the product in question. Other helpfulness determinants depend on whether experiential or utilitarian products are reviewed. Our research points e-shop providers towards two major improvements in their review systems.
DigitalAngewandte WirtschaftswissenschaftenTC Grafenau
J. Pfeiffer, Michael Scholz
A Low-Effort Recommendation System with High Accuracy
Business & Information Systems Engineering, vol. 5, no. 6, pp. 397-408
In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input.
In a simulation study, we demonstrate that recommendation systems using a choice-based conjoint analysis with hierarchical Bayes estimation require up to three times higher mental effort for the consumer than simple sorting mechanisms. However, consumers benefit from a choice-based conjoint analysis in terms of a significantly higher utility of the selected product. We further introduce the concept of a ranked Pareto-front which allows consumers to select a product with a better utility than they will select when using a choice-based conjoint analysis for the same low costs that using a simple sorting mechanism require.
NachhaltigAngewandte Naturwissenschaften und WirtschaftsingenieurwesenTSZ Weißenburg
Dmitry Rychkov, Rychkov, A. A., Efimov, N., A. Malygin, Gerhard, R.
Higher stabilities of positive and negative charge on tetrafluoroethylene–hexafluoropropylene copolymer (FEP) electrets treated with titanium-tetrachloride vapor
Applied Physics A - Materials Science and Processing, vol. 112, no. 2, pp. 283-287
Tetrafluoroethylene–hexafluoropropylene copolymer (FEP) films were treated with titanium-tetrachloride vapor in a molecular-layer deposition process. As a result of the surface treatment, significant improvements of the thermal and temporal charge stability were observed. Charge-decay measurements revealed enhancements of the half-value temperatures and the relaxation times of positively charged FEP electrets by at least 120 °C and two orders of magnitude, respectively. Beyond previous publications on fluoropolymer electrets with surface modification, we here report enhanced charge stabilities of the FEP films charged in negative as well as in positive corona discharges. Even though the improvement for negatively charged FEP films is moderate (half-value temperature about 20 °C higher), our experiments show that the asymmetry in positive and negative charge stability that is typical for FEP electrets can be overcome by means of chemical surface treatments. The results are discussed in the context of the formation of modified surface layers with enhanced charge-trapping properties.
Frank Denk, G. Rösel, Continental Automotive GmbH
[DE] Bestimmen des zeitlichen Bewegungsverhaltens eines Kraftstoffinjektors basierend auf einer Auswertung des zeitlichen Verlaufs von verschiedenen elektrischen Messgrößen [EN] Determining the movement ...