The Effect of Defaults and Task Difficulty on Consumer Satisfaction - Implications for Value Co-Creation Processes

Companies increasingly involve consumers in product design and development, e.g. through built-to-order and mass customization or by integrating data generated at the point of sale into product development and production processes. The latter approach requires that companies provide online tools and interfaces like product configurators for consumer participation in value co-creation. Our research addresses the question how to design such tools to i) ob-tain reliable data and ii) keep customers happy with both products and value co-creation processes. In a lab experiment, we show how two interface elements, default values and task difficulty, affect consumers\u27 product satisfaction, and satisfaction with the configuration process. Results indicate that product satis-faction is influenced by default values while process satisfaction is not, and task difficulty influences neither

The Recipe for the Perfect Review? - An Investigation into the Determinants of Review Helpfulness

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

Estimating Optimal Recommendation Set Sizes for Individual Consumers

Online consumers must burrow through vast piles of product information to find the best match to their preferences. This has boosted the popularity of recommendation agents promising to decrease consumers\u27 search costs. Most recent work has focused on refining methods to find the best products for a consumer. The question of how many of these products the consumer actually wants to see, however, is largely unanswered. This paper develops a novel procedure based on signal detection theory to estimate the number of recommendable products. We compare it to a utility exchange approach that has not been empirically examined so far. The signal detection approach showed very good predictive validity in two laboratory experiments, clearly outperforming the utility exchange approach. Our theoretical findings, supported by the experimental evidence, indicate conceptual inconsistencies in the utility exchange approach. Our research offers significant implications for both theory and practice of modeling consumer choice behavior

Think Twice Before You Buy! How Recommendations Affect Three-Stage Purchase Decision Processes

Consumer decision-making is usually modeled as a two-stage process of initial screening and subsequent in-depth consideration of attractive alternatives. Recent evidence indicates, however, that consideration is not necessarily the direct precursor of choice: consumers may narrow their consideration sets further to the choice set. We examine how choices in a three-stage purchase decision process evolve by observing consumer behavior in an online shopping experiment. Specifically, we examine the effects of system- and user-generated recommendations (SGR and UGR) moderated by gender. Our contribution to information systems research is threefold. First, we suggest a new experimental design for observing the stages in purchasing processes. Second, we show that effects of SGR and UGR indeed vary between stages. UGR reduce consideration set size and increase females’ choice probability while SGR reduce males’ transition probabilities. Third, our results suggest that omitting choice set formation can lead to incorrect estimates of choice probabilities

Leave a Comment! An In-Depth Analysis of User Comments on YouTube

User comments are the most popular but also extremely controversial form of communication on YouTube. Their public image is very poor; users generally expect that most comments will be of little value or even in thoroughly bad taste. Nevertheless, heaps of comments continue to be posted every day. We propose an explanation for this contradiction in user attitudes and behaviour based on a new comment classification approach which captures salient aspects of YouTube comments. We show that, based on our new classification, we are able to perform very fast lightweight semantic video analysis. In addition, our results indicate that users’ video perceptions (Likes and Dislikes) are indeed influenced by the dispersion of valuable and inferior comments

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 decision-making. 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 DECISION SUPPORT SYSTEM DESIGN TO OVERCOME RESISTANCE TOWARDS SUSTAINABLE INNOVATIONS

The concept of sustainability has been acknowledged as one of the central and most important issues of our time. However, technological innovations which provide a more sustainable way of living, for instance electric cars, are not always welcomed with open arms by consumers but often resisted at the beginning. As such, human resistance behavior can be explained as an interplay of different personality traits that favour the status quo. In this study, a decision support system design is introduced which bases on the concept of digital nudging that addresses innovation resistance on an individual’s cognitive level by de-biasing innovation trial decision-making. An experimental pre-study is conducted to test the influence of different DSS modifications on the selection of electric cars in an online rental car booking scenario. First results show that DSS which set sustainable innovations as default option have a significantly positive effect on their trial probability while priming consumers towards electric car trial has no significant effect

The Pulse Of Impulse Buying: An experimental study on the effects of background music tempo on impulse buying

Considering the importance of e-commerce, very little research has examined how marketing stimuli like music affect impulse buying behavior online. In general, the effect of music on impulse buying is not entirely understood yet. Prior research leads us to suggest that music may influence consumers’ ability to exert self-control and thus their receptiveness to product offers in a shopping situation. Most research has been done in offline settings via surveys or field studies, which often makes it difficult to measure traits, attitudes and cognitive resource and to control confounding factors. Our study aims to contribute to research on impulse buying in three ways: 1) observing impulse buying in an e-commerce setting, 2) exploring the relationship between self-control, background music and impulse buying, and 3) proposing an experimental design to study impulse buying in the lab

Does Amazon Scare Off Customers? The Effect of Negative Spotlight Reviews on Purchase Intention

Online retailers provide review systems to consumers in order to improve perceived trustworthiness and boost sales. We examine the effects of review valence and valence intensity on consumer purchase intention. Review adoption emerges as a novel, important moderating variable. We find that positive reviews have a stronger effect on consumer purchase intention than negative reviews. Moderate reviews always lead to higher purchase intention than extreme reviews, but the size of the effect is greater for extremely negative reviews than moderately negative reviews. The effect is reversed for positive reviews. Our results imply that a recent innovation in Amazon’s review system, highlighting negative reviews along with positive spotlight reviews, must be designed carefully to avoid losing customers. Choosing the wrong combination of reviews can diminish the positive effect of spotlight reviews on sales by nearly 20%