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Die Paper werden begutachtet:
The goal of the review process is to select a number of papers that will stimulate discussion and the development of new knowledge, with preference given to empirical research. Some of the papers will inevitably be more fully developed than others; however, it is hoped that each author will gain insights during the conference that will strengthen their research and introduce them to other scholars who share their interests.
The review process examines over 300 submissions each year. Authors submit an extended abstract of about 500 words in length. This abstract should identify the central research question, introduce the most important references and describe the methodology, empirical base, analysis, and results. Each abstract is evaluated by two or three members of the scientific committee. All are senior professors who are well positioned internationally in the field. Each abstract is evaluated using the following points.
Online reviews by users have become an increasingly important source of information. This is true not only for new users of goods or services, but also for their producers. They extend the insight into the acceptance of new goods and services, e.g. at the point of sale, from a mere sales and usage quantity oriented point of view to a cause and effect oriented one. Since online reviews by consumers of many goods and services are nowadays widespread and easily available on the internet, the question arises whether their analysis can replace the more traditional approaches to measure technology acceptance, e.g., using questionnaires with TAM (Technology Acceptance Model) items. This paper tries to answer this question using IKEA's mobile catalogue app as an example. For comparisons reasons, data on the acceptance of the current version of this catalogue is collected in four different ways, (1) as answers to batteries of TAM items, (2) as assignments to pre-defined adjective pairs, (3) as textual likes and dislikes of users (simulating online reviews), and (4) as publicly available (real) reviews by users. The source for (1)–(3) is a survey with a sample of respondents, the source for (4) an online forum. The data is analyzed using partial least squares (PLS) for TAM modelling and text mining for pre-processing the textual data. The results are promising: it seems that data collection via surveys can be replaced – with some reservations – by the analysis of publicly available (real) online reviews.