The Demand for Stored Value Payment Instruments

Due to their functionality, stored value purses based on smart card technology are prominent candidates for being the dominant medium of exchange for micropayments. However, the overall prospects of these payment systems are yet ambiguous, both from the perspective of practice and monetary theory, because their potential to substitute for cash is still largely unknown. As a contribution to the field, a model is proposed founding the potential utilization of stored value cards in microeconomic calculus. As a result, the model provides insight into the crucial parameters determining usage. Moreover, the model suggests that issuers should maximize demand and profits by offering interest payments or insurance against loss.Electronic Money, Stored Value, Digital Payment Instruments, Monetary Theory, Demand for Money

Risk in Electronic Commerce: It Does Matter, But Not Equally for all Companies

Based on the Electronic Commerce Enquête 97/98, one of the largest empirical investigations on business-tobusiness electronic commerce issues in the German-speaking world (n\u3e900), companies’ perception of risk factors in electronic commerce is revealed using multivariate statistical procedures. As a contribution to the field this paper presents the integral relationships and the importance of technical and nontechnical dimensions of risk related to electronic commerce in various industry segments. Furthermore, using the innovative Limit Conjoint Analysis the potential of Electronic Commerce in terms of market share under alternative risk scenarios is calculated. Companies belonging to different industry segments were asked to rate eight different electronic commerce scenarios with respect to their attitude and willingness to engage in electronic commerce transactions via the World Wide Web. The scenarios incorporate different levels of associated risks capturing the fundamental trade-off between opportunity for profit making versus danger of loss. Using Conjoint Analysis the relative importance of risk factors including psychological risk, financial risk, and technical/legal risk was then quantified. In most cases technical/legal risk over financial and psychological risk is of most importance to the vast majority of firms. In order to group the responding companies for further investigation we used the variable “industry segment”. Applying t-tests, main findings support the hypothesis that there are significant industry segment specific differences of risk perception. The potential of electronic commerce in terms of market share is roughly 40 times bigger if technical/legal risk is low compared to a scenario where technical/legal risk is high. In conclusion, risk matters in varying degrees for all companies and contributes largely to the potential of electronic commerce, thus helping us to better differentiate and assess the importance of risk in electronic commerce

Consumer-Oriented Tech Mining: Integrating the Consumer Perspective into Organizational Technology Intelligence - The Case of Autonomous Driving

To avoid missing technological opportunities and to counteract risks, organizations have to scan and monitor developments in the external environment through a structured process of technology intelligence. Previous approaches in tech mining—the application of text mining for technology intelligence —have primarily focused on the elicitation of technical or legal information from web, patent, or research databases. However, knowledge of consumers’ needs, fears, and hopes is a prerequisite for the success of an emerging technology in the marketplace. Thus, we claim that technology intelligence needs to also consider consumers’ technology perceptions. Hence, we propose a novel and comprehensive approach to collect user-generated content from the web and apply text mining to derive consumer perceptions. In doing so, we align with an established tech-mining process. This paper illustrates our approach on the emerging technology of autonomous driving and provides an initial indication of concurrent validity

Application Portfolio Management—An Integrated Framework and a Software Tool Evaluation Approach

Despite the growing number of organizations that have lost track of their application landscape and have suffered from a sharp increase in application portfolio complexity, a comprehensive and systematic approach to Application Portfolio Management (APM) still appears far from being adopted. To move the adoption process along, this paper develops a comprehensive framework assimilating and extending previous research and presents an APM process comprising data collection, analysis, decision-making, and optimization phases. This paper also presents an approach for evaluating software tools for APM and identifies which software tool families are best able to provide support for specific purposes. With this integrated conceptual guideline for APM and its translation into a model for measuring appropriate practical support, this paper not only allows for a move more deeply into the research area but also offers advice for both researchers and practitioners

Deep Learning Enabled Consumer Research for Product Development

“Needmining” is the analysis of user-generated content as a new source of customer needs, which are an important factor in new product development processes. Current approaches use supervised machine learning to condense large datasets by performing binary classification to separate informative content (needs) from uninformative content (no needs). This study introduces a transformer model and compares it to relevant approaches from the literature. We train the models on data composed from a single product category. Subsequently, we test the models’ ability to detect needs in a validation sample containing product categories not present in the training set, i.e. “out-of-category” prediction. Our cross-validated results suggest that, based on the F1-score, the transformer model outperforms previous approaches at both in-category and out-of-category predictions. This suggests that transformers can make needmining more relevant in practice by improving the efficiency of the needmining process by reducing the resources needed for data preparation

TOWARDS MINING BRAND ASSOCIATIONS FROM USER-GENERATED CONTENT (UGC): EVIDENCE FROM LINGUISTIC CHARACTERISTICS

Consumers’ brand associations offer qualitative explanations on a brand’s success or failure and are typically elicited using survey-based instruments. Marketers are interested in time- and cost-efficient, automated brand association elicitation approaches. To enable an automated brand association elicitation, we show that brand associations can be formalized and described by patterns of linguistic part-of-speech sequences that differ from ordinary speech which is required for an automated extraction via text mining. Furthermore, we provide evidence that UGC is an adequate data-source for an automated brand association elicitation. We do that by comparing survey-based and UGC data-sources using linguistic part-of-speech sequence- and n-gram analysis as well as sequential pattern mining. We contribute to exiting research by establishing prerequisites for the construction of novel information systems that use text mining to extract brand associations automatically from UGC

The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context

Designing a Methodology for Marketing Intelligence Systems – The Case of Brand Image Diagnostics

In situations of information overload and complexity, consumers consult their existing knowledge regarding brands as a guide in consumption decisions. This knowledge manifests as brand association networks (BANs) in consumers’ minds and reflects what the consumer thinks of when being confronted with a brand stimulus. BANs therefore characterize a brand’s image that determines consumers’ attitudes and behaviour. BANs serve as diagnostic instruments to explain a brand’s success or failure and to plan or control marketing activities. Traditionally, BANs are elicited directly from consumers utilizing survey-based instruments. However, in a dynamic and interactive environment, user-generated content (UGC) is increasingly relevant for a brand’s image and thus should be exploited for the elicitation of BANs. However, established elicitation instruments either follow another elicitation paradigm (i.e. surveys or interviews), or are unable to cope with volume, velocity, and variety of UGC as a big data source (e.g. content analysis). Hence, exploiting UGC for BAN elicitation requires the development of new, computer-supported instruments. Following a design science research approach, we contribute a novel methodology as our artefact to extract BANs from UGC using text-mining and net- work analysis. We evaluate our solution and demonstrate its utility for brand management on a study of automotive brands

Social Information Systems: Review, Framework, and Research Agenda

In this research-in-progress, we review the literature on an emerging new type of information systems: social information systems. Social information systems are information systems based on social technologies and open collaboration. The paper provides categories defining social information systems and a framework for existing and future research in this field of study