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

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

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

Measuring the performance of human-machine symbiosis

In today’s economy, machines are evolving and becoming increasingly important for supporting human’s daily life and work. Recently, scholars even discovered a symbiotic relationship between humans and machines, learning from each other to improve their joint processes and outputs. However, to our knowledge, this effect has not yet been rigorously measured. With this research in progress, we directly address this gap and propose to create a method consisting of evaluation criteria as well as an instrument for gathering data to measure human-machine symbiosis by interrogating users

Open Strategy: Consolidated Definition and Processual Conceptualization

Open approaches to operational work in organizations (e.g., crowdsourcing and open source development) have been of particular interest to information systems (IS) researchers. Recently, organizations, including IBM, Red Hat and Wikimedia Foundation, have also embraced openness principles for strategic work (e.g., planning, forming and implementing strategy). Little is known about such “open strategy” and, within it, the (critical) role of information technology (IT). Definitions and conceptualizations of open strategy in the literature are vague and inconsistent. In the study reported in this paper, we have reviewed the emerging literature on open strategy. By analysing the characteristics most commonly attributed to open strategy, we propose a consolidated definition of open strategy. Furthermore, we develop an open strategy process model and use it to re-analyse four open strategy case studies. This analysis contributes to our conceptual understanding of open strategy and of what is required for open strategy to work in practice

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