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Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes domains, including credit-risk assessment and medical diagnostics. Consequently, AI systems increasingly affect humans, yet many state-of-the-art systems lack transparency and thus, deny the individual’s “right to explanation”. As a remedy, researchers and practitioners have developed explainable AI, which provides reasoning on how AI systems infer individual predictions. However, with recent legal initiatives demanding comprehensive explainability throughout the (development of an) AI system, we argue that the pre-processing stage has been unjustifiably neglected and should receive greater attention in current efforts to establish explainability. In this paper, we focus on introducing explainability to an integral part of the pre-processing stage: feature selection. Specifically, we build upon design science research to develop a design framework for explainable feature selection. We instantiate the design framework in a running software artifact and evaluate it in two focus group sessions. Our artifact helps organizations to persuasively justify feature selection to stakeholders and, thus, comply with upcoming AI legislation. We further provide researchers and practitioners with a design framework consisting of meta-requirements and design principles for explainable feature selection.

Purpose – Although the health information seeking behavior of consumers through the internet has received great attention, limited attempt has been made to integrate both the health information seeking behavior and the usage behavior in a mobile online context. The purpose of this paper is to explore the factors that influence consumer mobile health information seeking (MHIS) and usage behavior based on information quality, perceived value, personal health value, and trust. Design/methodology/approach – A survey was conducted to collect data. A two-step approach of structure equation modeling based was used to test the measurement model and hypothesis model. Findings – Information quality, perceived value, and trust were found to have positive effects on both the intention to seek and to use health information, and that the intention to seek affects the intention to use. Among the three components of perceived value, the utilitarian and epistemic values were found to have significant effects on intention to seek. In addition, the current health status of health consumers moderates the relationships between MHIS and usage intention and their determinants. Originality/value – Studies have primarily focussed on online health information seeking behavior, whereas a few of these studies have examined the seeking behavior intention and the usage behavior intention in a general model. The results indicate that health information usage behavior intention is closely related to the seeking behavior intention in the mobile context, which enriches the research on the relationship between information seeking and its outcomes. Furthermore, this study highlights the impact of information quality, perceived value, and trust on the intention to seek, and the impacts of information quality and trust on the intention to use, which have been overlooked in previous studies on MHIS.

Network analytical metrics often seek to capture the structural dimension of social capital, but such data collections using traditional social research tools often suffer from biases like interviewer effects and are usually only suitable to study small groups of participants. Digital sources of social relations might offer great potential for facilitating such measures though, because they digitally store unprecedented amounts of relational data, free from the limitations associated with self-reported data. This study therefore compares individual node degrees collected through a contact diary (i.e., overall-social capital), and a counterpart extracted from digital footprint data from the social media network, Facebook (i.e., social media network-social capital). The findings suggest that researchers conducting empirical studies involving the concept thus should not ignore social media network-social capital as a practical alternative measure of overall-social capital; it provides a sound approximation but only after controlling for other influential factors. In particular, our results highlight that the usability of the digital social capital metric is conditional on the three-way interaction between the variables gender, age, and social media network-social capital. Thus, the evidence from our study, in turn, also intimates that individuals act heterogeneously in the digital sphere with respect to their networking behaviour.

Research on online content diffusion is vast but has rarely examined contextual factors, including the influence of online sharing mechanisms, such as social plugins (e.g., Facebook’s “Like” button), on online social networks (OSNs). While these mechanisms generally enable the content flow between senders and recipients, they vary in protecting users’ social and institutional privacy on OSNs. Additionally, sharing mechanisms might differ with respect to their labeling (e.g., positive versus neutral), which might interact with the sharable content. We examined the effects of these three design aspects on users’ sharing behavior in a controlled experiment and two analyses of observational data. The results show that two types of sharing mechanisms negatively affect content sharing in the domain of news sharing: those that allow greater information flow control over the sharing process and thus protect users’ social privacy and those that employ two-click designs to preserve users’ institutional privacy . These negative effects mainly stem from higher frictional costs associated with these features. For the average user in this domain, the disutility and additional cognitive effort generated by one additional click often mitigates the utility of sharing itself. Moreover, we find that neutral button labeling is important for fostering content sharing as users might encounter schema incongruity when using a positively connoted label to share bad news. Overall, a wrong decision in terms of the sharing mechanism can easily decrease the number of shares by up to 86%. Therefore, content providers can easily and substantially increase content sharing by properly designing the sharing mechanism on their websites. The online appendix is available at https://doi.org/10.1287/isre.2017.0738 .

High product return rates are an increasingly pressing challenge for many e-retailers around the world. To address this problem, this paper offers a new perspective by focusing on the critical moment of the package-opening process. Going beyond previous research, which has primarily focused on website information and the product itself, we examine the effects of the outside appearance (i.e., the color of the delivery package) and contents of the delivery package (i.e., extra gifts, coupons, and preprinted return labels) on consumer return behavior. Our findings across two experimental studies and an observational field study show that a well-considered package design, including colorful packaging and extra gifts, significantly lowers consumers' return intentions and actual returns. We also explore the process of consumers' cognitive-affective reactions after opening a delivery package. During this two-stage reaction process, pleasure plays a crucial role in the consumer's return choice.

Behavioral finance researchers have shown that the stock market can be driven by emotions of market participants. In a number of recent studies mood levels have been extracted from Social Media applications in order to predict stock returns. The paper tries to replicate these findings by measuring the mood states on Twitter. The sample consists of roughly 100 million tweets that were published in Germany between January, 2011 and November, 2013. In a first analysis, a significant relationship between aggregate Twitter mood states and the stock market is not found. However, further analyses also consider mood contagion by integrating the number of Twitter followers into the analysis. The results show that it is necessary to take into account the spread of mood states among Internet users. Based on the results in the training period, a trading strategy for the German stock market is created. The portfolio increases by up to 36 % within a six-month period after the consideration of transaction costs.

Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

Advances in new technologies affect private and professional lives alike, posing new opportunities and threats for companies, consumers, and society. In this context, the concept of corporate digital responsibility (CDR) gains traction enabling technologies benefitting humanity while exceeding mere technology advancements. Yet, theory and practice still lack a systematic in-depth understanding of the concept’s scope up to concrete activities. The aim of this paper is to enable a more concrete and deeper understanding of the concept scope by drawing on available knowledge in the thematically related discipline of information systems (IS) in general and electronic markets in particular. The study employs an extended systematic literature review to aggregate prior knowledge in this research domain relatable to the concept of CDR and to develop an in-depth classification of potential CDR activities inductively according to ten dimensions, corresponding sub-dimensions, and respective fields of action. This contributes to the overarching goal to develop the conceptualization of CDR and to anchor the concept in the context of electronic markets, thereby fostering human and social value creation.

The proliferation of the Internet has enabled platform intermediaries to create two-sided markets in many industries. Time-series data on the number of customers on both sides of the markets allow platform intermediaries for estimating the direction and magnitude of network effects, which can then support growth predictions and subsequent information technology (IT) or marketing investment decisions. This article investigates the conditions under which this estimation of same-side and cross-side network effects should distinguish between its impact on the number of new customers (i.e., acquisition) and existing customers (i.e., their activity). The authors propose an influx-outflow model for doing so and conduct a simulation study to benchmark the new model against the traditional model. Further they compare the models in an illustrative empirical study in which they study the growth of an Internet auction platform. The results show that this separation of effects is beneficial because the existing customers on both sides of the market can influence the acquisition and dropout of other customers asymmetrically. The paper thus makes an important contribution that should impact the way how researchers and business practitioners measure network effects in two-sided markets.