Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system's candidate recommendations on humans' hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.

COVID-19 has changed the way people live, bank, shop, and work by moving them toward digitalization. It has also driven the trend toward a cashless society, and this change has taken place in an increasingly uncertain and fearful environment. This study explores the social practice of mobile banking (MB) adoption during the global COVID-19 pandemic. Data were collected from banking customers and managers using online customer reviews, semi-structured interviews, and focus groups to develop an in-depth understanding of the subjective realities of their use of MB. This approach also ensured that social distancing practices were maintained during interviews conducted during the COVID-19 outbreak. Analysis of the data suggests that social media, social circles, family members, and teams of customer service agents play an important role in developing the social practice of MB. This study culminates in the presentation of the social practice of MB adoption (SPOTA) framework. This framework is based on extended social practice theory in the context of MB adoption. The study discusses the practical implications of the findings for systems developers. The many expectations of people with or without disabilities of MB are discussed and the findings could be used to improve the accessibility and habitual practice of MB adoption.

Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships.

Platform ecosystems have captured a variety of markets, enabling coordination, transactions, and value co-creation between independent actors. A focal platform constitutes the central nexus of e-commerce ecosystems and fosters the interaction among ecosystem participants through their boundary resources. Standardizing these interfaces simplifies ecosystem entry for developers and increases the number of participants propelling the network effects, and thus the overall value of the ecosystem. Currently, there is a lack of prescriptive design knowledge guiding platform owners in designing successful e-commerce ecosystems. Addressing this issue, we followed a dual approach, reporting on a systematic literature review in which we identified design requirements and complemented these with a multiple-case study on selected e-commerce ecosystems. Aggregating the requirements resulted in six meta-requirements and 19 design principles that foster the standardization of focal e-commerce platforms. Our design principles simplify the development of complements and enable multi-homing for developers due to possible standardization across ecosystems.

Open government data (OGD) holds great potential for firms and the digital economy as a whole and has attracted increasing interest in research and practice in recent years. Governments and organizations worldwide are struggling in exploiting the full potential of OGD and require a comprehensive understanding of this phenomenon. Although scientific debates in OGD research are intense and heterogeneous, the field lacks theoretical integration of OGD topics and their systematic consideration in the context of the digital economy. In addition, OGD has been widely neglected by information systems (IS) research, which promises great potential for advancing our knowledge of the OGD concept and its role in the digital economy. To fill in this gap, this study conducts a systematic literature review of 169 empirical OGD studies. In doing so, we develop a theoretical review framework of Antecedents, Decisions, Outcomes (ADO) to unify and grasp the accumulating isolated evidence on OGD in context of the digital economy and provide a theory-informed research agenda to tap the potential of IS research for OGD. Our findings reveal six related key topic clusters of OGD research and substantial gaps, opening up prospective research avenues and particularly outlining how IS research can inform and advance OGD research.

Data has become an indispensable input, throughput, and output for the healthcare industry. In recent years, omics technologies such as genomics and proteomics have generated vast amounts of new data at the cellular level including molecular, structural, and functional levels. Cellular data holds the potential to innovate therapeutics, vaccines, diagnostics, consumer products, or even ancestry services. However, data at the cellular level is generated with rapidly evolving omics technologies. These technologies use scientific knowledge from resource-rich environments. This raises the question of how new ventures can use cellular-level data from omics technologies to create new products and scale their business. We report on a series of interviews and a focus group discussion with entrepreneurs, investors, and data providers. By conceptualizing omics technologies as external enablers, we show how characteristics of cellular-level data negatively affect the combination mechanisms that drive venture creation and growth. We illustrate how data characteristics set boundary conditions for innovation and entrepreneurship and highlight how ventures seek to mitigate their impact.

This work analyzes the attitude formation process of individuals for central bank digital currencies (CBDCs), specifically for the digital euro. CBDCs are heavily researched, with pilot projects being conducted worldwide. Following the emergence of cryptocurrencies and a decrease in cash usage for retail transactions, CBDCs are viewed as the possible payment solution of the future. Using a qualitative approach, we conduct expert and non-expert interviews to apply and extend existing research on attitude formation to study how individuals form attitudes towards a CBDC in Germany. We find that individuals form their attitudes towards a digital euro through the perceived benefits, limitations, and concerns regarding related payment solutions, moderated through the perceived equivalence of these related technologies and the CBDC. The results contribute to the literature on CBDC and can be used by practitioners to develop a digital euro that offers a competitive advantage for retail transactions over established payment solutions.

Novel technologies such as smart personal assistants integrate digital services into everyday life. These services use personal data to offer personalized services. While they are subject to special data protection regulations at the time of development, there are few guidelines describing the transition from legal requirements to implementation. To reduce risks, services depend on external legal assessments. With developers and legal experts often missing either legal or technical knowledge, the challenge lies in bridging this gap. We observe that design patterns support both developers and legal experts, and we present an approach in which design patterns are leveraged to provide twofold value for both developers and legal experts when dealing with novel technologies. We conducted a revelatory case study for smart personal assistants and scaffolded the case interpretation through cognitive fit theory. On the basis of the findings, we develop a theoretical model to explain and predict the twofold value of design patterns to develop and assess lawful technologies.

In recent years, video games have been on the rise as entertainment goods, leading to a growing interest by practitioners, researchers, and, of course, consumers alike. While a few unusually successful video games produce overall high revenues, most released games struggle to break even. Hence, there is an urgent need to better understand what distinguishes financially successful games from nonsuccessful video games. Accordingly, several researchers have called for investigations into the drivers of the financial success of video games. However, empirical studies within this respect are still lacking. Based on longitudinal data of 351 video games, the current study strives to fill this research gap by investigating the relative importance of potential success factors for the short-term and long-term financial success of video games. The results of multiple regression analyses confirm that search qualities such as brand popularity, reviews, and awards as well as experience qualities such as graphics, sound, and game duration significantly drive financial success in terms of the total number of sold video games in Europe. Consequently, managers in the video game industry can boost their chances for the production of a successful video game by focusing on these factors.

The study models inter-relationship among key enablers that influence the growth of FinTechs that offer credit services to small and medium enterprises (SMEs). It focuses on emerging market of India, which is the world's third-largest FinTech centre. It employs Grey DEMATEL method to measure the cause-effect relationship based on the assessment given by FinTech practitioners, experts, policymakers, and investors. The results show that credit demand by SME borrowers, availability of alternate data sources, and Covid-19 are the critical enablers that exercise strong impact on FinTech system. Collaboration between FinTechs and traditional financial institutions, end-to-end financial solutions, and scalability of business operations are recognized as critical dependents that are hugely affected by others. The study recommends policymakers to foster collaborative environment, strengthen digital data landscape, and improve financial literacy to develop FinTech sector. It recommends practitioners to focus on data security and to offer end-to-end financial solutions to its SME borrowers.

We examine investor behavior on social media platforms related to the GameStop (GME) short squeeze in early 2021. Individual investors stimulated the stock market via Reddit social posts in the presence of institutional investors who bet against GME's success as short sellers. We analyzed r/WallStreetBets subreddit posts related to GME's trading patterns. We performed text-based sentiment analysis and compared the social informedness of posting users for GME trading on two social media platforms. The short squeeze occurred due to coordinated trading by individual investors, who discussed trading strategies on the platforms and drove collective social informedness-based trading behavior. Our findings suggest that the valence and number of submissions influenced GME's intraday transaction volumes and precursors for irrational trading behavior patterns to have emerged. We provide a theoretical interpretation of what occurred and call for tighter monitoring of social news platforms. We also encourage effort to create an in-depth understanding of the observed patterns and the linkages between them and the larger equity markets.

The rise of AI-based chatbots has gradually changed the way consumers shop. Natural language processing (NLP) technology and artificial intelligence (AI) are likely to accelerate this trend further. However, consumers still prefer to engage with humans and resist chatbots, which are often perceived as impersonal and lacking the human touch. While the predominant tendency is to make chatbots appear more humanlike, little is known about how anthropomorphic verbal design cues in chatbots influence perceived product personalization and willingness to pay a higher product price in conversational commerce contexts. In the current work, we set out to test this through one pre-test ( = 135) and two online experiments ( = 180 and 237). We find that anthropomorphism significantly and positively affects perceived product personalization, and that this effect is moderated by situational loneliness. Moreover, the results show that the interaction between anthropomorphism and situational loneliness has an impact on the willingness to pay a higher product price. The research findings can be used for future applications of AI-driven chatbots where there is a need to provide personalized and data-driven product recommendations.

Value creation in the financial services sector has been fundamentally transformed by digitally born financial technology (FinTech) companies. FinTech companies synthesize information systems with financial services. Given its disruptive power, the FinTech phenomenon has received great attention in academic research, practice, and media. Still, limited systematic research provides a structure and holistic view of FinTechs' success. Aiming to enhance understanding of the factors enabling FinTech success, we classify success factors across extant scientific literature on distinct FinTech business model archetypes. Our analysis reveals that the "cost-benefit dynamic of the innovation," "technology adoption," "security, privacy, and transparency," "user trust," "user-perceived quality," and "industry rivalry" are crucial factors for FinTech success and can be seen as "grand challenges" for the FinTech ecosystem. In addition, we validate and discuss our findings with real-world examples from the FinTech industry and two interviews with stakeholders from the FinTech ecosystem. Our study contributes to the knowledge of FinTechs by providing a classification system of success factors for practitioners and researchers.

Since late 2019, coronavirus disease 2019 (COVID-19) has led to a significant increase in the demand for medical resources. To publish data on face mask supplies, the Taiwanese government collaborated with program developers to construct a mask-supply information transitional platform (MITP). To comprehend the adoption of MITP, the study proposes a research model that integrates the health behavior model (HBM) and IS/IT continuance model for examining the factors affecting intention to use an MITP. Survey data collected from 524 respondents indicated that (1) intention to use an MITP is directly influenced by perceived threat of COVID-19 and beliefs toward using the MITP; (2) cues to action directly influence the perceived threat of COVID-19; and (3) perceived ease of use of MITP is a significant determinant of perceived usefulness of MITP. These results provide practical guidelines for health authorities and government to develop health information systems and strategies to control pandemics.

A citizen-centric view is key to channeling technological affordances into the development of future cities in which improvements are made with the quality of citizens' life in mind. This paper proposes City 5.0 as a new citizen-centric design paradigm for future cities, in which cities can be seen as markets connecting service providers with citizens as consumers. City 5.0 is dedicated to eliminating restrictions that citizens face when utilizing city services. Our design paradigm focuses on smart consumption and extends the technology-centric concept of smart city with a stronger view on citizens' roadblocks to service usage. Through a series of design workshops, we conceptualized the City 5.0 paradigm and formalized it in a semi-formal model. The applicability of the model is demonstrated using the case of a telemedical service offered by a Spanish public healthcare service provider. The usefulness of the model is validated by qualitative interviews with public organizations involved in the development of technology-based city solutions. Our contribution lies in the advancement of citizen-centric analysis and the development of city solutions for both academic and professional communities.

Online review systems try to motivate reviewers to invest effort in writing reviews, as their success crucially depends on the helpfulness of such reviews. Underlying cognitive mechanisms, however, might influence future reviewing effort. Accordingly, in this study, we analyze whether existing reviews matter for future textual reviews. From analyzing a dataset from Google Maps covering 40 sights across Europe with over 37,000 reviews, we find that textual reviewing effort, as measured by the propensity to write an optional textual review and (textual) review length, is negatively related to the number of existing reviews. However, and against our expectations, reviewers do not increase textual reviewing effort if there is a large discrepancy between the existing rating valence and their own rating. We validate our findings using additional review data from Yelp. This work provides important implications for online platforms with review systems, as the presentation of review metrics matters for future textual reviewing effort.

Smart retail stores have been gaining momentum in smart retailing. Instead of relying on in-store staff like traditional counterparts, smart retail stores provide an unmanned environment purely enabled by various in-store smart technologies that support customers throughout the shopping journey. This unstaffed operating model also enables smart retail stores to provide competitive prices by reducing labor costs. However, studies have overemphasized the unique value offered by smart technology but discounted the common value strengthened in smart retail. This study applies the situational factor framework to identify both unique and common factors empowered by smart technology from a comprehensive perspective; then, technology readiness is incorporated to explore consumer purchase intentions in smart retail stores. A total of 283 survey data were collected and analyzed. The main results indicate that most situational factors have a direct effect on purchase intention, and technology readiness enhances the unique situational factors enabled directly by smart technology.

Engagement platforms (EPs) are an essential technology to enable co-creation and service innovation. Therefore, the design and governance of these platforms are receiving increasing attention in research. In this study, we aim to identify which activities and mechanisms foster engagement and which governance mechanisms are implemented to avoid harm on EPs. To this end, we conducted expert interviews with founders, CEOs, and managers of 14 personal and household-related service platform companies from the DACH region (Germany(D), Austria(A), Switzerland(CH)), to gain insights into their activities and mechanisms for creating and maintaining successful EPs. We found eight mechanisms, e.g., moderation of content, limitations of entry and certification, employed by personal EPs (PEPs) as self-regulatory mechanisms to avoid misconduct and negative experiences of actors. The identified governance mechanisms may guide the design and governing of PEPs by providing tangible examples to foster actor engagement while considering externalities on a societal and individual level.

The amount of research related to financial technologies (fintech) has grown rapidly since these modalities have been implemented. A review of this literature base will help identify the topics that have been explored and identify topics for further research. This research project collects, synthesizes, and analyzes both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the literature, and then discusses an agenda for future research efforts. We searched for fintech research published in the last 20 years and analyzed 146 articles published in Finance and 70 articles published in Information Systems (IS) during this period in their respective A*, A, and B journals in the 2019 Australian Business Deans Council list. We found an increasing level of activity during the most recent 6-year period and a biased distribution of fintech articles focused on exploratory methodologies. We also found several research strategies that were either underrepresented or absent from the pool of fintech research and identified several subject areas that need further exploration. We also created four fintech topic categories to organize and classify this diverse research stream.

Reward-based crowdfunding broadens the scope of e-commerce transactions, as prototypical products are pre-sold under conditions of considerable uncertainty. To date, we know little about the mechanisms that underlie decisions to back reward-based crowdfunding campaigns. However, it is likely that startup founders' possibility of showcasing their personalities in video pitches signals their trustworthiness, particularly, as other features, such as quality seals and customer testimonials, are often unavailable. We use signaling theory to reinforce the move from a feature-oriented perspective to a signaling perspective, as signals can transmit information about startup founders' otherwise imperceptible qualities and abilities. Based on a survey ( = 108), we investigate how perceived hubris - proven to be particularly salient in startup contexts - influences the funding decision of potential backers. We find that abilities and legitimacy of a startup founder are rated positively when s/he is perceived as hubristic. These results have implications for crowdfunding campaigns and highlight the relevance of personality traits in electronic markets.

In the post-Covid world, our online personae have become increasingly essential mechanisms for presenting ourselves to the world. Simultaneously, new techniques for hacking online personae have become more widely available, easier to use, and more convincing. This combination, of greater reliance on online personae and easier malicious hacking, has created serious societal problems. Techniques for training users to detect false content have proved ineffective. Unfortunately, legal remedies for dealing with hacked personae have also been inadequate. Consequently, the only remaining alternative is to limit the posting of false content. In this discussion paper, we provide an overview of online personae hacking. As potential remedies, we propose to redesign search engine and social media algorithms allowing platforms to detect and restrict harmful false content and a new fundamental right for the EU Charter that would provide legal justification for platforms to protect online reputations. For those platforms that might choose not to protect online reputations, this new right would require that they do so.