The 50 most-cited articles in Journal of the Association for Information Systems, 2021

Although digital transformation offers a number of opportunities for today’s organizations, information systems scholars and practitioners struggle to grasp what digital transformation really is, particularly in terms of how it differs from the well-established concept of information technology (IT)-enabled organizational transformation. By integrating literature from organization science and information systems research with two longitudinal case studies—one on digital transformation, the other on IT-enabled organizational transformation—we develop an empirically grounded conceptualization that sets these two phenomena apart. We find that there are two distinctive differences: (1) digital transformation activities leverage digital technology in (re)defining an organization’s value proposition, while IT-enabled organizational transformation activities leverage digital technology in supporting the value proposition, and (2) digital transformation involves the emergence of a new organizational identity, whereas IT-enabled organizational transformation involves the enhancement of an…

Artificial intelligence (AI) technologies offer novel, distinctive opportunities and pose new significant challenges to organizations that set them apart from other forms of digital technologies. This article discusses the distinct effects of AI technologies in organizations, the tensions they raise and the opportunities they present for information systems (IS) research. We explore these opportunities in term of four business capabilities: automation, engagement, insight/decision making and innovation. We discuss the differentiated effects that AI brings about and the implications for future IS research.

Artificial intelligence (AI) systems in the workplace increasingly substitute for employees’ tasks, responsibilities, and decision-making. Consequently, employees must relinquish core activities of their work processes without the ability to interact with the AI system (e.g., to influence decision-making processes or adapt or overrule decision-making outcomes). To deepen our understanding of how substitutive decision-making AI systems affect employees’ professional role identity and how employees adapt their identity in response to the system, we conducted an in-depth case study of a company in the area of loan consulting. We qualitatively analyzed more than 60 interviews with employees and managers. Our research contributes to the literature on IS and identity by disclosing mechanisms through which employees strengthen and protect their professional role identity despite being unable to directly interact with the AI system. Further, we highlight the boundary conditions for introducing an AI system and contribute to the body of empirical research on the potential downsides of AI.

Conversational agents (CAs) are natural language user interfaces that emulate human-to-human communication. Because of this emulation, research on CAs is inseparably linked to questions about anthropomorphism—the attribution of human qualities, including consciousness, intentions, and emotions, to nonhuman agents. Past research has demonstrated that anthropomorphism affects human perception and behavior in human-computer interactions by, for example, increasing trust and connectedness or stimulating social response behaviors. Based on the psychological theory of anthropomorphism and related research on computer interface design, we develop a theoretical framework for designing anthropomorphic CAs. We identify three groups of factors that stimulate anthropomorphism: technology design-related factors, task-related factors, and individual factors. Our findings from an online experiment support the derived framework but also reveal novel yet counterintuitive insights. In particular, we demonstrate that not all combinations of anthropomorphic technology design cues increase perceived…

The paper presents an approach for implementing inscrutable (i.e., nonexplainable) artificial intelligence (AI) such as neural networks in an accountable and safe manner in organizational settings. Drawing on an exploratory case study and the recently proposed concept of envelopment, it describes a case of an organization successfully “enveloping” its AI solutions to balance the performance benefits of flexible AI models with the risks that inscrutable models can entail. The authors present several envelopment methods—establishing clear boundaries within which the AI is to interact with its surroundings, choosing and curating the training data well, and appropriately managing input and output sources—alongside their influence on the choice of AI models within the organization. This work makes two key contributions: It introduces the concept of sociotechnical envelopment by demonstrating the ways in which an organization’s successful AI envelopment depends on the interaction of social and technical factors, thus extending the literature’s focus beyond mere technical issues. Secondly,…

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars. However, an “uncanny valley” exists in which avatars trigger aversion when they are almost but not quite realistic. We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one that was close to human-realistic and one a cartoon caricature. We observed behavior, conducted one-on-one interviews, and collected survey data from SIGGRAPH conference attendees who either participated in a live discussion session between two avatars in a VR environment, or observed it via 3D VR headsets or on a large screen 2D video display. Eighteen sessions were conducted over four days, with the same person animating the human-realistic avatar and different guests animating the caricature avatars. The guests who interacted with the human-realistic avatar had a positive experience in the VR environment. The observers had positive evaluations of both avatars while…

Live streaming platforms such as Twitch and Periscope have become some of the most popular synchronous social networking services. To attract viewers, streamers are motivated to broadcast exciting video content while actively interacting with viewers. The emerging stream of research on the live streaming community has examined streamers’ motivations and how the viewers react to streamers. However, few studies have focused on understanding the characteristics of popular streamers. Popular streamers create tremendous business value for social media influencer investors, as they have high potential to create persuasive advertisements and endorsements for firms by promoting their products and services. We aim at examining the key characteristics associated with streamers’ viewer base, namely their personality, professionalism, and streaming affordance. Based on text mining and analyses of video content, our results show: (1) certain personality traits (such as openness) are negatively associated with both cumulative and current popularity, (2) professional players are more likely to…

Social media is one of the most disruptive technologies in executing a firm’s digital business transformation strategies. Does the firm’s ability to use social media affect its proficiency in exploring and exploiting knowledge? What should be the role of business analytics talent in this equation? We study theoretically and empirically these cutting-edge research questions. Our proposed research model argues that social media capability enables the development of knowledge exploration and knowledge exploitation, and business analytics talent exerts a positive reinforcing role in the impact of social media on knowledge exploration. We empirically tested the proposed research model with a secondary dataset from a sample of US firms using PLS path modeling. After running a robustness test by estimating eight alternatives/competing models, the empirical analysis revealed that social media capability is positively related to knowledge exploration and knowledge exploitation, but with a stronger effect on knowledge exploration. Moreover, business analytics talent plays a positive moderator…

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use…

This study investigates how different types of “big data analytics” (BDA) usage influence organizational decision-making in the area of supply chain management (SCM). Drawing on decision-making theory and organizational information processing theory, we conceptualize two patterns of BDA usage for supply chain (SC) activities (BDA use for SC optimization and BDA use for SC learning) and report two complementary channels via which the two BDA usage patterns impact a supply chain organization’s BDA-enabled decision-making capability. An analysis of questionnaire data from supply chain managers representing 157 companies based in North American suggests that BDA use for SC optimization is directly associated with better decision-making capability. In contrast, the influence of BDA use for SC learning does not impact decision-making directly but indirectly, as its effect is fully mediated by organizational integration. We discuss the implications of these findings for future academic research and for managers in practice who seek to maximize business values from BDA implementations.

This paper examines how signals and the herding factor originating from multiple sources complement or substitute for each other’s effects in the crowdfunding context. Drawing on the elaboration likelihood model, we propose that signals from campaigns (videos) and creators (experience) can mitigate information asymmetry concerns about project quality and creator credibility and that creator-originating signals offset the effect of campaign-originating signals on crowdfunding success. Further, we posit that online communication between creators and backers (backer comments and creator replies) complements or substitutes for the effects of signals originating from the campaign or creator. We collected and analyzed objective data on 9,884 crowdfunding projects from a major reward-based crowdfunding platform in China and were able to confirm most of our hypotheses.

Traditional passwords are difficult to remember and vulnerable to shoulder-surfing attacks. A graphical password was proposed to address these two problems. Users first identify their password images when they set up their passwords. During the authentication procedure, their password images are present with other distracting images. Instead of clicking their password images, users log in with the random numbers associated with their password images. Shoulder-surfing attackers will not be able to observe which images are the password images because users do not directly interact with them. To further increase the resistance to shoulder-surfing attacks, instead of directly entering the numbers associated with password images, users need to do some simple calculations, for example, adding the first number to the second, the second to the third, and so on so forth. The proposed scheme will be evaluated in a usability study in terms of shoulder-surfing resistance, usability, and user perceptions.

Obsessive online social gaming has become a worldwide societal challenge that deserves more scholarly investigation. However, this issue has not received much attention in the information systems (IS) research community. Guided by dual-system theory, we theoretically derive a typology of obsessive technology use and contextually adapt it to conceptualize obsessive online social gaming. We also build upon identity theory to develop a dual-identity perspective (i.e., IT identity and social identity) of obsessive online social gaming. We test our research model using a longitudinal survey of 627 online social game users. Our results demonstrate that the typology of obsessive technology use comprises four interrelated types: impulsive use, compulsive use, excessive use, and addictive use. IT identity positively affects the four obsessive online social gaming archetypes and fully mediates the effect of social identity on obsessive online social gaming. The results also show that IT identity is predicted by embeddedness, self-efficacy, and instant gratification, whereas social identity is…

Hybrid Intelligence is an emerging concept that emphasizes the complementary nature of human intelligence and artificial intelligence (AI). One key requirement for collaboration between humans and AI is the interpretability of the decisions provided by the AI to enable humans to assess whether to comply with the presented decisions. Due to the black-box nature of state-of-the-art AI, the explainable AI (XAI) research community has developed various means to increase interpretability. However, many studies show that increased interpretability through XAI does not necessarily result in complementary team performance (CTP). Through a structured literature review, we identify relevant factors that influence collaboration between humans and AI. Additionally, as we collect relevant research articles and synthesize their findings, we develop a research agenda with relevant hypotheses to lay the foundation for future research on human-AI complementarity in Hybrid Intelligence systems.

Although users often express strong positive intentions to follow security policies, these positive intentions fail to consistently translate to behavior. In a security setting, the inconsistency between intentions and behavior—termed the intention-behavior gap—is particularly troublesome, as a single failure to enact positive security intentions may make a system vulnerable. We address a need in security compliance literature to better understand the intention-behavior gap by explaining how an omnipresent competing intention—the user’s desire to minimize required effort—negatively moderates the relationship between positive intentions and actual security behavior. Moreover, we posit that this moderating effect is not accounted for in extant theories used to explain behavioral information security, introducing an opportunity to broadly impact information security research to more consistently predict behavior. In three experiments, we found that high levels of required effort negatively moderated users’ intentions to follow security policies. Controlling for this moderating effect…

Gender inequality in the IT profession is an acute issue with major individual, societal, and national implications. In this study, we build on the individual differences theory of gender and IT and extend it to account for subconscious processes that may drive women away from IT university majors and IT career choices. We specifically theorize on how the asymmetric roles of explicit and implicit gender identity facets impact the major selection of men and women students and affect their decisions to pursue the IT profession. To do so, this study introduces the concept of implicit gender identity, defined as the degree to which men and women subconsciously, automatically, and uncontrollably associate themselves with the masculine and feminine gender groups, respectively. We obtained data from 185 pre-major selection university students by means of a survey and the Implicit Association Test. The findings revealed that implicit gender identity was a significant predictor of IT major and career choices for women but not for men university students. Explicit gender identity had no…

This study aims to investigate the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Techniques considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Decision Tree with entropy, Random Forest, Gradient Boosting Decision Tree, and NGBoost algorithm. Following the activity recognition chain model for preprocessing, segmentation, feature extraction, and classification of human activities, we evaluate these ML techniques against classification performance metrics such as accuracy, precision, recall, F1 score, support, and run time on multiple HAR datasets. The findings highlight the importance to tailor the selection of ML technique based on the specific HAR requirements and the characteristics of the associated HAR dataset. Overall, this research helps in understanding the merits and shortcomings of ML techniques and guides the applicability of different ML techniques to various HAR datasets.

By enabling a new way to digitize transactions, distributed ledger technology allows to fundamentally change how value is digitally issued, transferred, and stored. Accordingly, «tokenization» refers to the concept of creating a singular identifier on a distributed ledger in terms of a token that may represent anything from financial assets, goods, to other valuable resources. Where tokenization may disrupt our economic system leading to more efficiency or democracy, it is required to gain insights and facilitate the development of use cases associated with this concept. To illustrate how firms can apply tokenization to innovate their businesses, we propose a framework of different token properties, drivers, and barriers for adoption based on literature and expert interviews and present eight archetypical cases derived from an analysis of 129 ventures. This work provides strategic guidance in a token economy and a starting point for future research of viable applications.

Insiders’ negligence or abuse is regarded as a leading cause of information security breaches in organizations. As most of the extant studies have largely examined insider threats at a high level of abstraction, the role of situational moral reasoning for information security policy (ISP) violations in specific situations has received little attention. To advance this line of research, this paper opens up a potentially fruitful path for IS researchers by applying situational action theory (SAT) to contextually examine why employees violate ISPs in particular situations. We consider the violations of password security policy, internet use policy, and confidential data security policy, and examine specific violation intents ranging from altruistic to malicious. The results support most of the assertions derived from SAT. We found situational moral beliefs to be the predominant driver for ISP violations across three situations in an organizational setting. However, the moderation effect of moral beliefs was only significant in situations involving sharing passwords and selling…

Chatbots gain more and more attention, both in research and in practice, and enter several application areas. While much research addresses technical or human-centered aspects, development, and adoption, little is known about Critical Success Factors (CSFs) and failure reasons of chatbots in practice. Design Science Research (DSR) oriented, we first analyze 103 real-world chatbots to examine the discontinuation rate of chatbots in 15 months. With a literature review and 20 expert interviews, we derive 12 specific CSFs and identify failure reasons which are evaluated in a focus group discussion with chatbot experts, afterwards. We explain chatbots’ failure in practice, improve chatbot knowledge in Information Systems (IS) and Human Computer Interaction (HCI), and finally deduce recommendations and further research opportunities.

Research on digital infrastructures and platforms studies large-scale systems that are characterized by constant evolution, loosely defined boundaries, and growing complexity. This research demonstrates that evolution is driven by tensions (between stability and change), which are in turn determined by the systems’ architecture and governance structures. This paper argues that architecture and governance are intrinsically related and conceptualizes them as a unified entity that we call an architecture-governance (A-G) configuration. We focus on the dynamics of A-G configurations—i.e., how architecture and governance interact and, in combination, shape the evolution of digital infrastructures, while, at the same time, change as emergent outcomes of the evolution of infrastructures. Toward this end, this paper applies assemblage theory as a lens for conducting a longitudinal study on an electronic prescription infrastructure. We identify three overall A-G configurations corresponding to different phases of the evolution of the infrastructure. This paper makes three contributions.…

Since the dotcom era around the year 2000, we observed a massive increase in technological progress. Nevertheless, with the emergence of new, elaborated digital technologies, theoretical complexity has increased simultaneously. Scholars often conceptualize the implications and the evolutionary process of adapting these technologies as digitization or digitalization. With our research, we aim to provide a clear and inter-subjective basis for common understanding of research terminology, that also supports practitioners in applying strategies with digital technologies. We conducted a systematic literature review, that sheds light on how digitization and digitalization are defined in IS literature, how the terms are used, and how researchers apply them. We present our approach to a definition and give researchers recommendations on how to use the terms explicitly. Subsequently, we discuss the different dimensions of our analysis, finding that to this point, there is no generalized conceptualization of the phenomena but a solid basis for formalizing one.

Although attractive to foreign and domestic firms, bottom-of-pyramid (BOP) markets pose unique challenges. Research suggests that IT-enabled supply chain information integration (IT-SCII) helps firms collaborate with suppliers and clients in broad business activities, operate in a unique context, and overcome salient challenges in BOP markets. Anecdotal evidence and research suggest that foreign and domestic firms have differing advantages: While foreign firms have considerable global experience, domestic firms have substantial local market knowledge. We draw on the ownership-location-internalization (OLI) framework to theorize that domestic and foreign firms leverage IT-SCII differently because of their differing ownership-based advantages in BOP markets. We hypothesize that the influence of IT-SCII on client business collaboration and the influence of client business collaboration on firm performance are stronger for domestic firms than for foreign firms. Conversely, we hypothesize that the influence of IT-SCII on supplier business collaboration and the influence of supplier…

Action research (AR) has developed extensively since the 1970s. We reviewed the AR literature within the information systems (IS) discipline and found 16 different methods, which constitutes a problematic situation for researchers. We describe and critique those methods before integrating their strengths to improve the AR method that is most frequently practiced in IS: canonical action research (CAR). The existing set of principles and criteria for CAR is modified and elaborated to enhance the foundation for undertaking AR consistently. We discuss the general implications of this improved form of the method, which we name integrated action research (IAR). We specifically suggest how IAR can be used to investigate the application of disruptive technologies, including those that embody artificial intelligence and enable more flexible and socially distanced work.

Owing to a lack of access and skill, most of the data that companies are creating today is unused, even though it is widely viewed as a strategic asset. To overcome this obstacle, enterprises are establishing data democratization initiatives that can empower employees to use data and extract additional business value from them. However, IS research on data democratization has been scarce and has yet to explain how companies build their data democratization capability. Leveraging a multiple case study involving eight companies, we identify five enablers of data democratization: (1) Broader data access, (2) Self-service analytics tools, (3) Development of data and analytics skills, (4) Collaboration and knowledge sharing, and (5) Promotion of data value. As academic contribution, our findings clarify the concept of data democratization and shed light on the differences between traditional and born-digital companies. For practitioners, our study delivers actionable insights to tailor their data democratization initiatives.

Many organizations are concerned about growing their direct investments in digital technologies and going through a digital transformation (DT) that offers value creation opportunities. It has been found that IT governance (ITG) plays an important role in organizations' digital initiatives. Previous studies have identified ITG structures, processes, and relational mechanisms to increase the firms’ performance. However, the traditional ITG mechanisms might no longer be valid in this digital era. Unfortunately, few studies have investigated this area. Therefore, a systematic literature review was performed to identify the ITG mechanisms that influence DT. During the review process, 46 articles were selected from top-rank journals and conference proceedings and analyzed. The process revealed 28 ITG mechanisms that influence DT. The study contributes to both research and practice, providing researchers with the ITG mechanisms that influence DT and informing practitioners on which ITG mechanisms to focus on towards a successful DT.

Cryptocurrencies, a new class of digital asset predominantly based on blockchain technologies, have gained immense popularity in recent years. Despite of advantages over traditional monetary systems such as lower transaction cost, increased transactional security, or transparency, cryptocurrencies are not free of disadvantages. The increase in popularity has also led to an increase in market manipulation in these markets. While there has been some attempt to identify and classify various cryptocurrency market manipulation schemes, there is a distinct need for a holistic description and classification of current cryptocurrency market manipulation schemes. Therefore, based on a systematic literature review, this paper provides an overview of cryptocurrency market manipulation methods using a concept-centric approach, a characterization of these methods, and identifies market vulnerabilities.

To efficiently manage information security, firms typically outsource part of their security functions to a managed security service provider (MSSP) under a variety of contractual arrangements. Based on this practice, we study a business setting in which the management of security outsourcing depends on the security efforts of both the MSSP and its clients, taking into account that their allocation of efforts can change during the contract horizon. Since their efforts are private to each other, a double moral hazard (DMH) problem can arise with the use of bilateral refund contracts, which have been widely adopted in the MSSP industry. Moreover, both the high probability of undirected attacks and system interdependency can exacerbate the DMH problem. We propose two new types of contracts to solve this problem. One is a monitoring contract, in which a cyberinsurance firm monitors the security efforts of the MSSP and its clients. The other is a liability contract, in which both parties take full liability for breaches through rewarding clients who are well protected and penalizing…

The recent emergence of financial technology (fintech) innovations offers a promising resolution to financial exclusion via mechanisms that empower financially underprivileged individuals to gain access rights in the traditional financial industry. However, academic research has provided little guidance on how to strategize the IT-enabled empowerment mechanisms for fintech innovations to realize both business success and financial inclusion. In this study, we conduct an in-depth revelatory case study on a novel Chinese fintech innovation, Yu’E Bao, a bellwether for the dramatic transformation of China’s financial industry through the successful empowerment of a large population of financially underprivileged individuals (i.e., “grassroots consumers,” translated from “cao gen” in Chinese). Through our systematic qualitative analysis of news articles collected since the product launch, we derive a two-stage theoretical model, examined through the lens of empowerment, and unravel the mechanisms underlying fintech innovation’s empowerment process on grassroots investment consumers. Most…

The rapid expansion of innovative smart technologies is creating exciting new opportunities and causing disruptions in many different sectors, including manufacturing, finance, insurance, and healthcare. Given its broad impacts across a variety of areas, smart technologies currently represent one of the hottest topics in both academia and industry. This special issue seeks to identify the latest trends and research directions on this topic by presenting research on the development, evaluation, management, and organization of smart technologies in the age of smart services. We identify three trending categories of smart services: (1) the Internet of Things and wearable technologies, (2) the sharing economy and platforms, and (3) intelligent agents. These innovative technologies and their corresponding production possibilities are of great interest to both researchers and practitioners, particularly in terms of their impacts under different application scenarios. This special issue offers a comprehensive review of IT-enabled smart services and we hope that it motivates further IS…

The digital economy holds new chances for value creation but also risks for both companies and customers alike. Within this context, the Corporate Digital Responsibility (CDR) movement gains traction. Building on the well-established Corporate Social Responsibility paradigm, CDR entails a set of rules through which it seeks to ensure an ethical and responsible development, deployment, and use of digital technologies. To date, the scholarly conceptualization of CDR is still in its infancy. This study pursues two main objectives: Firstly, this study seeks to contribute to CDR theory by providing a more in-depth conceptualization of the concept. Secondly, this study provides guidance for the implementation of CDR in practice, based on an empirical foundation. To this end, we conduct a series of Best-Worst Scaling studies with 515 German-speaking participants and examine consumers' perspectives on various ethical guidelines from CDR.

We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud. Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features. We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers. We perform a naive evaluation that assesses the classifiers’ performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures. The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection. We discuss implications for supervisory authorities, industry, and individual users.

Microtask crowdsourcing platforms foster digital assembly lines where microtasks are performed by on-demand microworkers. The sustainability of microtask crowdsourcing platforms and the timely completion of microtask batches hinge on microworkers’ continued participation, but our understanding of microworkers’ continuance remains unclear. Drawing on the theory of the allocation of time and the research on work motivational orientations, we hypothesize that three motivational orientations (i.e., compensation, enjoyment, and microtime structure) are positively associated with microworkers’ perceived relative advantage of microworking, which in turn enhances microworkers’ intent to continue microworking. The relationships between the three motivational orientations and relative advantage are moderated by microworkers’ perceived situational boredom and microwork status (i.e., ad hoc, part-time, and full-time). An online survey on Amazon’s Mechanical Turk provides empirical evidence for the predictive powers, which are contingent on situational boredom, of the three motivational…

Digital transformation research and practice has received significant attention across the information systems (IS) field in recent years. However, this area of research is still in its infancy and in particular most studies focus on the initial adoption of digital technology rather than the subsequent long-term normalisation and sustaining of that digital transformation. Little is known about normalising a digital transformation (i.e., embedding and sustaining a digital transformation). This paper presents research-in-progress to theorise about the normalisation of a digital transformation. This research presents a longitudinal case study on Hewlett Packard Enterprise Finance Services (HPEFS) and their efforts to normalise a digital transformation over seven years (2013-2020). The contribution of this research is threefold. Firstly, this research demonstrates the novelty of applying normalisation process theory (NPT) to examine a digital transformation process. Secondly, NPT goes beyond traditional IS theoretical perspectives and draws attention towards embedding and sustaining…

Blockchain systems become increasingly attractive targets for cybercrime due to the rising amount of value transacted in respective systems. However, researchers and practitioners alike lack a comprehensive overview of existing attacks and a directive discussion of resulting implications. Employing a structured literature review, we an-alyze academic research concerning malicious attacks on blockchain systems. We ex-tract 87 relevant attacks and structure those using the attack tree notation. Our re-sults show that the academic discourse revolves mainly around the analysis of a few individual attacks, and most publications deal with attacks on either Bitcoin or Ethereum. We further find that most attacks target the on-chain application logic component (smart contracts) of the blockchain technology stack as well as consensus mechanisms. A majority of attacks are mitigable, and socio-technical components play an important role in both attacks and applying effective countermeasures.

Organizations are increasingly engaging the community through crowdsourcing platforms to evolve innovative solutions to challenging business problems. Participants on such platforms often simultaneously cooperate and compete with one another to earn top honors. This paper addresses the imperative to understand the dynamics of knowledge sharing in such a coopetitive environment. Specifically, our study relies on the conceptual foundations of social exchange and social capital theories to investigate how help rendered (e.g., exchanging ideas or sharing knowledge) by participants in an online coopetitive crowdsourcing setting affects their performance. Furthermore, the study examines the moderating effects of the intensity of competition. Results of our econometrics analyses suggest that help given in a highly competitive contest, as opposed to a less competitive one, is more likely to be reciprocated, but less likely to improve the contributor’s contest performance. In addition, our study found that help received by participants positively impacts their contest performance, and…

In the increasingly connected business world, economic value is created less and less by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. However, one of the main obstacles to why actors are currently not motivated to engage in data ecosystems is that they are often not aware of the actual benefits of cross-organisational data sharing. This is partly because it is in many cases unclear what the incentives and ultimately the added value are for data providers when they share their data with others. To address this research gap we develop a taxonomy of incentive mechanisms for data sharing in data ecosystems which is based on a structured literature review. The resulting taxonomy consists of key dimensions and characteristics of incentive mechanisms for data sharing in data ecosystems and contributes to a better scientific understanding of these concepts.

This paper deals with the difficult yet increasingly important MIS phenomenon of online child sexual exploitation (online CSE). Through the use of secondary and publicly available data from the Federal Bureau of Investigation, as well as primary data from a cybercrime police unit in the United Kingdom, this study takes a grounded theory approach and organizes the role that technologies and social actors play in shaping online CSE. The paper contributes to IS theory by providing a consolidated model for online CSE, which we call the technology and imagery dimensions model. This model combines the staging of the phenomenon and the key dimensions that depict how the use of technology and imagery both fuels and defuses the phenomenon. In informing the construction of the model, the paper extracts, organizes, and generalizes the affordances of technology and discusses the role of information systems in detecting online CSE.

Inspired by the city planning metaphor, enterprise architecture (EA) has gained considerable attention from academia and industry for systematically planning an IT landscape. Since EA is a relatively young discipline, a great deal of its work focuses on architecture representations (descriptive EA) that conceptualize the different architecture layers, their components, and relationships. Beyond architecture representations, EA should comprise principles that guide architecture design and evolution toward predefined value and outcomes (prescriptive EA). However, research on EA principles is still very limited. Notwithstanding the increasing consensus regarding the role and definition of EA principles, the limited publications neither discuss what can be considered suitable principles nor explain how they can be turned into effective means to achieve expected EA outcomes. This study seeks to strengthen the extant theoretical core of EA by investigating EA principles through a mixed methods research design comprising a literature review, an expert study, and three case studies. The…

Artificial Intelligence (AI) is increasingly being used in various domains including highly regulated areas such as auditing. Although the use of AI in auditing may seem promising at the first glance, there are a number of implications that have so far prevented its broad application. By proposing the first Auditing Artificial Intelligence Maturity Model (A-AIMM), we assess the adoption and diffusion of AI in auditing by considering audit specific requirements. The resulting model contains eight different dimensions and five different maturity levels that foster audit firms in becoming AI-enabled organisations by providing recommendations for the further use of AI with their current capabilities. The development procedure represents a Design Science Research approach including a systematic literature review, a qualitative survey with audit experts and an iterative development process.

Due to the increasing amount of data, media companies increasingly use algorithms and artificial intelligence (AI) for both researching topics and autonomously creating journalistic articles and for publishing content on social media. However, previous studies found an increase of uncertainty of social media as a source of information, due to fake news and nontransparency of the source. Research on the credibility of using AI in journalism and trust in AI-generated articles is still in early stages. Therefore, we conducted an online survey (n = 122) to examine whether transparent communication and explanation of AI use in journalism can lead to more credibility. In contrast to previous findings, we could show that explanations and transparency do not have an impact on the credibility. However, we found that the credibility of media companies and users’ experience with social media as well as the AI experience positively impacts the trust in AI-generated content.

Artificial intelligence promises great potential to change, modify and enhance processes, products, and business models. Yet, many organizations struggle to leverage those potentials as they are not able to successfully implement AI technologies. While previous literature has already revealed success factors that are important to be considered when implementing AI projects, academia still lacks a sufficient overview in this field. With this background, this paper reviews existing literature on success factors related to the adoption of AI and provides a structured overview of 36 factors that have already been investigated by previous scholars. Specifically, we draw from the Technology-Organization-Environment (TOE) framework and reveal 12 factors related to the technological dimensions, 13 related to the organizational dimension, and 11 related to the environmental dimensions. Our results can help scholars and practitioners to include those factors in theory development and to implement AI projects more successfully.

Advanced information technologies (ITs) are increasingly assuming tasks that have previously required human capabilities, such as learning and judgment. What drives this technology anthropomorphism (TA), or the attribution of humanlike characteristics to IT? What is it about users, IT, and their interactions that influences the extent to which people think of technology as humanlike? While TA can have positive effects, such as increasing user trust in technology, what are the negative consequences of TA? To provide a framework for addressing these questions, we advance a theory of TA that integrates the general three-factor anthropomorphism theory in social and cognitive psychology with the needs-affordances-features perspective from the information systems (IS) literature. The theory we construct helps to explain and predict which technological features and affordances are likely: (1) to satisfy users’ psychological needs, and (2) to lead to TA. More importantly, we problematize some negative consequences of TA. Technology features and affordances contributing to TA can intensify…

Data-driven data science challenges our conceptualization of “data.” Significantly beyond capturing a given phenomenon, data, increasingly, are the phenomenon. Data may be iteratively manipulated algorithmically, undermining the “faithfulness” of data to any originating phenomenon. Crucially, data that are not “faithful” are inherently uncertain, as data risk becoming meaningless symbols. We empirically study how a community of commercially based geoscientists grapples with the phenomenon of offshore oil and gas reservoirs located kilometers below the seabed. The data available about these reservoirs are algorithmically manipulated sensor-based Internet of Things data. Our main contribution is the articulation of three patterns of work practices detailing how inherently uncertain data are woven into consequential work practices: (1) Accumulating is the cumulative process of supporting and triangulating one set of data with supplementary data sets; accumulating captures the conservative approach of backing up existing interpretations of the data. (2) Reframing describes the process…

This research explains why individuals resume using social networking sites (SNSs) after terminating their use. Drawing on return migration theory, we developed a theory-driven model of SNS resumption that includes two novel antecedents of SNS resumption behavior: nonuse-related dissatisfaction and use-related satisfaction. We also hypothesize that dispositional resistance to change moderates the impact of nonuse-related dissatisfaction and use-related satisfaction on resumption. We used a mixed methods approach to refine and evaluate the research model. Study 1 used the critical incident method to identify SNS-specific antecedents of nonuse-related satisfaction and use-related satisfaction, allowing us to refine the research model. Study 2 used structural equation modeling to evaluate our research model using two three-wave surveys: one with recent ex-users who recently decided to stop using and delete their profiles on Facebook and one with long-standing ex-users who stopped using and deleted their profiles on Facebook a long time ago. We found support for most relationships in…

As distance learning and large-scale learning environments continue to grow, interactive knowledge distribution is becoming a more challenging task. Although studies show that active and emotional student engagement is the best way to achieve promising educational outcomes, educational institutions still face challenges in providing students with interactive learning scenarios. Pedagogical conversational agents (PCAs) offer one way for educational settings to create such scenarios. Despite the increasing research interest of PCAs in research, there is a lack of shared knowledge about the different design elements of PCAs. Hence, our goal is to develop a taxonomy to classify PCAs into three main categories (structure, technology, task/people). In addition, we aim to provide preliminary results on possible outcome variables that could result from the presented design elements of the taxonomy. Our findings are intended to provide researchers and practitioners with deeper insight into the field of PCAs to possibly guide design decisions.

This study takes a review and theory development (RTD) approach to synthesizing the software platform literature, offering theoretical perspective and research guidance. In doing so, we conceptualize platform and complementary capabilities for software platform owners and complementors. The review indicates that three dimensions reflect platform capabilities: intermediarity, generativity, and ambidexterity, while complementary capabilities include creativity, interconnectivity, and appropriability dimensions. We derive an integrative framework of software platforms, which explains (1) how software platform owners and complementors improve performance by enhancing their capabilities, and (2) how software platform owners, complementors, and the ecosystem environment coevolve. We then discuss how future research can build on and enrich the research framework

Market research institutes forecast a growing relevance of Low-Code Development Platforms (LCDPs) for organizations. Moreover, the rising number of scientific publications in recent years shows the increasing interest of the academic community. However, an overview of current research focuses and fruitful future research topics is missing. This paper conducts a first scientific literature review on LCDPs to close this gap. The socio-technical system (STS) model, which categorizes information systems into a social and a technical system, serves to analyze the identified 32 publications. Most of current research focuses on the technical system (technology or task). In contrast, only three publications explicitly target the social system (structure or people). Hence, this paper enables future research to address the identified research gaps. Additionally, practitioners gain awareness of technical and social aspects involved in the development, implementation, and application of LCDPs.

A substantial amount of previous research has examined the efficacy of fear appeals to elicit security-enhancing behaviors from users. However, despite more than a decade of research on fear appeals in security contexts, researchers have yet to understand which factors drive users’ responses to fear appeals. Instead, the literature is riddled with inconsistent findings on the antecedents that predict fear-appeal outcomes, fueling controversy over, and inhibiting progress, on the problem. This research addresses the inconsistent findings by using construal level theory (CLT) to explain how temporal distance and argument nature affect fear-appeal appraisal. Based on two online experiments, we report evidence that temporal distance determines which antecedents drive fear-appeal outcomes, which helps explain inconsistent results found in prior literature. Moreover, we found that depending on the temporal distance condition, argument nature (i.e., how or why arguments) can affect the effectiveness of fear appeals. Overall, our findings refine understanding of when certain factors…