The 50 most-cited articles in Journal of the Association for Information Systems, 2019

Building on the promise of open data, government agencies support a continuously growing number of open data initiatives that are driven mainly by expectations of unprecedented value generation from an underutilized resource. Although data, in general, have undoubtedly become an essential resource for the economy, it has remained largely unclear how, or even whether, open data repositories generate any significant value. We addressed this void with a study that examines how sustainable value is generated from open data. Subsequently, we developed a model that explains how open data generate sustainable value through two underlying mechanisms. The first, the information sharing mechanism, explicates how open data are beneficial to forging informational content that creates value for society through increased transparency and improved decision-making. The second, the market mechanism, explicates how open data are beneficial as a resource in products and services offered on the market, as well as how open data are used to make processes more efficient or to satisfy previously unmet…

Blockchain technology, despite its origins as the underlying infrastructure for value transfer in the era of cryptocurrency, has been touted as the main disruptive force in modern businesses. Blockchain has the capacity to chronologically capture and store transactional data in a standardized and tamper-proof format that is transparent to all stakeholders involved in the transaction. This, in turn, has prompted companies to rethink preexisting business practices, thereby yielding a myriad of fascinating business models anchored in blockchain technology. In this study, we advance contemporary knowledge of business applications of blockchain by drawing on the theoretical lens of the digital business model and value configuration to decipher how pioneers in this space are leveraging blockchain to create and capture value. Through a comparative, multiple case study approach, we analyzed five companies in mainland China that have rolled out blockchain initiatives. From our case analyses, we derived a typology of five blockchain-inspired business models, each of which embodies a…

An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology could be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply…

Exploring theoretically plausible alternative models for explaining the phenomenon under study is a crucial step in advancing scientific knowledge. This paper advocates model selection in information systems (IS) studies that use partial least squares path modeling (PLS) and suggests the use of model selection criteria derived from information theory for this purpose. These criteria allow researchers to compare alternative models and select a parsimonious yet well-fitting model. However, as our review of prior IS research practice shows, their use—while common in the econometrics field and in factor-based SEM—has not found its way into studies using PLS. Using a Monte Carlo study, we compare the performance of several model selection criteria in selecting the best model from a set of competing models under different model set-ups and various conditions of sample size, effect size, and loading patterns. Our results suggest that appropriate model selection cannot be achieved by relying on the PLS criteria (i.e., R2, Adjusted R2, GoF, and Q2), as is the current practice in academic…

Contemporary C2C platforms, such as Airbnb, have exhibited considerable growth in recent years and are projected to continue doing so in the future. These novel consumer-to-consumer marketplaces have started to obliterate the boundaries between private and economic spheres. Marketing personal resources online is inherently associated with the disclosure of personal and sometimes intimate information. This raises unprecedented questions of privacy. Yet, there is so far little research on the role of privacy considerations in the sharing economy literature. Leveraging the theoretical perspective of privacy calculus, we address this gap by investigating how privacy concerns and economic prospects shape a potential provider’s intentions to share via different communication channels. We relate privacy concerns back to the provider’s perceptions of the audience. We evaluate our research model by means of a scenario-based online survey, providing broad support for our reasoning.

The use of Digital Twins has gained attraction in research and practice in recent years. Digital Twins are virtual representations of physical objects and they can be connected with their physical counterparts. Through this connection, Digital Twins contribute to the convergence of the real and the virtual world. While existing literature reviews focus strongly on the manufacturing industry, this paper analyzes Digital Twin applications across industries. Based on a systematic literature review, this paper examines 87 Digital Twin applications and proposes a classification scheme with six dimensions to describe the applications identified. The concept of Digital Twins is currently still underrepresented in Information Systems research, which opens up further research opportunities.

Information technology is rapidly changing the way how peoplecollaborate in enterprises. Chatbots integrated into enterprise collaborationsystems can strengthen collaboration culture and help reduce work overload. Inlight of a growing usage of chatbots in enterprise collaboration systems, weexamine the influence of anthropomorphic and functional chatbot design featureson user acceptance. We conducted a survey with professionals familiar withinteracting with chatbots in a work environment. The results show a significanteffect of anthropomorphic design features on perceived usefulness, with astrength four times the size of the effect of functional chatbot features. Wesuggest that researchers and practitioners alike dedicate priorities toanthropomorphic design features with the same magnitude as common forfunctional design features in chatbot design and research.

In this research, we develop a research model explaining the adoption of conversational agents for disease diagnosis. Healthcare is challenged by a parallel increasing demand for healthcare services and a decreasing supply of healthcare professionals. Mobile Health is proposed to overcome geographical, temporal, and organizational barriers of healthcare services. Conversational agents (CA), i.e. software programs that interact with users through natural language, are developed that are even able to diagnose a disease based on an individuals’ input using a chat interface. However, these systems face an adoption challenge. To understand that, we use UTAUT2 as theoretical lens and 35 semi-structured interviews with potential users of a CA for disease diagnosis. Based on that we propose a research model that contains (1) well-known UTAUT2 factors (performance and effort expectancy, facilitating conditions), (2) re-defined other factors to better fit the context (social influence, price value, habit), and (3) newly identified ones (privacy risk expectancy, trust in provider and system,…

The paper develops and tests theory that explains under what conditions the extent of email use is appraised as a stressor. Integrating concepts from information acquisition and person-environment fit theories, we hypothesize that individuals appraise their extent of email use as stressful based on the mismatch between their current and desired extents of email use. We define the mismatch as email misfit and the match as email fit. We first develop a conceptual framework that associates email misfit with the individual’s experience of three key workplace stressors—work relationships stressor, job control stressor, and job conditions stressor. We then develop hypotheses framing the relationship between email fit and misfit, and these stressors. We test our hypotheses by applying quadratic polynomial regressions and surface-response analysis to survey data obtained from 118 working individuals. The paper makes three theoretical contributions. First, in reporting a theoretical and empirical construction of email fit and misfit and their relationship to workplace stressors, it shows…

Taxonomies constitute one fundamental type of artefact in design science, describing and classifying existing or future objects of a domain. Taxonomies support researchers and practitioners with analysing and understanding a domain, which in turn is a prerequisite for theory building. Despite the increasing interest in taxonomies (and methodological guidance for building them), there is hardly any guidance for researchers on how to rigorously evaluate taxonomies. Based on a literature analysis, this study sheds light on the question of whether, when, and how researchers currently evaluate taxonomies. We critically synthesize and comprehensively review 306 articles that are concerned with taxonomies. Surprisingly, we find that taxonomies are rarely evaluated in IS research, nor is there any consistency in terms of methods used for evaluations. We describe the methods used by IS researchers to evaluate taxonomies after taxonomy building has been completed. Being the first to systematically analyse taxonomy evaluation, we propose a preliminary version of a framework for taxonomy…

Given the ubiquity of digital technologies, and increased use of autonomous algorithms, it is likely that many of today’s social and organizational processes will one day include autonomous elements. The Bitcoin blockchain is likely the first case of an increasingly generative and autonomous way of organizing, and the specific properties of blockchain infrastructures—distribution of control, openness to manipulation, and generativity of the underlying source code—make it an ideal case to study patterns of self-organizing. This paper investigates the phenomenon of self-organizing through a study of forking in the Bitcoin blockchain infrastructure between 2010 and 2016. It adds to the emerging body of research on digital infrastructures, and particularly blockchain infrastructures, by conceptualizing forking as a pattern of self-organizing in blockchain infrastructures that specifically involves the underlying infrastructure, the scale of code changes, individual objectives, and collective adoption, whether specific or general. Thus, this paper demonstrates how forking in blockchain…

Artificial intelligence has emerged as a research field. Recently, there has been a remarkable increase in the adoption of AI technology in organizations as new forms of work have increased substantially. Despite the envisaged benefits of AI adoption, many organizations still struggle to drive their AI adoption forward. This study leads to the closing of this gap by conducting a thorough analysis of the current state of AI adoption and the main barriers to AI adoption among Australian organizations. To do so, we draw on The Technology-Organizations-Environment (TOE) framework to categorize the factors inhibiting AI adoption at organization-level. This paper reports on the results of an online questionnaire involving 207 small, medium and large-sized organizations about their level of AI adoption and barriers. The study offers insights and a research agenda to help executives and top-level managers prepare for AI adoption, and to make informed decisions to speed up the adoption process.

Chatbots are software-based systems designed to interact with humans using text-based natural language and have attracted considerable interest in online service encounters. In this context, service providers face the challenge of measuring chatbot service encounter satisfaction (CSES), as most approaches are limited to post-interaction surveys that are rarely answered and often biased. Asa result, service providers cannot react quickly to service failures and dissatisfied customers. To address this challenge, we investigate the application of automated sentiment analysis methods as a proxy to measure CSES. Therefore, we first compare different sentiment analysis methods. Second, we investigate the relationship between objectively computed sentiment scores of dialogs and subjectively measured CSES values. Third, we evaluate whether this relationship also exists for utterance sequences throughout the dialog. The paper contributes by proposing and applying an automatic and objective approach to use sentiment scores as a proxy to measure CSES.

In this paper, we present the current state of the art of usingconversational agents for educational purposes. These so-called pedagogicalconversational agents are a specialized type of e-learning and intelligent tutoringsystems. The main difference to traditional e-learning and intelligent tutoringsystems is that they interact with learners using natural language dialogs, e.g. inthe form of chatbots. For the sake of our research project, we analyzed currenttrends in the research stream as well as research gaps. Our results show forinstance that (1) there is a trend towards using mobile conversational agents ineducation, (2) a proper generalization of existing research results (e.g. designknowledge) is missing, and (3) there is a need for comprehensive in-depthevaluation studies and corresponding process models. Based on our results, weoutline a research agenda for future research studies.

The goal of a review article is to present the current state of knowledge in a research area. Two important initial steps in writing a review article are boundary identification (identifying a body of potentially relevant past research) and corpus construction (selecting research manuscripts to include in the review). We present a theory-as-discourse approach, which (1) creates a theory ecosystem of potentially relevant prior research using a citation-network approach to boundary identification; and (2) identifies manuscripts for consideration using machine learning or random selection. We demonstrate an instantiation of the theory as discourse approach through a proof-of-concept, which we call the automated detection of implicit theory (ADIT) technique. ADIT improves performance over the conventional approach as practiced in past technology acceptance model reviews (i.e., keyword search, sometimes manual citation chaining); it identifies a set of research manuscripts that is more comprehensive and at least as precise. Our analysis shows that the conventional approach failed to…

This study aims to generate a systematic understanding of how digital platform firms can attain platform leadership. We explore the question by casting a boundary management lens over the complex network of interactions on a digital platform. Firms are faced with various boundaries—boundaries of efficiency, competence, power, identity, and ties—and must carefully address tensions within diverse groups of actors with their own interests. We conducted an in-depth case study on China’s largest online ticketing firm and established two contributions for attaining platform leadership. First, we conceptualized the development of a digital platform as a set of technology-based boundary management mechanisms (functional multiplexing, scope expansion, community curation, actor empowerment, and positional escalation) that includes a combination of boundary spanning, erecting, and reinforcing. Second, we uncovered the network dynamics of a digital platform by explicating the synergies and tensions of boundary management. Considering our novel findings, this study offers managerial and design…

Representation theory proposes that the basic purpose of an information system (IS) is to faithfully represent certain real-world phenomena, allowing users to reason about these phenomena more cost- effectively than if they were observed directly. Over the past three decades, the theory has underpinned much research on conceptual modeling in IS analysis and design and, increasingly, research on other IS phenomena such as data quality, system alignment, IS security, and system use. The original theory has also inspired further development of its core premises and advances in methodological guidelines to improve its use and evaluation. Nonetheless, the theory has attracted repeated criticisms regarding its validity, relevance, usefulness, and robustness. Given the burgeoning literature on the theory over time, both positive and negative, the time is ripe for a narrative, developmental review. We review representation theory, examine how it has been used, and critically evaluate its contributions and limitations. Based on our findings, we articulate a set of recommendations for…

This essay explores the fixation the field of information systems has with “theory” and my frustration with this focus, examining where this theory focus came from, why it has been so widely adopted, and how it has led to dysfunction. It also offers some recommended action items that the field can take to redirect its efforts in order to become more relevant, resilient, and resourceful. These actions include, broadening the aperture of what legitimate IS research should include, imploring journal editors to change the way “applied” research is handled, bringing back books as an accepted and valued publication outlet, and moving the field in the direction of engagement.

Leadership plays an important role in changing employees’ behavior. This paper aims to investigate the relationship between paternalistic leadership and employees’ information security policy (ISP) compliance. We adopt social bond theory as the theoretical lens to explain the effect of paternalistic leadership on ISP compliance through social bond formation. We developed a research model and tested it using data comprising 314 dyads of employees and their supervisors in organizations. The results show that all three dimensions of paternalistic leadership— benevolence, morality, and authoritarianism—positively influence employee ISP compliance. The social bond partially mediates the effects of benevolence and morality on compliance intention. Overall, this paper reveals the positive effect of paternalistic leadership in improving ISP compliance and the mediating role of the social bond in explaining the impact of paternalistic leadership on ISP compliance. In addition, the mediation effect of the social bond suggests that the non-IT related routine behavior of leaders can also affect…

Information systems security behavioral research has primarily focused on individual cognitive processes and their impact on information security policy noncompliance. However, affective processes (operationalized by affective absorption and affective flow) may also significantly contribute to misuse or information security policy noncompliance. Our research study evaluated the impact of affective absorption (i.e., the trait or disposition to allow one’s emotions to drive decision-making) and affective flow (i.e., a state of immersion with one’s emotions) on cognitive processes in the context of attitude toward and compliance with information security policies. Our conceptual model was evaluated using a laboratory research design. We found that individuals who were frustrated by work-related tasks experienced negative affective flow and violated information security policies. Furthermore, perceptions of organizational injustice increased negative affective flow. Our findings underscore the need for understanding affective processes as well as cognitive processes which may lead to a…

Prior research on the evolution of digital infrastructure has paid considerable attention to effective strategies for resolving contradictory tensions, yet what we still do not understand is the role of higher-level organizational capabilities that help balance the contradictory tensions that emerge during this evolution. In addressing this gap, two related questions guided our investigation: (1) How do organizations experience and resolve contradictory tensions throughout the evolution of digital infrastructure? and (2) What can we learn about the organizational capabilities that drive strategic actions in resolving these contradictory tensions? We approach these questions using an in-depth case study at RE/MAX LLC, a global real estate franchise. Based on our findings, we propose a theoretical model of digital infrastructure ambidexterity. The model recognizes three pairs of capabilities (identifying and germinating, expanding and legitimizing, and augmenting and implanting) and two supporting factors (leadership and structure) that are key to resolving contradictory tensions…

Despite the potential risks that social desirability (SD) bias poses to the validity of information systems (IS) research, little is known about the extent of such bias. This study examines the extent of SD bias in the IS domain and compares alternative techniques for measuring it. Our findings suggest that despite the popularity of the Marlowe-Crowne scale in IS research, the impression management scale functions better in assessing the extent of SD bias. We also found that under certain circumstances, SD bias can threaten the validity of IS research. This study contributes to the IS literature by showing the difference in SD bias across IS contexts and suggesting an effective way to test for the presence of SD bias.

The use of online live-streaming videos to promote brands and products in e-commerce is “exploding” in China. Whereas, little is known about the mechanism underlying viewers/ audience participating behavior in the rapidly growing online live-streaming videos for commerce phenomenon. Based on the stimulus–organism–response paradigm, this study endeavors to investigate the effects of content and social features (information quality, broadcaster attractiveness and para-social interaction) of on customers cognitive and emotional state (cognitive assimilation and emotional energy) and the subsequently their responses (hedonic consumption, impulsive consumption, and social sharing). 200 valid respondents were collected via cross-sectional online survey websites. The research results provide empirical evidences to support most of our hypotheses, indicating that hedonic consumption and social sharing behavior are determined by emotional energy and cognitive assimilation. These cognitive and emotional states, are influenced by information quality, broadcaster attractiveness, and para-social…

A number of specialized information systems for the digitally disadvantaged (SISD) have been developed to offset the limitations of people less able to participate in the information society. However, contributions from social identity theory and social markedness theory indicate that SISD can activate a stigmatized identity and thus be perceived unfavorably by their target audience. We identify two mechanisms by which functional limitations affect a digitally disadvantaged person’s adoption decision: (1) adoption decision as shaped through technology perceptions (i.e., perceived usefulness, perceived ease of use, and perceived access barriers), and (2) adoption decision as shaped through marked status awareness (i.e., stigma consciousness). We test our contextualized research model on digitally disadvantaged users with physical and/or sensory disabilities. Results of our mediation analysis show that the individuals who have the most to gain from SISD use (i.e., those with greater perceived functional limitations) are doubly disadvantaged: as a group, they find it more challenging…

Despite years of decision support systems (DSS) research, DSS artifacts are frequently criticized for lacking practitioner relevance and for neglecting configurability and contextual dynamism. Tailoring in end-user contexts can produce relevant emergent DSS artifacts, but design theory for this is lacking. Design science research (DSR) has important implications for improving DSS uptake, but generally this has not been promoted in the form of metadesigns with design principles applicable to other DSS developments. This paper describes a metadesign theory for tailorable DSS, generated through action design research studies in different primary industries. Design knowledge from a DSS developed in an agricultural domain was distilled and generalized into a design theory comprising: (1) a general solution concept (metadesign), and (2) five hypothesized design principles. These were then instantiated via a second development in which the metadesign and design principles were applied in a different domain (forestry) to produce a successful DSS, thus testing the metadesign and validating…

In the wake of an ongoing digital transformation organizations are seeking to understand and generate new data-driven business models. While data is a key resource, generating value from data is a key challenge for innovating data-driven business models. Extant tools for business model representation offer little help. In this paper, we propose the design of a Data Insight Generator (DIG) artefact that can support the design process of data-driven business models at a crucial step: connecting data to value propositions. The artefact is positioned as a complement to the Business Model Canvas, the most widely used tool for business model representation. The DIG connects two key elements of the business model, namely key resources and value proposition through six data-specific elements. Further, the DIG supports an iterative process of discovery for these elements as a boundary object between business and data science/IT participants of business model innovation. Based on a formative evaluation we demonstrate the usability and utility of the DIG.

Firms currently use teams extensively to accomplish organizational objectives. Furthermore, gamification has recently attracted much attention as a means of persuading employees and customers to engage in desired behaviors. Despite the importance of teams and the growing interest in gamification as a persuasion tool, past researchers have paid little attention to team-based gamification from a multilevel perspective. Based on motivational consistency theories, we hypothesize that at the team level, team performance has a positive effect on team cohesion. Drawing on the elaboration likelihood model (ELM), we further hypothesize two cross-level effects in the context of team-based gamified training: first, that team cohesion positively moderates the relationship between utilitarian perceptions (i.e., perceived quality of learning) and attitude; and second, that team cohesion negatively moderates the relationship between hedonic perceptions (i.e., perceived enjoyment of learning) and attitude. We tested our research model using an enterprise resource planning (ERP) simulation game…

Conversational agents (CA), i.e. software that interacts with its usersthrough natural language, are becoming increasingly prevalent in everyday lifeas technological advances continue to significantly drive their capabilities. CAexhibit the potential to support and collaborate with humans in a multitude oftasks and can be used for innovation and automation across a variety of businessfunctions, such as customer service or marketing and sales. Parallel to theincreasing popularity in practice, IS researchers have engaged in studying avariety of aspects related to CA in the last few years, applying different researchmethods and producing different types of theories. In this paper, we review 36studies to assess the status quo of CA research in IS, identify gaps regarding boththe studied aspects as well as applied methods and theoretical approaches, andpropose directions for future work in this research area.

Digital entrepreneurship—the intersection of digital technologies and entrepreneurship— is gaining increasing importance in the global economy and scholarly community. This PDW set out to establish a community platform for and shared understanding amongst information systems researchers who are interested in shaping the future of digital entrepreneurship research within and beyond the discipline’s boundaries. The simple framework presented in this short paper represents the first step of this endeavor and served as the foundation to structure thinking and discussions at the PDW. The framework identifies three fundamental dimensions of the digital entrepreneurship phenomenon—digital technologies as enablers, outcomes, or contexts of entrepreneurship processes—that form distinct sub-themes of digital entrepreneurship research and illustrates potential research topics that flow from each of them and their intersections.

How information systems impact task performance has attracted a significant amount of attention from information systems researchers and generated high interest among practitioners. A commonly accepted view is that the potential of information systems must be realized through system use. Nevertheless, existing findings regarding the impact of system use on task performance are not yet conclusive. We attributed this to the various conceptualizations of system use and the unclear mechanisms through which system use influences task performance. Thus, this research attempts to create a better understanding of how system use influences task performance. To this end, we developed an exploitative-explorative system use framework in order to reconcile the various conceptualizations of system use and to depict how both exploitative and explorative system use influences task performance through impacting task innovation, management control, and task productivity. We created an instantiation of the framework using USAGE (exploitative system use) and adaptive system use (ASU, explorative system…

Nudging has become a well renowned concept in policymaking around the globe, supporting active behavior changes like higher retirement saving, healthier nutrition or saving natural resources. Stemming from the behavioral economics (BE), nudge-interventions aim at changing individuals’ behaviors without limiting their freedom of choice, exerting coercion or significantly changing economic incentives. As nudging still impairs individuals’ autonomy, the concept has evoked heated debates about its ethical acceptability. Recently, IS scholars have begun to recognize the potential of nudging in digital contexts leading to the notion of digital nudging. The importance of ethical considerations when designing and implementing nudge-interventions has been acknowledged, however, a detailed analysis of these concerns in digital environments is still missing. In this article, we summarize, transfer and discuss the ethical considerations raised in BE in light of digital contexts. Three important ethical considerations for digital nudges are discussed: (1) Preserving individuals’ freedom of…

The objective of our paper is to conceptually and empirically challenge the idea of general information security policy (ISP) compliance. Conceptually, we argue that general ISP compliance is an ill-defined concept that has minimal theoretical usefulness because the policy-directed security actions vary considerably from threat to threat in terms of time, difficulty, diligence, knowledge, and effort. Yet, our senior IS scholars’ basket of journals has a strong preference to publish models in which the authors speculate that their findings are generalizable across all (or many) threats and controls contained in an organization’s ISP document. In our paper, we argue that compliance with each of the mandatory threat-specific security actions may require different (as opposed to similar) explanatory models, which makes constructing a universal model of ISP compliance problematic. Therefore, we argue that future ISP compliance literature will be more valuable if it focuses on the mechanisms, treatments, and behavioral antecedents associated with the required actions around specific…

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in…

With the increase in automated decision making using predictive analytics, the aspect of fairness of the resulting predictions for specific groups is increasingly considered in research and practice. Currently, the actual trade-off to achieve fairness, or a certain level of fairness, is not well understood, other than that an increase in fairness typically decreases other predictive analytics metrics such as accuracy. To enable a systematic evaluation of potential trade-offs between fairness and other metrics, a framework for exploring Algorithmic Fairness is proposed. Using a combination of multi-objective optimization and Pareto fronts, the framework allows for the exploration of fairness-performance trade-offs and enables the systematic comparison of different algorithmic techniques to increase fairness. A case study compares several fairness metrics and different algorithmic techniques, provides insight into trade-offs found between metrics, and shows how the framework can be leveraged to find a 'best' level of fairness for a given scenario.

Managing cultural change is crucial for the success of digital transformation. Yet while previous research acknowledges the importance of organizational culture for digital transformation, the majority of research remains largely silent on the specifics of culture change in the context of digital transformation and its management. We aim to gain first insights into the specifics of digital culture change and derive a characterization along four dimensions of change stemming from organizational transformation literature. Following a qualitative multiple-case study approach, we investigated digital culture change programs of 11 firms. Digital culture change is found to be characterized by a disrupted, constantly changing environment and an increased importance as well as intensified application of digital technologies. With this research, we contribute to digital transformation literature by providing first insights and deriving a characterization of digital culture change as theoretical basis for future research.

The digital age requires companies to invest in value-creating rather than routine activities to drive innovation as a future source of competitiveness and business success. Thus, many companies are reluctant to invest in large-scale, costly backend integration projects and seek adaptable solutions to automate their front-office activities. Bridging artificial intelligence and business process management, robotic process automation (RPA) provides the promise of robots as a virtual workforce that performs these tasks in a self-determined manner. Many studies have highlighted potential benefits of RPA. However, little data is available on operationalizing and automating RPA to maximize its benefits. In this paper, we shed light on the automation potential of processes with RPA and operationalize it. Based on process mining techniques, we propose an automatable indicator system as well as present and evaluate decision support for companies that seek to better prioritize their RPA activities and to maximize their return on investment.

This paper provides a framework of factors which positively influence a company’s ability to successfully initialize digital transformation. It was motivated by empirical data and cases which describe the phenomenon that, although a majority of organizations is aware of the necessity of engaging in digital transformation, only a small fraction can successfully initialize associated initiatives. Following a design science approach, a framework describing four categories and nine underlying factors was developed through a structured literature review, and then evaluated for its practical applicability and relevance in semi structured expert interviews. Whilst empirical data indicates that the categories defined within the initial version of the framework allow a holistic categorization of all enabling factors, the set of enabling factors deducted from literature was shown to be relevant, but not holistic.

While the co-creation of value between provider and customer(s) is a common theme in service research, continued theoretical conceptualization is required to guide more effective design of services. In order to advance understanding of value co-creation, we have conducted a pre-study consisting of 16 interviews in the application domain of data-driven services—services that support the decision-making process of customers through the provision of data and analytics. Adopting the theoretical lens of joint spheres, we present empirical evidence that the proportions of the joint sphere can vary with differences in interaction, access to customer processes or behaviors and decision power. As a next step, we propose a final case study phase to further explore factors that influence the proportion of joint sphere and, thus, the provider’s real value creation. Understanding how real value creation is impacted by the resource integration of provider and customer(s) will ultimately enable the purposeful design of data-driven services for effective value co-creation.

Prompted by perceived shortcomings of prevailing conceptualizations of digital technology in IS, we propose a theory aimed at capturing both the ontological complexity of digital objects qua objects, and how their identity and use is bound up with various social associations. We begin with what it is to be an object, the differences between material and nonmaterial objects, and various categories of nonmaterial objects including syntactic objects and bitstrings. Building on these categories we develop a conception of digital objects and a novel “bearer” theory of how material and nonmaterial objects combine. The role of computation is considered, and how the identity and system functions of digital objects flow from their social positioning in the communities in which they arise. Various implications of the theory are identified, focusing on its use as a conceptual frame through which to view digital phenomena, and its potential to inform existing perspectives with regard both to how digital technology per se and the relationship between people and digital technology should be…

Giving voice to the voiceless: The use of digital technologies by marginalized groups

In multiple research areas, scholars try to find determinants for business performance. Especially for entrepreneurship, this is of interest as failure rates are high. Qualitative research demonstrates that a firm’s business model influences its performance. However, research lacks large-scale quantitative studies to analyze if a firm’s applied business model explains heterogeneity in business performance. Therefore, this research builds on a dataset of 500 startups and analyzes the relationship of their applied business model patterns and their business performance (i.e., survival as proxy). Two business model patterns are significantly correlated with a higher survival rate (i.e., Freemium and Subscription), while four patterns are significantly correlated with a lower survival rate (i.e., Cross Selling, Hidden Revenue, Layer Player, and No Frills). For literature, we enrich qualitative research with statistical evidence that business models matter for business performance and strengthen the concept’s role as a useful theoretical construct in management and entrepreneurship…

The overall goal of this design science research project is to design and evaluate a chatbot-based learning system that is able to support novice programmers to learn to write software code in formal learning settings. Our results indicate that such a conversational intelligent programming tutor is suited to take over tasks of teaching assistants in times when no human teaching assistant or lecturer is available for help, e.g., due to resource constraints. For instance, our so-called ‘Coding Tutor’ is able to respond to open-ended knowledge questions, to assess submitted source code automatically or to guide students step-by-step through programming exercises using natural language communication. In addition to providing our situated software artifact, we document our results based on the core components of a design theory as proposed by Gregor and Jones (2007). Thus, we provide the first step toward a nascent design theory for chatbot-based learning systems in IS education.

Organizations appear to implement blockchain solutions based on fear of missing out instead of a clear understanding of blockchain usefulness. Actually, ninety percent of current blockchain projects do not need a blockchain to meet their requirements. Therefore, we employ a Design Science Research approach to develop a framework for evaluation of blockchain implementations. The framework incorporates common factors of blockchain decisions, including blockchain innovation, blockchain design, inter-organizational integration, and implementation environment. We contribute to the scientific literature by structuring previous research efforts in a four-step framework, which provides a fruitful ground for future conceptual and empirical studies. For practitioners, the framework is useful to identify blockchain projects that facilitate purposeful blockchain adoption.

Owing to technological advancements, individuals can increasingly automate and delegate private decisions. However, prior research shows that decision-makers tend to prefer human decision support despite the superiority of algorithms. Further, individuals prefer retaining control of decisions despite increased effort. We propose a model explaining underlying considerations of these paradoxes in decision support acceptance. In a vignette-based experiment in the context of investment management, we test the model to explain trade-offs in varying levels of automation and user control and analyze effects on the intention to utilize decision support. Results provide support for a positive effect of automation on performance expectancy and a negative effect of user control on perceived risk. These findings support the idea of increased automation in decision-making while letting users retain control over the process. This study extends our understanding of decision support in private contexts and holds implications for providers of decision support systems, particularly robo-advisors.

Speech interaction is becoming increasingly sophisticated and prevalent in everyday life due to recent advances in the area of artificial intelligence and high adoption rates of voice assistants (VAs). However, besides using a VA, individuals typically have various alternatives when pursuing certain tasks. To better understand when and why users choose speech interaction over traditional user interfaces, we conducted 31 one-to-one interviews. We analyzed the benefits and costs that users evaluate when using VA by applying means-end chain theory and value-focused thinking. Our findings reveal five fundamental (efficiency, convenience, ease of use, less cognitive effort, and enjoyment) and twelve means objectives that users evaluate when deciding on whether to use their VA or not. The resulting means-ends objectives network informs researchers by providing a comprehensive overview of relevant factors regarding VA use. Practitioners may learn how to further improve and sell VA characteristics.

An organization’s ability to successfully manage information security incidents is determined by the actions of its employees, as well as the actions of various groups of employees within its organizational boundaries. To date, information security research has primarily focused on individual-level phenomena and has not yet explored group-level phenomena such as how employee groups recognize and respond to security incidents in a way that is consistent with the organization’s security goals and objectives. The current study addresses this gap, thereby answering the research call for group-level security research perspectives. The present study explores how employee groups formulate their collective security efficacy, which influences how group members recognize and respond to information security incidents. Using a case study of a large healthcare research organization (HRO), we analyze two security incidents, a malware attack, and a physical security breach, to identify a unique set of ecological and social properties of employee groups that are salient to their collective security…

Software that interacts with its users through natural language, so-called conversational agents (CAs), is permeating our lives with improving capabilities driven by advances in machine learning and natural language processing. For organizations, CAs have the potential to innovate and automate a variety of tasks and processes, for example in customer service or marketing and sales, yet successful design remains a major challenge. Over the last few years, a variety of platforms that offer different approaches and functionality for designing CAs have emerged. In this paper, we analyze 51 CA platforms to develop a taxonomy and empirically identify archetypes of platforms by means of a cluster analysis. Based on our analysis, we propose an extended taxonomy with eleven dimensions and three archetypes that contribute to existing work on CA design and can guide practitioners in the design of CA for their organizations.