On the Combinatory Nature of Knowledge Transfer Conditions: A Mixed Method Assessment

2023 | Information Systems Frontiers | Citations: 1

Authors: Bacon, Emily; Williams, Michael D.; Davies, Gareth H.

Abstract: Organisations are increasingly creating inter-organisational ecosystem partnersh ... Expand

Abstract: Organisations are increasingly creating inter-organisational ecosystem partnerships to innovate openly. Despite effective knowledge management significantly supporting ecosystem infrastructures, empirical insights into the importance of and interdependencies between conditions for successful knowledge exchange across ecosystem contexts remain unexplored within existing literature. This study implements a mixed-method approach to ascertain which conditions are responsible for knowledge transfer success across innovation ecosystems. Interpretive Structural Modelling was employed to analyse questionnaires with key ecosystem stakeholders, in order to impose a hierarchical structure upon the conditions. The configurational nature of these conditions, and their combinations into solutions for success was ascertained through analysing semi-structured interviews using fuzzy-set Qualitative Comparative Analysis. Results reveal multiple, mutually exclusive pathways to knowledge transfer success, grouped into three solution types, increasing understanding of the interrelated nature of the knowledge transfer conditions. Limitations and implications for future research are provided. Collapse

Topics: knowledge sharing innovation management knowledge type open innovation digital transformation

Methods: qualitative interview survey computational algorithm literature study fuzzy set qualitative comparative analysis

Theories: complexity theory

YUMA -An AI Planning Agent for composing IT Services from Infrastructure-as-Code Specifications

2023 | HICSS | Citations: 0

Authors: Baer, F.; Leyer, M.

Abstract: Infrastructure-as-code enables cloud architects to automate IT service delivery ... Expand

Abstract: Infrastructure-as-code enables cloud architects to automate IT service delivery by specifying IT services through machine-readable definition files. To allow for a reusability of the infrastructure-as-code specifications, cloud architects specify IT services as compositions of sub-processes. As the AI planning agents for automated IT service composition proposed by prior research fall short in the infrastructure-as-code context, we design a search-based problem-solving agent named YUMA according to a design science research process to fill this research gap. YUMA holds a search tree reflecting the state space and transition model. It includes an algorithm for building the search tree and two algorithms for determining the minimum composition plan. The underlying IT service composition problem is explicated for the infrastructure-as-code context and formulated as a search problem. The results of the demonstration and evaluation show that YUMA fulfills the requirements necessary to solve this problem and digitizes an important task of cloud architects. Collapse

Topics: cloud computing artificial intelligence problem solving programming language source code

Methods: computational algorithm design artifact simulation reinforcement learning design process

Exploring the Interactions between Social Movement Activists and Target Organizations on Social Media

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bakhshi, Fateme; Faik, Isam

Abstract: Social movements are recognized as significant agents of change in modern socie ... Expand

Abstract: Social movements are recognized as significant agents of change in modern societies. Their influence is increasingly channeled through social media platforms to mobilize support and direct collective action toward targeted organizations. However, targeted organizations also resort to social media channels to counter the actions of social movements that are directed at them. There is little research on organizations' responses to movement's actions on social media. This study endeavors to explore the dynamics of interactions between social movement activists and their target organizations by utilizing narrative network analysis. Drawing on framing theory, this research endeavors to elucidate how social movement actors employ various frames. Specifically, we examine the Twitter accounts of various Canadian banks and activists targeting them for their investments in fossil fuels. We analyze the narrative networks and counter-networks to develop a theoretical understanding of how the social media interactions between activists and target organizations evolve over time. Collapse

Topics: social media Twitter web page design thinking

Methods: narrative network analysis network analysis computational algorithm event log

Theories: framing theory

Gold Rushes and Bandwagons: Blockchain’s Markets and Developers

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Barraza, Benjamin; Ravindran, Kiron; Arenas, Alvaro

Abstract: What drives Open-Source Software (OSS) development communities to participate i ... Expand

Abstract: What drives Open-Source Software (OSS) development communities to participate in Blockchain projects? Developer participation incentives in OSS are well studied but the nature of this incentive changes with a novel addition of the token or cryptocurrency. This longitudinal study followed nearly 6,000 OSS blockchain projects, their prices, and developer movements throughout 2020. We find that asset price does impact developer interest and contributions. We find lagged price movements of tokens statistically significant in later developer activity. Social aspects of developer interest are more complex and shift as we consider projects within different market cap strata. We find that lower profile blockchain projects benefit from both price movements and developer activity. Implications for managers include recruitment efforts and equity compensation. Collapse

Topics: blockchain cryptocurrency

Methods: descriptive statistic longitudinal research time series analysis

COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bartenschlager, Christina C.; Ebel, Stefanie S.; Kling, Sebastian; Vehreschild, Janne; Zabel, Lutz T.; Spinner, Christoph D.; Schuler, Andreas; Heller, Axel R.; Borgmann, Stefan; Hoffmann, Reinhard; Rieg, Siegbert; Messmann, Helmut; Hower, Martin; Brunner, Jens O.; Hanses, Frank; Römmele, Christoph

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map ... Expand

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model. Collapse

Topics: artificial intelligence mobile application pandemic logistics management data integration

Methods: computational algorithm machine learning normalization data transformation support vector machine artificial neural network

Clinical research from information systems practice

2023 | European Journal of Information Systems | Citations: 0

Authors: Baskerville, Richard; vom Brocke, Jan; Mathiassen, Lars; Scheepers, Helana

Abstract: An increasing presence of practitioners with doctoral degrees in Information Sys ... Expand

Abstract: An increasing presence of practitioners with doctoral degrees in Information Systems and related disciplines holds promise to advance Information Systems research. The prospect is to gain more knowledge from the practical experience of developing, using, and managing information systems in context. To scientifically capitalise on this opportunity, this EJIS special issue introduces the research genre of “Information Systems Clinical Research”. The genre presents knowledge generated from practitioner-researcher interventions to achieve desired outcomes in information systems development, use, and management practice contexts. In this editorial, we introduce and conceptualise the genre; we present a research framework that defines its four key elements; and we discuss how to address its key challenges in research projects. As a result, we derive ten criteria for rigorous Information Systems Clinical Research and provide examples on how the articles published in the special issue have addressed these criteria. We conclude with a call to further advance clinical research as an important part of the Information Systems discipline. Collapse

Topics: IS discipline innovation management IT skill systems development digital innovation

Methods: qualitative interview focus group field study literature sample qualitative coding

Optimizing Substance Use Treatment Selection Using Reinforcement Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Baucum, Matt; Khojandi, Anahita; Myers, Carole; Kessler, Larry

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in th ... Expand

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching. Collapse

Topics: electronic health record individual characteristic healthcare data missing data

Methods: hazard function machine learning random forest classification descriptive statistic propensity score method

Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver

Abstract: Because of a growing number of initiatives and regulations, predictions of moder ... Expand

Abstract: Because of a growing number of initiatives and regulations, predictions of modern artificial intelligence (AI) systems increasingly come with explanations about why they behave the way they do. In this paper, we explore the impact of feature-based explanations on users’ information processing. We designed two complementary empirical studies where participants either made incentivized decisions on their own, with the aid of opaque predictions, or with explained predictions. In Study 1, laypeople engaged in the deliberately abstract investment game task. In Study 2, experts from the real estate industry estimated listing prices for real German apartments. Our results indicate that the provision of feature-based explanations paves the way for AI systems to reshape users’ sense making of information and understanding of the world around them. Specifically, explanations change users’ situational weighting of available information and evoke mental model adjustments. Crucially, mental model adjustments are subject to the confirmation bias so that misconceptions can persist and even accumulate, possibly leading to suboptimal or biased decisions. Additionally, mental model adjustments create spillover effects that alter user behavior in related yet disparate domains. Overall, this paper provides important insights into potential downstream consequences of the broad employment of modern explainable AI methods. In particular, side effects of mental model adjustments present a potential risk of manipulating user behavior, promoting discriminatory inclinations, and increasing noise in decision making. Our findings may inform the refinement of current efforts of companies building AI systems and regulators that aim to mitigate problems associated with the black-box nature of many modern AI systems. History: Alessandro Acquisti, senior editor; Jason Chan, associate editor. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Projek 449023539), Volkswagen Foundation (ML2MT), and LeibnizInstitute for Financial Research SAFE. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1199. Collapse

Topics: explainable artificial intelligence artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making

Methods: F-test regression analysis method experiment field study robustness check

SNS Guilt Feelings: Causes and Influence on Discontinuance

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bayor, Laura; Weinert, Christoph; Maier, Christian; Weitzel, Tim

Abstract: Although experiencing guilt feelings after social networking service (SNS) usag ... Expand

Abstract: Although experiencing guilt feelings after social networking service (SNS) usage has become a prevalent phenomenon among SNS users, these SNS guilt feelings -and especially their causes -have received little scholarly attention. This paper therefore focuses on why SNS users may develop SNS guilt feelings and their impact on discontinuance intention. We utilize the lens of normative dissociation to develop a model to identify causes of SNS guilt feelings and apply a quantitative research approach to test the model. We contribute by demonstrating that two identified causes -perceived time spent and perceived procrastination -significantly influence SNS guilt feelings, while perceived meaningfulness does not. Furthermore, SNS guilt feelings influence discontinuance intention and mediate the relationship between several constructs and discontinuance intention. By employing the perspective of normative dissociation, we reinforce the relevance of SNS guilt feelings. Collapse

Topics: Instagram information system use

Methods: survey partial least squares path modeling post-hoc analysis PLS tool partial least squares regression

DLT-based Regulatory Systems Dynamics

2023 | HICSS | Citations: 0

Authors: Beck, Roman; Jain, Geetika

Abstract: In this research, we examine the interplay between ‘actors’ and ‘agents’ in Dist ... Expand

Abstract: In this research, we examine the interplay between ‘actors’ and ‘agents’ in Distributed Ledger Technology (DLT) systems. We identify regulatory interactions between off-chain agents setting the rules, as well as on-chain code as actors regulating the behavior of DLT users. We theorize about the relationship between agents and actors that mutually regulate each other in certain ways through the DLT system and identify the significant dimensions related to the trifecta in which the soft system agent sphere regulation of DLT is likely to interact with the hard system actor sphere regulation by DLT. By proposing the trifecta between DLT design, DLT protocol, and DLT use, we explain the relationship between these three and the role of DLT protocol as a mediator between DLT design and DLT use. Our research sheds light on the dynamics within DLT systems and the regulating forces at play from a systems’ thinking perspective.e. Collapse

Topics: blockchain IT governance distributed ledger technology peer-to-peer model information technology infrastructure

Methods: theory development design artifact structured literature research literature sample system dynamics

Theories: structuration theory