Workarounds as generative mechanisms for bottom-up process innovation—Insights from a multiple case study

2023 | Information Systems Journal | Citations: 0

Authors: Bartelheimer, Christian; Wolf, Verena; Beverungen, Daniel

Abstract: Workarounds are goal-driven deviations from the standard operating procedures pe ... Expand

Abstract: Workarounds are goal-driven deviations from the standard operating procedures performed to overcome obstacles constraining day-to-day work. Despite starting as temporary fixes, they can become established across an organisation and trigger the innovation of processes and IT artefacts that can resolve misfits permanently. Although prior research has elicited antecedents and types of workarounds, it is not known how workarounds diffuse in an organisation and, thereby, innovating co-workers' activities, IT artefacts, and organisational structures. The results of our multiple two-year case study provide unique empirical insights into the diffusion of workarounds and how they can act as generative mechanisms for bottom-up process innovation. Collapse

Topics: business process management innovation management business process innovation organization structure business process redesign

Methods: design artifact case study business process modeling qualitative interview qualitative coding

Theories: socio technical theory structuration theory

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

Empowering Users to Create Augmented Reality-Based Solutions – Deriving Design Principles for No-Code AR Authoring Tools

2023 | International Conference on Information Systems | Citations: 0

Authors: Bräker, Julia; Hertel, Julia; Semmann, Martin

Abstract: Grounded on an experimental study with 18 participants, we derive 15 design pri ... Expand

Abstract: Grounded on an experimental study with 18 participants, we derive 15 design principles for no-code AR authoring tools in an organizational setting. The study consists of two distinct treatments that aim to augment lightweight processes with AR. The outcomes are two interactive tutorials utilizing AR instructions. Following the no-code approach, the participants were empowered to create relevant AR content using a reduced interface and no need for advanced configurations or coding. The study thus combines two research streams with the aim of better understanding mechanisms for AR use in a professional context. As prior work has shown, despite the potential benefits, the adoption of AR authoring tools is limited because ramping up AR to productive use is heavily dependent on consulting and custom software solutions. Our novel approach bears the potential to broaden application domains and empower professionals to apply AR. Collapse

Topics: mobile application usability user experience information system use augmented reality

Methods: design principle design artifact survey cognitive walkthrough design science

User-Centered Requirements for Augmented Reality as a Cognitive Assistant for Safety-Critical Services

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Bräker, Julia; Osterbrink, Anna; Semmann, Martin; Wiesche, Manuel

Abstract: Augmented reality (AR) is widely acknowledged to be beneficial for services with ... Expand

Abstract: Augmented reality (AR) is widely acknowledged to be beneficial for services with exceptionally high requirements regarding knowledge and simultaneous tasks to be performed and are safety-critical. This study explores the user-centered requirements for an AR cognitive assistant in the operations of a large European maritime logistics hub. Specifically, it deals with the safety-critical service process of soil sounding. Based on fourteen think-aloud sessions during service delivery, two expert interviews, and two expert workshops, five core requirements for AR cognitive assistants in soil sounding are derived, namely (1) real-time overlay, (2) variety in displaying information, (3) multi-dimensional tracking, (4) collaboration, and (5) interaction. The study is the first one on the applicability and feasibility of AR in the maritime industry and identifies requirements that impact further research on AR use in safety-critical environments. Collapse

Topics: augmented reality logistics management mobile phone situational awareness implicit knowledge

Methods: cognitive walkthrough qualitative interview design science case study protocol analysis

Pay-for-performance schemes and hospital HIT adoption

2023 | Decision Support Systems | Citations: 0

Authors: Cheng, Ningning; Li, Hongfei; Bang, Youngsok

Abstract: Pay-for-performance (P4P) schemes are implemented to incentivize or penalize hos ... Expand

Abstract: Pay-for-performance (P4P) schemes are implemented to incentivize or penalize hospitals for their safe care­ giving. Given that health information technology (HIT) results in better healthcare outcomes, P4P schemes are expected to promote hospital HIT adoption. However, P4P schemes could also discourage hospitals from adopting HIT because they may take away resources initially allocated for HIT adoption. This paper is one of the first to empirically investigate the double-edged role of P4P schemes in HIT adoption. We leverage a natural experiment in the US healthcare system, which introduced P4P schemes in 2013. Our empirical analysis reveals an unintended side effect of P4P schemes that could potentially impede HIT adoption. We also find that P4P schemes have a negative spillover effect on nonparticipating hospitals in the same multihospital system (MHS) as participating hospitals, particularly for small nonparticipating hospitals. Our findings provide important impli­ cations for health policy design and MHS management. Collapse

Topics: health information system database system technology adoption organization learning missing data

Methods: longitudinal research experimental group natural experiment survey robustness check

Challenges in the Model Development Process: Discussions with Data Scientists

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Gerhart, Natalie; Torres, Russell; Giddens, Laurie

Abstract: Businesses are increasingly seeking out analytics to improve decision-making pr ... Expand

Abstract: Businesses are increasingly seeking out analytics to improve decision-making processes, although often with hesitations. Decision makers often do not have the sophisticated analytical skills needed to fully understand the analytics process. Contrastingly, data scientists may lack the business acumen needed to fully grasp the business context of the decision. In this research, we consider the perspective of the data scientist through a series of interviews to draw out challenges in the analytics process. We use principal-agent theory as a lens to shape our understanding of the conflict that arises due to goal misalignment and information asymmetry between the principal and agent. Findings are presented in the CRISP-DM process and a future research agenda is proposed. Collapse

Topics: decision making implicit knowledge data quality organizational impact organizational memory

Methods: Cross-Industry Standard Process for Data Mining qualitative interview theory development mathematical model literature study

Theories: agency theory lemon market theory theory of bounded rationality

AI UNCERTAINTY IN EXPERT DECISION-MAKING: A QUALITATIVE EVIDENCE SYNTHESIS

2023 | European Conference On Information Systems | Citations: 0

Authors: Grundstrom, Casandra; Mohanty, Pooja; Parmiggiani, Elena

Abstract: As Artificial Intelligence (AI) is becoming more prevalent in everyday work set ... Expand

Abstract: As Artificial Intelligence (AI) is becoming more prevalent in everyday work settings, the Information Systems (IS) discipline is perfectly poised to study the sociotechnical repercussions of AI supported decision-making part of expert's knowledge work. As expert know-how is tacit and socially situated, there are difficulties in capturing the nuances of the emerging human-machine collaborations. In this article, we review how epistemic uncertainty is evident in experts' decision-making practices when using AI tools. Building on rich primary studies, a qualitative evidence synthesis (QES) approach was used to synthesize relevant literature on this topic. Our findings unravel sources of expert uncertainty which point to strategies experts use to cope with uncertainty, and what attitudes experts have towards uncertainty when making complex decisions with AI-tools. Collapse

Topics: artificial intelligence decision making decision support knowledge sharing implicit knowledge

Methods: literature sample synthesis computational algorithm structured literature research literature filtering

The Currency of Wiki Articles – A Language Model-based Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Heinrich, Bernd; Huber, Maximilian; Krapf, Thomas; Schiller, Alexander

Abstract: Wikis are ubiquitous in organisational and private use and provide a wealth of ... Expand

Abstract: Wikis are ubiquitous in organisational and private use and provide a wealth of textual data. Maintaining the currency of this textual data is important and difficult, requiring large manual efforts. Previous approaches from literature provide valuable contributions for assessing the currency of structured data or whole wiki articles but are unsuitable for textual wiki data like single sentences. Thus, we propose a novel approach supporting the assessment and improvement of the currency of textual wiki data in an automated manner. Grounded on a theoretical model, our approach makes use of data retrieved from recently published news articles and a language model to determine the currency of fact-based wiki sentences and suggest possible updates. Our evaluation conducted on 543 sentences from six wiki domains shows that the approach yields promising results with accuracies over 80% and thus is well-suited to support assessment and improvement of the currency of textual wiki data. Collapse

Topics: wiki Wikipedia application programming interface implicit knowledge document management system

Methods: language model BERT literature sample stochastic model

The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

2023 | Information Systems Frontiers | Citations: 0

Authors: Harfouche, Antoine; Quinio, Bernard; Saba, Mario; Saba, Peter Bou

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accum ... Expand

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice. Collapse

Topics: artificial intelligence organizational memory explicit knowledge human in the loop human computer interaction

Methods: computational algorithm action research qualitative interview participant observation statistical hypothesis test

Theories: SECI model of knowledge dimensions organizational knowledge creation

ARTIFICIAL INTELLIGENCE-AUGMENTED DECISION MAKING IN SUPPLY CHAIN MONITORING: AN ACTION DESIGN RESEARCH STUDY

2023 | European Conference On Information Systems | Citations: 0

Authors: Herath Pathirannehelage, Savindu; Jóhannsson, Jóhann; Shrestha, Yash; Krogh, Georg von

Abstract: Organizations are progressively adopting hybrid human-artificial intelligence ( ... Expand

Abstract: Organizations are progressively adopting hybrid human-artificial intelligence (AI) systems in decisionmaking processes, with human decisions being augmented by AI insights. Among the promising AI applications in supply chain monitoring (SCMo) are predictive maintenance systems that predict potential device failures and augment maintenance decisions, allowing for timely and efficient interventions. Despite the growing proliferation of such systems, prescriptive knowledge encompassing technical, business, and organizational aspects on how to design, develop, and deploy them in actual operational environments remains limited. To address this shortcoming of evidence-based design principles in practice, our action design research developed a predictive maintenance system that predicts SCMo device failures and augments the maintenance decisions about those devices. By doing so, we outline generalizable design principles to guide prospective predictive maintenance systems in SCMo. Collapse

Topics: artificial intelligence predictive maintenance organizational context decision making systems evaluation

Methods: machine learning design principle design artifact artificial neural network random forest classification

Theories: evidence-based design