Business & Information Systems Engineering最新文献

With growing awareness of sustainability and convenience expectations, customers are increasingly demanding integrated and seamless mobility in the form of mobility-as-a-service (MaaS). However, as centralized MaaS platforms have thus far failed to integrate a critical share of mobility service providers (MSPs), travelers lack opportunities to efficiently combine the various mobility services required for seamless end-to-end itinerary coverage. Particularly, MSPs often refuse to collaborate by devolving control over customer interfaces or sensitive data owing to threats of market power concentration. While alternative blockchain-based approaches aim to provide equal market access, they cannot sufficiently align competing business goals and face substantial problems resulting from the replicated processing of sensitive data. Both researchers and practitioners have recently suggested decentralized digital identity management enabled by digital wallets as a promising mechanism to exchange verifiable identity attributes while mitigating problems related to data aggregation. Following a design science research approach, the article accordingly explores how digital wallets can address the shortcomings of existing approaches to MaaS. It contributes a novel IS architecture and principles for a design at the nexus of centralized and decentralized solutions to mitigate tensions between cooperation and competition. Further, the findings indicate that when building decentralized solutions, one should also consider components beyond blockchain and smart contracts.

This study draws on signaling theory to investigate the effect of hedonic signals in crowdfunding projects on funding performance. It compares the effect of hedonic signals across reward-, equity-, and donation-based crowdfunding platforms by combining archival data from 18 platforms and a large-scale panel of 64 experts that rate the strength of hedonic signals in 108 crowdfunding projects. Through the application of mixed linear modeling, the findings indicate a positive influence of stronger hedonic signals on funding performance. However, there are substantial differences across platform types. Increasing the strength of hedonic signals by one standard deviation increases funding performance by 28.9% on reward platforms, while there are no systematic effects on equity and donation platforms. This study contributes to existing crowdfunding research by clarifying the role of hedonic signals in crowdfunding and shedding light on the increasing need to better consider the characteristics of different crowdfunding platforms in crowdfunding research.

Cybersecurity threats faced by organizations are growing in volume and sophistication, with human issues being a significant reason underlying these threats. Organizations report the need for skills development to face these challenges. This need is addressed in this study by developing game-based learning (GBL) artefacts for process-related cybersecurity issues. The artefacts are developed using ADR (Action Design Research). These artefacts employ procedures prescribed by NIST (National Institute of Standards and Technology) and CCMP (Cyber Crisis Management Plan) to teach users cybersecurity concepts such as cyber crisis management, malware incident forensics, incident handling and password management. The paper details the artefacts and design process, highlights the implications for practitioners and academicians in GBL, and also describes a nascent theory for Design Science Research (DSR).

Abstract

Contextualization is an important challenge in process mining. While Internet of Things (IoT) devices are collecting increasing amounts of data on the physical context in which business processes are executed, the IoT and process mining fields are still considerably disintegrated. Important concepts such as event or context are not understood in the same way, which causes confusion and hinders cooperation between the two domains. Accordingly, in the paper, a consolidated model to bridge the conceptualization gap between the IoT and process mining fields, based on IoT ontologies and business process context models, is presented. This consolidation based on an initial model was obtained after an extensive validation both with an expert panel and with case studies. The results of the expert survey show that the model properly describes the links between the IoT and process mining and that it has added value for IoT process mining. Furthermore, the model was refined according to the experts’ feedback. Accordingly, the paper’s key contribution consists of a common reference model that can instigate true interdisciplinary research connecting IoT and process mining.

Workarounds, or deviations from standardized operating procedures, can indicate discrepancies between theory and practice in work processes. Traditionally, observations and interviews have been used to identify workarounds, but these methods can be time-consuming and may not capture all workarounds. The paper presents the Semi-automated WORkaround Detection (SWORD) framework, which leverages event log traces to help process analysts identify workarounds. The framework is evaluated in a multiple-case study of two hospital departments. The results of the study indicate that with SWORD we were able to identify 11 unique workaround types, with limited knowledge about the actual processes. The framework thus supports the discovery of workarounds while minimizing the dependence on domain knowledge, which limits the time investment required by domain experts. The findings highlight the importance of leveraging technology to improve the detection of workarounds and to support process improvement efforts in organizations.

The majority of (business) processes described in literature are discrete, i.e., they result in an identifiable and distinct outcome such as a settled customer claim or a produced part. However, there also exists a plethora of processes in process and control engineering that are continuous, i.e., processes that require real-time control systems with constant inlet and outlet flows as well as temporally stable conditions. Examples comprise chemical synthesis and combustion processes. Despite their prevalence and relevance a standard method for modeling continuous processes with BPMN is missing. Hence, the paper provides BPMN modeling extensions for continuous processes enabling an exact definition of the parameters and loop conditions as well as a mapping to executable processes. The BPMN modeling extensions are evaluated based on selected use cases from process and control engineering and interviews with experts from three groups, i.e., process engineers and two groups of process modelers, one with experience in industrial processes and one without. The results from the expert interviews are intended to identify (i) the key characteristics for the representation of continuous processes, (ii) how experts evaluate the current usability and comprehensibility of BPMN for continuous processes, and (iii) potential improvements can be identified regarding the introduced BPMN modeling extensions.

Abstract

Complex digitalization and sustainability challenges shape today’s management agendas. To date, the dedication of Information Systems research to both challenges has not been equal in terms of effort and reward. Building capabilities to leverage the synergetic potential of digital and sustainability transformation may enhance organizational performance and imply new value creation for the common good. To uncover such synergetic potential, this work conceptualizes the “twin transformation” construct as a value-adding reinforcing interplay between digital transformation and sustainability transformation efforts that improve an organization by leveraging digital technologies to enable sustainability and to guide digital progress by leveraging sustainability. The twin transformation conceptualization is complemented with a capability framework for twin transformation drawing from dynamic capability theory. This work contributes to descriptive knowledge of the interplay between digital transformation and sustainability transformation, setting a foundation for further theorizing on twin transformation and enabling organizations to twin transform.

Studying the behavior of users in software systems has become an essential task for software vendors who want to mitigate usability problems and identify automation potentials, or for researchers who want to test behavioral theories. One approach to studying user behavior in a data-driven way is through the analysis of so-called user interaction (UI) logs, which record the low-level activities that a user performs while executing a task. In the paper, the authors refer to the analysis of UI logs as User Behavior Mining (UBM) and position it as a research topic. UBM is conceptualized by means of a four-component framework that elaborates how UBM data can be captured, which technologies can be applied to analyze it, which objectives UBM can accomplish, and how theories can guide the analytical process. The applicability of the framework is demonstrated by three exemplary applications from an ongoing research project with a partner company. Finally, the paper discusses practical challenges to UBM and derives an agenda for potential future research directions.