The Influence of Mental Models on Employee-Driven Digital Process Innovation during Times of a Crisis

Digital technologies democratise the development of digital innovation. The resulting employee-driven digital innovation has become a major driver for digital transfor-mations and especially important during crisis times, such as the COVID 19 pandemic. To better understand cognitive factors influencing employee-driven digital process innovation (EDPI), we investigate the role of individual mental models for EDPI during times of a crisis compared to ‘normal’ times. Drawing from longitudinal data before and during the COVID 19 crisis, we find mental models having a significant influence on EDPI behaviour during ‘normal’ times. This relationship, however, loses robustness during the crisis, when employees with more accurate mental models show significant less EDPI behaviour before slowly recovering. We relate these findings to the mental models’ explanatory power and derive recommendations for management. Our study contributes explanatory knowledge on employee-driven digital innovation and related cognitive antecedents

TAXONOMY RESEARCH IN INFORMATION SYSTEMS: A SYSTEMATIC ASSESSMENT

Today’s world is changing at unprecedent speed and scale becoming more complex to understand. Taxonomies represent an important tool for understanding and analyzing complex domains based on the classification of objects. In the Information Systems (IS) domain, Nickerson et al. (2013) were the first to propose a taxonomy development method, addressing the observation that many taxonomies have been developed in an ‘ad-hoc’ approach. More than five years after Nickerson et al.’s (2013) publication, we examined to what extent recently published taxonomy articles account for existing methodological guidance. Therefore, we identified and reviewed 33 taxonomy articles published between 2013 and 2018 in leading Information Systems journals. Our results were sobering: We found few taxonomy articles that followed any specific development method. Although most articles correctly understood taxonomies as conceptually or empirically derived groupings of dimensions and characteristics, our study revealed that the development process often remained opaque and that taxonomies were hardly evaluated. We discuss these findings and potential root causes related to method design, method adoption, and the general positioning of taxonomy research in the IS domain. Our study proposes stimulating questions for future research and contributes to the IS community’s progress towards methodologically well-founded taxonomies

The effects of digital technology on opportunity recognition

Recognizing opportunities enabled by digital technology (DT) has become a competitive necessity in today’s digital world. However, opportunity recognition is a major challenge given the influence of DT, which not only disperses agency across various actors, but also blurs boundaries between customers, companies, products, and industries. As a result, traditional entrepreneurship knowledge needs to be rethought and the effects of DT on opportunity recognition need to be better understood. Drawing from opportunity recognition theory – as one of the central theories in the entrepreneurship domain – this study builds on a structured literature review to identify and explain three direct as well as three transitive effects of DT on opportunity recognition. These effects have been validated with real-world cases as well as interviews with academics and practitioners. In sum, this study contributes to descriptive and explanatory knowledge on the evolution from traditional to digital entrepreneurship. As a theory for explaining, the findings extend opportunity recognition theory by illuminating how and why DT influences opportunity recognition. This supports research and practice in investigating and managing opportunities more effectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12599-021-00733-9

DIGITALLY SOCIAL: REVIEW, SYNTHESIS, AND FUTURE DIRECTIONS FOR DIGITAL SOCIAL INNOVATION

Innovation contributes to solving the grand challenges of our time. Currently, two innovation research streams coexist mostly separated, without leveraging the potential at their interface: 1) Digital innovation using the generative power of digital technologies to trigger novel, incremental and/or disruptive solutions, and 2) social innovation accelerating sustainable development. To leverage the potential of digital innovations for reaching the goals of social innovation, we aim at advancing research on digital social innovation (DSI). A comprehensive literature review reveals 78 current DSI studies. We analyse them via a theory-based multidimensional framework. In that, we bring together both research streams, identify relevant research gaps at their interface, and derive a research agenda based on eight clear-cut research questions for DSI scholars. Our findings guide advancing DSI research and enable practitioners to leverage DSI in light of the current societal challenges

Illuminating Smart City Solutions – A Taxonomy and Clusters

With urban problems intensifying, Smart City solutions are recognized by researchers and practitioners as one of the most promising solutions to make urban areas economically, environmentally, and socially sustainable. While many elements of Smart City solutions have been explored, existing works either treat Smart City solutions as technical black boxes or focus exclusively on Smart City solutions’ technical or non-technical characteristics. Therefore, to conceptualize the unique characteristics of Smart City solutions currently available, we developed a multi-layer taxonomy based on Smart City solution literature and a sample of 106 Smart City solutions. Moreover, we identified three clusters, each covering a typical combination of characteristics of Smart City solutions. We evaluated our findings by applying the Q-sort method. The results contribute to the descriptive knowledge of Smart City solutions as a first step for a theory for analyzing and enable researchers and practitioners to understand Smart City solutions more holistically

Disentangling capabilities for industry 4.0 - an information systems capability perspective

Digital technologies revolutionise the manufacturing industry by connecting the physical and digital worlds. The resulting paradigm shift, referred to as Industry 4.0, impacts manufacturing processes and business models. While the ‘why’ and ‘what’ of Industry 4.0 have been extensively researched, the ‘how’ remains poorly understood. Manufacturers struggle with exploiting Industry 4.0’s full potential as a holistic understanding of required Information Systems (IS) capabilities is missing. To foster such understanding, we present a holistic IS capability framework for Industry 4.0, including primary and support capabilities. After developing the framework based on a structured literature review, we refined and evaluated it with ten Industry 4.0 experts from research and practice. We demonstrated its use with a German machinery manufacturer. In sum, we contribute to understanding and analysing IS capabilities for Industry 4.0. Our work serves as a foundation for further theorising on Industry 4.0 and for deriving theory-led design recommendations for manufacturers

Four Patterns of Digital Innovation in Times of Crisis

Exogenous shocks, such as COVID-19, significantly change fundamental premises on which economies and individual organizations operate. The light-asset nature of digital technologies provides the potential to not only facilitate an immediate crisis response, but also to catalyze novel innovation types to address the societal and economic changes caused by exogenous shocks. As digital innovation became a relevant part of organizations’ COVID-19 responses, and given that a corresponding structured knowledge base did not exist, we found the need to better understand crisis-driven digital innovation. Drawing on prior knowledge from crisis management and organizational ambidexterity as a theoretical lens, we present four patterns of crisis-driven digital innovation, classified along two dimensions: (1) driven by a sense of urgency or ambition and (2) focusing on exploitative or explorative innovation. Based on a thorough analysis of digital innovation cases during the COVID-19 crisis, we illustrate and discuss these four patterns and their emerging properties to explain how and why they led to digital innovation in the context of the crisis. Our work contributes to the explanatory knowledge on digital innovation in times of crisis, helping researchers and practitioners to understand and develop digital innovation in response to exogenous shocks

ECONOMIC PERSPECTIVE ON ALGORITHM SELECTION FOR PREDICTIVE MAINTENANCE

The increasing availability of data and computing capacity drives optimization potential. In the industrial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statistical measures such as absolute and relative prediction errors are considered. However, algorithm selection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Hotelling T² Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambiguous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of more than 17% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithms and supports predictive maintenance investment decisions