In this paper, we respond to Grover and Lyytinen (2022). We agree with them that the advent of the digital age is calling for a reconsideration of the role of theory and theorizing. We also think their proposal does not go far enough. The time is ripe to question the role of theory in our field more fundamentally. We propose to instead focus on establishing IS research as a platform through which we can collect, organize, and provide access to digital trace data from various sources to analyze contemporary socio-technical phenomena. We believe that such a move allows us to more fully unleash the unique socio-technical competences of our field in the digital age.
Process mining is a fast-growing technology concerned with managing and improving business processes. While the technology itself has been thoroughly scrutinized by prior research, we are only beginning to understand the managerial and organizational implications of process mining. Creating such knowledge is essential for a successful adoption and use of process mining in organizations. We conduct a qualitative-inductive interview study to explore how process mining can be leveraged in organizations. To this end, we systematically examine the needs and experiences of practitioners with process mining at different levels, including heads of process mining, process analysts, and data engineers. Complementing our tutorial, this article provides a theoretical background, outlines our research approach, and presents preliminary findings.
Despite widespread criticism, credit ratings continue to be commissioned and paid for by the firms they ought to scrutinize, raising concerns about the reliability of these issuer-paid ratings. We use an experiment to evaluate whether financial reputation concerns can effectively alleviate rating inflation and find that they are only partially sufficient to discipline rating agencies. However, introducing accountability mechanisms into the rating process effectively reduces rating inflation and almost extinguishes it in our model. Our results emphasize that financial reputation and accountability are important but different factors, which combined can effectively alleviate rating inflation and therefore provide a powerful mechanism of control over rating agencies.
This article explores how the Gender Pension Gap (GPG)—the relative difference in average pension received by men and women—might evolve in the future in various European countries, given past, current, and projected future labour market behaviour and earnings of women and men, and current pension regulations. The GPG reflects career inequalities between women and men, though these are partly mitigated by the redistributive impact of the public retirement pensions. They are further mitigated by survivor benefits. This study aims to document both mechanisms in the projections of the GPG. As the GPG varies widely across European countries, we analyse countries with a high (Luxembourg), high and low middle (Belgium and Switzerland Portugal), and low (Slovenia) GPG. We find that the GPG will fall significantly in all five countries over the coming decades. The fundamental drivers behind this development are discussed. In addition to the base scenario, we simulate two variants to show the impact of the Gender Pension Coverage Gap and of survivor pensions. Additionally, we project the GPG if current labour market gender gaps were to remain at their present level, and, conversely, if these were to disappear overnight. These alternative scenarios, one of which also serves as a robustness test, suggest that the future decline of the GPG is largely the result of labour market developments that have already happened during the past decades.
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such discrepancy has its root cause in the current state-of-the-art, which does not allow to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain—to any potential reader with an interest in this topic. We highlight the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity. Moreover, we elucidate various intrinsic problems affecting real ML deployments in cybersecurity. Finally, we present how various stakeholders can contribute to future developments of ML in cybersecurity, which is essential for further progress in this field. Our contributions are complemented with two real case studies describing industrial applications of ML as defense against cyber-threats.
While artificial intelligence (AI) governance is thoroughly discussed on a philosophical, societal, and regulatory level, few works target companies. We address this gap by deriving a conceptual framework from literature. We decompose AI governance into governance of data, machine learning models, and AI systems along the dimensions of who, what, and how "is governed". This decomposition enables the evolution of existing governance structures. Novel, business-specific aspects include measuring data value and novel AI governance roles.
Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization methods. Our theoretical analysis quantifies the learning behavior of weights of a single spatial filter. It is thus in contrast to a large body of work that discusses statistical properties of weights. It shows that uncorrelated initialization (1) might lead to poor convergence behavior and (2) training of (some) parameters is likely subject to slow convergence. Empirical analysis shows that these findings for a single spatial filter extend to networks with many spatial filters. The impact of (correlated) initialization depends strongly on learning rates and l2-regularization.
This article interrogates the term “periphery” by examining the forms of urbanisation unfolding in the Gauteng City‐Region (GCR) of South Africa. Among the urbanisation processes identified, it focuses on two, situating them among debates on informality and defining new vocabularies of urbanisation. Aligned with discussions of peripherality as a social phenomenon, the article first depicts how some marginalised groups of people using transversal means carve out “toeholds” near urban centralities and opportunities. Second, it conveys how peripherality is also a geographical phenomenon, describing “aspirational” mass housing for the lower‐middle class on urban peripheries that can generate unexpected forms of precarity. The article concludes that toehold urbanisation and aspirational urbanisation drive peripheralisation in the GCR, and considers the implications of these concepts for critical geography and urban studies.
Mean-semivariance and minimum semivariance portfolios are a preferable alternative to mean-variance and minimum variance portfolios whenever the asset returns are not symmetrically distributed. However, similarly to other portfolios based on downside risk measures, they are particularly affected by parameter uncertainty because the estimates of the necessary inputs are less reliable than the estimates of the full covariance matrix. We address this problem by performing PCA using Minimum Average Partial on the downside correlation matrix in order to reduce the dimension of the problem and, with it, the estimation errors. We apply our strategy to various datasets and show that it greatly improves the performance of mean-semivariance optimization, largely closing the gap in out-of-sample performance with the strategies based on the covariance matrix.
Objective This study aimed at examining the vulnerability of Central Africa to the Covid-19 pandemic. Methods Demographic, health and socio-economic indicators were used to describe the vulnerability. Results According to demographic indicators, populations appear younger than in Europe, Asia and North America, where evidence showed a higher lethality of Covid-19 and a higher frequency of hospitalization among the elderly. This highlights the protective effect of the age structure of the Central African populations. There is a significant vulnerability of their populations resulting from high morbidity and a considerable deficit in health care. Poverty indicators are not in their favour for a sustainable implementation of effective pandemic control measures. Very low literacy rates in some countries, misinformation and belief in conspiracy theory could affect the community involvement in the response. Several countries are weakened by other humanitarian crises, including; conflicts and other epidemics. The early easing in lockdown restrictions in certain countries could worsen the situation. Conclusion This Sub-region, where the largest proportion of the population lives in poverty, poor sanitary conditions, conflicts and humanitarian crises, the questions of standards of prevention could appear to them as luxurious idea relegated to the background. Central African Countries need financial and logistic support for a sustainable effective response. These observations could be easily extrapolated to other Sub-Saharan sub-regions.
Open-acess paper: https://journals.sagepub.com/doi/pdf/10.1177/02683962221096860 ================================================================== Wine tasting is a multisensory collective activity because it involves other senses in addition to sight and hearing. The importance of these multiple senses for wine tasting makes it more challenging to digitalize than other collective activities. We conducted an ethnography and used a semiotic analysis to explore the strategies to digitalize wine tasting session. In so doing, we examined how small artisanal winemakers and wine merchants in Austria, Germany, Liechtenstein, Luxembourg, and Switzerland moved their wine tasting sessions online to compensate for their lost key revenue streams during the global Covid-19 crisis. Based on our analysis, we present a typology of virtual wine tasting and illustrate how the approach to digitalize wine tasting evolved from a reactive approach to a more proactive one. We also identify strategies to digitalize wine tasting and characterize its social space. We discuss some avenues to regard virtual wine tasting as something more than just a digital representation of in-person wine tasting session by highlighting the mediating role of an information system. Finally, we propose some implications for digitalizing other multisensory collective activities.
Humans interact more and more with systems containing AI components. In this work, we focus on hand gestures such as handwriting and sketches serving as inputs to such systems. They are represented as a trajectory, i.e. sequence of points, that is altered to improve interaction with an AI model while keeping the model fixed. Optimized inputs are accompanied by instructions on how to create them. We aim to cut on effort for humans and recognition errors while limiting changes to original inputs. We derive multiple objectives and measures and propose continuous and discrete optimization methods embracing the AI model to improve samples in an iterative fashion by removing, shifting and reordering points of the gesture trajectory. Our quantitative and qualitative evaluation shows that mimicking generated proposals that differ only modestly from the original ones leads to lower error rates and requires less effort. Furthermore, our work can be easily adjusted for sketch abstraction improving on prior work.
Design science in the entrepreneurship field holds the promise of developing relevant knowledge with scientific rigor. Yet despite the promise of this approach, the entrepreneurship field still lacks guidance on how to plan, conduct, and assess design science work. In order to develop theoretically grounded principles, we first make our perspective on design science explicit. We characterize design science in entrepreneurship as a specific scientific approach that shares the values of practice (i.e., usefulness) and uses the methods of science (i.e., scientific method plus more specific, scrutable methods). We conceptualize design knowledge as a body of scientific knowledge that comprises both design object knowledge (e.g., situated artifact, and design principles), and design evaluation knowledge (e.g., usefulness, and social worth). Drawing on these foundations, we provide guidance on (1) how to make design knowledge contributions explicit, (2) how to position design science work, (3) how to effectively utilize prior knowledge, and (4) how to use fitting methods in design science work. The article contributes by further developing the conceptual foundations of design science in entrepreneurship and providing guidance on how to conduct and assess design science work in the entrepreneurship field.
Explorative business process management (BPM) is attracting increasing interest in the literature and professional practice. Organizations have recognized that a focus on operational efficiency is no longer sufficient when disruptive forces can make the value proposition of entire processes obsolete. So far, however, research on how to create entirely new processes has remained largely conceptual, leaving it open how explorative BPM can be put into practice. Following the design science research paradigm and situational method engineering, we address this research gap by proposing a method called the Five Diamond Method. This method guides explorative BPM activities by supporting organizations in identifying opportunities from business and technology trends and integrating them into business processes with novel value propositions. The method is evaluated against literature-backed design objectives and competing artefacts, qualitative data gathered from BPM practitioners, as well as a pilot study and two real-world applications. This research provides two contributions. First, the Five Diamond Method broadens the scope of BPM by integrating prescriptive knowledge from innovation management. Second, the method supports capturing emerging opportunities arising from changing customer needs and digital technologies.
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