A cost-sensitive learning algorithm for fuzzy rule-based classifiers

Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifica- tions may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule generation cost-sensitive. Furthermore, it will be shown how interpretability aspects and the costs of feature acquisition can be ac- counted for during classifier design. Natural language text is used to explain the generated fuzzy rules and their design proces

Colocation as a hybrid ICT sourcing strategy to improve operational agility

Fast access to communication networks and the availability of high-performance information and com-munication technology (ICT) infrastructures is indis-pensable for accelerating business transactions. Yet with increased environmental volatility, companies need to become more agile in identifying and responding to market- and technology-based challenges. Accordingly, a responsive and high-performance ICT infrastructure remains a top priority for firms. Thus, new ICT sourcing strategies may lead to significant competitive advantages, especially in dynamic business environments. This article analyzes a hybrid ICT sourcing strategy called colocation that allows firms to operate their own ICT resources in facilities of special-ized data center providers. Grounded in the theory of dynamic capabilities, we theorize and empirically ex-amine how colocation and top management support enable firms to improve their operational agility in the presence of environmental turbulence.</jats:p

Innovating Mindfully with Service-Oriented Grids - The Role of Organizational Mindfulness in Turbulent Environments

Environmental turbulence as being illustrated by the current international financial crisis leads to a high degree of uncertainty in decision-making processes. However, even in conventional economic cycles, demanding decision processes are exposed to varying levels of environmental turbulence and institutional pressure. Consistent with the extant literature, organizational mindfulness as cognitive pattern is supposed to attenuate arising uncertainty and error in the IS assimilation process. So far, little empirical research has been conducted to quantify the impact of organizational mindfulness on the IS assimilation and business value generation process in industry. Grounded in the technology-organization-environment framework this research approach contributes to the diffusion of innovations and IS assimilation theory by validating the role of organizational mindfulness in interacting with institutional pressure against the background of environmental turbulence. The planned crosscountry (North America, Europe) questionnaire-based field study aims at IS decision makers among the 3000 largest financial services providers worldwide allowing for cross-country comparisons

Interview with Peter Mertens and Wolfgang König: “From Reasonable Automation to (Sustainable) Autonomous Systems”

Peter Mertens is Professor Emeritus of Wirtschaftsinformatik at the Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg. After studying industrial engineering, he completed his doctoral studies and his habilitation at the TH Darmstadt (1961) and the TU München (1966), respectively. From 1966 to 1968, he worked for a large software and consulting firm in Switzerland, first as a system designer and later as a managing director. In 1968, Peter Mertens took over the first chaired professorship specialized in business data processing at the University of Linz. He is considered one of the founding fathers of Wirtschaftsinformatik in the German-speaking world. Until September 2005, Peter Mertens held the Chair of Business Administration, especially Wirtschaftsinformatik I at the Faculty of Business and Social Sciences of FAU. In parallel, he was head of the computer science research group “Business Applications” at FAU’s Faculty of Engineering. Since fall 2005, he works as an emeritus professor at his former chair. Peter Mertens is the author of numerous books, including 23 monographs. He has also been involved in the editing of 26 collective works. The first volume of his book “Integrated Information Processing” has been published in 18 editions. Some of his books have been translated into English, Chinese, Italian, and Russian. Among other awards, he is a Fellow of the German Informatics Society, an honorary doctor of five universities in Germany, Austria, and Switzerland, and has been awarded the Order of Merit of the Federal Republic of Germany. From 1990 until 2000, Peter Mertens served as Editor-in-Chief for WIRTSCHAFTSINFORMATIK (now: BISE). Until 2016, Wolfgang König was Professor of Business Administration, especially Information Systems and Information Management at the Faculty of Economics and Business Administration of Goethe University Frankfurt a. M., and until January 2022, he was Chairman of the E-Finance Lab (since 2020: efl – the Data Science Institute) at Goethe University. Since 2008, he holds the position of Executive Director of the House of Finance of Goethe University, and since 2016, he serves as Senior Professor at Goethe University. From 1998 until 2008, König served as Editor-in-Chief for WIRTSCHAFTSINFORMATIK (now: BISE). Both Peter Mertens and Wolfgang König are clearly among the research pioneers when it comes to automated systems, which can be seen as a precursor of the central topic of this special issue: autonomous systems (AS). The key difference between automated systems and AS is that, in AS, machines or other technology actors have at least some agency (i.e., they can act autonomously), whereas in automated systems, the agency still lies with humans – who, for example, define the relevant rule system – and machines/technologies merely automate the execution of these predefined rules

A Multi-Perspective Framework for Research on (Sustainable) Autonomous Systems

The ongoing digital transformation is challenging the way in which business is conducted and value is created and captured (Vial 2019). While prior digitalization waves focused on replacing paper as physical carrier of information, leveraging the Internet as global communication infrastructure, and developing reactive, partly automated business processes and systems (e.g. Legner et al. 2017), the next wave will be about transforming these processes/systems into proactive autonomous systems (AS). Such systems represent complex “systems of systems” with different maturities, qualities, reliabilities, and performances, which may develop their own dynamics (Boardman and Sauser 2006; Maier 1999). In the information systems (IS) context, a common characteristic of AS is their reliance on large amounts of data, along with the use of advanced technologies—such as the Internet of Things, Artificial Intelligence (AI), Machine Learning, or Blockchain—that allow for gathering and processing ‘big’ data with limited, or even no, human involvement. Today, AS can be found in various fields of application. Popular examples include driverless cars, smart cities, and smart homes, which often rely on a combination of sensors, algorithms, and self-executable code. Besides these tangible AS that link the physical world to the information world (Barrett 2006), we note a growing number of intangible AS in the form of software systems that operate either entirely in the background or at the interface with humans. Examples are intelligent chatbots, smart contracts, and recommender systems (Murray et al. 2021a; Pfeiffer et al. 2020; Rutschi and Dibbern 2020; Wang et al. 2019a, b), as well as algorithmic management and control systems, such as the ones used by Uber and other gig economy firms to manage their digital workforce (Cram and Wiener 2020; Möhlmann et al. 2021; Wiener et al. 2021). Even though AS are designed, developed, and implemented in a process of socio-technical interaction, once in use, the embedded technology takes on the role of an autonomous agent (or actor) that can make decisions and perform actions independently of humans (Baird and Maruping 2021). In other words, what has been created in a socio-technical way by implementing patterns—including organizational rules, as well as social norms and values—into a technical system, turns into a techno-social system once operating, where social agents in the organizational environment respond to the technical system and where the system may self-adapt to environmental changes. Thus, agency, decision rights, and responsibility are handed over to technology agents, while the ultimate accountability and decision rights to change these systems still reside with the governing entity owning those systems (Kellogg et al. 2020).Footnote1 This asks for a better understanding of AS in a broader context, where the autonomy of technical systems as agents must be analyzed in relation to human agents. In fact, changes in the autonomy of one (human or technology) agent may have consequences for the autonomy of another agent. Accordingly, the notion of “conjoined agency” between human and technology agents has been conceptualized as one way to acknowledge new types of interdependencies that arise in the course of increasing technology autonomy (Murray et al. 2021b). Another way to view AS is by consideration of their temporal dimension, as captured by the notion of sustainability, which generally refers to some long-term existence. This means that, once in use, AS should be able to exist and technology agents embedded in these systems should be able to fulfill their function for a longer period of time without human intervention, as otherwise they cannot be considered being really autonomous. In this sense, sustainable autonomous systems (SAS) may refer to self-learning technical systems that are constantly improving themselves, such as an autonomous vehicle that, on a daily commute, keeps optimizing the route it takes. Put differently, SAS are characterized by their ability to adapt to changing circumstances and be responsive to environmental changes. In doing so, SAS may not only optimize themselves in accordance with some predefined output criteria (e.g., quality or performance), but also with regard to their consumption of resources (e.g., an autonomous vehicle constantly improving its fuel consumption). On a larger scale, this points to another perspective on sustainability directed towards the effects of AS use and operation. As such, sustainability may also concern the long-term economic, social, and environmental effects of using AS (Hart and Milstein 2003), commonly referred to as the “3Ps” (profit, people, and planet) of the triple bottom line (Elkington 1997). This perspective includes the effects of SAS on the efficient use of tangible resources, such as energy (e.g., smart offices), space (e.g., smart cities), food (e.g., smart fridges), or natural resources (e.g., smart agricultures), as well as their effects on intangible resources, such as the longevity of data (e.g., for auditing purposes) or human and social capital in general. While the debate around SAS is not new, the emergence of blockchain has fueled innovative solutions, but also concerns regarding the energy consumption of blockchains based on the so-called “proof of work” consensus mechanism (Sedlmeir et al. 2020). While ecologic sustainability is one important aspect of SAS, there are further aspects that need to be considered. For example, as unintended and unforeseen second-order or spillover effects can result from the deployment of SAS, the question must be answered if we really want to rely on systems that are on ‘autopilot.’ Here, critical ethical questions arise (Tang et al. 2020), including questions of fairness regarding the decision rules according to which AS act (Dolata et al. 2021); for instance, how a driverless car should react to unforeseen circumstances affecting humans (Kirkpatrick 2015). In recent years, IS research has begun to pick up the concept of autonomy and to study it from different perspectives. Thereby it is important to note that the concept of autonomy is by no means new to the IS field. For example, autonomy has been an inherent characteristic of intelligent software agents (Jennings et al. 1998), which have been subject of research in various fields of application, such as supply-chain automation and improvement (Nissen and Sengupta 2006) or electronic auctions (Adomavicius et al. 2008). It is only recently, however, that the concept of autonomy has gained increasing interest with regard to the phenomena described above. Against this backdrop, in this editorial, we seek to synthesize and integrate different autonomy concepts and develop a framework that can serve as a basis for future research on (S)AS in various IS contexts and settings. In particular, drawing on the IS and related literatures, we first identify and review different autonomy concepts and their definitions. On this basis, we then elaborate on the relationships among those concepts and present a multi-perspective framework for studying (S)AS in a broader “systems of systems” context along with promising directions for future research. Our framework has been inspired by the existing literature on autonomy and AS, as well as the experiences we made as editors during the review process for our special issue on SAS in BISE. In total, we received 12 papers out of which two were accepted and published in this issue