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Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

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Wissenschaftlicher Artikel
Viele der wertvollsten Unternehmen der Welt betreiben ihr Geschäft auf Basis einer digitalen Plattform samt umgebendem Ökosystem. Während es in der Theorie zahlreiche Erklärungs-und Gestaltungsansätze für die erfolgreiche Umsetzung gibt, gelten diese wirtschaftlich attraktiven Geschäftsmodelle in der Praxis nach wie vor als herausfordernd. Auf der empirischen Grundlage von sieben Plattform-Innovationsprojekten und mit Methoden der Fallstudienforschung untersucht der vorliegende Artikel, welche Rolle digitale Plattformen in der Praxis spielen und wie diese Artefakte entwickelt werden können. Mit den Ergebnissen in Form von vier Einsatz- (Platform-as-a-Core, Platform-as-an-Evolution, Platform-as-an-Enabler, Platform-as-an-Add-On) und vier Entwicklungsmodellen (Methodic Problem Solvers, Methodic Strategists, Methodic Leaders, Ad-Hoc Developers) kann gefolgert werden: Digitale Plattformen können in der Praxis vielfältige Rollen einnehmen und deren Entwicklung kann mit unterschiedlicher Methodikintensität erfolgen. Für die Praxis profitieren Fach- und Führungskräfte von industrienahen Einblicken und abgeleiteten Handlungsempfehlungen. Für die Forschung wird der Wissensfundus im Bereich des Designs und der Entwicklung digitaler Plattformen erweitert.

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Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

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Viele CIOs (Chief Information Officer) in Unternehmen haben in der Pandemie einen hervorragenden Job gemacht. In der Zeit nach der Pandemie gilt es, diesen „Rückenwind“ und die Position des CIO neu zu definieren. Vor diesem Hintergrund wurden fünf CIOs, Hanna Hennig von der Siemens AG, Andreas Maier von der AXA Schweiz, Michael Müller-Wünsch von OTTO, Rolf Olmesdahl, ehemals Raiffeisen Schweiz, Ursula Soritsch-Renier von Saint-Gobain Paris, und ein Executive-Search-Spezialist, Patrick Naef, Boyden AG, gefragt wie sie die Zukunft des CIO sehen. Alle sechs Persönlichkeiten, die an dem Dialog teilnahmen, sind übereinstimmend der Meinung, dass es auch noch 2030 CIOs geben wird und sie auf der einen Seite verstärkt Treiber der digitalen Transformation in ihrem Unternehmen sein sollten und sie auf der anderen Seite nach wie vor Verantwortung für das Funktionieren der digitalen Infrastruktur tragen.

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Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.

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As expected, the digitalization of the economy and society has a major impact on the taxes of companies and on the tax department. There is no development in information and communication technology that can go unnoticed from the tax department's perspective. The digitalization of a business model can be of great importance, for example, when a digital platform is built. Putting all the facets described in this Chapter together, it is easy to see that the operational complexity for tax departments is already very high and will tend to increase further. International and digital business models meet new legal regulations and audit mechanics. At the same time, tax departments can deliver high quality financial data sets that can be used by business leaders to evaluate and steer new (digital) business models. The tax department should, therefore, increasingly be seen as a fundamental building block of a business . In interaction with other business units, external tax consultancies, technology providers and tax authorities, a new tax ecosystem is emerging, which requires new forms of internal and external collaboration. The increasing compliance requirements for companies, especially in the tax area, make it necessary to deal intensively with tax consequences before investing in a new business model. The digitalization of the tax department is progressing. New applications for the tax department are coming onto the market, new developments in information and communication technology are being used to digitalize processes in the tax department and to increase the level of automation. Data analytics; that is, the evaluation of large data sets using statistical methods and artificial intelligence, will become very important. It should always be clear that not only will companies digitalize their tax processes, but also the tax authorities will exploit all the potential of digitalization. A statement by former American President John F. Kennedy: ‘A rising tide lifts all boats’ can be applied to digitalization. Companies and government authorities will use and benefit from the potentials of digitalization.

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Effective Requirements Engineering is a crucial activity in software-intensive development projects. The human-centric working mode of Design Thinking is considered a powerful way to complement such activities when designing innovative systems. Research has already made great strides to illustrate the benefits of using Design Thinking for Requirements Engineering. However, it has remained mostly unclear how to actually realize a combination of both. In this chapter, we contribute an artifact-based model that integrates Design Thinking and Requirements Engineering for innovative software-intensive systems. Drawing from our research and project experiences, we suggest three strategies for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies.

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The algorithms of artificial intelligence are constantly being further developed and are being used in more and more products and applications in business and society. Numerous prototypes are being developed to open up the use of artificial intelligence in a wide variety of application areas. Nevertheless, only a few prototypes succeed in making the leap into productive applications that create sustainable business benefits. This paper series shows that processes and structures are needed for the management of artificial intelligence to ensure the sustainable success of AI systems.

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Im Rahmen einer Lehrveranstaltung des Masters in Business Innovation im Frühjahr 2021 gelang es, ohne vertiefte Kenntnisse der Bäckereibranche durch Verwendung bereits vorhandener Daten und durch Nutzung von Methoden der Statistik und der Künstlichen Intelligenz die Verkaufsprognosen der Filialleiterinnen und Filialleiter zu übertreffen und die Verkäufe des folgenden Tags mit einer Abweichung von ca. 13% bis 14% vorherzusagen. Diese unerwartet guten Ergebnisse lassen den Schluss zu, dass der Einsatz von Methoden der Statistik und der Künstlicher Intelligenz auch in KMUs wie Bäckereien in Zukunft wettbewerbsrelevant sein wird. Wenn es gelänge, die Prognosequalität zu verbessern, wäre in Bäckereien Nutzen in zwei Richtungen zu erwarten: Der unternehmerische Erfolg kann gesteigert und der Foodwaste reduziert werden. Allerdings bräuchte es mehr und bessere Daten, weiteren Austausch mit Experten der Domäne sowie geeignete Apps, um datengetriebene Prognosen in den Bäckereien zu implementieren.

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This paper introduces a framework for managing bias in machine learning (ML) projects. When ML-capabilities are used for decision making, they frequently affect the lives of many people. However, bias can lead to low model performance and misguided business decisions, resulting in fatal financial, social, and reputational impacts. This framework provides an overview of potential biases and corresponding mitigation methods for each phase of the well-established process model CRISP-DM. Eight distinct types of biases and 25 mitigation methods were identified through a literature review and allocated to six phases of the reference model in a synthesized way. Furthermore, some biases are mitigated in different phases as they occur. Our framework helps to create clarity in these multiple relationships, thus assisting project managers in avoiding biased ML-outcomes.

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Artificial Intelligence (AI) provides organizations with vast opportunities of deploying AI for competitive advantage such as improving processes, and creating new or enriched products and services. However, the failure rate of projects on implementing AI in organizations is still high, and prevents organizations from fully seizing the potential that AI exhibits. To contribute to closing this gap, we seize the unique opportunity to gain insights from five organizational cases. In particular, we empirically investigate how the unique characteristics of AI – i.e. experimental character, context sensitivity, black box character, and learning requirements – induce challenges into project management, and how these challenges are addressed in organizational (socio-technical) contexts. This shall provide researchers with an empirical and conceptual foundation for investigating the cause-effect relationships between the characteristics of AI, project management, and organizational change. Practitioners can benchmark their own practices against the insights to increase the success rates of future AI implementations.

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«Lost Generation» ist ein Begriff, den die amerikanische Schriftstellerin Gertrude Stein vor mehr als 100 Jahren prägte. Sie verwendete ihn für alle Kriegsbeteiligten des Ersten Weltkrieges, die sich nach Kriegsende respektlos verhielten und zu viel Alkohol konsumierten. Der Erste Weltkriege hatte ihnen ihre Zukunft gestohlen. In diesem Beitrag verwende ich den Begriff «Lost Generation» in Anlehnung an seine traditionelle Verwendung für eine Generation, die durch ein unbeeinflussbares heftiges Erlebnis, beispielsweise einen Krieg, eine Naturkatastrophe oder vielleicht auch eine Pandemie, in eine neue Lebenssituation gestellt wird, auf die sie nicht vorbereitet ist und die sie auch nicht will. Wenn auch die Pandemie, die derzeit Wirtschaft und Gesellschaft in Atem hält, nicht mit der Brutalität und den Gräueltaten des Ersten Weltkrieges vergleichbar ist, stellt sich für mich doch die Frage, wie stark die teilweise notwendigen harten Massnahmen des Jahres 2020, wie beispielsweise der Lockdown und die grossen Einschränkungen und Veränderungen im Privat- und Berufsleben nachhaltige Einflüsse auf die Zukunft junger Menschen haben, deren Pläne für die Zukunft komplett durcheinander gebracht und in vielen Fällen zumindest für die nahe Zukunft zerstört wurden.

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New technologies, changing customer expectations and a competitive business environment impact financial firms and their workforces in Switzerland. Financial firms are aware of the need to transform to be successful in the future. The measures and best practices they apply are analyzed and presented in this study. The analysis showed that there is no single successful approach, and that successful firms engage in a variety of activities to transform their workforce, learn fast and are capable to adapt to changing needs. The analysis also showed that the focus of financial firms is to educate, to re-educate and to create a culture of change in the workforce. The objective of this study was to examine how financial firms transform in the light of the outlined market and technology developments. More precisely, what measures do financial firms take regarding their workforces to meet changing customer expectations, to adopt new technologies, and to stay competitive in the market. Customer experience is the key driver of change in the financial industry. Financial firms have recognized that customer needs are changing based on customer centric experiences from other industries. In the new era of financial industry, where switching costs are low the bargaining power of customers has significantly increased. Digital workforce enablement is inevitable in a world where every interaction becomes digital. Financial firms are taking measures to prepare their workforce to master becoming more digital and interact with the customers accordingly. They are offering educational content on digital technology and enable digital collaboration through digital collaboration tools. Incubation and innovation are key transformational aspects since the pace and dynamic of the financial industry has significantly changed. Financial firms need to incubate and innovate faster, adapt to customer needs and implement new technologies in a way that benefits the organization. Financial firms take measures to embrace an innovative mindset in the work-force, pursue novel partnerships in ecosystems and enable the so called “startup mentality” in their organizations. Agility and agile culture is needed to foster an innovation and incubation on an organizational level The study found out that financial firms are moving away from rigid hierarchical structures to a more agile way of working. Financial firms are aware that working in teams and generating bottom-up innovations have become a necessity. Also, new performance systems are introduced as measures to award team spirit and collaborative working. The simplification of the application landscape is inevitable for financial firms to increase speed-to-market. Over the years, financial firms embedded many applications in their existing IT landscape which led to slowness and inefficiencies. The interviewed leaders recognize that the IT legacy is an obstacle for speed-to-market and good customer experience. IT legacy also hinders financial firms to effectively compete with new players entering the market. New players can build their application landscape from scratch and in a customer-oriented way which allow new entrants to provide exceptional customer experience at affordable prices. Automation & efficiency programs are needed because financial firms face operational efficiency pressures due to competition and changing regulations. Financial firms are much aware that 40% to 60% of current jobs in the financial industry could be automated in the future. Thus, financial firms are preparing their workforces with education and re-education measures, create new job roles and promote self-directed learning. Particularly, the study found out that the responsibility to prepare for the future is shifting from employer to employees. The employer provides the possibilities and it is up to the employees to prepare for future job roles. The study covered the measures taken by the financial firms to transform. However, our study shows that not enough urgency in the workforce is created to achieve the desired changes. Only if the measures can drive change on an organizational level, a financial firm can go through a successful transformation and remain competitive in the future.

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Design thinking is more than just a new, one-off method of innovation. Its focus is on establishing an innovation-friendly climate in companies and organizations for the long-term. To achieve this, an interdisciplinary team of authors has composed this "recipe book" that can be practically applied to your everyday business life. This book is for all who intend to understand and practice the design thinking method in the most rapid and uncomplicated way. The first part describes in depth what this method is all about. The second part of this comprehensive book offers you a step-by-step guide to practically apply design thinking. The subsequent sample cases show how to put theory into practice. The authors have gained their expertise in design thinking from both academic and scientific theory, and from countless long-term implementations at companies in various industries. So, benefit from this rich knowledge and start becoming innovative today. This book will show you how it's done. Readership: Business owners, lecturers, students and employees interested in design thinking and its implementation.

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Die pandemische Verbreitung des Virus COVID-19 bedeutet einen tiefen Einschnitt in Wirtschaft, Gesellschaft und das Leben fast aller Menschen. Die Auswirkungen des Lockdowns, der in zahlreichen Ländern stattgefunden hat und in einigen noch nicht beendet ist, zwingt nach unserer Meinung und Erfahrung Unternehmen, sich aktiv, intensiv und strategisch mit seinen Auswirkungen auseinanderzusetzen. Digitalisierung wird bei der Aufarbeitung des Lockdowns, das ist unbestritten, eine grosse Rolle spielen. Um besser verstehen zu können, wie Führungskräfte, die sich jeden Tag mit Digitalisierung auseinandersetzen, auf den Lockdown reagieren, haben wir Chief Information Officers und Chief Digital Officers grosser Schweizer Unternehmen nach ihren Erfahrungen in der COVID-19-Krise und mit dem Lockdown und ihren Plänen befragt. Sie alle erwarten grosse Veränderungen und beginnen mit der Aufarbeitung der Erkenntnisse und Erfahrungen. Die Ergebnisse unserer Befragung haben wir in sieben Thesen zusammengefasst. Einschränkend ist zu sagen, dass zum Zeitpunkt der Befragung, im Juni 2020, die Krise noch nicht überwunden ist. Entsprechend ist es möglich, dass der weitere Verlauf der COVID-19-Krise Aussagen in diesem Artikel in Frage stellt oder sie sich sogar als nicht richtig erweisen.

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get_appJennifer Hehn, Daniel Mendez, Falk Uebernickel, Walter Brenner, Manfred Broy
Wissenschaftlicher Artikel
In this position paper, we elaborate on the possibilities and needs to integrate Design Thinking into Requirements Engineering. We draw from our research and project experiences to compare what is understood as Design Thinking and Requirements Engineering considering their involved artifacts. We suggest three approaches for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies and point at open challenges for research and practice.

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Artificial Intelligence (AI) is considered being a disruptive force for existing companies and a promising avenue towards competitive advantage. A myriad of companies started investing in AI initiatives. However, a significant number of AI projects is not successfully deployed. Taking a closer look at financial service organizations, we aim at contributing to closing the gap between understanding the potential of AI and proactively leveraging the latter. We draw on affordance theory and socio-technical systems (STS) theory to identify the required socio-technical changes to actualize affordances of AI in financial service organizations. We present preliminary findings from a multiple case study approach with five financial service organizations based on rigorous interview coding that yields first insights into AI affordances. Building up on this, we will prioritize and structure future in-depth case studies to investigate how to orchestrate AI-induced changes in STS for actualizing AI affordances.

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Künstliche Intelligenz bietet Unternehmen neue Möglichkeiten Prozesse, Produkte, Dienstleistungen und Geschäftsmodelle zu innovieren und bestehende zu verändern. Daher wird das professionelle Management Künstlicher Intelligenz in Unternehmen zu einer zentralen Aufgabe, um die neuen Wertversprechen mit produktiven Systemen zu realisieren. Der Beitrag stellt das St. Galler Management Modell für KI (SGMM-KI) vor und zeigt sieben Handlungsfelder für den betrieblichen Einsatz von KI: (1) Management von Künstlicher Intelligenz, (2) Organisation des Betriebs, (3) Rechtliche Gestaltung, (4) Regulierung und Compliance, (5) Lebenszyklus-Management, (6) Management der Technologie-Infrastruktur, sowie (7) Cybersicherheit. Der vorliegende Artikel leitet konkrete erste Schritte an und richtet sich primär an Geschäftsleitungsmitglieder, IT- und Innovationsverantwortliche sowie Projektleiter, welche die neuen Wertversprechen der KI in der betrieblichen Praxis verwirklichen möchten.

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Digital and agile companies widely use chatbots in the form of integrations into enterprise messengers such as Slack and Microsoft Teams. However, there is a lack of empirical evidence about their action possibilities (i.e., affordances), for example, to link social interactions with third-party systems and processes. Therefore, we adopt a three-stage process. Grounded in a preliminary study and a qualitative study with 29 interviews from 17 organizations, we inductively derive rich contextual insights of 14 affordances and constraints, which serve as input for a Q-Methodology study that highlights five perceptional differences. We find that actualizing these affordances leads to higher-level affordances of chatbots that augment social information systems with affordances of traditional enterprise systems. Crossing the chasm between these, so far, detached systems contributes a novel perspective on how to balance novel digital with traditional systems, flexibility and malleability with stability and control, exploration with exploitation, and agility with discipline.

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The ability to generate business-relevant information and its use for business process improvements is a key success factor for businesses today. Answering the call for further research on success-relevant practices and instruments for managing business analytics, we report on the results of a three-year action design research project at a global car manufacturer. Drawing on the socio-technical systems theory, we identify seven meta-requirements and specify four principles for the design of an instrument to manage Analytics-as-a-Service (Aaas) portfolios. Our results reinforce the importance of coordinating different socio-technical components in business analytics initiatives and demonstrate how concrete management instruments, such as the proposed portfolio management tool, contribute to socio-technical alignment. For practitioners, the documented design components may provide guidance on how to design and implement similar instruments that support the management of Aaas portfolios.

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Increasing speed and flexibility is of strategic importance to almost any company in times of digital transformation. While startups or "born digital" companies are agile by nature, traditional companies are struggling with the question of how to increase organizational agility. Little knowledge exists about how enterprises adopt and scale agile practices and structures. This exploratory study with twelve global cases examines how traditional companies adopt and scale agile structures. We found that (1) agile structures are currently adopted by enterprises at large scale, (2) agile structures are adopted not only by IT, but also by business units, and (3) while Spotify's organization serves as a widespread template for a fully agile unit, enterprises adapt and fine-tune this template according to their needs and scale. Furthermore, we identified three additional models for fully agile structures where a fully agile unit with cross-product support is the most frequently observed model.

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Little is known about how digital work shapes the exchange of performance feedback, even though today’s digital and global world demands for more continuous feedback than annual reviews. This research investigates a feedback app in a naturalistic context within a globally leading financial service corporation (InsurCorp). Drawing on malleability and voluntary participation, the app offers possibilities to send and request feedback between employees. Rich contextual insights from a multinational pilot study with 568 users are gained by triangulating qualitative data from 21 semi-structured interviews and 69 feedback app user reviews with usage data. Anchored in the theory of affordances, we provide insights on use practices and find that the app affords operational-level feedback exchange on specific subjects, while general feedback on sensitive topics is preferably exchanged in person. To understand actualization facilitators and barriers, we take a social-technical systems perspective to elaborate contextual factors that influence the individual’s actualization decision.

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Daten und Informationen sind das Öl des digitalen Zeitalters; Industrie- und Serviceroboter sowie intelligente Algorithmen werden zu einem entscheidenden Produktionsfaktor. Diese beeinflussen nachhaltig die Formen klassischer Wertschöpfung. Gleichzeitig schaffen sie aber auch vollkommen neue Möglichkeiten unternehmerischer Wertschöpfung und beeinflussen damit Branchen- und Marktstrukturen. Dieser Beitrag erläutert die Veränderungen unternehmerischer Wertschöpfung durch den gezielten Einsatz digitaler und vernetzter Technologien. Dabei werden vor allem systematisiert die Einflüsse der Hardware- und Software-Robotik bzw. der intelligenten Algorithmen sowie von Advanced Analytics erörtert und eingeordnet. Auf dieser Basis werden an Beispielen Disruptionen in der unternehmerischen Wertschöpfungskette aufgezeigt und die entstehenden Veränderungen mit ihren Konsequenzen anhand von Beispielen diskutiert. Die veränderten Anforderungen an die Markt- und Branchenstrukturen sowie die Geschäftsmodelle mit ihren Geschäftsprozessen in diesen Wertschöpfungsstrukturen werden beleuchtet. Als Abschluss des Beitrags werden am Beispiel des Gesundheitswesens Veränderungen in der Wertschöpfung angerissen.

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Requirements Engineering (RE) has been aiding software-intensive development projects for quite some time now. However, today’s projects growingly demand for agile and human-centered approaches to discover and meet the often fuzzy needs of the various stakeholders involved. In that regard, Design Thinking (DT) has become one of the most promising methods to address complex problems and define innovative solutions. We see potential to combine both, the strongly human-oriented working mode of DT with the more formal, technology-driven world of RE, to develop human-centered solutions more effectively. Yet, little is known how such an integration could be realized and which concrete benefits and challenges to expect. To better understand this endeavor, we conduct a longitudinal case study to identify how DT and RE can work together in an agile development setting from the very beginning of idea conceptualization to market-ready implementation. In this paper, we summarize our research protocol and present first findings how Design Thinking can complement current RE practices through meeting known challenges encountered by the RE community of practitioners. We provide a better understanding of the multi-faceted potential of DT for RE for both, scholars and practitioners, and describe open issues and planned future steps in our study.

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This tutorial presents Design Thinking as a promising approach to creatively elicit human-centered requirements for software-intensive systems. Specifically, it contributes to Requirements Engineering practices by structuring the fuzzy process of developing creative and innovative ideas. Addressing academics and practitioners alike, the tutorial provides a 3.5 hour hands-on introduction to Design Thinking and links it with the realm of Requirements Engineering. The tutorial should be seen as a forum for the interchange of experience and learnings from combining both approaches and should raise awareness for the importance of human-centered methods and experimentation in early phases of software engineering. After the tutorial, the participants will have access to all materials, templates, and methods on our website for further usage.

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Effective requirements elicitation is perceived to be one of the most crucial activities in software-intensive development projects. While many scholars and practitioners have pointed out and agreed upon its numerous challenges, others consider the increasingly popular approach of Design Thinking to be the promising ‘cure.’ This paper provides robust empirical evidence of the role of Design Thinking for requirements elicitation and to help overcome its challenges. Specifically, this paper presents learnings in terms of process guidance, stakeholder communication, and requirements quality based on insights from a multiple-case study. In sum, Design Thinking introduces new structures and methods to enhance elicitation efforts and inspires a mind-shift towards a more human-centered and creative way of requirements elicitation. This work provides a better understanding of the multi-faceted potential of Design Thinking for requirements elicitation for both, scholars and practitioners.

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As our world is getting increasingly servitized and digitized, innovation is no longer bound to physical products. Customer experience and innovative services have become the new imperative for business success. Design Thinking is becoming a valid approach for service innovation in the digital age. However, a profound understanding of “how” Design Thinking contributes to service innovation is still missing. We show this by conducting a Delphi study which includes 14 Design Thinking experts across different company sizes and industries. As a result, we demonstrate consensus on 59 highly relevant methods for service innovation allocated to the respective Design Thinking phase. This is the first study to provide a comprehensive view on Design Thinking methods for service innovation. We enhance the understanding of Design Thinking and lay the foundation for further investigations into the service innovation process. Practitioners receive guidance from our findings for applying those methods.

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“Understanding the value and organizational implications of big data analytics: the case of AUDI AG” presents the case of AUDI AG and its attempts to implement big data analytics in its organization. The case highlights the situation of an original equipment manufacturer (OEM) in the automotive industry and the potentials and challenges the emerging technology big data analytics may entail for such organizations. The case tries to help students to grasp the technical characteristics, the value, and organizational implications of big data analytics as well as the distinct types of analytics services. The case is presented through the eyes of Hortensie, an aspiring manager at AUDI, who gained strong interest in the phenomenon of big data analytics and received the task to position it within AUDI. To ramp up the topic big data analytics, AUDI is engaging with industry and design experts as well as an external consultancy ITConsult.

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Design-inspired approaches to problem-solving, like design thinking, have been gaining rising recognition as a way to create human-centric solutions. A design thinking project provides a guiding, yet not rigid framework to explore and specify requirements for the development of a new product or service. We investigate the challenges concerned with specifying requirements in design thinking projects with the purpose of laying the groundwork for creating solutions to these challenges. We find that design thinking supports the process of discovering requirements but leads to challenges stemming from the design thinking process. We identify conclusions for practice and suggestions for further research to create better human-centric information systems in the future.

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Für produzierende Unternehmen hat sich Product Lifecycle Management in den letzten Jahrzehnten in wachsendem Maße zu einem strategisch wichtigen Ansatz entwickelt. Forciert durch steigende Effektivitäts- und Effizienzanforderungen stellen viele Unternehmen ihre Product Lifecycle Management-Prozesse und -Informationssysteme auf den Prüfstand, wobei besonders große, heterogene Industriekonzerne mit weitreichenden Aufgaben bei der Strategieentwicklung und -umsetzung betraut sind. Basierend auf einer Einzelfallstudie bei einem weltweit führenden, diversifizierten Konglomerat beschreibt der vorliegende Beitrag entlang eines etablierten Analyseframeworks Herausforderungen und Lösungsansätze im Product Lifecycle Management im Konzernumfeld. Als zentraler Inhalt werden Handlungsimplikationen auf strategischer, organisationaler, prozessualer und informationstechnologischer Ebene abgeleitet. Für Manager in fertigenden Unternehmen bietet die Fallstudie einen wertvollen Überblick über relevante Aspekte. Für industrienahe Forscher zielt die Fallstudie auf ein besseres Verständnis des Phänomens Product Lifecycle Management im Kontext von Großunternehmen ab und trägt zur Erweiterung des Wissensfundus bei.

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The pervasive infiltration of digital technology into physical products brings both tremendous challenges and opportunities to original equipment manufacturers. With the goal to support the initial stages of the product lifecycle, this article introduces a method compendium for the development of digitized products. More precisely, the compendium suggests (1) customer- and user-centric innovation methods, (2) agile and prototyping methods, (3) system and architecture modelling methods, (4) feedback- and data-driven methods, and (5) service and business modelling methods. Methodically, we draw on secondary data from a longitudinal single-case study scrutinizing the development of digitized trucks at a leading materials handling and intralogistics organization. Bounded to the business-to-business context of industrial equipment manufacturing, we enrich product lifecycle management with methodological contributions valuable for academia and practice alike.

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In this paper, we explore the evolution of product lifecycle management information systems projects in manufacturing industries over time. There is critical need because initiated projects routinely fail in terms of time, budget, or quality to which the academic discourse has not given adequate consideration. Therefore, we build up on an in-depth case study within the project setting of a leading European automotive supplier kicked-off in January 2016. As central results, the paper provides insights (1) how product lifecycle management information systems projects develop over time, (2) what may be underlying causes, and (3) which implications on project management may be deduced. In view of the limitations by the applied case study research strategy, we illumine the specifics of these information systems projects for scholars. For project managers, an overview on essential developments and their implications supports the successful project execution.

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The rise of chatbots poses new possibilities to link social interactions within instant messengers with third-party systems and business processes. While many companies use chatbots within the enterprise in the form of Slack apps and integrations, little is known about their affordances. Grounded in a qualitative research endeavour, we conducted 12 explorative interviews in 8 organizational settings to inductively gain rich contextual insights. Our results reveal 14 functional affordances in 4 categories, elucidating how their actualization leads to the perception of higher level affordances and constraints. First, we discuss how chatbots augment social information systems with affordances of traditional enterprise systems, and therefore, enable bottom-up automation. Second, we elaborate on how the actualization of an affordance by one user may facilitate its perception by other users. Thus, we contribute towards a better understanding of how chatbots create value.

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get_appThomas Bieger, Paul Achleitner, Urs Landolf, Luca Serratore, Yannik Breitenstein, Bernadette Dilger, Marc Angélil, Sou Fujimoto, Andreas Böhm, Miriam Meckel, Damian Borth, Urs Gasser, Melinda Florina Lohmann, Heike Bruch, Sandra Berenbold, Oliver Gassmann, Barbara Josef, Andrea Back, Falk Uebernickel, Marc Kohler, Jennifer Hehn, Jan Marco Leimeister, Matthias Söllner, Walter Brenner, Thomas Gutzwiller, Markus Frank, Wolfgang Schürer, Henry Kissinger
Buch
Wie wird das HSG Learning Center aussehen und was wird es leisten? Wie wird die digitale Transformation unser Leben und unser Lernen verändern? Wie können wir sie gestalten? Das Buch zeigt das Potential des HSG Learning Centers auf und zeichnet ein Panorama der Aktivitäten in Forschung und Lehre im Kontext des digitalen Wandels auf.

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Evaluation activities in Design Science Research not only verify utility but also scientific rigour and the truth-like value of prescriptive knowledge contributions. Assuming a lack of guidance, evaluation frameworks like the one of Sonnenberg and vom Brocke have been proposed prior to evaluating their actual utility to scholars. This research now aims at evaluating how scholars actually apply the framework in practice and their reasoning for doing so. The research-in-progress paper at hand presents preliminary results from a citation analysis. While we find an increasing awareness of the relevance of ex-ante evaluations, there is still a lack of comprehensive detail on ex-ante evaluation activities and an emphasis on ex-post evaluations in artificial settings. The call to accumulate incremental prescriptive knowledge has mostly been ignored and artifact changes are rarely disclosed. Therefore, we question the missing guidance as the sole reason that scholars emphasise building rather than evaluation. We propose to conduct a case study that investigates the reasons behind scholars’ evaluation decisions.

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New digital services in consumer-facing organizations offer novel value propositions, closer consumer relationships and higher automation of consumer-facing processes. But transforming to fully digital services requires an organization to acquire specific capabilities. This article presents a digital services capability model that allows an organization to assess its current capabilities and identify gaps. Two in-depth case studies demonstrate the application of the model and show how it identifies the capabilities in urgent need of improvement. Our recommendations are built around four scenarios for using the model.

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Over the last decade, a plethora of Enterprise Social Software (ESS) has emerged in various shapes, yet difficult to compare what they enable or constrain their users to do. Neither the prior frameworks nor the ambiguous concepts shed light on the fine-granular similarities and differences among them. In particular, organizations can consciously design and adjust their ESS artifacts. Hence, it is relevant to assess the possibilities for goal-oriented action they offer and spot the differences among them. Following a structured method, we identify eight distinct dimensions with subordinate characteristics that enable the classification of functional affordances of ESS. This paper presents the resulting taxonomy that has been built and evaluated over six iterations. We contribute to practice by supporting practitioners to assess ESS, inspire the innovation of existing ESS and the development of future ESS. Furthermore, we build a foundation for future research to systematically develop and investigate ESS.

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get_appWalter Brenner, Manfred Broy, Jan Marco Leimeister
Monograph
Die Informatik und mit ihr unsere Fähigkeiten, große Softwaresysteme zu schaffen und mit wirtschaftlichem Erfolg einzusetzen, haben sich in etwa 75 Jahren dramatisch entwickelt. Waren anfänglich Fragen der effizienten Lösung von Problemen im Vordergrund, die bis dahin insbesondere manuell behandelt worden waren, hat sich die Informatik in Stufen mehr und mehr zu einer Disziplin entwickelt, die völlig neue Möglichkeit eröffnet und Innovationen schafft und auf dieser Basis von höchster wirtschaftlicher Bedeutung ist. Diese vergleichsweise schnelle Veränderung der Informatik ist bisher weder in der Informatikausbildung, noch in der Anwendung der Informatik in der Praxis, gerade in etablierten Unternehmen, entsprechend berücksichtigt. Die schnelle Veränderung erfordert entschlossenes Handeln. Wirtschaft wie Hochschulen müssen sich auf eine Informatik neuer Prägung einstellen und ausrichten, denn nur dann wird es möglich sein, dass die bisher sehr erfolgreichen Unternehmen in Europa ihre führende Position verteidigen und mit Hilfe der Informatik in neue Innovationsbereiche vordringen können.

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Digital transformation, which often includes establishing big data analytics capabilities, poses considerable challenges for traditional manufacturing organizations, such as car companies. Successfully introducing big data analytics requires substantial organizational transformation and new organizational structures and business processes. Based on the three-stage evolution of big data analytics capabilities at AUDI, we provide recommendations for how traditional manufacturing organizations can successfully introduce big data analytics and master the related organizational transformations.

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The automotive industry represents one of the most relevant industrial sectors of the global economy. In response to a plethora of challenges, e-collaboration for product development has become a nexus of competitive advantage in the automotive world. Since new dynamics in organizational forms on the one hand and advancements in engineering information systems on the other hand have led to increased complexity, a classification model to organize and structure the manifold manifestations seems analytically useful. Hence, the paper at hand (1) proposes, (2) describes, and (3) validates archetypes of e-collaboration for product development in the automotive industry. Anchored in (1) a structured literature review and (2) rich empirical evidence from a multiple-case study in the automotive ecosystem, we organize our research study along a well-established, two-stage research method on archetypes adopting a socio-technical systems perspective. Key findings include the archetypes (1) mechanical development-dominant, (2) software development-dominant, (3) systems engineering-oriented, and (4) non-development-focused e-collaborations for product development as basic patterns. Thereby, “importance of mechanical development” and “importance of software development” act as essential classification dimensions. Keeping the inherent limitations of the qualitative research tradition in mind, this paper offers theoretical, methodological, managerial, and cross-disciplinary contributions.

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In the light of pervasive digitalization, traditional physical products get augmented with digital components that create the potential of making the whole product lifecycle visible for product developers. As numerous opportunities sketch out how feedback such as sensor data might be leveraged for future products, a comprehensive model to describe, particularly a classification model to organize and structure these opportunities seems analytically useful. Hence, this paper pursues a scenario-based approach and proposes a taxonomy for feedback-driven product development scenarios in manufacturing industries. Grounded on (1) empirical data from case studies and focus groups and (2) a systematic literature review, we follow an established taxonomy development method employing the general systems theory as meta-characteristic. With the limitation of a (1) qualitative, interpretive empirical research design and a (2) representative literature review, we contribute to the body of knowledge by shedding light on feedback-driven product development from a classification perspective which may act as structuring and creativity fostering tool.

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“Understanding the Value and Organizational Implications of Big Data Analytics – The Case of AUDI AG” presents the case of Audi AG and its attempts to implement big data analytics in its organization. The case highlights the situation of an original equipment manufacturer (OEM) in the automotive industry and the potentials and challenges the emerging technology big data analytics may entail for such organizations. The case tries to help students to grasp the technical characteristics, the value, and organizational implications of big data analytics as well as the distinct types of analytics services. The case is presented through the eyes of Hortensie, an aspiring manager at AUDI, who gained strong interest in the phenomenon of big data analytics and received the task to position it within AUDI. To ramp up the topic big data analytics AUDI is engaging with industry and design experts as well as an external consultancy ITConsult.

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In 2010, FC Bayern München (FCB), one of the world’s most successful football clubs, had just finished a successful season from a commercial perspective as well as on the field. With regard to its digital service portfolio, FCB had successfully established its digital content distribution and an ecommerce platform. However, the club seemed to have missed one major trend: the rise of social media. European competitors were already attracting large crowds on Facebook. The management was unsure about how social media would contribute to the digital strategy. In this teaching case, students need to prepare materials for a board meeting in June 2010, in which the digital road map for the upcoming season has to be decided upon. Furthermore, they are supposed to reflect on FCB’s evolution from today’s perspective. This teaching case helps to understand how value can be generated through the use of social media. Additionally, it illus-trates how social media enriches content distribution and how it contributes to fan loyalty.

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Original equipment manufacturers (OEMs) face the challenge of building the capabilities to effectively harness the digital data streams flowing from their digitized industrial products to create innovative data-driven services. Based on insights from the digitization journeys of thyssenkrupp and SIEMENS, we provide a capability framework and actions that will guide OEMs as they progress through a stepwise evolution of six strategic service stages.

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Abstract: Against the backdrop of a steady shift in value added from the automotive original equipment manufacturers to the automotive suppliers, product lifecycle management in the automotive supply industry gains importance. Prior literature has acknowledged product lifecycle management as paradigm for manufacturing industries, yet little is known about the specific characteristics and boundary conditions in this emerging industry branch. Grounded on extensive empirical evidence from a typical and revelatory case study at a global leader for mechatronic assemblies, this exploratory paper identifies, illustrates, and discusses challenges in product lifecycle management in the automotive supply industry. With the limitation of an exploratory and interpretive single-case study approach, we supply scholars and practitioners with grounded, stakeholder-related insights.

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