Publikations-Übersicht

The centrality of information systems (IS) customization to match companies' needs with software systems available in the market has been researched extensively. The distinctive characteristics of Artificial Intelligence (AI) systems compared to other types of IS suggest that customization needs a new conceptualization in this context. We draw on evidence from expert interviews to conceptualize customization of AI systems as composed of four layers: data, models, algorithms, infrastructures. We identify a continuum of levels of customization, from no to complete customization. Since companies customize AI systems in response to business needs, we develop a theoretical model with six antecedents of AI systems' customization choices. In so doing, we contribute to both AI management research, by introducing the IS customization perspective in the field, and IS customization literature, by introducing AI systems as a novel class of systems and enlarging the understanding of customization for a specific class of software systems.

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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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Agile work organization has experienced a significant increase in acceptance in recent years. However, literature falls short in explaining the transformation process that results from the implementation of agile work organization as a means of achieving increased adaptability, rate of speed and flexibility. We apply a process ontology to agile work organization by following three multinational firms that apply agile work organization, utilizing a case study approach over three years. At the macro level, our theory describes the transformation process set in motion by agile work organization as a three-phase process. At the micro level, we show that this transformation process is driven by specific design decisions on individual elements. Thus, our process theory contributes to a better understanding of agile work organization as a means to achieve organizational agility and to STS theory by showing that the emergence and constitution of STS are mainly driven by micro-level processes.

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Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.

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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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Many organizations struggle to measure, control, and manage agility in a manner of continuous improvement. Therefore, we draw on Design Science Research to develop and test a tool for Continuously Assessing and Improving Agile Practices (CAIAP). CAIAP helps agile practitioners to monitor the alignment of “as is” agile practices on individual, team levels with the overall agile strategy of the organization. To develop CAIAP, we first empirically gather requirements, draw on the ICAP framework to base the tool development on a solid conceptual and theoretical basis. CAIAP helps agile practitioners to constantly monitor their agile practices on individual and team levels and to identify areas for improvement to gain greater organizational agility. To researchers, CAIAP helps to make the unit of analysis of agile work explainable, predictable and helps researchers to guide their own empirical research as well as serve as a basis for designing further tool support.

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With looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.

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The Swiss Journal of Business Research and Practice invited submissions related to the question of how the digitalization may shape the future of work. The rapid development of innovative digital technologies and the associated digital transformation have changed the way in which we live, communicate, and work. Digital platforms and the increasing pursuit of becoming more effective and flexible have affected many traditional work structures within and across organizations. Work is potentially becoming more distributed, flexible, and autonomous. At the same time, many approaches of digital work are associated with inferior working conditions, low payment, or even increasing surveillance (Durward et al. 2020, Aloisi/Gramano 2019). Phenomena such as artificial intelligence, smart devices, or robotics might further accelerate these developments and could lead to an augmentation and automation of knowledge work - work that requires extensive education and training and that is today performed by humans. Similarly, organizations and management practices may become more digital such that new jobs, roles, and skill profiles as well as innovative modes of management and leadership could emerge. These developments will not only impact individuals and organizations, but also our society in its entirety.

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Agile work organization has experienced a significant increase in acceptance in recent years. However, literature falls short in explaining the transformation process that results from the implementation of agile work organization as a means of achieving increased adaptability, rate of speed and flexibility. We apply a process ontology to agile work organization by following three multinational firms that apply agile work organization, utilizing a case study approach over three years. At the macro level, our theory describes the transformation process set in motion by agile work organization as a three-phase process. At the micro level, we show that this transformation process is driven by specific design decisions on individual elements. Thus, our process theory contributes to a better understanding of agile work organization as a means to achieve organizational agility and to STS theory by showing that the emergence and constitution of STS are mainly driven by micro-level processes.

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Many organizations struggle to measure, control, and manage agility in a manner of continuous improvement. Therefore, we draw on Design Science Research to develop and test a tool for Continuously Assessing and Improving Agile Practices (CAIAP). CAIAP helps agile practitioners to monitor the alignment of “as is” agile practices on individual, team levels with the overall agile strategy of the organization. To develop CAIAP, we first empirically gather requirements, draw on the ICAP framework to base the tool development on a solid conceptual and theoretical basis. CAIAP helps agile practitioners to constantly monitor their agile practices on individual and team levels and to identify areas for improvement to gain greater organizational agility. To researchers, CAIAP helps to make the unit of analysis of agile work explainable, predictable and helps researchers to guide their own empirical research as well as serve as a basis for designing further tool support.

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The role of the Chief Information Officer (CIO) in the organization has received a lot of attention in recent years. While traditionally most CIOs had faced the difficulty of stepping out of the shadow of being coined as a "Utility & Infrastructure Director", they have been found to establish themselves as a driving force in defining and shaping the digital agenda and strategic direction of their organization (Peppard et al. 2011). This crisis-driven development is, however, now paving the way for a new era of the CIO role. As research shows, crises usually do not lead to a trend reversal, but to a trend acceleration (Gassmann and Ferrandina 2021). Therefore, this opportunity should be seized by CIOs in order to leverage the digitalization momentum gained through the COVID-19 crisis, and to build lean digital organizational structures and use strategic sourcing of services for cost efficiency. Thus, the focus here should not be on rebuilding old barriers, but to use the crisis induced dynamic to empower the CIO to successfully master the future challenges of efficiency, flexibility, resilience, scalability and innovation in the organization.

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The increasing availability of data and advances in data processing and analysis methods have led to a flourishing of data science and business analytics. This not only constitutes new research efforts in information systems research (e.g. artificial intelligence (AI), processing of unstructured data, decision support systems, or visualization), but also has a significant impact on established topics in information systems research such as business intelligence and decision support systems. In this track, we welcomed the entire diversity of information systems research efforts in the fields of data science and business analytics and were open to all methodological approaches.

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Internal crowdsourcing showed a substantial increase of use in recent years, since it describes a promising alternative to traditional orchestration of employees in today’s digital era. However, literature falls short in explaining the transformation process that is enacted by such approaches of platform-based work organization. By using a work organizational perspective with the existing body of knowledge in combination with a revelatory case study, this paper develops a process theory explaining the transformation process of internal crowdsourcing over time and how the organization of work transform during this process. Moreover, we discovered four different forms of organizing work with a completely new form of work organization: the “Hybrid Flash Organization”. Scholars can identify critical incidents and process phases, while practitioners use our findings as a transformation guideline of internal crowdsourcing to detect potential threads, opportunities and constraints along the way of a successful implementation."

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Crowd work reflects a new form of gainful employment on the Internet. We study how the nature of the tasks being performed and financial compensation jointly shape work perceptions of crowdworkers in order to better understand the changing modes and patterns of digital work. Surveying individuals on 23 German crowd working platforms, this work is the first to add a multi-platform perspective on perceived working conditions in crowd work. We show that crowd workers need rather high levels of financial compensation before task characteristics become relevant for shaping favorable perceptions of working conditions. We explain these results by considering financial compensation as an informational cue indicating the appreciation of working effort that is internalized by well-paid crowd workers. Resulting boundary conditions for task design are discussed. These results help us understand when and under what conditions crowd work can be regarded as a fulfilling type of employment in highly developed countries.

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Crowdsourced tasks are very diverse – and so are platform types. They fall into four categories, each demanding different governance mechanisms. The main goal of microtasking crowdsourcing platforms is the scalable and time-efficient batch processing of highly repetitive tasks. Crowdsourcing platforms for information pooling aggregate contributions such as votes, opinions, assessments and forecasts through approaches such as averaging, summation, or visualization. Broadcast search platforms collect contributions to solve tasks in order to gain alternative insights and solutions from people outside the organization, and are particularly suited for solving challenging technical, analytical, scientific, or creative problems. Open collaboration platforms invite contributors to team up to jointly solve complex problems in cases where solutions require the integration of distributed knowledge and the skills of many contributors. Companies establishing crowdsourcing platforms of any type should continuously monitor and adjust their governance mechanisms. Quality and quantity of contributions, project runtime, or the effort for conducting the crowdsourcing project may be good starting points.

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Internal crowdsourcing showed a substantial increase of use in recent years, since it describes a promising alternative to traditional orchestration of employees in today’s digital era. However, literature falls short in explaining the transformation process that is enacted by such approaches of platform-based work organization. We apply a process ontology on internal crowdsourcing as platform-based mode of work organization, following two organizations employing internal crowdsourcing in a case study approach for over four years. On a macro level, our theory describes the transformation process enacted by internal crowdsourcing as three-phased process. On the micro-level, we illustrate that this transformation process is driven by specific design choices on single elements. In so doing, our process theory contributes to a better understanding of internal crowdsourcing as means for transformation work organization and to STS theory by showing that the emergence and constitution of STS is mainly driven by processes on a micro-level.

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While many firms in recent years have started to offer public Application Programming In-terfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and the demands of external developers. We address the lack of theory that explains the performance impacts of three API archetypes (professional, media-tion, and open asset services). We couple survey data from 152 API product managers with manually coded API design classifications. With this data, we conduct cluster and regression analyses that reveal moderating effects of two value creation strategies (economies of scope in production and innovation) on the relationships between API archetype similarity and two API performance outcomes: return on investment and diffusion. We contribute to IS litera-ture by developing a unifying theory that consolidates different theoretical perspectives on API design, by extending current knowledge on the performance effects of API design, and by empirically studying the distinct circumstances under which digital platforms facilitate economies of scope in production or in innovation. Our results provide practical implica-tions on how API providers can align API archetype choice with the value creation strategy and the API’s business objective.

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Crowdsourcing represents a powerful approach for organizations to collect data from large networks of people. While research already made great strides to develop the technological foundations for processing crowdsourced data, little is known about decision-making patterns that emerge when decision-makers have access to such large amounts of data on people's behavior, opinions, or ideas. In this study, we analyze the characteristics of decision-making in crowdsourcing based on interviews with decision-makers across 10 multinational corporations. For research, we identify four common patterns of decision-making that range from structured and goal-oriented to highly dynamic and data-driven. In this way, we systematize how decision-makers typically source, process, and use crowdsourced data to inform decisions. We also provide an integrated perspective on how different types of decision problems and modes of acquiring information induce such patterns. For practice, we discuss how information systems should be designed to provide adequate support for these patterns.

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During the last years, crowdfunding gained attention as alternative source of funding for a variety of projects. More and more creative, artistic and entrepreneurial projects search funding through the crowd. However, crowdfunding markets are often considered inefficient and shaped by information asymmetries. Although first project characteristics towards governance mechanisms have been identified, the general use of governance mechanisms in crowdfunding and their impact on funding success have mostly remained uncovered. With that in mind, we present preliminary results on the influence of governance mechanisms on funding success of crowdfunding projects. We assessed 108 projects from 18 platforms in order to measure the use of governance mechanisms and to discover differences between the types of crowdfunding. We find that archetypes of governance mechanisms with influence on the funding success exist and intend to contribute to theory that explains the use of governance mechanisms in crowdfunding.

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Dieser Artikel hilft Organisationen ihre Mitarbeiter mittels Crowdsourcing in einem digitalen Umfeld zu reorganisieren, um Agilität, Produktivität und Effektivität der Geschäftsprozesse zu erhöhen. Internes Crowdsourcing, in welchem die kollektive Arbeitskraft, Kreativität und Intelligenz der Mitarbeiter effektiver genutzt wird, stellt eine flexible, dezentrale sowie digitale Form der Arbeitsorganisation dar. Dieser Prozess resultiert in einer Transformation der Organisation, der über die einfache Einrichtung der Crowdsourcing-Plattform hinausgeht. Aus diesem Grund stehen viele Unternehmen vor Problemen, diesen Transformationsprozess erfolgreich zu gestalten und können die Vorteile von Crowdsourcing nicht oder nur eingeschränkt nutzen. Daher identifizieren wir in unserem Artikel, basierend anhand einer multiplen Fallstudie fünf Herausforderungen und dazu passende Maßnahmen, um eine erfolgreiche Transformation zu managen. In diesem Rahmen gehen wir in einem Fall auf die Herausforderungen und Lösungen im Detail ein, auf denen wir dann in den zusätzlichen Fällen aufbauen. Schließlich präsentieren wir fünf Herausforderungen, wie inadäquate IT Fähigkeiten und die passenden Lösungsansätze, wie gezielte Schulungen. Diese erlauben es Unternehmen Internes Crowdsourcing als eine innovative Form der Arbeitsorganisation zu nutzen und Agilität sowie Effektivität interner Arbeitsprozesse zu erhöhen.

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In this paper, we analyze internal crowd work as Neo-STS from an employee’s perspective. Based on qualitative interviews, we describe in our model how employees perceive empowerment through participation in internal crowd work. As our main contribution, we detail and extend existing research regarding internal crowd work, Neo-STS as well as empowerment by identifying structural antecedents that affect psychological empowerment of internal crowd workers.

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Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures' Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.

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Internal crowd work has emerged as a new form of digital gainful employment that changes the nature of work. However, the possible effects of internal crowd work on the individual level have been largely neglected. In this paper, we therefore present our research in progress which is concerned with the effects of work characteristics in internal crowd work that have impact on the individual’s empowerment and satisfaction. Thus, we developed our research model and conducted an online survey amongst 118 internal crowd workers of a Swiss bank who were asked to test new software. Our expected contribution will increase the understanding of internal crowd work and provide important insights for organizations to (re-) design work on internal IT-platforms.

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Crowdfunding emerged as new way of funding by matchmaking capital givers and seekers. However, traditional financial intermediation theory falls short in explaining how crowdfunding brings demand and supply for capital to equilibriums. We thus develop a system theory of crowdfunding intermediation by unraveling specific mechanisms of crowdfunding intermediation and identifying dominant configurations of them. Following a mixed method approach, we collect data on implemented crowdfunding intermediation mechanisms by content-analyzing 160 crowdfunding intermediaries. We then apply unsupervised and supervised machine learning techniques in order to identify three timely robust archetypes of crowdfunding intermediation – philanthropic, hedonistic, and profit-oriented crowdfunding. This study contributes to crowdfunding literature by proposing a theory of crowdfunding intermediation that unravels the inner workings of crowdfunding intermediaries and reflects a theoretically grounded, empirically validated, and temporally stable taxonomy of crowdfunding intermediaries. Further, it extends financial intermediation theory by improving the understanding of how the Internet disrupts traditional financial intermediation.

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While the crowdsourcer’s job is to encourage valuable contributions and sustained commitment in a cost-effective manner, it seems as if the primary attention of management and research is still centered on the evaluation of contributions rather than the crowd. As many crowdsourcers lack the resources to successfully execute such projects, crowdsourcing intermediaries play an increasingly important role. First studies dealt with internal management challenges of incorporating an intermediary. However, the issue of how intermediaries influence crowdsourcees’ psychological and behavioral responses, further referred to as engagement, has not been addressed yet. Consequently, two leading research questions guide this paper: (1) How can the engagement process of crowdsourcees be conceptualized? (2) How and why do crowdsourcing intermediaries impact crowdsourcees’ engagement? This study extends existing knowledge by offering IS-researchers a process perspective on engagement and exploring the underlying mechanisms and IT-enabled stimuli that foster value-creation in a mediated and non-mediated setting. A theoretical process model is first conceptualized and then explored with insights from two common cases in the growing field of crowd testing. By triangulating platform and interview data, initial propositions concerning the role of specific stimuli and the intermediary within the engagement process are derived. It is proposed that crowdsourcing enterprises, incorporating intermediaries, have the potential to generate a desired engagement state when perceived stimuli under their control belong to the so-called group of “game changers” and “value adders”, while the intermediary controls mainly “risk factors” for absorbing negative experiences. Apart from the theoretical relevance of studying mediated engagement processes and explaining voluntary use and participation in a socio-technical system, findings support decisions on how to effectively incorporate platform intermediaries.

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We investigate whether digital traces can be used to predict early stage startup survival. Based on common survival factors from the entrepreneurship literature, we mined the digital footprints of 542 entrepreneurs and their ventures. Using a context-specific text mining approach, we performed a bootstrapping simulation in which we predict 5-year survival for different survival rates that range from 50% to 10%. Our results indicate that we can predict 5-year survival with an accuracy of up to 91%. With this study, we will provide an evidence-based taxonomy of digital traces for predicting early stage startup survival, identify the most important digital traces for doing so and benchmark our predictive approach against the actual investments of 339 business angels.

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Crowdsourcing ermöglicht Unternehmen, die Intelligenz, Problemlösungsfähigkeit und Kreativität großer, weltweit verteilter Menschengruppen zu nutzen. Viele Organisationen nutzen diese Möglichkeiten seit längerem. Insbesondere aufgrund der zunehmenden Vernetzung und Digitalisierung hat sich ein breit gefächertes, ausdifferenziertes Crowdsourcing-Angebot etabliert, das von Unternehmen einfach genutzt werden kann. Während Crowdsourcing sich gut für die Entwicklung neuer Produkt- oder Serviceideen eignet, stoßen die bisherigen Methoden an Grenzen, wenn die Entwicklung der organisatorischen Kernbereiche wie das Geschäftsmodell, die Strategie oder die Kultur des Unternehmens im Fokus der Entwicklung steht. Für die Unterstützung der Unternehmensentwicklung eignen sich länger angelegte, iterative und durch Coaching begleitete Prozesse. Am Beispiel einer innovativen Crowdsourcing-Methode wird ein iteratives Verfahren vorgestellt und mit konventionellen Ansätzen verglichen.

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Recently, the application of internal crowdsourcing in companies as a new form of orchestrating work has increased substantially. Early research has shown that organizations should apply internal crowdsourcing due to its benefits, such as fast access to internal knowledge and increased productivi-ty. Although studies have identified some advantages, internal crowdsourcing is a complex initiative and we do not sufficiently know how to rollout internal crowdsourcing initiatives in a company and to guide them to a state of stable operations in the adaptation stage. Some papers derived barriers for internal crowdsourcing and solutions on how to overcome them. However, these barriers address mostly the operational stage, when the initiative is already stable. Some papers address adaptation barriers, but the assessment frameworks in current literature used to detect them were incomprehen-sive resulting in only few adaptation barriers and solutions. Therefore, we identify the adaptation bar-riers of internal crowdsourcing comprehensively through the technochange theory in a multiple case study, assess what solutions the companies applied and describe how the solutions work in order to display how to overcome barriers in a consolidated introduction model for internal crowdsourcing.

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To profit from crowdsourcing, organizations can engage in four different approaches: microtasking, information pooling, broadcast search, and open collaboration. This article presents 21 governance mechanisms that can help organizations manage their crowdsourcing platforms. It investigates the effectiveness of these governance mechanisms in 19 case studies and recommends specific configurations of these mechanisms for each of the four crowdsourcing approaches. Also, it offers guidance to organizations that host a crowdsourcing platform by providing recommendations for implementing governance mechanisms into their platforms and building up governance capabilities for crowdsourcing.

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Crowd work has emerged as new pattern of digitally mediated collaboration. In this paper, we focus on the determinants and effects of crowd workers’ job satisfaction – a perspective that has been largely neglected by current crowdsourcing research. We report results from a survey of 161 crowd workers participating in crowdsourced software testing. Our research shows that job satisfaction mediates the effects of monetary rewards, hedonic value, and cognitive stimulation on the intention to participate in future testing tasks. By contrast, factors of work context (i.e., flexibility and provided information) have no effects. We contribute to the literature by unraveling job satisfaction as causal mechanism influencing future participation. For practice, our results help to design more effective tasks in crowd work.

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Digitalization and the Internet changed our life. Many phenomena are responsible for this change. A relatively new one is crowdsourcing. Companies such as Amazon or Procter and Gambles use crowdsourcing successfully. The change will continue and we need to fully understand this subject to use the potential offered by this new phenomenon. This literature review summarizes and structures the crowdsourcing literature with focusing on the crowdsourcability of tasks. We analyzed the outsourcing literature to build a framework and adopted 7 perspectives, which were used to describe the outsourcability of tasks. The framework helps us to structure and analyze the crowdsourcing literature with focusing on the crowdsourcability of tasks. We found relevant literature regarding every perspective, but great research gaps were shown concerning these perspectives, leading to the assumption that the task characteristics of crowdsourcing are not sufficiently explored by the state-of-the-art literature. More research is needed to fully understand and use the potential of crowdsourcing.

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Digitalization gives rise to dynamic forces shaping future working structures. In practice, companies are increasingly interested in using their own employees as an internal crowd. Drawing on socio-technical systems (STS) perspective, organizations must understand how they can embed an internal crowdsourcing system effectively in order to exploit its potential. In particular, organizations need assistance in designing internal crowdsourcing systems. Thus, we follow an action design research approach and develop comprehensive design guidelines for designing an internal crowdsourcing system. The paper in hand develops the design guidelines by deriving the requirements from literature, developing them further in a bank project and evaluating them with external experts. Furthermore, we assess what tools can cover which part of the system and implement the system. The short paper will present the derived requirements, the first design of the internal crowdsourcing system and explain next steps of the full paper as well as the contribution.

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Although peer assessment is a widely used didactical method in higher education, little is known about a) how many peer assessors are required to receive a stable assessment on a peer's solution and b) how valid this assessment is compared to an expert assessment. To fill these gaps, we conducted a peer assessment in a large-scale lecture. Overall, 136 students participated in the peer assessment. Each student had to complete an assignment, which was then anonymously evaluated by five randomly selected peers, and three independent experts. We applied a bootstrap-based Monte Carlo simulation based on our data. The results show that a) three peer assessors are sufficient for a stable assessment, and b) the peer assessors are less critical compared to experts. We thus contribute to literature by providing insights on how many peer assessors are required when applying peer assessment, and how comparable peer assessment is with expert assessment.

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Crowdsourcing represents a powerful approach for organizations to engage in distant search and mobilize knowledge distributed amongst a diverse network of people. While organizations generally succeed in generating large amounts of knowledge, they frequently fail to identify useful ideas that have the potential to solve problems or serve as innovation. We combine text mining and network analysis to examine how such contributions emerge on crowdsourcing platforms and how organizations may identify them. We find that useful ideas typically originate from members in a crowd with only few network ties and that these contributions become especially useful when they are enriched with local knowledge provided by experienced members on the platform. We extend existing research by examining the effects of network relationships and knowledge (re)combination in crowdsourcing. We also discuss the potential of network analysis and text mining to support organizations in tracking the origin of contributions and analyzing their content.

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With an increasing amount of arising crowdsourcing initiatives, insights are needed on how to successfully drive initial and sustained platform-activity, as a form of value co-creation between crowdsourcer and crowdsourcees. Therefore, the engagement concept, known as a micro-foundation of value co-creation, serves to holistically understand crowdsourcees’ psychological and behavioral responses along the IT-mediated crowdsourcing journey. Due to the multidimensionality of the concept, a mixed method approach is proposed for exploring qualitatively and quantitatively stimuli’s effect on psychological states and engagement behaviors. Therefore, two measuring approaches, the Sequential Incident Laddering Technique and a Panel Poisson Model, are presented. Preliminary findings suggest that, next to other factors, crowdsourcer-interaction and high-effort tasks serve as dominant drivers, fostering psychological engagement beyond the interaction process, while crowd-interaction rather drives within-process engagement behavior. This research in progress provides IS-researchers and practitioners initial insights into IT-enabled value co-creation processes.

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In recent years, many firms have published public Application Programing Interfaces (APIs). However, firms struggle with how to successfully implement API-enabled service innovation. APIs are either boundary resources through which platform strategies are enacted or they represent distribution channels for software, data, or infrastructure. We use a service innovation framework to integrate ten elements that determine business-oriented API design and that were discussed by prior API research. We report the preliminary results of a cluster analysis of 96 randomly sampled and qualitatively coded APIs. We identify three archetypes of API-enabled service innovation that are characterized by distinct configurations of API elements. We use a measure of API popularity to find structural differences of the archetypes' market impact. With our planned research addressing the interplay of API design, ecosystem-based value creation strategies and API performance, we intend to contribute to theory that explains the impact of API design on digital innovation.

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Crowdsourcing ermöglicht Unternehmen, die Intelligenz, Problemlösungsfähigkeit und Kreativität großer, weltweit verteilter Menschengruppen zu nutzen. Viele Organisationen nutzen diese Möglichkeiten seit längerem. Insbesondere aufgrund der zunehmenden Vernetzung und Digitalisierung hat sich ein breit gefächertes, ausdifferenziertes Crowdsourcing-Angebot etabliert, das von Unternehmen einfach genutzt werden kann. Während Crowdsourcing sich gut für die Entwicklung neuer Produkt- oder Serviceideen eignet, stoßen die bisherigen Methoden an Grenzen, wenn die Entwicklung der organisatorischen Kernbereiche wie das Geschäftsmodell, die Strategie oder die Kultur des Unternehmens im Fokus der Entwicklung steht. Für die Unterstützung der Unternehmensentwicklung eignen sich länger angelegte, iterative und durch Coaching begleitete Prozesse. Am Beispiel einer innovativen Crowdsourcing-Methode wird ein iteratives Verfahren vorgestellt und mit konventionellen Ansätzen verglichen.

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The rapid development of new IT-enabled business models, a fast-growing hardware market, and that market's segmentation are making software testing more complex. So, manual testing is becoming less applicable--economically and practicably. One approach to overcome these issues is crowdtesting--using crowdsourcing to perform testing. To profit from crowdtesting, companies can use three approaches: engage an external crowd of Internet users, engage their employees, or engage their customers. Three case studies illustrate these approaches' differences, benefits, and challenges, and the potential solutions to those challenges. Researchers' experiences with these approaches have led to guidelines that can help software development executives establish crowdtesting in their organizations.

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Crowd work has emerged as a new form of digital gainful employment whose nature is still a black box. In this paper, we focus on the crowd workers – a perspective that has been largely neglected by research. We report results from crowd worker interviews on two different platforms. Our findings illustrate that crowd aggregators as new players restructure the nature of crowd work sustainably with different effects on the behavior as well as the existing relationships of crowd workers. We contribute to prior research by developing a theoretical framework based on value chain and work aggregation theories which are applicable in this new form of digital labor. For practice, our results provide initial insights that need to be taken into account as part of the ongoing discussion on fair and decent conditions in crowd work.

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Crowdfunding gained momentum over the last few years. In contrast to traditional forms of funding, the service provision of crowdfunding platforms is performed within service systems. These comprise a complex combination of IT and non-IT services, different stakeholders, and diverging contexts and purposes. The design and operation of such service systems represents a tough challenge. Therefore, we developed a crowdfunding service configuration framework in the form of a morphological box and derived three dominant design patterns by following a design science approach. Therefore, we followed three iterations, which comprise in total twelve expert interviews, three case studies and the analysis of 161 crowdfunding platforms. The configuration framework extends research on crowdfunding and service science by providing insights in how to support the systematic design of crowdfunding service systems, reducing their complexity, and giving a comprehensive overview over their building blocks.

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The Internet has affected and partially radically changed the business models of traditional industries. Crowdfunding as a new concept of funding over the Internet by a large crowd has especially gained maturity. Crowdfunding offerings range from funding charitable projects or innovative gadgets to a funding alternative for start-ups or small businesses. Therefore, crowdfunding represents an innovative way to provide liquidity for illiquid markets. With regard to the banking crisis and the growing skepticism toward banks, crowdfunding is seen as a more transparent, democratic, and entertaining way of funding, which makes it highly attractive for banks. A senior innovation manager of The Bank of Switzerland (TBOS), one of Switzerland's largest and most traditional banks, recognized the disruptive and beneficial potential of crowdlending. By facing strong resentments, he developed the idea of TBOS engaging in crowdlending by collaborating with a start-up by bundling competencies in a service system.

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Crowd work has emerged as a new form of digital gainful employment that changes the nature of work. However, an increasing number of people perform certain tasks in the crowd and start to identify with this work. In this paper, we outline our research in progress which is concerned with the effects of work characteristics in crowd work that have impact on the individual’s identification. Thus, we developed our research model and conducted an online survey amongst 434 crowd workers to ex-amine their perception of work and illustrate the antecedences of identification. Our expected contribution will increase the understanding of crowd work and extend prior research on self-determination theory (SDT) and work design. For practice, we provide important insights for platform providers to (re-) design work on platform in order to increase identification among their crowd. In addition, our findings can serve as common basis for future discussions on decent crowd work.

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Crowdsourcing represents a powerful approach that seeks to harness the collective knowledge or creativity of a large and independent network of people for organizations. While the approach drastically facilitates the sourcing and aggregating of information, it represents a latent challenge for organizations to process and evaluate the vast amount of crowdsourced contributions – especially when they are submitted in an unstructured, textual format. In this study, we present an on-going design science research project that is concerned with the construction of a design theory for semi-automated information processing and decision support in crowdsourcing. The proposed concept leverages the power of crowdsourcing in combination with text mining and machine learning algorithms to make the evaluation of textual contributions more efficient and effective for decision-makers. Our work aims to provide the theoretical foundation for designing such systems in crowdsourcing. It is intended to contribute to decision support and business analytics research by outlining the capabilities of text mining and machine learning techniques in contexts that face large amounts of user-generated content. For practitioners, we provide a set of generalized design principles and design features for the implementation of these algorithms on crowdsourcing platforms.

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Crowdsourcing represents an innovative approach that allows companies to engage a diverse network of people over the internet and use their collective creativity, expertise, or workforce for completing tasks that have previously been performed by dedicated employees or contractors. However, the process of reviewing and filtering the large amount of solutions, ideas, or feedback submitted by a crowd is a latent challenge. Identifying valuable inputs and separating them from low quality contributions that cannot be used by the companies is time-consuming and cost-intensive. In this study, we build upon the principles of text mining and machine learning to partially automatize this process. Our results show that it is possible to explain and predict the quality of crowdsourced contributions based on a set of textual features. We use these textual features to train and evaluate a classification algorithm capable of automatically filtering textual contributions in crowdsourcing.

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Virtual idea communities (VIC) are a relatively new phenomenon in business. These communities, in which distributed groups of individual customers focus on voluntarily sharing and elaborating innovation ideas, are used by firms to integrate customers into the ideation for new product development rooted in Chesbrough’s (2003) open innovation paradigm. Developers and decision makers realized especially within the last decade that games or game-like appeals could serve as appropriate gamifications to attract people to participate in VICs. Therefore, gamification gained momentum and has been widely implemented into VICs. The use of gamification does, however, not lead to this intended positive outcome per se. Because of that, obstacles and challenges in the use of gamification have to be considered, which has often been neglected in practice. Therefore, the goal of this chapter is to address this topic and to describe major obstacles and challenges in the use of gamification in VICs.

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A largely neglected aspect in crowdsourcing research is the “Crowdsourcing Experience” itself, which every crowdsourcee is necessarily exposed to throughout the IT-mediated interaction process, potentially stimulating engagement towards the crowdsourcer. Hence, the crowdsourcees’ engagement process is conceptualized and illustrated with empirical findings from a pilot case. It exemplifies that crowdsourcing has the potential to generate high levels of attitudinal and behavioral engagement, depending on prior experiences and perceived cognitions and emotions. Related stimuli characteristics are identified, which serve as a first indication of the foundations of the engagement process. This study offers IS-researchers first insights on the so far under-researched topic of IT-enabled engagement processes between individuals and entities.

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