With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate ...human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.
Software-as-a-Service (SaaS) is said to become an important cornerstone of the Internet of Services. However, while some market research and IT provider firms fervently support this point of view, ...others already conjure up the failure of this on-demand sourcing option. Oftentimes based on weak empirical data and shaky reasoning, these inconsistent perspectives lack scientific rigor and neglect to present a more differentiated picture of SaaS-adoption. This study seeks to deepen the understanding of factors driving the adoption of Software-as-a-Service (SaaS). Grounded in transaction cost theory, the resource-based view, and the theory of planned behavior, we develop a research model to assess SaaS-adoption at the application level. Survey data of 297 firms in Germany with 374 valid response items across different industries were collected to test the theoretical model. Our analysis revealed that patterns on the decision on SaaS-adoption differ across application types. Social influence, attitude toward SaaS-adoption, adoption uncertainty, and strategic value turned out to be the strongest and most consistent drivers across all application types. Furthermore, we found that firm size does not matter in SaaS-adoption, since large enterprises and small- and medium-sized companies had similar adoption rates. Overall, this study provides relevant findings that IT vendors can use to better appeal to potential companies that consider adopting SaaS.
Pricing Strategies of Software Vendors Lehmann, Sonja; Buxmann, Peter
Business & information systems engineering,
12/2009, Letnik:
1, Številka:
6
Journal Article
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Due to the economic characteristics specific to the software industry, pricing concepts existing in other industries cannot be transferred without adaptation. Therefore, this article provides an ...overview of pricing models for software. In this context we discuss the six parameters formation of prices, structure of payment flow, assessment base, price discrimination, price bundling, and dynamic pricing strategies. Furthermore, we refer to recent software delivery models, such as Software as a service. The results are based on literature research and empirical studies.
With millions of users worldwide, online dating platforms strive to assert themselves as powerful tools to find dates and form romantic relationships. However, significant differences exist in male ...and female use of this mate-matching technology with respect to motivation, preferences, self-presentation, interaction and outcomes. While existing research has routinely reported on gender differences in online dating, these insights remain scattered across multiple studies. To gain a systematic insight into existing findings, in this study we conduct a meta-review of existing research. We find that evolutionary theory generally holds true in online dating: Users still follow natural stereotypes when it comes to choosing a mate online. Physical attractiveness is the key criteria for men, while women, being much more demanding, prioritize socio-economic attributes when choosing a male partner. Together, our structured findings offer a deeper insight into the underlying dynamics of gender differences in online dating.
Clinical decision support systems (CDSSs) based on machine learning (ML) hold great promise for
improving medical care. Technically, such CDSSs are already feasible but physicians have been
skeptical ...about their application. In particular, their opacity is a major concern, as it may lead physicians
to overlook erroneous outputs from ML-based CDSSs, potentially causing serious consequences for
patients. Research on explainable AI (XAI) offers methods with the potential to increase the
explainability of black-box ML systems. This could significantly accelerate the application of MLbased CDSSs in medicine. However, XAI research to date has mainly been technically driven and
largely neglects the needs of end users. To better engage the users of ML-based CDSSs, we applied a
design science approach to develop a design for explainable ML-based CDSSs that incorporates
insights from XAI literature while simultaneously addressing physicians’ needs. This design comprises
five design principles that designers of ML-based CDSSs can apply to implement user-centered
explanations, which are instantiated in a prototype of an explainable ML-based CDSS for lung nodule
classification. We rooted the design principles and the derived prototype in a body of justificatory
knowledge consisting of XAI literature, the concept of usability, and an online survey study involving
57 physicians. We refined the design principles and their instantiation by conducting walk-throughs
with six radiologists. A final experiment with 45 radiologists demonstrated that our design resulted in
physicians perceiving the ML-based CDSS as more explainable and usable in terms of the required
cognitive effort than a system without explanations.
Providers of online services are under increasing pressure to leverage the value inherent in customer data to remain competitive. At the same time, however, Internet users' privacy has become more ...and more subject to public debate, and companies must protect this privacy if they want to attract and retain customers. It seems that companies struggle to satisfy these competing demands, and neglecting either could have detrimental effects on their success. Different streams of research underline the importance of either privacy protection or the collection and use of customer information. But they remain largely silent about the resulting challenges that companies face. In this study, we address this gap by exploring how companies perceive and handle the tensions between their organizational information needs that necessitate some degree of privacy intrusion and their need to attract and retain customers, which requires privacy protection. We develop a grounded theory that explains how companies balance these opposing demands from an organizational perspective. More specifically, we identify four challenging tensions that define the conditions under which companies must operate. We find that companies try to achieve and maintain a state of balance in the presence of these tensions, and we discover three categories of tactics that help companies in their balancing acts. Our research contributes to a provider‐centric perspective on information privacy and uncovers the management of customer information and privacy as a new but important context in which organizational ambidexterity is required. We offer a detailed perspective on the specific challenges that companies face with respect to customer information and privacy, and our results can guide managers who have to deal with these challenges.
This editorial book presents twelve contributions from the German "Wirtschaftsinformatik that exemplify the contemporary approach of theory-guided modeling and empiricism which complements existing ...approaches. It summarizes recent research which has been presented at major international and national conferences in order to demonstrate the growing importance of this stream of research.
Future Work and Enterprise Systems vom Brocke, Jan; Maaß, Wolfgang; Buxmann, Peter ...
Business & information systems engineering,
08/2018, Letnik:
60, Številka:
4
Journal Article
Recenzirano
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Much of the current discourse about future work systems addresses automation, as work is increasingly performed by machines. For example, blockchain and smart contracts can automate large parts of ...the supply chain (Mendling et al. 2018), and machine learning now facilitates automation in business areas that were once too unstructured for automation (Willcocks et al. 2015). In such settings, people are likely to contribute to work systems by means of creative work and exploration (as opposed to exploitation), a distinction that O’Reilly and Tushman (2013) referred to as organizational ambidexterity. Therefore, from the perspective of BISE research, the future of work poses questions about the interplay of people and machines, as Lehrer et al. (2018) outlined in their work on digital service innovation. In this discussion, we differentiate between the social intensity and the technical intensity of work and define four basic types of work systems.
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