Christoph Riedl

Cloud Computing – Outsourcing 2.0 or a new Business Model for IT Provisioning?

The term cloud computing is sometimes used to refer to a new paradigm – some authors even speak of a new technology – that flexibly offers IT resources and services over the Internet. Gartner market research sees cloud computing as a so-called “emerging technology” on its way to the hype. When looking at the number of searches for the word pair “cloud computing” undertaken with the Google search engine one can get an imagination of the high interest on the topic. Even terms like “outsourcing”, “Software-as-a-Service (SaaS)” or “grid computing” have already been overtaken.

Quality Management in Service Ecosystems

Service-oriented architectures and Web services mature and have become more widely accepted and used by industry. This growing adoption increased the demands for new ways of using Web service technology. Users start re-combining and mediating other providers’ services in ways that have not been anticipated by their original provider. Within organisations and cross-organisational communities, discoverable services are organised in repositories providing convenient access to adaptable end-to-end business processes. This idea is captured in the term Service Ecosystem. This paper addresses the question of how quality management can be performed in such service ecosystems. Service quality management is a key challenge when services are composed of a dynamic set of heterogeneous sub-services from different service providers. This paper contributes to this important area by developing a reference model of quality management in service ecosystems. We illustrate the application of the reference model in an exploratory case study. With this case study, we show how the reference model helps to derive requirements for the implementation and support of quality management in an exemplary service ecosystem in public administration.

Towards a generic value network for cloud computing

With the rise of a ubiquitous provision of computing resources over the past years, cloud computing has been established as a prominent research topic. In contrast to many other research works, this paper does not focus on technical aspects of cloud computing but rather takes a business perspective. By taking this perspective we examine the ecosystem that has developed around cloud computing. Here, new market players emerged, breaking up the traditional value chain of IT service provision. In this paper we describe the roles of different market actors and develop a generic value network of cloud computing, using the e3-value method. Based on interviews with domain experts we were able to draw first estimates regarding possible future value streams within the ecosystem. Extending the prevailing technical perspective of cloud computing, this paper shifts the focus to a broader understanding of business opportunities and business value. Researchers can apply the developed generic value network as an analytical framework to guide their research, while practitioners might apply it to position themselves in the cloud computing market and identify possible business opportunities.

Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science

Selecting among alternative projects is a core management task in all innovating organizations. In this paper, we focus on the evaluation of frontier scientific research projects. We argue that the “intellectual distance” between the knowledge embodied in research proposals and an evaluator’s own expertise systematically relates to the evaluations given. To estimate relationships, we designed and executed a grant proposal process at a leading research university in which we randomized the assignment of evaluators and proposals to generate 2,130 evaluator–proposal pairs. We find that evaluators systematically give lower scores to research proposals that are closer to their own areas of expertise and to those that are highly novel. The patterns are consistent with biases associated with boundedly rational evaluation of new ideas. The patterns are inconsistent with intellectual distance simply contributing “noise” or being associated with private interests of evaluators. We discuss implications for policy, managerial intervention, and allocation of resources in the ongoing accumulation of scientific knowledge.

Tweeting to Feel Connected: A Model for Social Connectedness in Online Social Networks

Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, a theoretical model of social connectedness based on the functional and structural characteristics of peoples communication behavior within an online social network is developed and tested. The study investigates how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, the study looks at the concurrent direct and moderating effect of two structural constructs characterizing peoples online social network: network size and frequency of usage. The research model is tested using data (n = 121) collected from two sources: (a) an online survey of Twitter users and (b) their usage data collected directly from Twitter. Results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, whereas network size has a moderating effect. Social presence is found to partially mediate the relationship between social awareness and social connectedness. The findings of the analysis are used to outline design implications for online social networks from a human–computer interaction perspective.

The Effect of Rating Scales on Decision Quality and User Attitudes in Online Innovation Communities

Given the rise of the Internet, consumers increasingly engage in co-creating products and services. Whereas most co-creation research deals with various aspects of generating user-generated content, this study addresses designing ratings scales for evaluating such content. In detail, we analyze functional and perceptional aspects of two frequently used rating scales in online innovation communities. Using a multimethod approach, our experiments show that a multicriteria scale leads to higher decision quality of users than a single-criterion scale, that idea elaboration (i.e., idea length) negatively moderates this effect such that the single-criterion rating scale outperforms the multicriteria scale for long ideas, and finally that the multicriteria scale leads to more favorable user attitudes toward the Web site. To ensure robustness of our results, we applied a bootstrap-based Monte Carlo simulation based on our experimental data. We found that around 20 user ratings per idea are sufficient for creating stable idea rankings and that a combination of both rating scales leads to a 63 percent performance improvement over the single-criterion rating scale and 16 percent over the multicriteria rating scale. Our work contributes to co-creation research by offering insights as to how the interaction of the technology being used (i.e., rating scale) and the attributes of the rating object affects two central outcome measures: the effectiveness of the rating in terms of decision quality of its users and the perception of the scale by its users as a predictor of future use.

Quality aspects in service ecosystems: areas for exploitation and exploration

Service Science, Management, and Engineering (SSME) is a research area with significant relevance to research and practice. Networked systems of web services are a field of service science that enjoys growing interest from researchers. The complex and dynamic environment of these service ecosystems poses new requirements on quality management that are insufficiently addressed by current approaches that focus mainly on the technical aspects of quality. This focus is a severe limitation for the development of service networks because it neglects perceived service quality from the viewpoint of service consumers. In this paper we propose a reference model for quality management in service ecosystems. This reference model is linked in particular to innovation and new service development. Towards the end we propose premises for the implementation and outline a future research agenda.

Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing

Information technology (IT) has created new patterns of digitally-mediated collaboration that allow open sourcing of ideas for new products and services. These novel sociotechnical arrangements afford finely-grained manipulation of how tasks can be represented and have changed the way organizations ideate. In this paper, we investigate differences in behavioral decision-making resulting from IT-based support of open idea evaluation. We report results from a randomized experiment of 120 participants comparing IT-based decision-making support using a rating scale (representing a judgment task) and a preference market (representing a choice task). We find that the rating scale-based task invokes significantly higher perceived ease of use than the preference market-based task and that perceived ease of use mediates the effect of the task representation treatment on the users’ decision quality. Furthermore, we find that the understandability of ideas being evaluated, which we assess through the ideas’ readability, and the perception of the task’s variability moderate the strength of this mediation effect, which becomes stronger with increasing perceived task variability and decreasing understandability of the ideas. We contribute to the literature by explaining how perceptual differences of task representations for open idea evaluation affect the decision quality of users and translate into differences in mechanism accuracy. These results enhance our understanding of how crowdsourcing as a novel mode of value creation may effectively complement traditional work structures.

Conceptualizing a bottom-up approach to service bundling

Offering service bundles to the market is a promising option for service providers to strengthen their competitive advantages, cope with dynamic market conditions and deal with heterogeneous consumer demand. Although the expected positive effects of bundling strategies and pricing considerations for bundles are covered well by the available literature, limited guidance can be found regarding the identification of potential bundle candidates and the actual process of bundling. The contribution of this paper is the positioning of bundling based on insights from both business and computer science and the proposition of a structured bundling method, which guides organizations with the composition of bundles in practice.

From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers

Organizations are increasingly turning to crowdsourcing to solve difficult problems. This is often driven by the desire to find the best subject matter experts, strongly incentivize them, and engage them with as little coordination cost as possible. A growing number of authors, however, are calling for increased collaboration in crowdsourcing settings, hoping to draw upon the advantages of teamwork observed in traditional settings. The question is how to effectively incorporate team-based collaboration in a setting that has traditionally been individual-based. We report on a large field experiment of team collaboration on an online platform, in which incentives and team membership were randomly assigned, to evaluate the influence of exogenous inputs (member skills and incentives) and emergent collaboration processes on performance of crowd-based teams. Building on advances in machine learning and complex systems theory, we leverage new measurement techniques to examine the content and timing of team collaboration. We find that temporal “burstiness�? of team activity and the diversity of information exchanged among team members are strong predictors of performance, even when inputs such as incentives and member skills are controlled. We discuss implications for research on crowdsourcing and team collaboration.