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Experimental And Quasi Experimental Designs For Research

Models that depict innovation as a smooth, well-behaved linear process badly misspecify the nature and direction of the causal factors at work. Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization.

An Overview of Innovation

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Public Management Reform. A Comparative Analysis

Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

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Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model

Although a significant number of public organizations have embraced the idea of open data, many are still reluctant to do this. One root cause is that the publicizing of data represents a shift from a closed to an open system of governance, which has a significant impact upon the relationships between public agencies and the users of open data. Yet no systematic research is available which compares the benefits of an open data with the barriers to its adoption. Based on interviews and a workshop, the benefits and adoption barriers for open data have been derived. The findings show that a gap exists between the promised benefits and barriers. They furthermore suggest that a conceptually simplistic view is often adopted with regard to open data, one which automatically correlates the publicizing of data with use and benefits. Five ‘myths’ are formulated promoting the use of open data and placing the expectations within a realistic perspective. Further, the recommendation is given to take a user’s view and to actively govern the relationship between government and its users.

/pdf/benefits-adoption-barriers-and-myths-of-open-data-and-open-5ccio5woyu.pdf

Benefits, Adoption Barriers and Myths of Open Data and Open Government

https://repository.tudelft.nl/islandora/object/uuid%3A5f010109-d312-4af2-a388-3361a41b85a0/datastream/OBJ/download

Open data policies, their implementation and impact: A framework for comparison

https://cronfa.swan.ac.uk/Record/cronfa23851/Download/0023851-16122015125707.pdf

Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology

Open data ecosystems are expected to bring many advantages, such as stimulating citizen participation and innovation. However, scant attention has been given to what constitutes an open data ecosystem. The objective of this paper is to provide an overview of essential elements of open data ecosystems for enabling easy publication and use of open data. To achieve this objective, the literature has been reviewed and a scenario about the publication and use of open data has been analyzed. It was found that various applications, tools and portals are available which together can form an ecosystem. The best functionalities of this ecosystem can be selected and utilized by open data providers and users. To create an open data ecosystem at least four key elements should be captured, namely, 1) releasing and publishing open data on the internet, 2) searching, finding, evaluating and viewing data and their related licenses, 3) cleansing, analyzing, enriching, combining, linking and visualizing data and 4) interpreting and discussing data and providing feedback to the data provider and other stakeholders. Furthermore, to integrate the ecosystem elements and to let them act as an integrated whole, there should be three additional elements 5) user pathways showing directions for how open data can be used, 6) a quality management system and 7) different types of metadata to be able to connect the elements.

/pdf/innovation-with-open-data-essential-elements-of-open-data-3ux8jdskj1.pdf

Innovation with open data: Essential elements of open data ecosystems

Many public organizations are opening their data to the general public and embracing social media in order to stimulate innovation. These developments have resulted in the rise of new, infomediary business models, positioned between open data providers and users. Yet the variation among types of infomediary business models is little understood. The aim of this article is to contribute to the understanding of the diversity of existing infomediary business models that are driven by open data and social media. Cases presenting different modes of open data utilization in the Netherlands are investigated and compared. Six types of business models are identified: single-purpose apps, interactive apps, information aggregators, comparison models, open data repositories, and service platforms. The investigated cases differ in their levels of access to raw data and in how much they stimulate dialogue between different stakeholders involved in open data publication and use. Apps often are easy to use and provide predefined views on data, whereas service platforms provide comprehensive functionality but are more difficult to use. In the various business models, social media is sometimes used for rating and discussion purposes, but it is rarely used for stimulating dialogue or as input to policy making. Hybrid business models were identified in which both public and private organizations contribute to value creation. Distinguishing between different types of open data users was found to be critical in explaining different business models.

Anneke Zuiderwijk

Papers

Benefits, Adoption Barriers and Myths of Open Data and Open Government

Open data policies, their implementation and impact: A framework for comparison

Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology

Innovation with open data: Essential elements of open data ecosystems

Infomediary Business Models for Connecting Open Data Providers and Users