35 research outputs found
EXPLORING BARRIERS IN EXPERTISE SEEKING: WHY DON’T THEY ASK AN EXPERT?
Get PDFThis is the published version: Helms, R. W., Diemer, D. and Lichtenstein, S. 2011, Exploring barriers in expertise seeking
Who Reads Corporate Tweets? Network Analysis of Follower Communities
Get PDFSocial networking sites became very popular since the introduction of Six Degrees in 1997 and companies started to utilize them to build online communities. This research aims to further understand online communities by analyzing the network structure and composition of Twitter follower networks. An explorative analysis is conducted on the Twitter follower network of Europe’s twenty-five largest product software vendors, which includes 95,895 unique followers. A proprietary tool was built for data collection and social network analysis was used to analyze the data. Structural analysis shows that the networks have small-world characteristics and have high reciprocity in terms of following relationships. Analyzing the composition of the networks shows that a) they have a small internal audience and large external audience, b) Twitter is used for monitoring competitors by some, and c) the companies have very distinct and unique follower communities. Especially the uniqueness of the communities demonstrates the value of such communities to companies
Developments in knowledge discovery processes and methodologies:Anything new?
No full textThe process of turning data into knowledge is referred to as “knowledge discovery” (KD) and originated in the 1990s. Since that time many different process models and methodologies have been developed. A genealogy presented in 2010, showed how the different models evolved and presented a refined process model, which represents a synthesis of the models presented before. However, the rise of data analytics and big data have changed how organizations do business. The key to these changes is to use data and turn it into knowledge to create value for the organization. Therefore, this study aims to update our understanding of knowledge discovery processes by reviewing the research into KD processes since 2010 in order to understand if there have been considerable changes and developments in this field. The developments in KD process models and methodologies that were found are threefold: tasks, steps and agile practices
ASSESSING THE NEW WAY OF WORKING: BRICKS, BYTES AND BEHAVIOUR
Get PDFAbstract The world in which we work is changing. Informatio
Data Analytics Project Methodologies: Which one to choose?
Get PDFDevelopments in big data have led to an increase in data analytics projects conducted by organizations. Such projects aim to create value by improving decision making or enhancing business processes. However, many data analytics projects still fail to deliver the expected value. The use of process models or methodologies is recommended to increase the success rate of these projects. Nevertheless, organizations are hardly using them because they are considered too rigid and hard to implement. The existing methodologies often do not fit the specific project characteristics. Therefore, this research suggests grouping different project characteristics to identify the most appropriate project methodology for a specific type of project. More specifically, this research provides a structured description that helps to determine what type of project methodology works for different types of data analytics projects. The results of six different case studies show that continuous projects would benefit from an iterative methodology
Reference Model for Generic Capabilities in Maturity Models
Get PDFMany Maturity Models (MMs) have been designed for over 40 years now but selecting the constructs which chart the application areas is at variance. When comparing MMs, application area-specific constructs appear to be divers. Nevertheless, some constructs are often similar. This research aims at finding generic constructs in existing MMs as reference for con-structing MMs. We conducted literature research for generic MM constructs in organisational readiness MMs. We applied card sorting as a classification method and sorted cards according to Metaplan technique with peers. This research resulted in a limited set of generic capabilities for constructing MMs. Organising these capabilities according to widely accepted reference models in Information Systems (IS) literature results in the Generic Capability Reference (GCR) model. The GCR model serves as a reference model for (re-) designing MMs for the part of the generic capabilities in MMs besides application area-specific capabilities.</p
Predictive quality performance control in BPM: proposing a framework for predicting quality anomalies
No full textTowards a Framework for Data Analytics Governance Mechanisms
Get PDFThe rise of big data has led to many new opportunities for organizations to create value from data. However, at the same time the increasing dependence on data poses many challenges for organizations in managing data analytics activities. For example, data analytics activities are fragmented across the organization resulting in incompatible outcomes. This inhibits the organization from gaining full potential of their data analytics activities. To overcome these challenges organizations have to implement governance for their data analytics activities. IT and Data Governance literature shows that governance can be implemented through several types of governance mechanisms: structural, procedural and relational mechanisms. However, the literature is not very abundant when it comes to describing these mechanisms. Therefore, there is a need to identify data analytics governance mechanisms to better understand how data analytics governance can be achieved. To this end, a literature review was conducted to identify a preliminary framework. The framework was validated, and extended, in three case studies by identifying practical implementations of governance mechanisms. It resulted in an extended reference framework for data analytics governance describing several structural, process and relational mechanisms. This framework can assists managers in designing data analytics governance mechanisms for their specific organization
