Towards interoperable E-Government – identifying and classifying G2B services in the European metropolitan area Rhine-Neckar

E-Government can transform and improve the entire scope of administrative actions and political processes. Hence, EGovernment is both, vision of a future government and the reality we can experience today. E-Government is not an objective per se; it has rather to be seen as measures of organizing public governance for better serving citizens and enterprises. Conversely, E-Government services are often developed and implemented by a top-down approach without reflecting the use of the services by involving the potential users. Hence, this paper advocates implementing services on regional level – assumed to be the social context of E-Government. The presented survey conducted in the European metropolitan area Rhine-Neckar (MRN) among self-employed citizens identified the most requested E-Government services in the region as a first step. The case of the MRN allowed classifying the services into several groups distinguishing by the level of regional influence. The case suggests that the more influence a region – or public administration at local level has – the more a bottom-up approach of identifying and implementing E-Government services should be followed. In decentralized structures such as the MRN the question is also raised whether E-Government programs should move outside the organizational boundaries of known hierarchical administrative structures

Warehousing and Analyzing Streaming Data Quality Information

The development of integrative IS architectures focuses typically on solving problems related to the functionality of the system. It is attempted to design optimally flexible interfaces that can achieve the most agile architecture. The quality of the data that will be exchanged across these interfaces is often disregarded (implicitly or explicitly). This results in distributed applications which are functionally correct but cannot be deployed due to the low quality of the data involved. In order to avoid wrong business decisions due to ‘dirty data’, quality characteristics have to be captured, processed, and provided to the respective business task. However, the issue of how to efficiently provide applications with information about data quality is still an open research problem. Our approach tackles the problems posed by data quality deficiencies by presenting a novel concept to stream and warehouse data together with its describing data quality information

Reflecting the Past Decades of ICIS, ECIS and AMCIS Proceedings - A Design Science Perspective

The methodological pluralism in IS research is topic of an ongoing discussion. Several claims have been made both in favor of methodological pluralism and against it. The debate focuses mainly the relationship between IS research methods (i.e. empirical/behavioural or constructional orientation) and the underlying IS research paradigms, especially positivism and interpretivism. As an integrative discipline Information System (IS) research has a multi-disciplinary and multinational focus per definition. Further investigating that methodical pluralism we want to investigate how the methodical discussion effected the development of Design Science Research in the last decade. Therefore, we conducted a literature analysis of the proceedings of the three major IS conferences over the last decade. Our analysis of more than 7500 articles showed that the Design Science Research agenda indeed differs from the common IS research agenda in respect to the use of methods and seems to be more open for a multi-methodological research approach

MADNESS OF THE CROWD - HOW BIG DATA CREATES EMOTIONAL MARKETS AND WHAT CAN BE DONE TO CONTROL BEHAVIOURAL RISK

In the recent years the term Big Data has been vividly discussed in management, the IS community and in the IT departments. Du to its potential for corporate performance and competitive advantage it has gained large attention up into the C-level-management. Observations on the possible negative consequnces of living in a data-driven world have mostly been limited to the perspective of an individual. For instance, concerns about data privacy have been vividly discussed when the growing hunger of governmental or private institutions for ever more and more personalized data was made public. This article starts with a critical reflection on the phenomena of Big Data, focusing on the consequnces for organizations and decision making. Next a case from the field of risk management is investigated in more detail using behavioural economics. Upon a series of experiments this paper sheds light on the possibility to create emotional markets using Big Data analytics in an un-reflected way. As a key takeaway this article should raise the awareness of behavioural risk. The presented work suggests extending the organizational risk framework by addressing behavioural risk

Towards the Development of CRM Capabilities for Leveraging Big Data Assets – A Conceptual Framework Derived from Literature

The growing availability of a high volume of continuously generated data being heterogeneous in structure, i.e. big data, represents a change of an organization’s data assets. While these novel (big) data assets promise valuable insights, e.g. into customers’ preferences and behavior, leveraging them requires the development of corresponding capabilities. By taking the domain of CRM as a functional reference domain, this paper investigates how capabilities for leveraging big data could be developed to create valuable relationships with customers. Based on a literature review of scholarly papers, a conceptual framework for leveraging big data in the domain of CRM is presented. From a preliminary validation of the framework grounded on the analysis of big data success stories, the paper suggests first evidence of our propositions. Future research is intended to further validate the framework in order to provide a systematic understanding of big data and relevant capabilities to leverage these assets

CAPABILITIES TO ACHIEVE BUSINESS INTELLIGENCE AGILITY – RESEARCH MODEL AND TENTATIVE RESULTS

The class of business intelligence (BI) systems is used as a basis for decision making in most big organizations. Extensive initiatives have been launched to accomplish adequate and timely decision support as an important factor to achieve and sustain competitive advantage. Within turbulent market environments it is challenging to keep up a distinguishable long-term strategy while quickly reacting to changing circumstances. This area of conflicts holds particularly true for BI as it is originally used to retrospectively reflect an organization’s performance and built upon stability and efficiency. Therefore, we investigate how dynamic BI capabilities, i.e. adoption of assets, market understanding and intimacy as well as business operations, impact the agility of BI. We approach our goal from a dynamic capability perspective. Starting from a literature review of dynamic capabilities of information systems (IS) and BI, we propose hypotheses to connect dynamic BI capabilities and BI agility. Derived hypotheses based on existing literature will be tested in our prospective research agenda. A small pre-study showed promising results. In-memory (IM) technology seems to be a technology enabler for agile BI. However, adoption of BI assets and the focus on market orientation and business operations may even intensify the positive effect

Increasing the Level of Customer Orientation - A Big Data Case Study from Insurance Industry

The paper positions Big Data as a challenge of information integration into existing analytical infrastructures. The presented arguments have been derived by means of a case study. The case is selected from the domain of insurance industry that intends to leverage the potential of Big Data for the purpose of increased customer orientation. Particularly the application of advanced analytics on a broader information base, i.e. include data that has been collected by the distributed sales force, promised to be a fruitful approach. Yet, we can mainly learn from areas in which the project initially failed. It will become obvious that the ability of a cross-functional process alignment is prerequisite to providing a consolidated view of customer information. It also seems to be essential for integrating external data sources. As a key take away, this paper will provide first heuristics and drafts a maturity model on how these challenges of integration will manifest themselves when applying Big Data techniques

Exploring the Future Shape of Business Intelligence: Mapping Dynamic Capabilities of Information Systems to Business Intelligence Agility

A major challenge in today’s turbulent environments is to make appropriate decisions to sustainably steer an organization. Business intelligence (BI) systems are often used as a basis for decision making. But achieving agility in BI and cope with dynamic environments is no trivial endeavor as the classical, data-warehouse (DWH)-based BI is primarily used to retrospectively reflect an organization’s performance. Using an exploratory approach, this paper investigates how current trends affect the concept of BI and thus their ability to support adequate decision making. The key focus is to understand dynamic capabilities in the field of information systems (IS) and how they are connected to BI agility. We therefore map dynamic capabilities from the IS literature to agility dimensions of BI. Additionally, we propose a structural model that focusses on DWH-based BI and analyze how current BI-related trends and environmental turbulence affect the way that BI is shaped in the future

BI Systems Managers’ Perception of Critical Contextual Success Factors: A Delphi Study

The present article investigates critical contextual success factors (CCSFs) that influence business intelligence (BI) system success in terms of their relevance and controllability. The initial set of CCSFs is based on an analysis of existing literature and serves as the basis for further exploration of these factors. Advances to previous studies are the validation of possible CCSFs influencing BI system design by domain experts in a Delphi Study and the multi-dimensional view of these factors. A carefully selected expert panel investigated CCSFs not only with regard to the dimensions of relevance – which is typical for ranking-type Delphi studies – they also assessed each factor in the dimension of controllability. This two-dimensional approach allowed us to identify five distinct clusters of CCSFs that influence BI system success. This paper contributes to information systems (IS) research on critical success factors in general and provides the BI domain with specific insights. The results contribute to the BI success factor literature and can potentially be generalized to other IS. BI managers may use the results to assess their daily challenges in BI system development and maintenance projects