A Historical Perspective on Information Systems: A Tool and Methodology for Studying the Evolution of Social Representations on Wikipedia

In recent years, scholars’ interest in developing historically informed explanations of information systems has surged. Several scholars have suggested that doing so can help information systems scholars to examine shifts in the academic nature of our discipline, trace the origins of prominent information systems phenomena, and reflect on and critique their own work. To enable such inquiry, we draw on the theory of social representation to build an analytical tool, WikiGen, and develop a methodology for examining the evolution of collective knowledge on Wikipedia. We demonstrate the usefulness of the tool and methodology by applying it to an illustrative case study, the Wikipedia article on cloud computing. After presenting the results of the analysis, we discuss the applicability of the tool and methodology, the contributions of our study, and possibilities for future research

Tool Support for Design Science Research—Towards a Software Ecosystem: A Report from a DESRIST 2017 Workshop

The information systems (IS) field contains a rich body of knowledge on approaches, methods, and frameworks that supports researchers in conducting design science research (DSR). It also contains some consensus about the key elements of DSR projects—such as problem identification, design, implementation, evaluation, and abstraction of design knowledge. Still, we lack any commonly accepted tools that address the needs of DSR scholars who seek to structure, manage, and present their projects. Indeed, DSR endeavors, which are often complex and multi-faceted in nature and involve various stakeholders (e.g., researchers, developers, practitioners, and others), require the support that such tools provide. Thus, to investigate the tools that DSR scholars actually need to effectively and efficiently perform their work, we conducted an open workshop with DSR scholars at the 2017 DESRIST conference in Karlsruhe, Germany, to debate 1) the general requirement categories of DSR tool support and 2) the more specific requirements. This paper reports on the results from this workshop. Specifically, we identify nine categories of requirements that fall into the three broad phases (pre-design, design, and post design) and that contribute to a software ecosystem for supporting DSR endeavors

An Empirical Study of Behavioral Factors Influencing Text Messaging Intention

This manuscript provides a comprehensive review of many of the behavioral factors associated with the use of technology and tests their applicability to text messaging. The theories explored included End User Computer Satisfaction, Theory of Reasoned Action, Diffusion of Innovation, Theory of Planned Behavior, and Technology Acceptance Model. In addition, Positive and Negative Emotion factors were developed and tested to examine their influence on text messaging behavioral intention. Several statistical processes were utilized to develop and confirm the factors. The results of the study suggest that no one model can fully explain texting behavior but several factors did have a significant influence on intention at p \u3c .05. These factors were Attitude, Compatibility, Ease of Use, Satisfaction, and Visibility. These factors can serve as areas that practitioners and researchers can focus on to improve text messaging intention and obtain the significant benefits of this technology

A Framework for the Implementation of RFID Systems

There are an increasing number of organizations planning to implement Radio Frequency Identification (RFID) systems to enhance their competitiveness. Due to the novelty of the technology, many managerial challenges exist in determining and planning for the implementation of such systems. These challenges often lead to wasted efforts and resources, as well as to failed implementations. This paper presents a systematic and holistic RFID implementation framework which has been validated by both users and experts. The framework outlines the important tasks to be performed in each step of the implementation process. To enable practitioners to make informed go/no‐go decisions, essential considerations of implementation are also discussed in this paper. Furthermore, the critical success factors for the deployment of such systems are also elaborated

Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications