Abudulkadir Sajeev

Bio: Abudulkadir Sajeev is an academic researcher from University of New England (Australia). The author has contributed to research in topics: Public sector & Procurement. The author has an hindex of 11, co-authored 42 publications receiving 800 citations.

Critical factors that influence e-procurement implementation and success in the public sector

Abstract: This paper presents the results of a literature survey developed to support a proposed model of the Critical Success Factors (CSFs) likely to impact the success of e-Procurement initiatives in the public sector. It identifies a number of relevant variables for each CSF and presents a model for future research. It also analyses the relative importance of different CSFs and observes that organization and management factors are the most important category for success of e-Procurement initiatives. If e-Procurement initiatives in the public sector are to assist the development of e-Procurement across the information economy, there should be wider discussion and agreement on what constitutes the relevant CSFs and how the achievement of success can be assessed.

Critical factors that influence e-procurement implementation success in the public sector

Abstract: This paper presents the results of a literature survey developed to support a proposed model of the Critical Success Factors (CSFs) likely to impact the success of e-Procurement initiatives in the public sector. It identifies a number of relevant variables for each CSF and presents a model for future research. It also analyses the relative importance of different CSFs and observes that organization and management factors are the most important category for success of e-Procurement initiatives. If e-Procurement initiatives in the public sector are to assist the development of e-Procurement across the information economy, there should be wider discussion and agreement on what constitutes the relevant CSFs and how the achievement of success can be assessed.

Offshore outsourcing: the risk of keeping mum

Abstract: Breaking the code of silence.

An Empirical Study of Error Patterns in Industrial Business Process Models

Abstract: Business processes play an important role in organizations; however, not enough attention is given to analyzing and modeling errors in them. In this paper, we study syntactic and control flow error frequencies in business processes from real industry projects. Our samples come from a number of application domains such as Banking and Capital Markets, Insurance and Healthcare, and Retail. We consider industrial business processes modeled in Business Process Modeling Notation (BPMN) and use graph-theoretic techniques and Petri net-based analyses to detect syntactic and control flow-related errors, respectively. We then use a set of metrics that capture different network characteristics of the models and study the empirical relations between the metrics and process errors. The major results of the empirical investigation are: 1) multiple edges to or from tasks as well as hanging nodes are the predominant forms of syntactic errors, 2) syntactic errors occur frequently in Retail & Logistics domain and significantly less in the Insurance and Healthcare domain, and 3) the probability of error occurrence can be modeled as a function of node size and coefficient of connectivity through a logistic regression model which correctly classified 97.6 percent of the cases.

Using Multivariate Statistics

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A Novel Approach to Mapped Correlation of ID for RFID Anti-Collision

Abstract: One of the key problems that should be solved is the collision between tags which lowers the efficiency of the RFID system. The existed popular anti-collision algorithms are ALOHA-type algorithms and QT. But these methods show good performance when the number of tags to read is small and not dynamic. However, when the number of tags to read is large and dynamic, the efficiency of recognition is very low. A novel approach to mapped correlation of ID for RFID anti-collision has been proposed to solve the problem in this paper. This method can increase the association between tags so that tags can send their own ID under certain trigger conditions, by mapped correlation of ID, querying on multi-tree becomes more efficient. In the case of not too big number of tags, by replacing the actual ID with the temporary ID, the method can greatly reduce the number of times that the reader reads and writes to tag's ID. In the case of dynamic ALOHA-type applications, the reader can determine the locations of the empty slots according to the position of the binary pulse, so it can avoid the decrease in efficiency which is caused by reading empty slots when reading slots. Experiments have shown this method can greatly improve the recognition efficiency of the system.

Understanding the Occurrence of Errors in Process Models Based on Metrics

Abstract: Business process models play an important role for the management, design, and improvement of process organizations and process-aware information systems. Despite the extensive application of process modeling in practice, there are hardly empirical results available on quality aspects of process models. This paper aims to advance the understanding of this matter by analyzing the connection between formal errors (such as deadlocks) and a set of metrics that capture various structural and behavioral aspects of a process model. In particular, we discuss the theoretical connection between errors and metrics, and provide a comprehensive validation based on an extensive sample of EPC process models from practice. Furthermore, we investigate the capability of the metrics to predict errors in a second independent sample of models. The high explanatory power of the metrics has considerable consequences for the design of future modeling guidelines and modeling tools.