Bidding for Complex Projects: Evidence From the Acquisitions of IT Services

Competitive bidding (as auctions) is commonly used to procure goods and services. Public buyers are often mandated by law to adopt competitive procedures to ensure transparency and promote full competition. Recent theoretical literature, however, suggests that open competition can perform poorly in allocating complex projects. In exploring the determinants of suppliers’ bidding behavior in procurement auctions for complex IT services, we find results that are consistent with theory. We find that price and quality do not exhibit the classical tradeoff one would expect: quite surprisingly, high quality is associated to low prices. Furthermore, while quality is mainly driven by suppliers’ experience, price is affected more by the scoring rule and by the level of expected competition. These results might suggest that (scoring) auctions fail to appropriately incorporate buyers’ complex price/quality preferences in the tender design.Procurement Auctions, Scoring Rules, IT Contracts, Price/Quality Ratio

Evaluating eREVERSE auctions (EeRA): A research note

This eGISE network paper seeks to evaluate issues relating to the implementation of electronic reverse auctions (eRA) within local government procurement processes. The adoption of an eRA invites pre-qualified suppliers to compete with each other for a specified good or service. Consequently, there is a unique opportunity for the buyer to receive a reduced cost through the successful bidder. However, the literature identifies a number of adverse effects within these arrangements depending upon the nature of the buyer/supplier relationship. The objectives of the research involves identifying a set of business scenarios to demonstrate the impact of different eRA strategies in this respect. This will be achieved through a structured case analysis approach to enable qualitative data to be modelled through a visual toolset simulation. It is believed the outcome of the investigation will provide valuable insights into the complexities associated with the eProcurement process

Evaluation of anthropomorphic feedback for an online auction and affordances

This paper describes an experiment investigating the effectiveness and user satisfaction of using anthropomorphic feedback at the user interface. The context chosen was online bidding due to this kind of activity being very much used in current times by general users. The main results of the experiment were that there was a statistically significant effect observed for the time taken to place a bid in the anthropomorphic text condition. However there were no other significant effects for effectiveness issues and user satisfaction indicators. The results were also analysed in terms of the affordances and the main findings were that each of the four conditions tested in the experiment were probably equivalent in terms of their facilitating the affordances. Overall it may be more important to facilitate the affordances rather than a type of feedback being anthropomorphic in nature or not

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper