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The purpose of this paper is to varied a conceptual model for e-Tendering readiness in any construction organisation prior of implementing e-Tendering system.

Based on conceptual model called e-Tendering readiness model (e-TRM), this paper empirically examines the e-TRM’s interactions and causal relationships between e-Tendering constructs and e-Tendering readiness. The paper uses the structural equation modelling technique to test the hypothesised positive inter-relationships. A questionnaire survey is conducted for respondents of construction organisations in Saudi Arabia to understand their current e-Tendering readiness and importance of e-Tendering variables.

Supported by empirical evidence, this paper recognised that three out of nine constructs have direct influences on the e-Tendering readiness. However, one of the constructs, which is for the first time hypothesised and tested has the most effect.

Ultimately, the empirical test for the e-TRM is conducted in certain case (Saudi Arabia); however, the e-TRM needs to be tested in other case area for more verification.

The study findings update previous information technology/information system models in construction by adding this tested model to the research literature on traditional and electronic tendering and the body of knowledge in the construction industry.

The service providers construct is proposed and tested for the first time, which is necessary to support the successful e-Tendering implementation.

Unbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.

The identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.

The proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.

The data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

Communications regarding this column should be addressed to Mrs. Cheney, Peabody Library School, Nashville, Tenn. 37203. Mrs. Cheney does not sell the books listed here. They are available through normal trade sources. Mrs. Cheney, being a member of the editorial board of Pierian Press, will not review Pierian Press reference books in this column. Descriptions of Pierian Press reference books will be included elsewhere in this publication.

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

During the 20th century, the United States rapidly developed its research capacity by fostering a broad base of institutions of higher education led by a small core of highly productive research universities. By the latter half of the century, scientists in a greatly expanded number of universities across the United States published the largest annual number of scholarly publications in STEM+ fields from one nation. This expansion was not a product of some science and higher education centralized plan, rather it flowed from the rise of mass tertiary education in this nation. Despite this unprecedented productivity, some scholars suggested that universities would cease to lead American scientific research. This chapter investigates the ways that the United States’ system of higher education underpinned American science into the 21st century.

The authors present a historical and sociological case study of the development of the United States’ system of higher education and its associated research capacity. The historical and sociological context informs our analysis of data from the SPHERE team dataset, which was compiled from the Thomson Reuters’ Science Citation Index Expanded (SCIE) database.

We argue that American research capacity is a function of the United States’ broad base of thousands of public and broadly accessible institutions of higher education plus its smaller, elite sector of “super” research universities; and that the former serve to culturally support the later. Unlike previous research, we find that American higher education is not decreasing its contributions to the nation’s production of STEM+ scholarship.

The chapter provides empirical analyses, which support previous sociological theory about mass higher education and super research universities.