Corruption in the Public Construction Sector: A Holistic View [1 ed.] 9789811395499, 9811395497

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Ming Shan · Yun Le · Albert P. C. Chan · Yi Hu

Corruption in the Public Construction Sector A Holistic View

Corruption in the Public Construction Sector

Ming Shan Yun Le Albert P. C. Chan Yi Hu •

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Corruption in the Public Construction Sector A Holistic View

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Ming Shan School of Civil Engineering Central South University Changsha, Hunan, China

Yun Le School of Economics and Management Tongji University Shanghai, China

Albert P. C. Chan Department of Building and Real Estate The Hong Kong Polytechnic University Kowloon, Hong Kong

Yi Hu School of Economics and Management Tongji University Shanghai, China

ISBN 978-981-13-9549-9 ISBN 978-981-13-9550-5 https://doi.org/10.1007/978-981-13-9550-5

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

According to the well-recognized Bribe Payer’s Index released by Transparency International, the public construction sector has been found the most corrupt sector in the world. Such a judgment is not new and is mainly due to the unique characteristics of the public construction sector. For instance, contracts in this sector are usually large and construction projects in this sector are often exclusive and unique, making it difficult to benchmark for costs and time, and thereby making it easier to hide and inflate additional expenditure. Furthermore, the public construction sector is a fragmented sector often involving many parties such as government, clients, contractors, subcontractors, consultancies, and suppliers, making the tracing of payments complex and challenging as well. Apart from that, the public construction sector is relatively more prosperous in developing countries where established legal systems may lack, which also ‘encourages’ the corruption in the sector largely. The costs of corruption in the public construction sector are also extremely damaging. Poor procurement, contract, and investment decisions caused by corruption easily cheat the public out of their money. Also, they are harmful to the long-term growth prospects of countries, particularly in the developing countries where the public construction is so important. Corruption can also affect the quality of projects in the public construction sector, which will further threaten the safety and health of the public who are using the public buildings and facilities every day. More importantly, corruption in the public construction sector will tarnish the government and deprive the government of the trust of public, causing social stability issue eventually. This book presents a holistic view on corruption in the public construction sector and was structured into nine chapters. Chapter 1 introduces the motivation, scope, and objectives of this book. It also presents the significance and the structure of this book. Chapter 2 provides a comprehensive review of the corruption research in the area of construction engineering and management. The review covers a broad range of topics including forms of corruption, impacts of corruption, and the prevailing anti-corruption strategies. Chapter 3 systematically investigates the underlying factors of corruption in the public construction sector. Chapter 4 explores the

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principal causes of corruption in the public construction sector. Chapter 5 assesses the effectiveness of the prevailing anti-corruption strategies in the public construction sector. Chapter 6 proposes an assessment model that can measure corruption in the public construction projects. Chapter 7 conducts a systematic examination of collusive practices in the public construction projects. Chapter 8 develops an artificial neural network model that can assess the collusion risks in the public construction projects. Lastly, Chap. 9 provides a comprehensive summary and lists up specific recommendations for the future research of corruption. Although corruption is a critical problem in the public construction sector and has attracted considerable attention from various parties including authorities, industries, and academics, there remains a lack of books on this issue. Therefore, this book bridges the knowledge gap and contributes to the current body of knowledge of corruption research. Furthermore, the findings from this book can enhance the policymakers and industry practitioners’ understanding of corruption in the public construction sector, thus benefiting the practice as well. Changsha, China Shanghai, China Kowloon, Hong Kong Shanghai, China

Ming Shan Yun Le Albert P. C. Chan Yi Hu

Contents

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1 1 2 4 4 7 8

2 Corruption in Construction: A Global Review . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Corruption in Construction . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Literature Search Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Major Research Areas of Corruption in Construction . . . . . 2.4.1 Forms of Corruption in Construction . . . . . . . . . . . 2.4.2 Impacts of Corruption in Construction . . . . . . . . . . 2.4.3 Anti-corruption Strategies . . . . . . . . . . . . . . . . . . . . 2.5 Research Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Identification of Corruption in Construction in Developing Countries . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Evaluation of Corruption in Construction . . . . . . . . 2.5.3 Examination of the Effectiveness of Anti-corruption Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Underlying Factors of Corruption in the Public Construction Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 Introduction . . . . . . . . . . . . . . . . . 1.1 Research Motivation . . . . . . . . 1.2 Research Scope and Objectives 1.3 Research Approach . . . . . . . . . 1.4 Significance and Value . . . . . . 1.5 Structure of the Book . . . . . . . References . . . . . . . . . . . . . . . . . . .

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3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Corruption in the Construction Industry . . . . . . . . . . 3.2.2 Corruption in the Chinese Public Construction Sector 3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Structured Interviews . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Questionnaire Survey . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Stepwise Multiple Regression Analysis . . . . . . . . . . . 3.3.5 Partial Least Squares-Structural Equation Modelling . 3.3.6 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Stepwise Multiple Regression Analysis Results . . . . . 3.4.3 Partial Least Squares Structural Equation Modelling Analysis Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Discussions and Recommendations . . . . . . . . . . . . . . . . . . . 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Principal Causes of Corruption in the Public Construction Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Causes of Corruption . . . . . . . . . . . . . . . . . . . . . 4.2.2 Vulnerabilities to Corruption . . . . . . . . . . . . . . . 4.2.3 Hypothesis Development . . . . . . . . . . . . . . . . . . 4.3 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Structured Interviews . . . . . . . . . . . . . . . . . . . . . 4.3.2 Questionnaire Survey . . . . . . . . . . . . . . . . . . . . . 4.4 Tools for Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 PLS-SEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Evaluation of Measurement Models . . . . . . . . . . 4.5.3 Evaluation of Hierarchical Models . . . . . . . . . . . 4.5.4 Evaluation of Structural Model . . . . . . . . . . . . . . 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 The Flawed Regulation Systems . . . . . . . . . . . . . 4.6.2 Lack of Positive Industrial Climate . . . . . . . . . . .

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4.7 Research Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Effectiveness of Prevailing Anti-corruption Strategies . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Conceptual Framework and Hypothesis Development . 5.2.1 Corruption Vulnerabilities . . . . . . . . . . . . . . . 5.2.2 Response Strategies . . . . . . . . . . . . . . . . . . . . 5.2.3 Hypothesis Development . . . . . . . . . . . . . . . . 5.3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Questionnaire Survey . . . . . . . . . . . . . . . . . . . 5.4 Tools for Data Analysis . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 PLS-SEM . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Analysis Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Evaluation of Measurement Models . . . . . . . . 5.5.3 Evaluation of Hierarchical Models . . . . . . . . . 5.5.4 Evaluation of Structural Models . . . . . . . . . . . 5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Rules and Regulations . . . . . . . . . . . . . . . . . . 5.6.3 Sanctions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.4 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Measuring Corruption in Public Construction Project: A Case of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Semi Structured Interviews . . . . . . . . . . . . . . 6.3.2 Questionnaire Survey . . . . . . . . . . . . . . . . . . 6.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Model Development—Fuzzy Measurement . . . . . . . 6.6 Illustrative Case . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Research Limitation . . . . . . . . . . . . . . . . . . . . . . . . 6.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Collusive Practices in Public Construction Projects: A Case of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Research Methods . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Delphi Survey . . . . . . . . . . . . . . . . . . . . . 7.3.2 Questionnaire Survey . . . . . . . . . . . . . . . . 7.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Discussions of the Primary Collusive Practices . . . 7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Assessing Collusion Risks in Public Construction Projects: A Case of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Delphi Interview . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Artificial Neural Network . . . . . . . . . . . . . . . . . . 8.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Identification of Collusive Practices . . . . . . . . . . 8.3.2 Delphi Interview . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Development of ANN Model . . . . . . . . . . . . . . . . . . . . 8.4.1 Calculations of Inputs . . . . . . . . . . . . . . . . . . . . 8.4.2 Training, Validating, and Testing of Network . . . 8.4.3 Validation of the Model . . . . . . . . . . . . . . . . . . . 8.5 Model Application and Discussion . . . . . . . . . . . . . . . . 8.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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9 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . 9.1 Research Findings and Conclusions . . . . . . . . . . . . . . . . . . . . . 9.1.1 Underlying Factors of Corruption in the Public Construction Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 Principal Causes of Corruption in the Public Construction Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.3 Effectiveness of Prevailing Anti-corruption Strategies . . 9.1.4 A Measurement Model for Corruption in Public Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.5 Primary Collusive Practices in Public Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.6 An ANN Model to Assess Collusion Risks in Public Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . .

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9.2 Contributions to the Literature and to the Practice . . . . . . . . . . . . 183 9.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 9.4 Recommendations for Future Research . . . . . . . . . . . . . . . . . . . . 184

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 3.1 Fig. Fig. Fig. Fig. Fig.

3.2 4.1 4.2 4.3 5.1

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

5.2 5.3 6.1 6.2 7.1 8.1 8.2 8.3

Objectives of the book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overall flow of the book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The hypothesized structural equation model of the underlying factors of corruption and the overall corruption situation . . . . . Network of the related parties in the case study . . . . . . . . . . . . Initial theoretical model and research hypothesis . . . . . . . . . . . Refined proposed theoretical model . . . . . . . . . . . . . . . . . . . . . Testing results of the theoretical model . . . . . . . . . . . . . . . . . . Hypothesized model of corruption vulnerabilities and response strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Refined hypothesized model . . . . . . . . . . . . . . . . . . . . . . . . . . . Testing results of the hypothesized model . . . . . . . . . . . . . . . . Membership functions of linguistic values . . . . . . . . . . . . . . . . Values of various constructs of the illustrative case . . . . . . . . . Collusion network in construction projects . . . . . . . . . . . . . . . . MSE versus the number of hidden neurons . . . . . . . . . . . . . . . MAPE versus the number of hidden neurons . . . . . . . . . . . . . . Configuration of the developed model . . . . . . . . . . . . . . . . . . .

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List of Tables

Table Table Table Table Table Table Table

1.1 3.1 3.2 3.3 3.4 3.5 3.6

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3.7 3.8 3.9 4.1 4.2 4.3 4.4 4.5 4.6 4.7

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4.8 4.9 5.1 5.2 5.3 5.4 5.5

Table 5.6 Table 5.7 Table 5.8

Research objectives and research methods . . . . . . . . . . . . . . . IRCs identified from structured interviews . . . . . . . . . . . . . . . Profile of the respondents. . . . . . . . . . . . . . . . . . . . . . . . . . . . Factor analysis result of IRCs . . . . . . . . . . . . . . . . . . . . . . . . Stepwise multiple regression analysis results . . . . . . . . . . . . . Evaluation results of the hypothesized model. . . . . . . . . . . . . Correlation matrix and the square root of each UFC’s AVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross loadings for IRCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation results of the structural model . . . . . . . . . . . . . . . Irregularities related to corruption involved in the case . . . . . Backgrounds of interviewees . . . . . . . . . . . . . . . . . . . . . . . . . Measurement items of causes of corruption . . . . . . . . . . . . . . Measurement items of vulnerabilities to corruption . . . . . . . . Added measurement items and evaluations . . . . . . . . . . . . . . Backgrounds of respondents . . . . . . . . . . . . . . . . . . . . . . . . . Measurement model evaluation . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix and square root of average variance extracted of constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross loadings for individual measurement items . . . . . . . . . Evaluation of hierarchical models . . . . . . . . . . . . . . . . . . . . . Backgrounds of interviewees . . . . . . . . . . . . . . . . . . . . . . . . . Measurement items of corruption vulnerabilities . . . . . . . . . . Sources and evaluations of added measurement items . . . . . . Backgrounds of respondents . . . . . . . . . . . . . . . . . . . . . . . . . Factor analysis results of measurement items of response strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of measurement models . . . . . . . . . . . . . . . . . . . . Correlation matrix and square root of average variance extracted of constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross loadings for individual measurement items . . . . . . . . .

. . . . . .

. . . . . .

5 28 29 32 33 35

. . . . . . . . . .

. . . . . . . . . .

35 36 36 40 54 55 57 61 63 66

. . . . . . .

. . . . . . .

66 67 69 84 85 89 90

.. ..

93 94

.. ..

95 96 xv

xvi

List of Tables

Table Table Table Table Table Table Table Table

5.9 6.1 6.2 6.3 6.4 6.5 6.6 6.7

Table Table Table Table Table Table Table Table Table Table Table Table Table

6.8 7.1 7.2 7.3 7.4 7.5 7.6 7.7 8.1 8.2 8.3 8.4 8.5

Table 8.6

Evaluation results of hierarchical models . . . . . . . . . . . . . . . . Backgrounds of interviewees . . . . . . . . . . . . . . . . . . . . . . . . . Measurement items refined by the interviewees . . . . . . . . . . . Measurement items supplemented by the interviewees . . . . . . Profile of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluations of the measurement items for corruption . . . . . . . Factor analysis results and weighting calculation . . . . . . . . . . Correlation matrix among the five constructs of measurement items of corruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illustrative example of the model application . . . . . . . . . . . . . Collusive practice papers identified from literature review . . . Collusive practices identified from literature review . . . . . . . . Profile of the Delphi panel . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the two-round Delphi survey . . . . . . . . . . . . . . . . . Profile of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rankings of collusive practices . . . . . . . . . . . . . . . . . . . . . . . Statistical test results of collected data . . . . . . . . . . . . . . . . . . Profile of the Delphi panel . . . . . . . . . . . . . . . . . . . . . . . . . . . Collusive practices spotted in existing literature . . . . . . . . . . . Results of the two-round Delphi interview . . . . . . . . . . . . . . . Profile of questionnaire respondents . . . . . . . . . . . . . . . . . . . . Evaluations of collusive practices and the relevant statistical test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of actual and assessed results of collusion risk . .

. . . . . . .

. . . . . . .

98 108 110 113 114 115 118

. . . . . . . . . . . . .

. . . . . . . . . . . . .

119 123 135 138 140 141 143 144 146 157 162 167 168

. . 169 . . 173

Chapter 1

Introduction

1.1 Research Motivation Corruption exists in both developed and developing countries of various political and economic systems, and its occurrence is highly associated with the economic growth and development stages of the country (Bardhan 1997; Ehrlich and Lui 1999). As a result of the continued economic growth and rapid urbanization worldwide over the past decades, investments in infrastructure and urban development projects increase dramatically (Gupta and Abed 2002), which also triggers the increased vulnerabilities to corruption in managing these projects (Goldie-Scot 2008). Corruption can bring about many kinds of negative effects. It can reduce economic efficiency and growth, inhibit provisions of public services, and result in income inequality (Gupta 1998). This wrong doing has been identified as the greatest obstacle to the global economic and social development (Marquette 2001). As a core industrial sector, construction industry plays a vital role in building national economies and has constantly contributed to improving the built environment of human societies (Hillebrandt 2000). However, these positive social images have been increasingly diminished by the issue of corruption in recent years (Le et al. 2014b). Corruption has damaged the construction industry at multiple levels causing many problems including quality defects, cost and time overruns, failure in upholding professional ethics (Chan and Owusu 2017). According to Sohail and Cavill (2008), the annual loss caused by corruption in the global construction market reaches about USD 340 billion, accounting for 1% of the global construction market value (about USD 3.2 trillion). Compared with private construction sector, public construction sector is particularly important from the perspective of development. This is because public construction sector requires decisions to be made with respect to the use and ownership of a country’s core resources and infrastructure, which has profound consequences for the well-being of future generations (Le et al. 2014a). Based on the Bribe Payers Index issued by the Transparency International (1999, 2002, 2006, 2008, 2011), public construction sector has consistently been regarded as the most corrupt sector around © Springer Nature Singapore Pte Ltd. 2020 M. Shan et al., Corruption in the Public Construction Sector, https://doi.org/10.1007/978-981-13-9550-5_1

1

2

1 Introduction

the world in the past decade. Thus, it is important and necessary to examine the issue of corruption in the public construction sector. However, current literature shows that little research has been done in this regard. As a result, this book attempts to bridge the knowledge gap by addressing the issue of corruption in the public construction sector. Moreover, this book also investigates the issue of collusion. According to Le et al. (2014b), collusion is the most common form of corruption in the public construction sector.

1.2 Research Scope and Objectives A comprehensive understanding of vulnerabilities to corruption is necessary to preventing corruption and achieving integrity and transparency in the public construction sector. Only by understanding the baseline of corruption, that is, how much corruption, in what forms, and what causes it, could effective policy responses be formulated. In addition, this book also investigates the issue of collusion particularly, which is the most common form of corruption in the public construction sector (Le et al. 2014b). This book attempts to address the following research problems: (1) What are the underlying factors of corruption in the public construction sector? (2) What are the principal causes of corruption in the public construction sector? (3) What are the prevailing anti-corruption strategies in the public construction sector? How is their effectiveness? (4) How can corruption in public construction projects be assessed? (5) What are the common collusive practices in public construction projects? (6) Can the collusion risk in public construction projects be predicted? To tackle these problems, ten objectives were set as follows: Objective 1 To identify the corruption indicators in the public construction sector; Objective 2 To identify the causes of corruption in the public construction sector; Objective 3 To identify the prevailing anti-corruption strategies in the public construction sector; Objective 4 To evaluate the perceived level of corruption in the public construction sector; Objective 5 To explore the underlying factors of corruption in the public construction sector; Objective 6 To explore the principal causes of corruption in the public construction sector; Objective 7 To check the effectiveness of the prevailing anti-corruption strategies in the public construction sector; Objective 8 To develop an evaluation model to assess the vulnerability to corruption in public construction projects; Objective 9 To identify the common practices of collusion in public construction projects;

1.2 Research Scope and Objectives

3

Objective 10 To develop an assessment model that can predict the collusion risks in public construction projects. Figure 1.1 presents the contents of the book by illustrating the relationships among different objectives. As depicted in Fig. 1.1, this book begins by identifying corruption indicators (Objective 1), causes of corruption (Objective 2), prevailing anticorruption strategies (Objective 3), and the perceived level of corruption (Objective 4) in the public construction sector. Then this book explores the underlying factors of corruption (Objective 5) by investigating the relationships between the underlying factors of corruption and the perceived level of corruption in the public construction

Objective 1

Objective 4

Identify corruption indicators in the public construction sector

Perceived level of Corruption in the public construction sector

Objective 2

Objective 5

Objective 3

Identify causes of corruption in the public construction sector

Explore underlying factors of corruption in the public construction sector

Identify prevailing anticorruption strategies in the public construction sector

Objective 6

Objective 7

Explore principal causes of corruption in the public construction sector

Check the effectiveness of the prevailing anticorruption strategies

Objective 8 Develop a model to assess the vulnerability to corruption

Objective 9

Objective 10

Identify common collusive practices in public construction projects

Develop a model to predict collusion risks in public construction projects

Fig. 1.1 Objectives of the book

4

1 Introduction

sector. Subsequently, principal causes of corruption (Objective 6) were explored by investigating the causal relationships between causes of corruption and the underlying factors of corruption. After that, the effectiveness of the prevailing anti-corruption strategies (Objective 7) was checked by investigating the relationships between the prevailing anti-corruption strategies and the underlying factors of corruption. Then, an evaluation model that can assess the vulnerability to corruption (Objective 8) was developed based on the corruption indicators and the underlying factors of corruption in the public construction sector. Lastly, this book identified the common collusive practices that may occur in public construction projects and proposed a model that can help predict the collusion risks in public construction projects.

1.3 Research Approach Nine research methods, including qualitative and quantitative research methods, were adopted to conduct the research work described in the book. These research methods are (1) literature review, (2) structured interview, (3) Delphi survey, (4) questionnaire survey, (5) exploratory factor analysis, (6) partial least squares structural equation modelling (PLS-SEM), (7) fuzzy set theory, (8) case study, and (9) artificial neural network approach. To be specific, the corruption indicators, causes of corruption, and prevailing anti-corruption strategies in the public construction sector were first identified through a comprehensive literature review and a series of structured interviews first. Then data were collected by conducting a questionnaire survey with target respondents having public project experiences. After that, underlying factors of corruption, principal causes of corruption, and the constructs of anti-corruption strategies were obtained through exploratory factor analysis, together with PLS-SEM analysis and case study. Subsequently, an evaluation model was developed to measure the vulnerability to corruption in Chinese public projects using the approach of fuzzy set theory. Lastly, common collusive practices in public construction projects were identified from a Delphi survey. A model that can predict collusion risks in a given project was developed using the approach of artificial neural network. Table 1.1 summarizes the research methods adopted for each research objective. Figure 1.2 shows the overall flow of the study.

1.4 Significance and Value The issue of corruption should be addressed not only because it is a moral issue that negatively affects the construction industry, but also because people everywhere, particularly those in developing countries that are undergoing transition economies, pay the price of corruption in many ways. It has been widely proved that corruption can create poverty and inequality (Gupta 1998; Shacklock et al. 2016). Although corruption has been widely investigated in the fields of social science and public

1.4 Significance and Value

5

Table 1.1 Research objectives and research methods Research objectives

Research methods

Objective 1: to identify the corruption indicators in the public construction sector

• Literature review • Structured interview

Objective 2: to identify the causes of corruption in the public construction sector

• Literature review • Structured interview

Objective 3: to identify the prevailing anti-corruption strategies in the public construction sector

• Literature review • Structured interview

Objective 4: to obtain the perceived level of corruption in the public construction sector

• Questionnaire survey

Objective 5: to explore the underlying factors of corruption in the public construction sector

• • • •

Objective 6: to explore the principal causes of corruption in the public construction sector

• Questionnaire survey • Exploratory factor analysis • PLS-SEM

Objective 7: to check the effectiveness of the prevailing anti-corruption strategies in the public construction sector

• Questionnaire survey • Exploratory factor analysis • PLS-SEM

Objective 8: to develop an evaluation model that can assess the vulnerability to corruption in public construction projects

• • • •

Objective 9: to identify the common collusive practices in public construction projects

• Delphi survey • Questionnaire survey

Objective 10: to develop a model that can predict collusion risks in public construction projects

• Questionnaire survey • Artificial neural network • Case study

Questionnaire survey Exploratory factor analysis PLS-SEM Case study

Questionnaire survey Exploratory factor analysis Fuzzy set theory Case study

management (Ades and Di Tella 1999; Andvig et al. 2001; Bardhan 1997; Lambsdorff 1999), little attention was given to the corruption issue in the public construction sector (Le et al. 2014b; Tabish and Jha 2012), the most corrupt sector in the world (Transparency International 2011). Therefore, this book can contribute to the current body of knowledge. This book carried out a comprehensive investigation into the corruption issue in the public construction sector. It identified the indicators of corruption, causes of corruption, prevailing anti-corruption strategies, and collusive practices in the public construction sector. It explored the underlying factors of corruption, the principal causes of corruption, and the effectiveness of the prevailing anti-corruption strategies in the public construction sector. It also developed a model that can assess vulnerability to corruption in public construction projects, and a model that can predict collusion risk in public construction projects. This book can provide the industry and the academia with a good and clear understanding of vulnerabilities to cor-

6

1 Introduction Research Strategies

Research Input

Research Process

Research Output

Research Objectives

Literature review & structure interview

Information on corrupt practices in construction

Data consolidation

Corruption indicators in the public construction sector

1

Literature review & structure interview

Information on causes of corruption in construction

Data consolidation

Causes of corruption in the public construction sector

2

Literature review & structure interview

Information on anti-corruption strategies in construction

Data consolidation

Prevailing anticorruption strategies in the public construction sector

3

Questionnaire survey

Evaluation of the level of corruption

Data consolidation

Perceived level of corruption in the the public construction sector

4

Questionnaire survey

Corruption indicators & perceived level of corruption

Exploratory factor analysis PLS-SEM

Underlying factors of corruption in the public construction sector

5

Questionnaire survey

Causes of corruption & corruption indicators

Exploratory factor analysis PLS-SEM

Principal causes of corruption in the public construction sector

6

Questionnaire survey

Prevailing anticorruption strategies & corruption indicators

Exploratory factor analysis PLS-SEM

Effectiveness of prevailing anticorruption strategies

7

Questionnaire survey

Corruption indicators

Fuzzy model to evaluate vulnerability to corruption

8

Literature review & structure interview

Information on collusive practices

Delphi survey

Common collusive practices in public construction projects

9

Questionnaire survey

Information on collusive practices

Artificial neural network

Model to predict collusion risks in public construction projects

10

Fig. 1.2 Overall flow of the book

Fuzzy set theory

1.4

Significance and Value

7

ruption in the public construction sector, thereby helping to develop more effective countermeasures against corruption.

1.5 Structure of the Book The structure of the book is as follows: This chapter introduced the book by discussing the research background, research problems, the significance and value of research, research objectives, research process of the study, as well as the structure of the book. Chapter 2 presented a comprehensive literature review of corruption research in the field of construction engineering and management. The review covered the definitions, forms, causes, and impacts of corruption, as well as the prevailing anticorruption strategies being implemented in the public construction sector. Chapter 3 identified the corruption indicators in the public construction sector. It also assessed the perceived level of corruption in the public construction sector. Then it explored the underlying factors of corruption by testing the hypothetical relationship between corruption indicators and the perceived level of corruption. Chapter 4 identified the causes of corruption in the public construction sector. It then explored the principal causes of corruption in the public construction sector by testing the hypothetical relationship between causes of corruption and corruption indicators. Chapter 5 identified the prevailing anti-corruption strategies in the public construction sector. It then checked the effectiveness of those prevailing anti-corruption strategies by testing the hypothetical relationship between the prevailing anticorruption strategies and corruption indicators. Chapter 6 developed an evaluation model to assess the vulnerability to corruption in public construction projects. Two illustrative applications of the model in real public construction projects were also provided in the chapter. Chapter 7 identified and assessed the common collusive practices in public construction projects. The top ten collusive practices were thoroughly discussed in the chapter. Chapter 8 developed a model that can help industry practitioners predict collusion risks in public construction projects. The chapter also included the applications of the model in the context of Chinese public construction sector, which proved model’s reliability and validity. Chapter 9 concluded the book. The limitations of the book and the future directions for the corruption research were also presented in the chapter. Figure 1.2 shows the overall flow of the book.

8

1 Introduction

References Ades, A., & Di Tella, R. (1999). Rents, competition, and corruption. American Economic Review, 89(4), 982–993. Andvig, J. C., Fjeldstad, O.-H., Weltzien, Å., Amundsen, I., Sissener, T. K., & Søreide, T. (2001). Corruption. A review of contemporary research. Bardhan, P. (1997). Corruption and development: A review of issues. Journal of Economic Literature, 35(3), 1320–1346. Chan, A. P., & Owusu, E. K. (2017). Corruption forms in the construction industry: Literature review. Journal of Construction Engineering and Management, 143(8), 04017057. Ehrlich, I., & Lui, F. T. (1999). Bureaucratic corruption and endogenous economic growth. Journal of Political Economy, 107(S6), S270–S293. Goldie-Scot, H. (2008). Briefing: Corruption in construction in developing countries. Paper presented at the Proceedings of the Institution of Civil Engineers-Municipal Engineer. Gupta, M. S. (1998). Does corruption affect income inequality and poverty? International Monetary Fund. Gupta, M. S., & Abed, M. G. T. (2002). Governance, corruption, and economic performance. International Monetary Fund. Hillebrandt, P. M. (2000). Economic theory and the construction industry. Berlin: Springer. Lambsdorff, J. G. (1999). Corruption in empirical research: A review. Transparency International, processed, 6. Le, Y., Shan, M., Chan, A. P., & Hu, Y. (2014a). Overview of corruption research in construction. Journal of Management in Engineering, 30(4), 02514001. Le, Y., Shan, M., Chan, A. P., & Hu, Y. (2014b). Investigating the causal relationships between causes of and vulnerabilities to corruption in the Chinese public construction sector. Journal of Construction Engineering and Management, 140(9), 05014007. Marquette, H. (2001). Corruption, democracy and the World Bank. Crime, Law and Social Change, 36(4), 395–407. Shacklock, A., Galtung, F., & Sampford, C. (2016). Measuring corruption. Taylor & Francis. Sohail, M., & Cavill, S. (2008). Accountability to prevent corruption in construction projects. Journal of Construction Engineering and Management, 134(9), 729–738. Tabish, S., & Jha, K. N. (2012). The impact of anti-corruption strategies on corruption free performance in public construction projects. Construction Management and Economics, 30(1), 21–35. Transparency International, T. (1999). Bribe Payers Index 1999. Retrieved from https://www. transparency.org/research/bpi/bpi_1999/0. Transparency International, T. (2002). Bribe Payers Index 2002. Retrieved from https://www. transparency.org/research/bpi/bpi_2002/0. Transparency International, T. (2006). Bribe Payers Index 2006. Retrieved from https://www. transparency.org/research/bpi/bpi_2006/0. Transparency International, T. (2008). Bribe Payers Index 2008. Retrieved from https://www. transparency.org/research/bpi/bpi_2008/0. Transparency International, T. (2011). Bribe Payers Index 2011. Retrieved from https://www. transparency.org/research/bpi/bpi_2011/0.

Chapter 2

Corruption in Construction: A Global Review

2.1 Introduction In the past decade, the corruption issue in the construction industry has attracted wide attention not only from researchers in developed countries such as the United States (Crist 2009; Sohail and Cavill 2008), the United Kingdom (Amaee 2011), and Australia (Brown and Loosemore 2015; Hartley 2009), but also from those in developing countries such as India (Tabish and Jha 2011, 2012), Nigeria (Alutu 2007; Alutu and Udhawuve 2009), Pakistan (Choudhry and Iqbal 2012), Ghana (Owusu et al. 2019), and South Africa (Bowen et al. 2007a, b, 2012). Ample evidences indicate that corruption in construction has become a significant global challenge faced by all these countries. In order to provide a thorough view of corruption research in construction, this chapter conducted a systematic review of the corruption related papers that have been published in peer-reviewed journals. This chapter attempts to answer the following questions: 1. What are the major areas of construction corruption research, according to the existing literature? 2. What are the future directions for research on corruption in construction?

2.2 Corruption in Construction Corruption is regarded as one of the major obstacles to economic and social development (Foster et al. 2012). In the construction industry, corruption may occur in any phase of a project, namely, project initiation, planning and design, bidding and construction, as well as operation and maintenance (Tabish and Jha 2011). Recent investigations by the Transparency International (1999, 2002, 2006, 2008, 2011) revealed that the construction industry has become the most corrupt industry along with the rapid growth of construction market worldwide after entering the 21st century. This © Springer Nature Singapore Pte Ltd. 2020 M. Shan et al., Corruption in the Public Construction Sector, https://doi.org/10.1007/978-981-13-9550-5_2

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is mainly due to the fragmented nature of the construction industry (involving clients, designers, contractors, consultants, and suppliers), which imposes difficulties in tracing payment information and providing ample opportunities for corruption (Kenny 2009; Mauro 1995). Sohail and Cavill (2008) estimated that the annual loss from corruption in the global construction market reaches about USD$340 billion, which accounts for 1% of the global construction market value (about USD$3.2 trillion). Some efforts have been made to investigate causes of corruption in the construction industry. In some cases, corruption is regarded as the result of an unethical decision (Liu et al. 2004; Moodley et al. 2008; Zarkada-Fraser and Skitmore 2000). For developing countries in societal transition that lacks mature law system, corruption may be caused by insufficient legal punishments and penalties (Bologna and Del Nord 2000). Bowen et al. (2012) considered the lack of positive role models of public officials one of the key causes of corruption in construction. Tabish and Jha (2011) emphasized that corruption in construction is due to the lack of standardized execution in construction projects. Sohail and Cavill (2008) summarized several main causes of corruption in construction: (1) over-competition in the tendering process, (2) insufficient transparency in the selection criteria for tenderers, (3) inappropriate political interference in cost decisions, (4) complexity of institutional roles and functions, and (5) asymmetric information amongst project parties. Owusu et al. (2017) conducted a comprehensive review of the causes of corruption. The researchers identified 44 causes of corruption and categorized them into five categories: Psychosocial-Specific Causes, Organizational-Specific Causes, RegulatorySpecific Causes, Project-Specific Causes and Statutory-Specific Causes. To prevent corruption caused by the above factors, several industrial associations, non-governmental organizations (NGOs), and international organizations have made considerable efforts. The American Society of Civil Engineers (ASCE) promoted a “zero tolerance” policy to cultivate an anti-corruption culture in the U.S. construction industry (Crist 2009). In collaboration with the Global Infrastructure Anti-Corruption Centre (GIACC), the Transparency International developed an integrated anti-corruption system, namely, the Project Anti-Corruption System (PACS). The PACS proposes a group of anti-corruption strategies to prevent corruption, such as the appointment of an independent assessor, commitment of all participants, disclosure of project information, and the use of anti-corruption agreements (Stansbury 2009). The World Economic Forum also established a global Partnering Against Corruption Initiative that provides a platform to companies for preventing corrupt practices (Lee 2009). Despite considerable efforts that have been made, the construction industry seems still to be facing an increasingly serious challenge in various countries of the world, especially in those developing countries (Goldie-Scot 2008).

2.3 Literature Search Strategy This chapter adopted the structured review method advocated by Ke et al. (2009) to identify corruption-related papers published from 2000 to 2018.

2.3

Literature Search Strategy

11

First, a list of peer-reviewed construction engineering and management (CEM) journals was formulated as the source for identifying corruption related papers, following the CEM journal lists recommended by Chau (1997) and Hu et al. (2013). Selected journals included Construction Management and Economics, Journal of Construction Engineering and Management, Engineering, Construction and Architectural Management, Journal of Management in Engineering, International Journal of Project Management, Automation in Construction, Project Management Journal, Journal of Professional Issues in Engineering Education and Practice, Science and Engineering Ethics, Building Research and Information, Journal of Civil Engineering and Management, and International Journal of Construction Management. A full search of the corruption related papers in each of the twelve journals was conducted in the database of Web of Science Core Collection using the keyword of “corruption.” A total of 49 papers were obtained from the search. Then, to identify more papers on corruption in the construction industry, a separate research was conducted using the databases of Web of Science Core Collection and ASCE Library. The keywords of “corruption” and “construction industry” were used simultaneously in the topic search in Web of Science Core Collection, which generated 102 papers. The keyword of “corruption” was searched in the field of Title in ASCE Library which identified 33 papers. Therefore, a total of 184 papers were obtained from the initial searches. After reviewing research topics of these 184 papers, 78 papers were found highly correlated with the topic of corruption. Thus, these papers were considered as valid papers and were then thoroughly reviewed.

2.4 Major Research Areas of Corruption in Construction Three main areas were identified to categorize the research interests of the 78 papers, including the forms of corruption in construction, impacts of corruption in construction, and anti-corruption strategies.

2.4.1 Forms of Corruption in Construction Twelve forms of corruption in the construction industry were identified, including bribery, fraud, collusion, bid rigging, embezzlement, kickback, conflict of interest, dishonesty and unfair conducts, extortion, negligence, front companies, and nepotism. Bribery is the most common and serious form of corruption in the construction industry, particularly in developing countries (Alutu 2007; Bowen et al. 2012; GoldieScot 2008; Le et al. 2014; Sohail and Cavill 2008; Vee and Skitmore 2003). This misconduct refers to “offering, giving, receiving or soliciting of anything of value to influence the action of an official in the procurement or selection process or in

12

2 Corruption in Construction: A Global Review

contract execution” (Hartley 2009). Bowen et al. (2007a, b) examined the process of bribery and found that it could take various forms, such as gifts, cash, overseas and holiday trips, special favours/privileges, and affirmative appointments. Chan and Owusu (2017) shared similar opinions and stated that bribery in construction can be in many forms, including rewards, fees, gifts, loans, or any supplementary advantage such as donations, services, or special treatment. Fraud is also a common form of corruption in construction. It refers to the act of deception with the intention to cheat and takes place when a party deceives another person or part with the aim of gaining an illegal or unfair advantage like contract award (Chan and Owusu 2017). Fraud mainly takes forms of misinformation (e.g., deliberate intention to mislead and withhold information, and alteration of documents), deceit (e.g., making invoices and payment for materials without being received), and theft (e.g., materials and equipment) (Bowen et al. 2007a; De Jong et al. 2009; Tabish and Jha 2011; Van den Heuvel 2005; Vee and Skitmore 2003). According to the two questionnaire surveys conducted in Australia and South Africa (Bowen et al. 2007a, b; Vee and Skitmore 2003), deceit and misinformation are perceived by the industry practitioners as the most common form of fraud. Collusion is a form of corruption in which a secret agreement is reached between two or more parties for a fraudulent or deceitful purpose (Besfamille 2004; Brockmann 2009; Chotibhongs and Arditi 2012a, b; De Jong et al. 2009; Van den Heuvel 2005). It has three major forms which are cartels, bid rigging, and price fixing (Chan and Owusu 2017). Collusion can benefit the involved parties by sacrificing the normal benefits of the project or the public (Bowen et al. 2007a, b; Dorée 2004). ZarkadaFraser and Skitmore (2000) stated that most collusive practices are conducted by tenderers during project biddings. Zarkada-Fraser (2000) also emphasized that collusion can seriously corrodes the foundation of the competitive principle in the construction industry. Bid rigging is a major form of corruption that usually occurs between a tenderee and a tenderer (Hartley 2009; Sichombo et al. 2009). It refers to a collusive act where consenting participants settle on the results of a bid process beforehand (Chan and Owusu 2017). In the case, a tenderee may intentionally set up some constraints (e.g., a short time limit and inappropriate qualification requests) in bidding documents to help its favored tenderer attend the tendering and win the contract (De Jong et al. 2009). Bowen et al. (2007a, b) identified several common forms of bid rigging, such as cover pricing, bid cutting, hidden fees and commissions, and compensation for tendering costs of unsuccessful tenderers. Embezzlement is a crime in which a person fraudulently misuses the power or the resource in his or her position intentionally to grab personal benefits (Hartley 2009; Stansbury 2009). In the construction industry, a typical example of embezzlement is the misappropriation of project funds (Tow and Loosemore 2009). Sohail and Cavill (2008) highlighted that embezzlement can seriously affect the cost management of construction projects. For example, payment for a contractor can be defaulted by the client’s embezzlement of the project funds, which may delay project delivery or even result in project failure.

2.4 Major Research Areas of Corruption in Construction

13

Kickback refers to illegal economic incentives that a person uses to seek a favourable decision from a person in power (Bowen et al. 2012; Sohail and Cavill 2008). For instance, a client staff may receive kickbacks in form of economic reward from a tenderer by helping him win the contract. A questionnaire survey administered in the construction industry of Nigeria revealed that the contractors who win contracts mostly use kickbacks and they always include the amount of the kickback into the price quotation for bidding (Alutu 2007). Conflict of interest refers to a situation in which a professional in a position of trust, such as a site supervisor, an auditor, or a cost consultant cannot fulfil his or her duty impartially because of ambivalent professional or personal interests (Bowen et al. 2007b; Chan and Owusu 2017; De Jong et al. 2009). Despite the lack of improper activity evidences, a conflict of interest can cause an appearance of impropriety and thus undermine confidence in the professional opinions or actions, which may negatively affect the performances of projects (Bowen et al. 2012). Dishonesty and unfair conducts mostly occur in the phases of bidding, contract negotiation and signing, and project construction phase (Vee and Skitmore 2003). Bowen et al. (2007a, b) collected the opinions of the key stakeholders of construction projects regarding dishonesty and unfair conducts. They found that, architects always think contractors not honest in following contractual specifications, and that most of the contractors will use cheaper and inferior alternatives; contractors always complain that the tendering adjudication process is unfair, and that there exist a bias in professionals’ acts when the process is high intervened by clients; and quantity surveyors report that contractors always repeatedly over-claim in the project construction phase. Extortion refers to corrupt conducts motivated by personal desire for extra income, which usually take the form of forcing extraction of bribes and asking for favors from vulnerable project parties (Bowen et al. 2012; Hartley 2009; Sichombo et al. 2009; Sohail and Cavill 2008; Stansbury 2009; Tabish and Jha 2011). Extortion usually occurs from a party to another party involved in a project, such as (1) from client staff to contractors or material suppliers, (2) from a major contractor to his subcontractor, (3) from a potential subcontractor to a material/equipment supplier, and (4) from regulatory/permitting agencies to clients, contractors, or material/equipment suppliers. Extortion can result in the misuse of project funds and bring some individuals illegal incomes (De Jong et al. 2009). Negligence is a common form of corruption in construction projects that is characterized with failures to exercise the due care of a responsible professional (Richard 1972). Specific forms of negligence include inadequate quality specifications, poor workmanship, insufficient safety specifications, low-quality materials, poor process supervision, and lack of project management and skills (Vee and Skitmore 2003). Bowen et al. (2007a, b) observed that more than 90% of architects and cost consultants have committed negligence in the South African construction industry. Front companies refer to corporate entities that are established by persons who hold senior positions in the government or client organizations to obtain illegal benefits in awarding construction contracts (De Jong et al. 2009). Although these companies are not familiar to the public, they can secure construction contracts because

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of the power of their owners and delegate them to other contractors or suppliers in a lower price (Hartley 2009). The price difference is exactly the illegal incomes to these corruptors. Nepotism refers to corrupt conducts by which a person may provide assistance to a tenderer who has some kinds of relational links with him or her, such as the common race, same origins, and good friendship (Bowen et al. 2012; Hartley 2009; Kadembo 2008; Ling and Tran 2012). Nepotism, which is also called as the “good old boys’ network” (Singh and Shoura 1999), can have multiple negative impacts on performances of construction projects, such as low construction productivity and low managerial efficiency (Kale and Arditi 1998).

2.4.2 Impacts of Corruption in Construction The 56 identified papers revealed three main impacts of corruption on various levels of the construction industry, namely, corruption risks in construction projects (micro), impacts on the expansion strategies of global companies (medium), as well as social and economic impacts (macro). Corruption is an extremely significant risk that has great impacts on core management tasks in construction projects, particularly in those developing countries where mature legislative and administrative system lacks (Choudhry and Iqbal 2013; Deng et al. 2013; Fernandez-Dengo et al. 2013; Ofori 1999). Wang et al. (1999, 2000) identified corruption as one of the major risks in managing build-operate-transfer (BOT) projects, and found that bribing governmental officials is the major corruption risk in Chinese BOT projects. Numerous researchers stated that public-private partnership projects in China and Turkey also face a high risk in corruption prevention (Chan et al. 2011; Gurgun and Touran 2014; Ke et al. 2011; Xu et al. 2010). Meduri and Annamalai (2013) added that corruption risks can lead to an increase project costs and a waste of public funds in India because of extra bribe expenditure. Corruption can also affect the execution of expansion strategies of global companies in the international construction market (Ling and Hoang 2010). Crosthwaite (1998) sated that, despite the great construction demand and enormous latent profits in some developing countries, the level of corruption in a country may be a key consideration for global companies to decide whether or not to enter into the market in the country. Tang et al. (2012) also stated that corruption combined with political and physical factors is critical for an overseas company to successfully enter into the Chinese construction market. However, Barco (1994) pointed out that bribery is commonly used as a strategy by global companies to gain competitive advantage in wining overseas construction contracts. Corruption can hinder the social and economic development of human societies worldwide (Snaith and Khan 2008). Empirical studies have revealed that corruption causes economic problems and worsen current economic crises in some European countries. For instance, Jimenez (2009) noted that corruption in the construction industry led to the speculative bubble in Spain. Romero et al. (2012) stated that cor-

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ruption has resulted in many unsuccessful urban expansion cases in Spain. Skorupka (2008) and Badun (2011) reported that the slow development of infrastructures in Poland and Croatia are due to corrupt practices. Developing countries in Asia and Africa face a more severe situation in the aspect. For instance, many global contractors abandoned water and irrigation projects in Nigeria (Sonuga et al. 2002) and road projects in Afghanistan (Unruh and Shalaby 2012) because of serious corruption in the two countries.

2.4.3 Anti-corruption Strategies The third stretch of existing corruption research centers on anti-corruption strategies in the construction industry. It mainly involves four major strategies, namely, transparency mechanism, ethical code, project governance, and audit and information technology. Transparency mechanism is an important strategy for corruption prevention in construction projects (Deng et al. 2003). Sohail and Cavill (2008) observed that transparency mechanisms can provide the public with access to information on construction projects so that project performance can be monitored, and decision makers can be held accountable for their decisions. Kenny (2012) further indicated that the regular exposure of contract and implementation details is a common method for improving project transparency. Goldie-Scot (2008) noted that some developing countries such as Tanzania, Zambia, the Philippines, and Vietnam have already made considerable efforts in introducing transparency initiatives to prevent corruption in construction projects. Ethical code is another important proactive strategy commonly used to prevent corrupt practices (Fan et al. 2001). For instance, a National Code of Practice for the Construction Industry has been promoted in Australia to discipline all industry professionals (Hartley 2009). Sohail and Cavill (2008) noted that ethical training programs can help prevent corruption, and that developing an ethical code for a particular stakeholder may be more useful because the universal industry ethical code cannot include exhaustive guidelines for all situations that different stakeholders face in their work. Goldie-Scot (2008) added that, to construct a positive industry atmosphere, ethical behavior should be rewarded. Several project governance strategies can also contribute to prevent corruption in construction. Kenny (2009) argued that the separation of project ownership and regulatory functions of the government in construction projects can effectively mitigate corruption because it can restore the competitive nature of the construction sector. Bowen et al. (2012) stated that the good leadership can facilitate corruption prevention, thereby contributing to project success. Tabish and Jha (2012) stressed that harsh punishment should also be considered in the design of anti-corruption strategies because it can increase the fear of professionals and reduce their potential corrupt practices.

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Audit and information technology play an increasingly important role in corruption prevention in the construction industry worldwide (Wu et al. 2013; Zou 2006). Sichombo et al. (2009) stated that technical auditing in the project pre-contract phase can minimize or prevent corrupt practices in construction projects. Sohail and Cavill (2008) suggested that the integrity pact and information technologies widely applied worldwide can also help prevent corruption. The European Union has promoted a debarment system, which records companies and individuals found guilty of corruption, and helps prevent the corrupt companies and individuals from participating in EU projects (De Jong et al. 2009). Several international organizations and industria associations have made substantial efforts to promote the mixed use of the two or three above strategies for preventing corruption in the construction industry. For instance, the Transparency International published a special report on corruption in construction in 2005 and consequently developed PACS in 2007 to assist project participants in corruption prevention (Krishnan 2009). Similarly, the International Federation of Consulting Engineers developed some corruption prevention information systems for its members, such as Business Integrity Management System and Government Procurement Integrity Management System (Boyd and Padilla 2009). The Global Infrastructure Anti-corruption Center (GIACC) established the GIACC Resource Centre and provided industrial professionals the free access to advice and tools on corruption identification and prevention. The ASCE has established a Committee of Global Principles for Professional Conduct and an Engineer’s Charter in the organization, which has developed related policies, such as the Statement 510 Combating Corruption, and reviewed anti-corruption issues in annual meetings (Crist 2009). In the UK, an AntiCorruption Forum that involves the Institution of Civil Engineers, the Chartered Institute of Building, the Royal Institution of Chartered Surveyors, and the Association of Consulting Engineers and other local institutions has been held annually since 2003 and many useful guidelines have been provided on corruption prevention in the construction industry (Goldie-Scot 2008).

2.5 Research Potentials 2.5.1 Identification of Corruption in Construction in Developing Countries Identification of corrupt practices is essential to address the corruption issues in the construction industry, particularly in those developing countries facing a greater challenge in preventing corruption due to the lack of sufficient legislative and institutional support (Ofori 1999). However, this area has just received growing research concerns in the past few years from researchers in a few developing countries, such as South Africa, Nigeria, Pakistan, and India (Alutu 2007; Alutu and Udhawuve 2009; Ameh and Odusami 2010; Bowen et al. 2007a, b, 2012; Choudhry and Iqbal 2013; Tabish

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and Jha 2011). With the recognition that a small ratio of developing countries (4 in 1999) have been engaged in this area (United Nations Development Program 2010), existing research are still limited and may be not fully address related issues. Thus, greater research efforts, particularly from researchers in those developing countries where related research has not been conducted before, should be directed to this area.

2.5.2 Evaluation of Corruption in Construction Evaluating corruption is crucial in achieving anti-corruption progress for greater integrity, higher transparency, and better accountability performance (Foster et al. 2012; Goel and Nelson 2011). Sampford et al. (2006) and Zou (2006) also emphasized that only by understanding the extent of corruption, can effective anti-corruption strategies be formulated and then implemented. However, the review of the 56 related papers indicate that previous studies seldom provide systematic and evaluation approaches for the evaluation of corruption in the construction sector, representing a great opportunity for future research.

2.5.3 Examination of the Effectiveness of Anti-corruption Strategies Although several anti-corruption strategies have already been proposed and employed in the construction industries of various countries, the effectiveness of these strategies has seldom been systematically examined before. Additionally, the severe situation of corruption in construction seems not to be alleviated by far as the construction is still the most corrupt industry recognized by the public (Transparency International 2002, 2006, 2008, 2011). Thus, there is an urgent need to conduct depth examinations of the effectiveness of anti-corruption strategies that are being implemented. By doing this, better development and execution of anti-corruption strategies for a more transparent, healthy and sustainable industry can be attained.

2.6 Summary This chapter has undertaken a critical review of 78 corruption-related papers published in the period of 2000–2018. Research interests of these papers were categorized under three main areas, namely, forms of corruption in construction, impacts of corruption in construction, and anti-corruption strategies. Review of these papers has revealed main developments and different perspectives of corruption research in construction, representing the state of the art on this topic. Three areas for future research were also proposed in this chapter, including identification of corruption in

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construction in developing countries, evaluation of corruption in construction, and examination of the effectiveness of anti-corruption strategies.

References Alutu, O. (2007). Unethical practices in Nigerian construction industry: Prospective engineers’ viewpoint. Journal of Professional Issues in Engineering Education and Practice, 133(2), 84–88. Alutu, O., & Udhawuve, M. (2009). Unethical practices in Nigerian engineering industries: Complications for project management. Journal of Management in Engineering, 25(1), 40–43. Amaee, R. (2011). UK’s new wide-ranging Bribery Act comes into force in April 2011. Paper presented at the Proceedings of the Institution of Civil Engineers-Civil Engineering. Ameh, O. J., & Odusami, K. T. (2010). Professionals’ ambivalence toward ethics in the Nigerian construction industry. Journal of Professional Issues in Engineering Education and Practice, 136(1), 9–16. https://doi.org/10.1061/(asce)1052-3928(2010)136:1(9). Badun, M. (2011). The relationship between political corruption and public investment—The case of Croatia. Drustvena Istrazivanja, 20(2), 295–316. https://doi.org/10.5559/di.20.2.01. Barco, A. L. (1994). International expansion, ethics, and prohibited foreign trade practices. Journal of Management in Engineering, 10(5), 34–40. Besfamille, M. (2004). Collusion in local public works. International Economic Review, 45(4), 1193–1219. Bologna, R., & Del Nord, R. (2000). Effects of the law reforming public works contracts on the Italian building process. Building Research & Information, 28(2), 109–118. Bowen, P., Akintoye, A., Pearl, R., & Edwards, P. J. (2007a). Ethical behaviour in the South African construction industry. Construction Management and Economics, 25(6), 631–648. Bowen, P. A., Edwards, P. J., & Cattell, K. (2012). Corruption in the South African construction industry: A thematic analysis of verbatim comments from survey participants. Construction Management and Economics, 30(10), 885–901. Bowen, P., Pearl, R., & Akintoye, A. (2007b). Professional ethics in the South African construction industry. Building Research and Information, 35(2), 189–205. Boyd, J. M., & Padilla, J. D. (2009). FIDIC and integrity: A status report. Leadership and Management in Engineering, 9(3), 125–128. https://doi.org/10.1061/(ASCE)1532-6748(2009)9:3(125). Brockmann, C. (2009). The mechanics of collusion. Organization, Technology & Management in Construction: An International Journal, 1(2), 51–58. Brown, J., & Loosemore, M. (2015). Behavioural factors influencing corrupt action in the Australian construction industry. Engineering, Construction and Architectural Management, 22(4), 372–389. Chan, A. P., & Owusu, E. K. (2017). Corruption forms in the construction industry: Literature review. Journal of Construction Engineering and Management, 143(8), 04017057. Chan, A. P. C., Yeung, J. F. Y., Yu, C. C. P., Wang, S. Q., & Ke, Y. (2011). Empirical study of risk assessment and allocation of public-private partnership projects in China. Journal of Management in Engineering, 27(3), 136–148. https://doi.org/10.1061/(asce)me.1943-5479.0000049. Chau, K. W. (1997). The ranking of construction management journals. Construction Management & Economics, 15(4), 387–398. Chotibhongs, R., & Arditi, D. (2012a). Analysis of collusive bidding behaviour. Construction Management and Economics, 30(3), 221–231. Chotibhongs, R., & Arditi, D. (2012b). Detection of collusive behavior. Journal of Construction Engineering and Management, 138(11), 1251–1258. Choudhry, R. M., & Iqbal, K. (2012). Identification of risk management system in construction industry in Pakistan. Journal of Management in Engineering, 29(1), 42–49.

References

19

Choudhry, R. M., & Iqbal, K. (2013). Identification of risk management system in construction industry in Pakistan. Journal of Management in Engineering, 29(1), 42–49. https://doi.org/10. 1061/(asce)me.1943-5479.0000122. Crist, R. A., Jr. (2009). The ASCE committee of global principles for professional conduct. Leadership and Management in Engineering, 9(3), 144–146. Crosthwaite, D. (1998). The internationalization of British construction companies 1990–96: An empirical analysis. Construction Management and Economics, 16(4), 389–395. https://doi.org/ 10.1080/014461998372178. De Jong, M., Henry, W. P., & Stansbury, N. (2009). Eliminating corruption in our engineering/construction industry. Leadership and Management in Engineering, 9(3), 105–111. https:// doi.org/10.1061/(asce)1532-6748(2009)9:3(105). Deng, F., Liu, G., & Jin, Z. (2013). Factors formulating the competitiveness of the chinese construction industry: Empirical investigation. Journal of management in engineering, 29(4), 435–445. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000161. Deng, X. M., Tian, Q., Ding, S. H., & Boase, B. (2003). Transparency in the procurement of public works. Public Money & Management, 23(3), 155–162. https://doi.org/10.1111/1467-9302.00363. Dorée, A. G. (2004). Collusion in the Dutch construction industry: An industrial organization perspective. Building Research & Information, 32(2), 146–156. Fan, L., Ho, C., & Ng, V. (2001). A study of quantity surveyors’ ethical behaviour. Construction Management & Economics, 19(1), 19–36. Fernandez-Dengo, M., Naderpajouh, N., & Hastak, M. (2013). Risk assessment for the housing market in Mexico. Journal of Management in Engineering, 29(2), 122–132. https://doi.org/10. 1061/(asce)me.1943-5479.0000128. Foster, J. E., Horowitz, A. W., & Méndez, F. (2012). An axiomatic approach to the measurement of corruption: Theory and applications. The World Bank Economic Review, 26(2), 217–235. Goel, R. K., & Nelson, M. A. (2011). Measures of corruption and determinants of US corruption. Economics of Governance, 12(2), 155–176. https://doi.org/10.1007/s10101-010-0091-x. Goldie-Scot, H. (2008). Briefing: Corruption in construction in developing countries. Paper presented at the Proceedings of the Institution of Civil Engineers-Municipal Engineer. Gurgun, A. P., & Touran, A. (2014). Public-private partnership experience in the international arena: Case of Turkey. Journal of Management in Engineering, 30(6). https://doi.org/10.1061/(asce)me. 1943-5479.0000213. Hartley, R. (2009). Fighting corruption in the Australian construction industry: The national code of practice. Leadership and Management in Engineering, 9(3), 131–135. Hu, Y., Chan, A. P., Le, Y., & Jin, R.-Z. (2013). From construction megaproject management to complex project management: Bibliographic analysis. Journal of management in engineering, 31(4), 04014052. Jimenez, F. (2009). Building boom and political corruption in Spain. South European Society and Politics, 14(3), 255–272. https://doi.org/10.1080/13608740903356541. Kadembo, E. (2008). Corruption and the distortion of technology transfer and marketing processes: An insight into Africa’s economic malaise and the decadence of its social fabric. Journal of Sustainable Development, 1(1). Kale, S., & Arditi, D. (1998). Business failures: Liabilities of newness, adolescence, and smallness. Journal of Construction Engineering and Management, 124(6), 458–464. https://doi.org/10.1061/ (ASCE)0733-9364(1998)124:6(458). Ke, Y., Wang, S., Chan, A. P., & Cheung, E. (2009). Research trend of public-private partnership in construction journals. Journal of Construction Engineering and Management, 135(10), 1076–1086. Ke, Y., Wang, S., Chan, A. P. C., & Cheung, E. (2011). Understanding the risks in China’s PPP projects: Ranking of their probability and consequence. Engineering, Construction and Architectural Management, 18(5), 481–496. https://doi.org/10.1108/09699981111165176. Kenny, C. (2009). Transport construction, corruption and developing countries. Transport Reviews, 29(1), 21–41.

20

2 Corruption in Construction: A Global Review

Kenny, C. (2012). Publishing construction contracts to improve efficiency and governance. Proceedings of the Institution of Civil Engineers-Civil Engineering, 165(5), 18–22. https://doi.org/ 10.1680/cien.11.00007. Krishnan, C. (2009). Combating corruption in the construction and engineering sector: The role of transparency international. Leadership and Management in Engineering, 9(3), 112–114. https:// doi.org/10.1061/(ASCE)1532-6748(2009)9:3(112). Le, Y., Shan, M., Chan, A. P., & Hu, Y. (2014). Overview of corruption research in construction. Journal of Management in Engineering, 30(4), 02514001. Lee, T. (2009). Partnering against corruption initiative leads industry battle against corruption. Leadership and Management in Engineering, 9(3), 123–124. Ling, F. Y. Y., & Hoang, V. T. P. (2010). Political, economic, and legal risks faced in international projects: Case study of Vietnam. Journal of Professional Issues in Engineering Education and Practice, 136(3), 156–164. https://doi.org/10.1061/(asce)ei.1943-5541.0000015. Ling, F. Y. Y., & Tran, P. Q. (2012). Effects of interpersonal relations on public sector construction contracts in Vietnam. Construction Management and Economics, 30(12), 1087–1101. https://doi. org/10.1080/01446193.2012.729848. Liu, A. M., Fellows, R., & Ng, J. (2004). Surveyors’ perspectives on ethics in organisational culture. Engineering, Construction and Architectural Management, 11(6), 438–449. Mauro, P. (1995). Corruption and growth. The Quarterly Journal of Economics, 110(3), 681–712. Meduri, S. S., & Annamalai, T. R. (2013). Unit costs of public and PPP road projects: Evidence from India. Journal of Construction Engineering and Management, 139(1), 35–43. https://doi. org/10.1061/(asce)co.1943-7862.0000546. Moodley, K., Smith, N., & Preece, C. N. (2008). Stakeholder matrix for ethical relationships in the construction industry. Construction Management and Economics, 26(6), 625–632. Ofori, G. (1999). Challenges of construction industries in developing countries: Lessons 300 from various countries. In 2nd International Conference on Construction in Developing Countries: Challenges Facing the Construction Industry in Developing Countries, Gaborone, November 15–17, 1999. Owusu, E. K., Chan, A. P., & Ameyaw, E. (2019). Toward a cleaner project procurement: Evaluation of construction projects’ vulnerability to corruption in developing countries. Journal of Cleaner Production. Owusu, E. K., Chan, A. P., & Shan, M. (2017). Causal factors of corruption in construction project management: An overview. Science and Engineering Ethics, 1–31. Richard, A. P. (1972). A theory of negligence. The Journal of Legal Studies, 1(1), 29–96. Romero, J., Jimenez, F., & Villoria, M. (2012). (Un)sustainable territories: Causes of the speculative bubble in Spain (1996–2010) and its territorial, environmental, and sociopolitical consequences. Environment and Planning C-Government and Policy, 30(3), 467–486. https://doi.org/10.1068/ c11193r. Sampford, C., Shacklock, A., Connors, C., & Galtung, F. (2006). Measuring corruption. Hampshire, England: Ashgate Publishing Ltd. Sichombo, B., Muya, M., Shakantu, W., & Kaliba, C. (2009). The need for technical auditing in the Zambian construction industry. International Journal of Project Management, 27(8), 821–832. Singh, A., & Shoura, M. M. (1999). Improvement of management parameters in public construction agency. Journal of Management in Engineering, 15(3), 52–59. https://doi.org/10.1061/ (asce)0742-597x(1999)15:3(52). Skorupka, D. (2008). Identification and initial risk assessment of construction projects in Poland. Journal of Management in Engineering, 24(3), 120–127. https://doi.org/10.1061/(asce)0742597x(2008)24:3(120). Snaith, M. S., & Khan, M. U. (2008). Deleterious effects of corruption in the roads sector. Proceedings of the Institution of Civil Engineers-Transport, 161(4), 231–235. https://doi.org/10.1680/ tran.2008.161.4.231.

References

21

Sohail, M., & Cavill, S. (2008). Accountability to prevent corruption in construction projects. Journal of Construction Engineering and Management-Asce, 134(9), 729–738. https://doi.org/ 10.1061/(asce)0733-9364(2008)134:9(729). Sonuga, F., Aliboh, O., & Oloke, D. (2002). Particular barriers and issues associated with projects in a developing and emerging economy. Case study of some abandoned water and irrigation projects in Nigeria. International Journal of Project Management, 20(8), 611–616. https://doi. org/10.1016/s0263-7863(02)00029-7. Stansbury, C. (2009). The global infrastructure anticorruption centre. Leadership and Management in Engineering, 9(3), 119–122. Tabish, S., & Jha, K. N. (2011). Analyses and evaluation of irregularities in public procurement in India. Construction Management and Economics, 29(3), 261–274. Tabish, S., & Jha, K. N. (2012). The impact of anti-corruption strategies on corruption free performance in public construction projects. Construction Management and Economics, 30(1), 21–35. Tang, L. C. M., Atkinson, B., & Zou, R. R. (2012). An entropy-based SWOT evaluation process of critical success factors for international market entry: A case study of a medium-sized consulting company. Construction Management and Economics, 30(10), 821–834. https://doi.org/10.1080/ 01446193.2012.697180. Tow, D., & Loosemore, M. (2009). Corporate ethics in the construction and engineering industry. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 1(3), 122–129. Transparency International, T. (1999). Bribe Payers Index 1999. Retrieved from https://www. transparency.org/research/bpi/bpi_1999/0. Transparency International, T. (2002). Bribe Payers Index 2002. Retrieved from https://www. transparency.org/research/bpi/bpi_2002/0. Transparency International, T. (2006). Bribe Payers Index 2006. Retrieved from https://www. transparency.org/research/bpi/bpi_2006/0. Transparency International, T. (2008). Bribe Payers Index 2008. Retrieved from https://www. transparency.org/research/bpi/bpi_2008/0. Transparency International, T. (2011). Bribe Payers Index 2011. Retrieved from https://www. transparency.org/research/bpi/bpi_2011/0. Unruh, J., & Shalaby, M. (2012). A volatile interaction between peacebuilding priorities: Road infrastructure (re)construction and land rights in Afghanistan. Progress in Development Studies, 12(1), 47–61. https://doi.org/10.1177/146499341101200103. Van den Heuvel, G. (2005). The parliamentary enquiry on fraud in the Dutch construction industry collusion as concept between corruption and state-corporate crime. Crime, Law and Social Change, 44(2), 133–151. Vee, C., & Skitmore, C. (2003). Professional ethics in the construction industry. Engineering, Construction and Architectural Management, 10(2), 117–127. Wang, S. Q., Tiong, R. L. K., Ting, S. K., & Ashley, D. (1999). Political risks: Analysis of key contract clauses in China’s BOT project. Journal of Construction Engineering and ManagementASCE, 125(3), 190–197. https://doi.org/10.1061/(asce)0733-9364(1999)125:3(190). Wang, S. Q., Tiong, R. L. K., Ting, S. K., & Ashley, D. (2000). Evaluation and management of political risks in China’s bot projects. Journal of Construction Engineering and ManagementASCE, 126(3), 242–250. https://doi.org/10.1061/(asce)0733-9364(2000)126:3(242). Wu, Y., Yang, Y., Feng, T., & Huang, Y. (2013). Government investment projects’ collusion deterrence model based on prospect theory. Journal of Industrial Engineering and Engineering Management, 27(2), 168–176. Xu, Y., Yeung, J. F. Y., Chan, A. P. C., Chan, D. W. M., Wang, S. Q., & Ke, Y. (2010). Developing a risk assessment model for PPP projects in China—A fuzzy synthetic evaluation approach. Automation in Construction, 19(7), 929–943. https://doi.org/10.1016/j.autcon.2010.06.006. Zarkada-Fraser, A. (2000). A classification of factors influencing participating in collusive tendering agreements. Journal of Business Ethics, 23(3), 269–282.

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Zarkada-Fraser, A., & Skitmore, M. (2000). Decisions with moral content: Collusion. Construction Management & Economics, 18(1), 101–111. Zou, P. X. W. (2006). Strategies for minimizing corruption in the construction industry in China. Journal of Construction in Developing Countries, 11(2), 15–29.

Chapter 3

Underlying Factors of Corruption in the Public Construction Sector

3.1 Introduction As a core sector, construction has played a vital role in contributing to the economic and social developments of human societies (De Jong et al. 2009). However, in the recent years, its positive social images have been increasingly diminished by corruption issues (Le et al. 2014a, b). Corruption has damaged the construction industry at multiple levels and resulted in the underperformances of construction projects such as cost overruns and quality defects (Kenny 2009). Corruption has also brought about considerable economic loss to the construction sector. According to Sohail and Cavill (2008), the annual loss from corruption in the global construction market reached approximately USD 340 billion, accounting for 1% of the global construction market value (roughly USD 3.2 trillion). Transparency International estimated that 10% of the global infrastructure investment was lost through corruption annually (ASCE 2015). Particularly, as an important section of the construction sector, the public construction sector has been plagued by corruption constantly, and it has also been assessed as the most corrupt sector worldwide by the Transparency International’s Bribe Payers Index since 1999 (Transparency International 2011). In 2014, Le and Shan (2014) conducted a comprehensive literature review of corruption research in the construction engineering and management field and found that its primary research efforts mainly focused on identifying the forms of construction corruption, investigating the impacts of construction corruption, and exploring the anti-corruption strategies for the construction industry. Subsequent to the study of Le et al. (2014), several studies continued examining this topic. Gunduz and Önder (2013) scrutinized the internal fraud and the corruption problem in the Turkish construction industry. Brown and Loosemore (2015) explored the behavioral factors influencing corruption in the Australian construction industry. Bowen et al. (2015) surveyed the experiences and opinions of construction professionals on corruption in the South African construction industry. (Nag 2015) investigated corruption in the Indian public procurement sector and recommended measures to combat such corruption. Concentrating on corruption in the Chinese public construction sector, © Springer Nature Singapore Pte Ltd. 2020 M. Shan et al., Corruption in the Public Construction Sector, https://doi.org/10.1007/978-981-13-9550-5_3

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Le and Shan and their partners successively investigated the principal causes of corruption (Le et al. 2014a), evaluated the effectiveness of prevailing anti-corruption strategies (Shan et al. 2015a), and developed a model to measure corruption in public construction projects (Shan et al. 2015b). Although continuing efforts have been exerted to examine the corruption issue in the construction sector, the exploration of the underlying factors causing corruption has been minimal. Exploring the underlying factors causing corruption is extremely crucial to the control of corruption as it can help deconstruct the phenomenon and reveal the areas that are most vulnerable to corruption, thereby facilitating the development of more effective anti-corruption strategies. Hence, this chapter attempts to bridge the knowledge gap by exploring the underlying factors in the public construction sector. This chapter was conducted within the context of the Chinese public construction sector and is an extension of Le et al. (2014a), Shan et al. (2015a, b). This chapter contributes to the body of knowledge by exploring the underlying factors causing corruption in the public construction sector. This chapter is also beneficial to the industry as its findings can provide industrial practitioners with an in-depth understanding of construction corruption and thereby introduce more effective anti-corruption strategies.

3.2 Background 3.2.1 Corruption in the Construction Industry Corruption is a type of dishonest or fraudulent practice conducted by those morally depraved individuals in power, who misuse the public power for their private benefit (Gray and Kaufmann 1998). This wrongdoing distorts markets and the allocation of resources, and is therefore regarded as a major obstacle to economic and social development worldwide (Jain 2001; Tanzi 1998). Corruption also prevails in the construction industry (De Jong et al. 2009). In construction projects, corruption may occur with practitioners at any level and in any project phase such as project inception, planning and design, bidding and construction, and operation and maintenance (Bowen et al. 2007a, b, 2012; Nag 2015; Tabish and Jha 2011). The common forms of corruption in the construction industry are bribery, fraud, collusion, bid rigging, embezzlement, kickbacks, conflicts of interest, extortion, negligence, front companies, and nepotism (Ameh and Odusami 2010; Bowen et al. 2007a, b, 2012; Le et al. 2014a, b; Shan et al. 2017; Sichombo et al. 2009; Sohail and Cavill 2008; Tabish and Jha 2011; Vee and Skitmore 2003). Some efforts have been made to explore the causes of corruption in the construction industry. In some cases, corruption was deemed to be the result of unethical decision-making (Moodley et al. 2008; Zarkada-Fraser and Skitmore 2000). For developing countries under societal transition that may lack mature law systems, corruption might be incurred by the defective institutional systems or insufficient legal punishments (Bologna and Del Nord 2000). Sohail and Cavill (2008) sum-

3.2 Background

25

marized several primary causes of corruption in construction as follows: (1) over competition in the tendering process, (2) insufficient transparency in the tenderer selection criteria, (3) political interference, (4) complicated institutional roles and functions, and (5) asymmetric information amongst project parties. Tabish and Jha (2011) emphasized that corruption in construction was attributable to the lack of standardized execution in construction projects. Bowen et al. (2012) stated that the construction industry was susceptible to corruption owing to its particular characteristics such as the complexity and uniqueness of construction projects, the considerable number of contractual links and the culture of secrecy. Le et al. (2014a) stressed that the principal causes of corruption in the Chinese public construction sector are flawed regulation systems and lack of a positive industrial climate. The impacts of corruption in the construction industry can be tagged at three levels, namely, impacts at the project level, the organizational level, and the national level. At the project level, corruption is considered to be an extremely significant risk to construction projects in various countries, particularly those in developing countries that lack mature legislative and administrative system (Choudhry and Iqbal 2013; Deng et al. 2003, 2014); while the typical consequences of corruption risk are the increase in project costs and waste of public funds (Hwang et al. 2016; Meduri and Annamalai 2013). At the organizational level, corruption affects the execution of the expansion strategies of global construction companies in the international construction market greatly (Crosthwaite 1998; Deng et al. 2014; Ling and Hoang 2010; Tang et al. 2012). This is understandable because normally global construction companies would avoid conducting business with those host countries having a serious problem of corruption. As for the impact at national level, corruption in the construction industry has hindered the social and economic development of various countries worldwide (Snaith and Khan 2008). For instance, Jimenez (2009) and Romero et al. (2012) noted that construction corruption led to the speculative bubble in Spain and resulted in many unsuccessful urban expansion cases in this country. Skorupka (2008) and Badun (2011) stated that the slow development of infrastructure in Poland and Croatia was attributable to corrupt practices in the civil and construction sectors. To prevent corruption in the construction industry, various strategies have been proposed. The commonly advocated strategies are developing leadership, enforcing rules, regulations and sanction systems, implementing training and education, transparency mechanism, ethical code, project governance, and using audit and information technologies (Bowen et al. 2012; Kenny 2012; Shan et al. 2015a; Sichombo et al. 2009; Sohail and Cavill 2008; Tabish and Jha 2012; Zou 2006). In addition, several construction industry associations, nongovernmental organizations, and international organizations have also devoted considerable efforts to fight against corruption in the construction industry. The American Society of Civil Engineers promoted a “zero tolerance” policy to cultivate an anticorruption culture in the U.S. construction industry (Crist 2009). In collaboration with the Global Infrastructure Anti-Corruption Centre, Transparency International developed an integrated anticorruption system: The Project Anti-Corruption System (PACS), which has promoted a group of anticorruption strategies to prevent corruption (Transparency International 2013). The World Economic Forum also established the Global Partnering Against Corruption Initia-

26

3 Underlying Factors of Corruption in the Public …

tive which provided a platform for construction companies to gain anticorruption knowledge (World Economic Forum 2013).

3.2.2 Corruption in the Chinese Public Construction Sector Over the past three decades, the government of China has continuously been using increasing fixed-asset investments to boost its economic development (Zeng et al. 2016), and a considerable fixed-asset investment has been devoted to the public construction sector (Wu et al. 2012). According to the National Bureau of Statistics of China (2015), the total investment in the public construction sector increased almost 100 times from 27 billion (Chinese Yuan, CNY) (approximately USD 4 billion) in 1981 to CNY 267.5 billion (approximately USD 400 billion) in 2014. However, such huge investments have also caused numerous corruption cases within the Chinese public construction sector. The National Bureau of Corruption Prevention reported 15,010 cases of corruption recorded in the public construction sector between 2009 and 2011, which resulted in an economic loss of CNY 3 billion (approximately USD 490 million) (Xinhua Net 2011). Findings from a research project funded by the Ministry of Science and Technology showed that, among the 164 provincial officials who were prosecuted between 1986 and 2014, more than 40% of them were associated with corruption in the public construction sector (Wang 2014). These statistics suggests that China is facing a significant, serious and continuing challenge in preventing corruption in the public construction sector.

3.3 Methodology The research efforts described in this chapter were conducted in five steps. First, structured interviews were conducted to generate the irregularities related to corruption in the Chinese public construction sector. Second, based on the interview results, a questionnaire was developed and disseminated within the public construction sector to collect the opinion-based data of those irregularities related to corruption. Third, based on the collected data, a factor analysis was conducted to extract the underlying factors of corruption. Fourth, the stepwise multiple regression analysis and the partial least squares structural equation modelling analysis were carried out separately to analyse the collected data to explore the most influential underlying factors of corruption. Finally, a case study was conducted to triangulate the findings from the statistical analysis.

3.3 Methodology

27

3.3.1 Structured Interviews First, this chapter conducted a series of structured interviews to identify the irregularities related to corruption in the Chinese public construction sector. In this chapter, irregularities related to corruption are defined as professionals’ malpractices that are caused by corruption. These corruption-related irregularities can reflect the internal attributes of corruption at large and are helpful in exploring the underlying factors of corruption. However, in their comprehensive literature review, Le et al. (2014) found that few studies have investigated these irregularities systematically, with the exception of Tabish and Jha (2011) that summarized a detailed list of 61 irregularities in the Indian public construction sector. This chapter therefore used the Tabish and Jha (2011) framework as the initial framework to derive the corruption-related irregularities in the Chinese public construction sector. This choice could be justified by two reasons: (1) the Tabish and Jha (2011) framework comprised 61 detailed irregularities gathered from the entire project life cycle, indicating that the framework was fairly comprehensive; and (2) both China and India have a booming public construction sector and are facing a similar challenge of preventing corruption in the sector (Shan et al. 2015). Nevertheless, there should be a compatibility issue when applying the Tabish and Jha (2011) framework directly in the context of China. Thus, this chapter conducted structured interviews with 14 experienced experts from the Chinese public construction sector to fit the original framework in the context of China. Lastly, a total of 24 corruption-related irregularities were finalized through the interviews, as listed in Table 3.1. More specific details of the structured interviews could be found in Le et al. (2014a).

3.3.2 Questionnaire Survey Based on the interview results, a questionnaire survey was administered to collect the opinion-based data of corruption-related irregularities from respondents. Data were collected from two perspectives, namely, probability (i.e., the possibility of occurrence of each irregularity) and severity (i.e., the impact of the consequence of each irregularity), using a five-point rating scale (i.e., 1 for very low, 2 for low, 3 for medium, 4 for high, and 5 for very high). Moreover, the questionnaire also collected the perception data of the overall corruption situation in the Chinese public construction sector from respondents with a five-point rating scale (i.e., 1 for not serious at all, 2 for not serious, 3 for medium, 4 for serious, and 5 for extremely serious). The population of the survey targets the officials, professionals and researchers that are involved in the Chinese public construction sector. As for the sampling approach, this chapter used a nonprobability sampling approach instead of a probability sampling approach. This was because it would be extremely difficult to conduct probability sampling in the Chinese construction sector which had about 29,212,000

28

3 Underlying Factors of Corruption in the Public …

Table 3.1 IRCs identified from structured interviews Codea

Irregularity related to corruption

IRC1

Administrative approval and financial sanction not taken even before the work starts

IRC2

The provisions are not as per the laid down yardstick

IRC3

Work is not executed for the same purpose for which the provisions were sanctioned

IRC4

The consultant is not appointed after proper publicity and open competition

IRC5

The criteria adopted in prequalification of consultant are restrictive and benefit only few consultants

IRC6

The selection of consultant is not done by the appropriate authority

IRC7

Adequate and wide publicity is not given to tender

IRC8

Adequate time for submission of tender/offer not given

IRC9

Prequalification criteria for selection of contractor are stringent

IRC10

The evaluation of tenders is not done exactly as per the notified criteria

IRC11

The negotiation on the tender not done as per the laid down guidelines

IRC12

The conditions/specifications are relaxed in favor of the contractor to whom the work is being awarded

IRC13

The work order/supply order is not placed within justified rates

IRC14

Work is executed without the availability of funds for the said purpose

IRC15

The work is not executed as per the original design

IRC16

Compliance with conditions regarding obtaining licenses, insurance policies and deployment of technical staff not being followed by contractor

IRC17

The proper record of hindrances is not being maintained from the beginning

IRC18

The deviations, especially in abnormally high rated and high value items are not properly monitored and verified

IRC19

The escalation clause is not applied correctly for admissible payment

IRC20

A large project that should have called for bids is split into several small projects and contracted without bidding

IRC21

Contractors provide false certificates in bidding

IRC22

Confidential information of bidding is disclosed to a specific bidder

IRC23

Substitution of unqualified materials in construction

IRC24

Site supervisor neglects his duties by taking bribes from contractor

a IRC

= irregularity related to corruption

employees across the country (National Bureau of Statistics of China 2015). In addition, among the commonly used nonprobability sampling methods such as convenience sampling, judgement sampling, quota sampling, and snowball sampling method (Jessen 1969), this chapter selected the convenience sampling and snowball sampling methods. Convenience sampling was selected as it was a method where subjects were selected because of their convenient accessibility and proximity to the researcher. This method was often used in exploratory research where the researcher was interested in getting an inexpensive approximation to the truth (Hultsch et al.

3.3 Methodology

29

2002). The snowball sampling was selected as it was a method where existing study subjects recruited future subjects from among their acquaintances, and it was often used when the desired sample characteristic was rare (Noy 2008). These two nonrandom sampling methods were particularly suitable to this chapter as it had a sensitive survey topic (i.e., corruption) which meant that few respondents would like to participate willingly. These two sampling methods were the most appropriate strategies to gather as many replies as possible for the survey. The questionnaire was disseminated through three channels including an online survey, interviews with qualified attendants of an industrial summit, and field surveys at three public construction sites. A total of 188 valid replies were received, with 87 collected from the online survey, 20 from the industrial summit, and 81 from the field surveys. The profiles of the respondents are shown in Table 3.2. More specific details of the questionnaire survey can be found in Le et al. (2014a). Table 3.2 Profile of the respondents Personal attributes

Categories

Number of respondents

Percentage

Organization

Government

20

10.6

Client

43

22.9

Contractor

43

22.9

Consultant

46

24.5

Designer

26

13.8

Position

Years of experience

Working place

Academic

10

5.3

Top managerial level (e.g. director, general manager, professor)

49

26.1

Middle managerial level (e.g. project manager)

88

46.8

Professional (e.g. engineer, quantity surveyor)

51

27.1

>20

24

12.8

11–20

40

21.3

6–10

76

40.4

20

24

12.8

12.8

11–20

40

21.3

34.1

6–10

76

40.4

74.5

2.56). Values of Composite Reliability are also over 0.7, which suggests a satisfactory level of reliability of first-order constructs with the corresponding second-order construct (Bagozzi and Yi 1988; Ling et al. 2013).

4.5.4 Evaluation of Structural Model The path coefficient between causes of and vulnerabilities to corruption has a t-value higher than 2.58, indicating its statistical significance at the 0.01 level (Henseler et al. 2009). The hypothesis that causes of corruption are positively correlated with corruption vulnerabilities is supported in the hypothesized sign. Figure 4.3 shows the testing results of the theoretical model.

4.6 Discussion According to the PLS-SEM results, all the statistical indicators were found to be acceptable, which validated the hypothesis developed in the study (Hair et al. 2011). The PLS-SEM results suggested that the causes of corruption have a positive correTable 4.9 Evaluation of hierarchical models

Paths

Path coefficient

T-value

CR

FRS → CC

0.605

15.330

0.8320

LPIC → CC

0.560

14.306

VC → IMM

0.820

22.166

VC → UNF

0.861

51.096

VC → OPA

0.738

17.325

VC → PRV

0.685

16.841

VC → COV

0.640

12.106

Note CC represents for causes of corruption VC represents for vulnerabilities to corruption

0.9045

70

4 Principal Causes of Corruption in the Public Construction Sector IMM1 IMM1

Immorality (IMM)

0.687 0.732 0.719 0.772 0.789

FRS1 FRS1 FRS3 FRS3 FRS4 FRS4

IMM5 IMM5 IMM6 IMM6

0.683 0.737

Flawed regulation systems (FRS)

UNF1 UNF1

0.820, t=22.166

0.830

Unfairness (UNF)

0.605, t=15.330 Causes of corruption

+ 0.459, t=6.2612

0.861, t=51.096

0.767 0.801 0.767 0.689 0.712

Vulnerabilities to corruption

0.669 0.783 0.691

Lack of positive industrial climate (LPIC)

0.685, t=16.841

Opacity (OPA)

0.615 0.801 0.789 0.752

OPA3 OPA3 OPA4 OPA4 OPA5 OPA5

0.640, t=12.106

0.789

LPIC6 LPIC6

UNF4 UNF4 UNF5 UNF5

OPA1 OPA1

0.560, t=14.306

LPIC2 LPIC2

UNF2 UNF2

UNF6 UNF6 0.738, t=17.325

LPIC5 LPIC5

IMM4 IMM4

0.600

FRS5 FRS5

LPIC4 LPIC4

IMM3 IMM3

PRV1 PRV1 Procedural violation (PRV)

0.794 0.658 0.758

PRV2 PRV2 PRV3 PRV3

Contractual violation (COV)

0.799 0.836

COV1 COV1 COV2 COV2

Fig. 4.3 Testing results of the theoretical model

lation with corruption vulnerabilities in the Chinese public construction sector. The results also showed that flawed regulation systems (FRS) and the lack of a positive industrial climate (LPIC) had significant correlations with the second-order construct the causes of corruption. The flawed regulation systems (FRS) emerged as the most principal set of causes of corruption with a path coefficient of 0.605. The lack of a positive industrial climate (LPIC) emerged as the second most principal set of causes of corruption with a path coefficient of 0.560.

4.6.1 The Flawed Regulation Systems Negative leader roles (FRS5) received the highest factor loading (0.830) on the flawed regulation systems (FRS). Leadership plays a vital role in the formation of an organization’s ethically-oriented culture (Schein 2006; Sims 1992, 2000). Positive leader roles can facilitate achievement of a mission via fair and honest actions (Tabish and Jha 2012). Conversely, negative leader roles can lead to corruption if leaders engage in corrupt practices themselves or, they overlook such practices performed by their friends, relatives, or colleagues. Under such circumstance, their subordinates may not behave differently (Tanzi 1998). According to Li et al. (2013), in most cases, corruption is undertaken by the collective involving executives and staff within an organization. In a recent survey in South Africa, Bowen et al. (2012) also reported

4.6 Discussion

71

similar findings that corrupt practices by an organization’s leaders could have negative effects on their subordinates, which would be followed by the subordinates. Inadequate sanctions (FRS4) had the second-high factor loading (0.737) on the flawed regulation systems (FRS). Theoretically, imposing significant sanctions on corrupt crimes to a large extent reduce the occurrence of corruption (Tanzi 1998; Zarkada-Fraser 2000). However, the Chinese publics believe that only very limited suspects receive sanctions for their corrupt crimes (He 2000). Even though the suspects may be sentenced to jail for their corrupt crimes, their terms of imprisonment are usually commuted by paying bribery to the judicial department (Xinhua Net 2014). Lack of rigorous supervision (FRS3) received the third ranking among the measurement items on the flawed regulation systems (FRS). Rigorous supervision is usually regarded one of the most effective anti-corruption measures (Tanzi 1998). However, there seems to be a significant gap between the specification of supervising rules and its execution in the Chinese context (Ko and Weng 2011). This may be due to a high social cost that is reluctant to be afforded by the supervisors, such as losing friend (Guo and Yang 2008). In addition, supervisors themselves may have been accessible to corruption, which could also lead to the lack of rigorous supervision (Li et al. 2013). Under such circumstance, small corrupt practices could have the opportunity to evolve into bigger ones. The item multifarious licenses/permits (FRS1) had the fourth high factor loading (0.600) in the list of measurement items on the flawed regulation systems (FRS). Obtaining several compulsory licenses/permits from government agencies are indispensable for a company to enter into the public construction sector (Zou et al. 2007). It is estimated that, a company need to obtain 108 licenses/permits to enter into the public construction market of Guangdong Province (Southern Metropolis Daily 2013). There also exists a lack of access to information and procedures regarding obtaining related licenses and permits in developing countries (Neelankavil 2002; Tanzi 1998). To accelerate the process of obtaining licenses and permits, some companies may choose to bribe to government officials (Argandona 2001; Tanzi 1998).

4.6.2 Lack of Positive Industrial Climate Interpersonal connections (LPIC6) had the highest factor loading (0.789) on the lack of a positive industrial climate (LPIC). Previous studies indicated that interpersonal connections are regarded a critical factor for doing business in China (Alston 1989). In a transitional society which lacks mature legislative and administrative systems, a company can gain competitive advantages and achieve business success by developing good interpersonal connections with governmental officials (Chan et al. 1999). Although interpersonal connections can gain company competitive and achieve business benefits, these benefits are often obtained by exchanging favors of various parties, especially by exchanging money and power (Fan 2002). In China, interpersonal connections are to a certain extent regarded as a synonym for corrupt

72

4 Principal Causes of Corruption in the Public Construction Sector

acts such as bribery, nepotism and fraud (Yang 2016). Although corruption is common in every country, interpersonal connections provide a more fertile soil in China than in any other country for corruption to flourish (Fan 2002). Over-close relationships among contracting parties (LPIC4) had the second high factor loading (0.783) on the lack of positive industrial climate (LPIC). Although close relationships among contracting parties (LPIC4) is regarded as a critical factor for the success of public construction project (Ning and Ling 2013), over-close relationships can also trigger a risk in collusion, a form of corruption. Zarkada-Fraser and Skitmore (2000) defined collusion as a corrupt act in which various parties coordinate their behaviours surreptitiously and gain benefits by bringing loss to project benefits. In practice, it is very difficult to identify collusion. Such wrong doing is quite common a kind of corrupt practice referring to various contracting parties including clients, contractors, designers, consultants and suppliers in Chinese public construction sector (Legal Daily 2012). Great project complexity (LPIC5) received the third with factor loading of 0.691 on the lack of positive industrial climate (LPIC). Project complexity may impose pressure on parties involved in a construction project and thus trigger corruption risk (El-Sayegh 2008). Tanzi and Davoodi (1998) further stated that project complexity may increase difficulties in project management tasks such as contractual design, engineering design, project construction, and site supervision. Task uncertainty caused by project complexity also provides opportunities for potential corruptors (e.g. contractors) to reap personal benefits (Tanzi et al. 1998). Le et al. (2013) and Li et al. (2013) reported that the complex and non-standard production process of construction projects in the Chinese context may foster asymmetric information stocks between contracting parties, thus providing opportunity for the occurrence of corruption. Poor professional ethical standards (LPIC2) ranked fourth in the factor loadings of all items on the lack of a positive industrial climate (LPIC). The profession refers to a group of well-trained people organized to serve a body of specialized knowledge in the interests of society (Appelbaum et al. 1990). Professional ethics is a set of moral principles that govern the conduct for these professionals (Allen 1990). Sohail and Cavill (2008) highlighted the seven principles for being an ethical professional, namely fair reward, integrity, honesty, objectivity, accountability, reliability, and fairness. However, previous studies have revealed the lack of professional and public morality in the construction sector of developing countries (Bowen et al. 2007; Bowen et al. 2007; Vee and Skitmore 2003). Poor professional ethical standards (LPIC2is a root cause of this situation in developing countries.

4.7 Research Limitation The main research limitation lies in the sample size of the questionnaire survey. Although this chapter has made great efforts in disseminating questionnaires and collecting feedbacks from various regions of China and the empirical data obtained

4.7

Research Limitation

73

has supported the developed hypothesis, this chapter still has room for collecting more empirical data and providing stronger evidences for model validation.

4.8 Summary To examine relationships between causes of and vulnerabilities to corruption in the public construction sector, an empirical survey was conducted in this chapter. PLSSEM results of the survey strongly supported the hypothesis that causes of corruption are positively correlated with corrupt vulnerabilities. Analysis results showed that the causes of corruption could be grounded under two constructs, namely, “flawed regulation systems”, and “lack of a positive industrial climate”. In addition, “flawed regulation systems” were found to have a higher path coefficient on corruption vulnerabilities in the public construction sector than “lack of a positive industrial climate”. This result indicates that “flawed regulation systems” have a higher influence on corruption vulnerabilities than “lack of a positive industrial climate”. Consequently, more anti-corruption efforts should be directed to this aspect. Based on the factor loading of each measurement item on its corresponding construct, the prioritization of various causes of corruption under each construct was identified. With respect to the construct “flawed regulation systems”, the descending order of the measurement items is “negative leader roles”, “inadequate sanctions”, “lack of rigorous supervision”, and “multifarious authorizations”. As to the construct “lack of a positive industrial climate”, the descending order of the measurement items is, “interpersonal connections”, “over-close relationships among contracting parties”, “great project complexity”, and “poor professional ethical standards”. Considering these results, several anti-corruption strategies for the public construction sector were proposed in this chapter as follows: (1) improve procedure design and implementation, and information disclose of awarding public construction projects; (2) impose rigorous supervision and auditing on public projects, and Enforce the execution of corruption-related laws and regulations in practice; and (3) establish the professional ethical standard and strengthen related training.

References Aibinu, A. A., & Al-Lawati, A. M. (2010). Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Automation in Construction, 19(6), 714–724. https://doi.org/10.1016/j.autcon.2010.02.016. Alam, M. S. (1989). Anatomy of Corruption: An Approach to the Political Economy of Underdevelopment. American Journal of Economics and Sociology, 48(4), 441–456. https://doi.org/10. 1111/j.1536-7150.1989.tb02132.x. Allen, R. E. (1990). The Concise Oxford Dictionary of Current English (8th ed.). Oxford: Clarendon Press.

74

4 Principal Causes of Corruption in the Public Construction Sector

Alston, J. P. (1989). Wa, Guanxi, and Inhwa: Managerial principles in Japan, China, and Korea. Business Horizons, 32(2), 26–31. https://doi.org/10.1016/S0007-6813(89)80007-2. Alutu, O. (2007). Unethical practices in Nigerian construction industry: Prospective engineers’ viewpoint. Journal of Professional Issues in Engineering Education and Practice, 133(2), 84–88. Alutu, O., & Udhawuve, M. (2009). Unethical practices in Nigerian engineering industries: Complications for project management. Journal of management in engineering, 25(1), 40–43. Appelbaum, D., & Lawton, S. (1990). Ethics and the Professions: Prentice-Hall. NJ: Englewood Cliffs. Argandona, A. (2001). Corruption: the corporate perspective. Bus. Ethics Eur. Rev., 10(2), 163–175. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327. Bologna, R., & Del Nord, R. (2000). Effects of the law reforming public works contracts on the Italian building process. Building Research & Information, 28(2), 109–118. Bowen, P., Akintoye, A., Pearl, R., & Edwards, P. J. (2007a). Ethical behaviour in the South African construction industry. Construction Management and Economics, 25(6), 631–648. Bowen, P., Pearl, R., & Akintoye, A. (2007b). Professional ethics in the South African construction industry. Building Research and Information, 35(2), 189–205. Bowen, P. A., Edwards, P. J., & Cattell, K. (2012). Corruption in the South African construction industry: A thematic analysis of verbatim comments from survey participants. Construction Management and Economics, 30(10), 885–901. Brent, R. J. (2009). Cost-benefit analysis and the evaluation of the effects of corruption on public projects. In Handbook of research on cost-benefit analysis (pp. 388–413). Cenfetelli, R. T., & Bassellier, G. (2009). INTERPRETATION OF FORMATIVE MEASUREMENT IN INFORMATION SYSTEMS RESEARCH. MIS Quarterly, 33(4), 689–707. Chan, W. K. L., Wong, F. K. W., & Scott, D. (1999). Managing construction projects in China The transitional period in the millennium. International Journal of Project Management, 17(4), 257–263. https://doi.org/10.1016/S0263-7863(98)00037-4. Chan, A. P. C., Lam, P. T. I., Chan, D. W. M., Cheung, E., & Ke, Y. (2010). Critical success factors for PPPs in infrastructure developments: Chinese perspective. Journal of Construction Engineering and Management, 136(5), 484–494. https://doi.org/10.1061/(asce)co.1943-7862.0000152. Choi, T. N. Y., Chan, D. W. M., & Chan, A. P. C. (2011). Perceived benefits of applying Pay for Safety Scheme (PFSS) in construction - A factor analysis approach. Safety Science, 49(6), 813–823. https://doi.org/10.1016/j.ssci.2010.10.004. Conway, J. M., & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6(2), 147–168. https:// doi.org/10.1177/1094428103251541. De Jong, M., Henry, W. P., & Stansbury, N. (2009). Eliminating corruption in our engineering/construction industry. Leadership and Management in Engineering, 9(3), 105–111. https:// doi.org/10.1061/(asce)1532-6748(2009)9:3(105). Doig, A. (1997). The privatisation of the Property Services Agency: Risk and vulnerability in contract-related fraud and corruption. Public Policy and Administration, 12(3), 6–27. https://doi. org/10.1177/095207679701200302. Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358. Ehrlich, I., & Lui, F. T. (1999). Bureaucratic corruption and endogenous economic growth. Journal of Political Economy, 107(S6), S270–S293. El-Sayegh, S. M. (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431–438. https://doi.org/10.1016/j.ijproman.2007. 07.004. Fan, Y. (2002). Guanxi’s consequences: Personal gains at social cost. Journal of Business Ethics, 38(4), 371–380. https://doi.org/10.1023/A:1016021706308. Guo, Z., & Yang, M. (2008). New standards of payment for the site supervisor and the corresponding strategies. Project Manage. (4), 46–47, 59.

References

75

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis.: New Jersey: Prentice Hall Upper Saddle River. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202. Haque, N. U., & Sahay, R. (1996). Do government wage cuts close budget deficits? Costs of corruption. Staff Papers-International Monetary Fund, 754–778. Harboon, D., & Heinrich, F. (2011). Bribe payers index 2011. Berlin, Germany: Transparency International. He, Z. K. (2000). Corruption and anti-corruption in reform China. Communist and Post-Communist Studies, 33(2), 243–270. https://doi.org/10.1016/s0967-067x(00)00006-4. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) The use of partial least squares path modeling in international marketing. In: Vol. 20. Advances in International Marketing (pp. 277–319). Hon, C. K. H., Chan, A. P. C., & Yam, M. C. H. (2012). Empirical Study to Investigate the difficulties of implementing safety practices in the repair and maintenance sector in Hong Kong. Journal of Construction Engineering and Management, 138(7), 877–884. https://doi.org/10.1061/(asce)co. 1943-7862.0000497. Hsueh, P.-R., Graybill, J. R., Playford, E. G., Watcharananan, S. P., Oh, M.-D., Ja’alam, K., et al. (2009). Consensus statement on the management of invasive candidiasis in Intensive Care Units in the Asia-Pacific Region. International Journal of Antimicrobial Agents, 34(3), 205–209. https:// doi.org/10.1016/j.ijantimicag.2009.03.014. Hwang, B.-G., Zhao, X., & Ng, S. Y. (2013). Identifying the critical factors affecting schedule performance of public housing projects. Habitat International, 38, 214–221. https://doi.org/10. 1016/j.habitatint.2012.06.008. Jain, A. K. (2001). Corruption: A review. Journal of Economic Surveys, 15(1), 71–116. Kenny, C. (2009). Transport construction, corruption and developing countries. Transport Reviews, 29(1), 21–41. Ko, K., & Weng, C. (2011). Critical Review of Conceptual Definitions of Chinese Corruption: a formal-legal perspective. Journal of Contemporary China, 20(70), 359–378. https://doi.org/10. 1080/10670564.2011.565170. Le, Y., Zhang, B., Guan, X., & Li, Y. (2013). Collusion study of public investment projects based on SNA. Journal of Public Management, 10(3), 29–40. Le, Y., Shan, M., Chan, A. P. C., & Hu, Y. (2014). Overview of Corruption Research in Construction. Journal of management in engineering, 30(4). https://doi.org/10.1061/(asce)me.1943-5479. 0000300. Lee, S.-H., Oh, K., & Eden, L. (2010). Why Do Firms Bribe? Insights from Residual Control Theory into Firms’ Exposure and Vulnerability to Corruption. Management International Review, 50(6), 775–796. https://doi.org/10.1007/s11575-010-0057-9. Legal Daily, L. (2012). Corruption tricks in the construction sector, revealed by the prosecutors. Levy, R. (2000). FUBAI: differing Chinese views of corruption since Tiananmen: does a road paved with corruption lead to socialism? Int. J. Public Adm, 23(11), 1863–1898. Li, Y., Le, Y., B., Z., & Shan, M. (2013). The correlations among corruption severity, power and behavior features in construction industry: An empirical study based on 148 typical cases. Manage. Rev., 25(8), 21–31. Ling, F. Y. Y., & Tran, P. Q. (2012). Effects of interpersonal relations on public sector construction contracts in Vietnam. Construction Management and Economics, 30(12), 1087–1101. https://doi. org/10.1080/01446193.2012.729848. Ling, F. Y. Y., Ning, Y., Ke, Y., & Kumaraswamy, M. M. (2013). Modeling relational transaction and relationship quality among team members in public projects in Hong Kong. Automation in Construction, 36, 16–24. https://doi.org/10.1016/j.autcon.2013.08.006. Liu, A. M., Fellows, R., & Ng, J. (2004). Surveyors’ perspectives on ethics in organisational culture. Engineering, Construction and Architectural Management, 11(6), 438–449. Meon, P. G., & Sekkat, K. (2005). Does corruption grease or sand the wheels of growth? Public Choice, 122(1–2), 69–97. https://doi.org/10.1007/s11127-005-3988-0.

76

4 Principal Causes of Corruption in the Public Construction Sector

Mo, P. H. (2001). Corruption and economic growth. Journal of Comparative Economics, 29(1), 66–79. https://doi.org/10.1006/jcec.2000.1703. Mohamed, S. (2002). Safety climate in construction site environments. Journal of Construction Engineering and Management-Asce, 128(5), 375–384. https://doi.org/10.1061/(asce)07339364(2002)128:5(375). Moodley, K., Smith, N., & Preece, C. N. (2008). Stakeholder matrix for ethical relationships in the construction industry. Construction Management and Economics, 26(6), 625–632. National Bureau of Statistics of China, N. (2012). China Statistical Yearbook, 2012. Retrieved from http://www.stats.gov.cn/tjsj/ndsj/2012/indexch.htm. Neelankavil, J. P. (2002). International business corruption: A framework of causes, effects, and prescriptions. 28th European International Business Academy Conference, Athens. Ning, Y. (2014). Quantitative effects of drivers and barriers on networking strategies in public construction projects. International Journal of Project Management, 32(2), 286–297. https://doi. org/10.1016/j.ijproman.2013.04.003. Ning, Y., & Ling, F. Y. Y. (2013). Reducing Hindrances to Adoption of Relational Behaviors in Public Construction Projects. Journal of Construction Engineering and Management, 139(11). https://doi.org/10.1061/(asce)co.1943-7862.0000745. Norušis, M. J. (2008). SPSS 16.0 Advanced Statistical Procedures Companion.: New Jersey: Prentice Hall Higher Education. Nye, J. S. (1967). Corruption and political development: A cost-benefit analysis. American Political Science Review, 61(2), 417–427. https://doi.org/10.2307/1953254. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A Critical Look at the Use of PLS-SEM in MIS Quarterly. Mis Quarterly, 36(1), III-XIV. Rose-Ackerman, S. (2008). Briefing: Risks of corruption in government infrastructure projects. Proceedings of the Institution of Civil Engineers-Municipal Engineer, 161(3), 149–150. https:// doi.org/10.1680/muen.2008.161.3.149. Schein, E. H. (2006). Organizational culture and leadership,: third edition, Jossey-Bass, San Francisco, C.A., U.S.A. Sha, K. X. (2004). Construction business system in China: an institutional transformation perspective. Building Research and Information, 32(6), 529–537. https://doi.org/10.1080/ 0961321042000280778. Sims, R. R. (1992). The challenge of ethical behavior in organizations. Journal of Business Ethics, 11(7), 505–513. https://doi.org/10.1007/BF00881442. Sims, R. R. (2000). Changing an organization’s culture under new leadership. Journal of Business Ethics, 25(1), 65–78. https://doi.org/10.1023/A:1006093713658. Sohail, M., & Cavill, S. (2008). Accountability to prevent corruption in construction projects. Journal of Construction Engineering and Management-Asce, 134(9), 729–738. https://doi.org/ 10.1061/(asce)0733-9364(2008)134:9(729). Southern Metropolis Daily, S. (2013). The administrative authorizations in Guangdong may continue to be reduced in next month. Retrieved from http://epaper.oeeee.com/A/html/2013-11/26/ content_1978414.htm. Tabish, S., & Jha, K. N. (2011). Analyses and evaluation of irregularities in public procurement in India. Construction Management and Economics, 29(3), 261–274. Tabish, S., & Jha, K. N. (2012). The impact of anti-corruption strategies on corruption free performance in public construction projects. Construction Management and Economics, 30(1), 21–35. Tanzi, V. (1998) Corruption around the world: causes, consequences, scope, and cures. In IMF Staff Papers (Vol. 45, pp. 559–594). Tanzi, V., & Davoodi, H. (1998). Corruption, Public Investment, and Growth. H. Shibata and T. Ihori, eds., Springer Japan,, 41–60.

References

77

Treisman, D. (2000). The causes of corruption: a cross-national study. Journal of Public Economics, 76(3), 399–457. https://doi.org/10.1016/s0047-2727(99)00092-4. Vee, C., & Skitmore, C. (2003). Professional ethics in the construction industry. Engineering, Construction and Architectural Management, 10(2), 117–127. Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). USING PLS PATH MODELING FOR ASSESSING HIERARCHICAL CONSTRUCT MODELS: GUIDELINES AND EMPIRICAL ILLUSTRATION. MIS Quarterly, 33(1), 177–195. https://doi.org/10.2307/20650284. Xinhua Net, X. (2011). More efforts will be imposed on the investigation of corruption in public construction sector in China. Retrieved from http://news.xinhuanet.com/legal/2011-05/17/c_ 121426891.htm. Xinhua Net, X. (2014). Irregularities in biddings of three gorges project: Nepotism, and waste of public money. Retrieved from http://news.xinhuanet.com/finance/2014-02/19/c_126159860. htm. Yang, M. M. H. (2016). Gifts, favors, and banquets: The art of social relationships in China. Zarkada-Fraser, A. (2000). A classification of factors influencing participating in collusive tendering agreements. Journal of Business Ethics, 23(3), 269–282. Zarkada-Fraser, A., & Skitmore, M. (2000). Decisions with moral content: collusion. Construction Management & Economics, 18(1), 101–111. Zhao, X., Hwang, B. G., & Low, S. P. (2013). Critical success factors for enterprise risk management in Chinese construction companies. Construction Management and Economics, 31(12), 1199–1214. https://doi.org/10.1080/01446193.2013.867521. Zhao, X., Hwang, B. G., & Low, S. P. (2014). Investigating enterprise risk management maturity in construction firms. Journal of Construction Engineering and Management, 140(8). https://doi. org/10.1061/(asce)co.1943-7862.0000873. Zou, P. X., Fang, D., Wang, S. Q., & Loosemore, M. (2007). An overview of the Chinese construction market and construction management practice. J. Technol. Manage. China, 2(2), 163–176.

Chapter 5

Effectiveness of Prevailing Anti-corruption Strategies

5.1 Introduction Corruption vulnerabilities in the public construction sector have raised in various countries around the world, particularly in those developing ones, which are caused by continual economic growth and rapid urbanization worldwide (Transparency International 2002, 2006, 2008, 2011). Corruption vulnerabilities can ruin the public construction sector at multiple levels and lead to underperformance of public projects, such as quality defects, cost overruns and delivery delay (Kenny 2009). It is estimated that corruption vulnerabilities may result in a loss ratio of project cost ranging from 10 to 50% (Jain 2001). Therefore, a growing number of research efforts have been devoted to related issues in recent years (Alutu 2007; Bowen et al. 2012; De Jong et al. 2009; Gunduz and Onder 2013; Le et al. 2014a, b; Sohail and Cavill 2008; Tabish and Jha 2011, 2012). Corruption vulnerabilities commonly exist in both developed and developing countries with various political and economic systems (Cendrowski et al. 2015; Ehrlich and Lui 1999; Melgar et al. 2009). As a result of the lack of mature legislative and institutional systems, developing countries face a greater challenge in preventing corruption than developed countries do (Ofori 2000). China is one example. For instance, the National Bureau of Corruption Prevention reported 15,010 cases of corruption recorded in the public construction sector between 2009 and 2011, which caused an estimated loss of CNY 3 billion (approximately USD 490 million) (Xinhua Net 2011). The serious corruption situation has forced the government to pay more attention to anti-corruption issues and improve relevant supervision in the Chinese public construction sector (Xinhua Net 2009). Various response strategies, such as economic (e.g., raising wage level, tax reform), administrative (e.g., public procurement reform, decentralization of decision-making), political (e.g., political competition, transparency in party financing), legislative (e.g., anti-corruption legislation, respect for the rule of law), and auditing strategies (e.g., independent judiciary, independent/free media), have been proposed in previous studies to mitigate corruption vulnerabilities (Chandler 2002; © Springer Nature Singapore Pte Ltd. 2020 M. Shan et al., Corruption in the Public Construction Sector, https://doi.org/10.1007/978-981-13-9550-5_5

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Desta 2006; Karhunen and Ledyaeva 2012; Klinkhammer 2013; Peisakhin and Pinto 2010; Riley 1998). However, few studies have evaluated the effectiveness of these response strategies. Therefore, this chapter focuses on the Chinese public construction sector, and aims to evaluate the effectiveness of existing response strategies by examining its relationships with corruption vulnerabilities.

5.2 Conceptual Framework and Hypothesis Development The conceptual framework of this chapter was developed based on Tabish and Jha (2011, 2012), which investigated corruption vulnerabilities and response strategies in the Indian public project procurement. Their frameworks were adopted as the theoretical foundation of this chapter for the following reasons. First, few researchers, apart from Tabish and Jha (2011, 2012), have examined the vulnerabilities to corruption and response strategies in the public construction sector of developing countries. Second, China and India have many similar aspects, such as close locations, economy, population and industrial structures (Cheng et al. 2007). Most importantly, both China and India are undergoing rapid urbanization and face a similar challenge of preventing corruption in the public construction sector (Le et al. 2014a). Furthermore, in order to make the framework of Tabish and Jha (2011, 2012) to fit with the Chinese context, a series of interviews were conducted by interviewing with experienced experts in China.

5.2.1 Corruption Vulnerabilities Corruption vulnerabilities play a critical role in corruption research, particularly in developing countries which lack a sound legislative and administrative system (Doig 1997; Lee et al. 2010). Sohail and Cavill (2008) outlined various corruption vulnerabilities and related stakeholders in the project execution and delivery process. Tabish and Jha (2011) further conceived key corruption vulnerabilities in public procurement in terms of irregularities. In their study, Tabish and Jha (2011) identified 61 irregularities in the Indian public procurement projects, and categorized these irregularities into five groups, namely transparency, professional standards, fairness, contract monitoring and regulation, and procedural accountability irregularities. Based on the consideration, these five groups and their affiliated irregularities were used in this chapter as the initial measurement framework of corruption vulnerabilities in the Chinese public construction sector.

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81

5.2.2 Response Strategies According to Tabish and Jha (2012), the response strategies for corruption vulnerabilities in the public construction sector consist of four constructs, namely, leadership, rules and regulations, training, and sanctions. Leadership can develop and facilitate values of integrity in an organization which are manifested in via appropriate actions (Tabish and Jha 2012). An eligible leader always communicates values of integrity to the rest of the organization and creates conditions that motivate people to behave in an upright way (Sööt 2012). Meanwhile, the openness and strictness of leaders are also found to have a direct impact on the frequency of integrity violations by employees (Huberts et al. 2007). Therefore, selecting good leaders is vital for an organization to fight against potential corruption vulnerabilities (Mumford et al. 2003). Harbouring the belief that corruption can be completely curbed without rules and regulations is perhaps naive given the long history of corruption in business and the understanding of the human behaviour that cannot be disciplined under a circumstance without any constraint (Ashforth et al. 2008). Rules and regulations have been deemed as the core component of anti-corruption strategies, because an organization must implement its mission and vision of anti-corruption policies with the aid of relevant rules and regulations (Ivancevich et al. 2003; Klitgaard and Klitgaard 1988; Tabish and Jha 2012). A thorough regulation system is usually developed to increase transparency and accountability and to enforce penal codes against corruption, and can thus aid the “good guys” in controlling unsavoury competitors and creating an impartial playing field (Ashforth et al. 2008; Misangyi et al. 2008). Imposing training on industry practitioners is indispensable to corruption prevention in the construction industry (Smith 2009). This is because training can help practitioners acquire knowledge on the damaging effects of corruption on society and teach them corruption risk in the project execution and concrete skills coping for these risks (Boehm and Nell 2007; Schwartz 2004; Schwartz 2009). Many international associations, such as the International Federation of Consulting Engineers, the American Society of Civil Engineers, the U.K. Institution of Civil Engineers, the U.K. Chartered Institute of Building, and the U.K. Royal Institution of Chartered Surveyors, have incorporated training as an important component into their anti-corruption guidelines (Boyd and Padilla 2009; Crist 2009; Le et al. 2014b). Sanctions should be imposed for corrupt practices that have been caught (Tabish and Jha 2012). Imposed sanctions is an indispensable response strategy that is affected by four factors, namely, probability of being caught, enforcement, independence of the judiciary from politicians, and equal access to the law for every one (Arvey and Ivancevich 1980; Jain 2001; Mulder et al. 2009). An adequate sanction can curb corruption, because the harsh punishment will undoubtedly change the cost-benefit calculation of potential corruptors, particularly in cases when the risk of being caught is sufficiently high (Johannsen and Pedersen 2012).

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5 Effectiveness of Prevailing Anti-corruption Strategies

Transparency irregularities Leadership Professional standards irregularities Rules and Regulations Response Strategies

-

Corruption Vulnerabilities

Fairness irregularities

Training Contract monitoring and regulation irregularities Sanctions Procedural accountability irregularities

Fig. 5.1 Hypothesized model of corruption vulnerabilities and response strategies

5.2.3 Hypothesis Development A hypothesized model (Fig. 5.1) based on the conceptual framework was proposed to investigate the relationships between corruption vulnerabilities and response strategies in the Chinese public construction sector. As shown in Fig. 5.1, response strategies in the hypothesized model are considered a four-dimensional and second-order construct composed of leadership, rules and regulations, training, and sanctions. Corruption vulnerabilities are deemed as a five-dimensional and second-order construct composed of transparency, professional standards, fairness, contract monitoring and regulation, and procedural accountability irregularities. The development of the model adopted the second-order construct approach recommended by Wetzels et al. (2009), because it maximizes the interpretability of both measurement and hierarchical models. In the proposed model, the hypothesis that response strategies are negatively correlated with corruption vulnerabilities in the public construction sector, is to be tested.

5.3 Research Methodology The whole research process consists of four steps. First, a hypothesized model for defining the relationships between corruption vulnerabilities and response strategies was formulated based on Tabish and Jha (2011, 2012). Second, the model was refined by interviewing with selected experts to fit in the Chinese context. Third, a questionnaire instrument was developed based on the refined framework and was used in the

5.3 Research Methodology

83

survey to collect opinion-based data from target respondents. Lastly, both factor analysis (FA) and partial least squares structural equation modelling (PLS-SEM) were conducted to analyse the data collected and validate the hypothesized model. Qualitative and quantitative methods were sequentially adopted in this chapter. Results obtained from diverse methods can triangulate and complement each other, thus yielding stronger and more reliable findings (Xia et al. 2009; Zhao et al. 2014).

5.3.1 Interviews To verify the hypothesized model derived from Tabish and Jha (2011, 2012) and make it fit in with the Chinese context, a series of face-to-face interviews were conducted between July and August 2013. Each interview contains two sections. In Section A, the interviewee was asked to provide his/her opinion on the measurement items of response strategies of Tabish and Jha (2012), in terms of their involvement in the Chinese public construction sector. In Section B, the interviewee was asked to provide his/her opinions on the measurement items of corruption vulnerabilities derived from Tabish and Jha (2011) in terms of five-point Likert scale: “1—strongly disagree,” “2—disagree,” “3—neutral,” “4—agree,” and “5—strongly agree.” Each interviewee was also encouraged to supplement the measurement items of corruption vulnerabilities that were not recorded in the interview. A total of 14 experienced industrial and academic experts were invited to participate in the interviews. To ensure the reliability and quality of interviews, a purposive approach was adopted to select interviewees. All the interviewees had at least tenyear experience in the public construction sector and senior positions within their organizations. The selection of interviewees also considered the diversity of professional expertise of experts, which helped increase the heterogeneity of the interview panel and thus improve the validity of interviews. Table 5.1 shows the backgrounds of interviewees. All interviewees agreed with the applicability of Tabish and Jha’s (2012) categorization of response strategies for corruption vulnerabilities in the Chinese context. Only a few statements of measurement items were adjusted as suggested by interviewees. According to Interviewees A, C, and L, the items of ‘fear of suspension’, ‘fear of disciplinary action’, and ‘fear of caution/warning letter’ proposed by Tabish and Jha (2012) were revised to ‘fear of economic sanction’, ‘fear of penal sanction’, and ‘fear of administrative sanction’, respectively. According to the interview feedback, the mean score of each measurement item of Tabish and Jha (2011) was calculated. Only those achieving a value of 2.5 or above were used in the final questionnaire for the survey. This method is suggested by Hsueh et al. (2009). Finally, 19 measurement items regarding corruption vulnerabilities were extracted and used in the questionnaire survey (Table 5.2). In addition, five new measurement items (i.e., contractors provide false certificates in bidding, substitution of unqualified materials in construction, site supervisor neglects his duties for taking bribe from contractor, confidential information of bidding is disclosed to a spe-

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5 Effectiveness of Prevailing Anti-corruption Strategies

Table 5.1 Backgrounds of interviewees No.

Employer

Position

Years of experience

Largest project ever managed/consulted

A

Government Director

20

USD 363 million

B

Government Deputy Director

16

USD 308 million

C

Client

Project Manager

19

USD 363 million

D

Client

Project Manager

17

USD 308 million

E

Client

Director

13

USD 167 million

F

Contractor

General Manager

25

USD 363 million

G

Contractor

Project Manager

20

USD 122 million

H

Contractor

Director

15

USD 85 million

I

Consultant

General Manager

20

USD 363 million

J

Consultant

Project Manager

16

USD 122 million

K

Consultant

Project Manager

15

USD 85 million

L

Academic

Professor

22

USD 197 million

M

Academic

Professor

17

USD 73 million

N

Academic

Associate Professor

13

USD 363 million

cific bidder, and a large project should have called for bids is split into several small projects and contracted without bidding) regarding corruption vulnerabilities advocated by most experts were added to elaborate the hypothesized model and make a tailor fit with the Chinese context (Table 5.3). Correspondingly, five categories of corruption vulnerabilities were renamed as opacity (formerly transparency), immorality (formerly professional standards), unfairness (formerly fairness), contractual violation (formerly contract monitoring and regulation), and procedural violation (formerly procedural accountability). Figure 5.2 shows the revised hypothesized model.

5.3.2 Questionnaire Survey A questionnaire survey was administered based on the measurement items consolidated in the interviews. The target respondents included clients, contractors, designers, consultants, governmental officials, and academics involved in public construction projects in China. To maximize the number of potential respondents, a number of government agencies, research institutions, and companies within the construction industry were contacted. In the end, eight institutions, namely, (1) Research Institute of Complex Engineering & Management, Tongji University, (2) Shanghai Construction Consultants Association, (3) Shanghai Xian Dai Architectural Design (Group) Co., Ltd., (4) School of Civil Engineering and Transportation, South China University of Technology, (5) College of Civil Engineering, Shenzhen University, (6) Construction Commission of Zhengzhou Municipality, (7) Zhengzhou Metro Group

5.3 Research Methodology

85

Table 5.2 Measurement items of corruption vulnerabilities Construct

Code

Measurement item

Evaluation Factor loading

Variance explained

Immorality

IMM1

The work is not executed as per original design accorded

3.93

0.727

33.679%

IMM2

Work is executed without the availability of funds for the said purpose

3.93

0.474b

IMM3

The changes, especially in abnormally high rated and high value items are not properly monitored and verified

3.29

0.696

IMM4a

Contractors provide false certificates in bidding

3.96

0.673

IMM5a

Substitution of unqualified materials in construction

3.54

0.735

IMM6a

Site supervisor neglects his duties for taking bribe from contractor

3.91

0.750

UNF1

The consultant is not appointed after proper publicity and open competition

3.64

0.797

UNF2

The criteria adopted in prequalification of consultant are restrictive and benefit only few consultants

3.43

0.849

Unfairness

9.718%

(continued)

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5 Effectiveness of Prevailing Anti-corruption Strategies

Table 5.2 (continued) Construct

Opacity

Code

Measurement item

Evaluation Factor loading

UNF3

The selection of consultant not done by appropriate authority

3.57

0.451b

UNF4

The criteria for selection of contractor are restrictive and benefit only few contractors

3.00

0.708

UNF5

The condi3.50 tions/specifications are relaxed in favour of contractor to whom the work is being awarded

0.636

UNF6a

Confidential information of bidding is disclosed to a specific bidder

3.76

0.654

OPA1

Adequate and wide publicity is not given to tender

2.71

0.720

OPA2

Adequate time for submission of tender/offer not given

2.64

0.482b

OPA3

The evaluation of tenders is not done exactly as per the notified Criteria

2.57

0.752

OPA4

The negotiation on tender not done as per laid down guidelines

3.00

0.759

Variance explained

6.644%

(continued)

5.3 Research Methodology

87

Table 5.2 (continued) Construct

Procedural violation

Contractual violation

Code

Measurement item

Evaluation Factor loading

OPA5a

A large project should have called for bids is split into several small projects and contracted without bidding

3.40

0.616

PRV1

Administrative approval and financial sanction not taken to execute the work

2.79

0.742

PRV2

Lack of the sanctioned financial provisions from the government

3.86

0.707

PRV3

Work is not executed for the same purpose for which the sanction was accorded

2.93

0.640

PRV4

The proper record of hindrances is not being maintained from the beginning

2.93

0.440b

COV1

Escalation clause is not applied correctly for admissible payment

3.57

0.746

Variance explained

6.300%

5.281%

(continued)

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5 Effectiveness of Prevailing Anti-corruption Strategies

Table 5.2 (continued) Construct

a IMM4, b IMM2,

Code

Measurement item

Evaluation Factor loading

COV2

Compliance with conditions regarding deployment of technical staff not being followed by contractor

3.71

0.573

COV3

The work order/supply order is not placed within justified rates

2.71

0.443b

Variance explained

IMM5, IMM6, UNF6, and OPA5 were added by the interviewees UNF3, OPA2, PRV4, and COV3 were excluded with factor loadings lower than 0.5 IMM1 IMM1 IMM2 IMM2 Immorality (IMM)

IMM3 IMM3 IMM4 IMM4

LEA1 LEA1

IMM5 IMM5

LEA2 LEA2

IMM6 IMM6

LEA3 LEA3 LEA4 LEA4

UNF1 UNF1 Leadership (LEA)

UNF2 UNF2

LEA5 LEA5

Unfairness (UNF)

LEA6 LEA6 LEA7 LEA7

RAR3 RAR3

Rules and Regulations (RAR)

Response strategies (RS)

-

UNF6 UNF6

Corruption vulnerabilities (CV)

OPA1 OPA1 OPA2 OPA2 Opacity (OPA)

RAR4 RAR4 TRA1 TRA1 TRA2 TRA2

OPA3 OPA3 OPA4 OPA4

Training (TRA)

OPA5 OPA5

TRA3 TRA3

PRV1 PRV1

SAN1 SAN1 SAN2 SAN2

UNF4 UNF4 UNF5 UNF5

RAR1 RAR1 RAR2 RAR2

UNF3 UNF3

Sanctions (SAN)

Procedural violation (PRV)

SAN3 SAN3

PRV2 PRV2 PRV3 PRV3 PRV4 PRV4 COV1 COV1

Contractual violation (COV)

COV2 COV2 COV3 COV3

Fig. 5.2 Refined hypothesized model

Measurement item

Interpersonal connections

Contractors provide false certificates in bidding

Substitution of unqualified materials in construction

Site supervisor neglects his duties for taking bribe from contractor

Confidential information of bidding is disclosed to a specific bidder

A large project should have called for bids is split into several small projects and contracted without bidding

Code

LPIC6

IMM4

IMM5

IMM6

UNF6

OPA5

√

√

√

√

√

√

√

√

D √

√

√

C

√

B

√

A

Interviewee

Table 5.3 Sources and evaluations of added measurement items

√

√

√

E √

√

F √

√

√

G

√

√

H √

√

√

√

I

√

√

√

√

J √

√

√

√

K √

√

√

√

√

√

L √

√

√

√

M

√

√

√

√

N √

3.40

3.76

3.91

3.54

3.96

3.96

Evaluation

5.3 Research Methodology 89

90

5 Effectiveness of Prevailing Anti-corruption Strategies

Table 5.4 Backgrounds of respondents Personal attributes

Categories

Number of respondents

Percentage

Organization

Government

20

10.6

Client

43

22.9

Contractor

43

22.9

Consultant

46

24.5

Designer

26

13.8

Academic

10

5.3

Top managerial level (e.g. director, general manager, professor)

49

26.1

Middle managerial level (e.g. project manager)

88

46.8

Professional (e.g. engineer, quantity surveyor)

51

27.1

Position

Years of experience

>20

24

12.8

11–20

40

21.3

6–10

76

40.4

2.58). The values of Composite Reliability are also over 0.7, which indicates a satisfactory level of reliability of first-order constructs with the corresponding second-order construct (Bagozzi and Yi 1988; Ling et al. 2013).

5.5.4 Evaluation of Structural Models The path coefficient between response strategies and corruption vulnerabilities has a t-value that is higher than 1.96, suggesting its statistical significance at the 0.05 level (Henseler et al. 2009). The hypothesis that response strategies are negatively correlated with corruption vulnerabilities is supported in the hypothesized sign. Figure 5.3 shows the testing results of the hypothesized model.

0.7887

−0.0470

−0.1520

−0.0820

−0.0601

−0.0529

−0.1632

−0.1426

0.3434

0.4502

0.4895

0.3763

0.4300

−0.0222

−0.0600

−0.0746

−0.0788

−0.0837

−0.1854

−0.0951

0.1268

0.2881

0.1791

0.0864

0.3165

0.2344

0.3247

IMM1

IMM3

IMM4

IMM5

IMM6

LEA1

LEA2

LEA3

LEA4

LEA5

LEA6

LEA7

OPA1

OPA3

OPA4

OPA5

PRV1

PRV2

PRV3

0.4297

0.2000

0.2806

0.4454

0.3517

0.3402

0.1417

0.7716

0.7316

0.6867

0.7199

0.3943

0.7994

0.5163

0.8356

COV2

IMM

COV1

COV

0.4022 0.3282 0.2740 0.3458 0.3049

−0.0374 −0.0160 −0.1122 −0.1206 −0.1880

0.3021 0.3235

0.0079 −0.0897

0.3927

0.7515

−0.0561 0.0018

0.7895

0.0711

0.6162 0.8011

0.0249

−0.0166

−0.0821

−0.0865

−0.0440

0.0497

−0.0189

−0.1177

0.7947

0.8010

0.6849

0.7800

0.8332

0.8291

−0.0032

0.2111

0.7747

0.1700

−0.0506

OPA

−0.1244

LEA

Table 5.8 Cross loadings for individual measurement items

0.7574

0.6581

0.7948

0.3925

0.3891

0.3040

0.2725

−0.0352

−0.1172

0.0256

−0.0040

0.0465

−0.0304

−0.0953

0.3865

0.3249

0.2382

0.2988

0.2998

0.3705

0.2863

PRV

−0.0227

0.1141

0.0492

0.0387

0.0590

−0.0219

0.0007

0.3790

0.3443

0.1937

0.2317

0.3403

0.3658

0.3719

−0.1083

−0.0188

−0.1154

0.0411

−0.0488

−0.1132

−0.1166

SAN

0.1140

−0.0907

−0.0721 −0.1028

0.0396 −0.0166

−0.1313

0.1818

−0.0284 −0.0912

0.0794

0.1446

0.1767

0.2713

0.2240

0.2790

0.2079

0.2581

0.2850

−0.1047

−0.1083

−0.1653

−0.0706

0.0369

−0.1290

−0.0234

TRA

−0.1176

0.0507

0.4947

0.4287

0.2578

0.2766

0.4119

0.4320

0.4002

−0.2663

−0.1833

−0.1153

−0.0428

−0.1926

−0.1473

−0.1651

RAR

(continued)

0.3800

0.3585

0.3743

0.4976

0.4163

0.5065

0.3146

−0.1821

−0.1389

−0.1516

−0.1090

−0.1680

−0.1592

−0.1607

0.4506

0.3896

0.3224

0.2962

0.5499

0.3394

0.4122

UNF

96 5 Effectiveness of Prevailing Anti-corruption Strategies

−0.1782

−0.0466

0.0653

−0.2058

−0.1842

−0.1494

−0.1687

0.1218

−0.0370

−0.2115

−0.2080

−0.1115

−0.0412

−0.1899

−0.1398

−0.1410

−0.1342

0.0594

−0.0138

−0.1737

0.2632

0.3276

0.3383

0.2793

0.4876

SAN1

SAN2

SAN3

RAR1

RAR2

RAR3

RAR4

TRA1

TRA2

TRA3

UNF1

UNF2

UNF4

UNF5

UNF6

0.5630

0.4125

0.3893

0.3228

0.3447

IMM

COV

Table 5.8 (continued)

0.1223 0.4044 0.3447 0.5198 0.4828 0.4559

−0.2572 −0.1138 −0.0761 −0.0740 −0.2010

0.1273

0.0915

0.0016

−0.0932

−0.0552

−0.0646

−0.0004

0.0728

0.0024

OPA

0.3364

0.2304

0.1021

0.1052

0.4007

0.4905

0.4353

0.3252

0.3445

0.4279

LEA

0.4172

0.1846

0.4685

0.3751

0.4006

−0.0253

0.0643

0.0041

−0.1616

−0.0971

−0.0878

−0.1311

0.1000

0.1093

−0.0404

PRV

−0.0634

−0.1495

0.0072

−0.0078

−0.0637

0.2874

0.2215

0.0929

0.1554

0.2227

0.2650

0.2254

0.8747

0.9444

0.8871

SAN

−0.3069

−0.0408

−0.1378

−0.1421

−0.2490

0.3727

0.2166

0.0636

0.5491

0.8070

0.8602

0.8553

0.1935

0.2357

0.3227

RAR

−0.0118

−0.0599

−0.0212

−0.0553

−0.1231

0.8621

0.8733

0.6805

0.1508

0.2641

0.3035

0.2322

0.1918

0.2025

0.3316

TRA

0.7110

0.6890

0.7669

0.8017

0.7676

−0.1407

−0.0035

0.0479

−0.1631

−0.1889

−0.2144

−0.1889

−0.0334

−0.0422

−0.1097

UNF

5.5 Analysis Results 97

98

5 Effectiveness of Prevailing Anti-corruption Strategies

Table 5.9 Evaluation results of hierarchical models

Paths

Path coefficient

T-value

CR 0.9008

LEA → RS

0.6359

17.8615

RAR → RS

0.2830

10.2842

TRA → RS

0.1428

5.2634

SAN → RS

0.2356

8.1213

CV → PRV

0.6857

17.1155

CV → UNF

0.8629

51.1495

CV → OPA

0.7402

17.7132

CV → COV

0.6377

11.7899

CV → IMM

0.8157

21.6029

0.9045

Note RS represents for response strategies CV represents for corruption vulnerabilities IMM1 IMM1 LEA1 LEA1 Immorality (IMM)

LEA2 LEA2 LEA3 LEA3 LEA4 LEA4 LEA5 LEA5

0.775 0.829 0.833 0.780 0.685 0.801 0.795

Leadership (LEA)

Unfairness (UNF)

RAR1 RAR1

RAR3 RAR3

Rules and 0.283, t=10.284 Regulations (RAR)

-

0.180, t=2.457

Corruption vulnerabilities (CV)

0.681 0.873 0.862

UNF2 UNF2 UNF4 UNF4 UNF5 UNF5

OPA1 OPA1 Opacity (OPA)

0.616 0.801 0.790 0.752

OPA3 OPA3 OPA4 OPA4 OPA5 OPA5

0.638, t=11.790

TRA3 TRA3

PRV1 PRV1

SAN1 SAN1 SAN2 SAN2

IMM5 IMM5

UNF6 UNF6

0.236, t=8.121 Training (TRA)

0.768 0.802 0.767 0.689 0.711

0.740, t=17.713

0.686, t=17.116

TRA1 TRA1 TRA2 TRA2

Response strategies (RS)

0.863, t=51.150

0.143, t=5.263

RAR4 RAR4

IMM4 IMM4

UNF1 UNF1

0.636, t=17.862

LEA7 LEA7

0.855 0.860 0.807 0.549

IMM3 IMM3

IMM6 IMM6 0.816, t=21.603

LEA6 LEA6

RAR2 RAR2

0.720 0.687 0.732 0.772 0.789

0.887 0.944 0.875

Sanctions (SAN)

SAN3 SAN3

Procedural violation (PRV)

0.795 0.658 0.757

PRV2 PRV2 PRV3 PRV3

Contractual violation (COV)

0.836 0.799

COV1 COV1 COV2 COV2

Fig. 5.3 Testing results of the hypothesized model

5.6 Discussion Based on the PLS-SEM results, all the statistical indicators were found to be acceptable, which loosely supported the hypothesis in the study. Analysis results also revealed that four response strategies grouped under various constructs did not play an effective role in preventing corruption vulnerabilities as predicted in prior studies received. The most effective response strategy, Leadership (LEA), only received a

5.6 Discussion

99

path coefficient of 0.636; the path coefficients of other three strategies were about 0.200, which were relatively low.

5.6.1 Leadership Leadership (LEA) was regarded as the most useful response strategy in the survey, which has reinforced the findings of earlier studies (Ashforth and Anand 2003; Sims 2000; Tabish and Jha 2012). Compared with western countries, leadership plays a more critical role in China. This can be due to the tradition of rule by man, although rule by law has been gradually accepted and practiced improving the legislative and administrative systems in the country and it still has a long road to incorporate it into the existing institutions. Consequently, accountability for integrity of leadership needs to be improved in future public construction (People’s Liberation Army Daily 2003). By establishing this mechanism, leaders have duty to secure the integrity of the projects with the exercise of his/her leadership, which can also produce a positive impact on his/her subordinates’ corrupt practices.

5.6.2 Rules and Regulations This response strategies received a low path coefficient of 0.283 (t-value = 10.28), which indicated that the effectiveness of rules and regulations (RAR) is loosely supported by the respondents. This may be due to the fact that the existing response rules and regulations at the macro level are reactive, which seldom address the need of proactively preventing corrupt practices at the micro level (He 2000). Although the China government has already recognized this fact and begun promulgating a series of more detailed and workable rules and regulations focusing on the micro level (Legal Weekly 2014), such as the interpretation of issues that are applicable to the Disciplinary Regulations of the Chinese Communist Party because of illegal interference on construction projects by the leader members of the Party, and Implementing regulations of the Law of Bidding of People’s Republic of China (People Net 2010; The State Council of P.R. China 2011), which have been evidenced by a growing number of corruption cases revealed in recent years, it still has a long waiting to see the effectiveness of these new rules and regulations.

5.6.3 Sanctions This strategy received a low path coefficient of 0.236 (t-value = 8.12). Although imposing serious sanctions on corrupt crimes is regarded the most useful strategy for preventing corruption (Tanzi 1998), the effectiveness of this strategy is merely

100

5 Effectiveness of Prevailing Anti-corruption Strategies

regarded as acceptable by the respondents, which has echoed the belief of the Chinese public that only very limited suspects have receive sanctions for their corrupt crimes (He 2000). In extreme cases, some suspects may be sentenced to jail for their corrupt crimes, but their terms of imprisonment may be commuted by paying bribery to the judicial department (Xinhua Net 2014). This fact has explained why the respondents are reluctant to provide a high evaluation on the effectiveness of sanctions (SAN). In order to change this situation, a series of reforms have been made by the Chinese Government. According to the China Ministry of Supervision, 11,273 people received administrative sanction, and 5698 people received penal sanction for their corrupt crimes in the public construction sector between September 2009 and March 2011 (Xinhua Net 2011), which indicated that the execution of sanctions for corruption crimes seems to be gradually strengthened.

5.6.4 Training Training (TRA) received the lowest path coefficient of 0.143 (t-value = 5.26) among the four response strategies, which indicated that most survey respondents held a belief that existing training on corruption remains lacking. Undoubtedly, training is regarded as an indispensable response strategy for corruption prevention for its proactive role of forestalling corruption (Heineman and Heimann 2006). Thus, related training need to be implemented in all Chinese public construction projects. Zou (2006) stated that existing training seldom address doubts on emergent ethical dilemmas, such as conflicts of interest, and gift giving/receiving. Similar problems are common to industry practitioners as a result the inappropriate response to ethical dilemmas (Luo 2002). Therefore, future professional training should incorporate corruption issues and help industrial professionals maintain the highest integrity standards.

5.7 Summary A questionnaire survey was conducted in this chapter to evaluate the effectiveness of the response strategies for vulnerabilities to corruption in the public construction sector. The survey results showed that the effectiveness of four response strategies, namely, “leadership”, “rules and regulations”, “training”, and “sanctions”, only achieved an acceptable level in corruption prevention. Although “leadership” was found to be the most effective construct of response strategies and plays a decisive role in preventing corruption vulnerabilities, the effectiveness of this strategy remained limited and need to be improved in future. The effectiveness of “rules and regulations”, “sanctions”, and “training” are found to be loosely supported by the respondents, implicating more efforts should be directed to these aspects. The major

5.7 Summary

101

findings of this chapter can help researchers and practitioners obtain more knowledge about anti-corruption strategies in developing countries, particularly in China.

References Aibinu, A. A., & Al-Lawati, A. M. (2010). Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Automation in Construction, 19(6), 714–724. https://doi.org/10.1016/j.autcon.2010.02.016. Alutu, O. (2007). Unethical practices in Nigerian construction industry: Prospective engineers’ viewpoint. Journal of Professional Issues in Engineering Education and Practice, 133(2), 84–88. Arvey, R. D., & Ivancevich, J. M. (1980). Punishment in organizations: A review, propositions, and research suggestions. Academy of Management Review, 5(1), 123–132. Ashforth, B. E., & Anand, V. (2003) The normalization of corruption in organizations. In Research in organizational behavior (Vol. 25, pp. 1–52). Ashforth, B. E., Gioia, D. A., Robinson, S. L., & Trevino, L. K. (2008). Re-viewing organizational corruption—Introduction. Academy of Management Review, 33(3), 670–684. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327. Boehm, F., & Nell, M. (2007). Anti-corruption training and education. U4 Brief, 2007(13). Bowen, P. A., Edwards, P. J., & Cattell, K. (2012). Corruption in the South African construction industry: A thematic analysis of verbatim comments from survey participants. Construction Management and Economics, 30(10), 885–901. Boyd, J. M., & Padilla, J. D. (2009). FIDIC and integrity: A status report. Leadership and Management in Engineering, 9(3), 125–128. https://doi.org/10.1061/(ASCE)1532-6748(2009)9:3(125). Cendrowski, H., Martin, J. P., & Petro, L. W. (2015). The handbook of fraud deterrence. Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–707. Chan, A. P. C., Lam, P. T. I., Chan, D. W. M., Cheung, E., & Ke, Y. (2010). Critical success factors for PPPs in infrastructure developments: Chinese perspective. Journal of Construction Engineering and Management, 136(5), 484–494. https://doi.org/10.1061/(asce)co.1943-7862.0000152. Chandler, D. (2002). Anti-corruption strategies and democratization in Bosnia-Herzegovina. Democratization, 9(2), 101–120. https://doi.org/10.1080/714000252. Cheng, H. F., Gutierrez, M., Mahajan, A., Shachmurove, Y., & Shahrokhi, M. (2007). A future global economy to be built by BRICs. Global Finance Journal, 18(2), 143–156. https://doi.org/ 10.1016/j.gfj.2006.04.003. Choi, T. N. Y., Chan, D. W. M., & Chan, A. P. C. (2011). Perceived benefits of applying Pay for Safety Scheme (PFSS) in construction—A factor analysis approach. Safety Science, 49(6), 813–823. https://doi.org/10.1016/j.ssci.2010.10.004. Conway, J. M., & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6(2), 147–168. https:// doi.org/10.1177/1094428103251541. Crist, R. A., Jr. (2009). The ASCE committee of global principles for professional conduct. Leadership and Management in Engineering, 9(3), 144–146. De Jong, M., Henry, W. P., & Stansbury, N. (2009). Eliminating corruption in our engineering/construction industry. Leadership and Management in Engineering, 9(3), 105–111. https:// doi.org/10.1061/(ASCE)1532-6748(2009)9:3(105). Desta, Y. (2006). Designing anti-corruption strategies for developing countries: A country study of Eritrea. Journal of Developing Societies, 22(4), 421–449. https://doi.org/10.1177/ 0169796X06072568.

102

5 Effectiveness of Prevailing Anti-corruption Strategies

Doig, A. (1997). The privatisation of the property services agency: Risk and vulnerability in contractrelated fraud and corruption. Public Policy and Administration, 12(3), 6–27. https://doi.org/10. 1177/095207679701200302. Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358. Ehrlich, I., & Lui, F. T. (1999). Bureaucratic corruption and endogenous economic growth. Journal of Political Economy, 107(S6), S270–S293. Gunduz, M., & Onder, O. (2013). Corruption and internal fraud in the Turkish construction industry. Science and Engineering Ethics, 19(2), 505–528. https://doi.org/10.1007/s11948-012-9356-9. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis. New Jersey, Upper Saddle River: Prentice Hall. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202. He, Z. K. (2000). Corruption and anti-corruption in reform China. Communist and Post-Communist Studies, 33(2), 243–270. https://doi.org/10.1016/s0967-067x(00)00006-4. Heineman, B. W., & Heimann, F. (2006). The long war against corruption. Foreign Affairs, 85(3), 75±. https://doi.org/10.2307/20031968. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) The use of partial least squares path modeling in international marketing. In Advances in international marketing (Vol. 20, pp. 277–319). Hsueh, P.-R., Graybill, J. R., Playford, E. G., Watcharananan, S. P., Oh, M.-D., Ja’alam, K., … Padiglione, A. A. (2009). Consensus statement on the management of invasive candidiasis in Intensive Care Units in the Asia-Pacific Region. International Journal of Antimicrobial Agents, 34(3), 205–209. https://doi.org/10.1016/j.ijantimicag.2009.03.014. Huberts, L. W. J. C., Kaptein, M., & Lasthuizen, K. (2007). A study of the impact of three leadership styles on integrity violations committed by police officers. Policing-an International Journal of Police Strategies & Management, 30(4), 587–607. https://doi.org/10.1108/13639510710833884. Ivancevich, J. M., Duening, T. N., Gilbert, J. A., & Konopaske, R. (2003). Deterring white-collar crime. Academy of Management Executive, 17(2), 114–127. https://doi.org/10.5465/ame.2003. 10025206. Jain, A. K. (2001). Corruption: A review. Journal of Economic Surveys, 15(1), 71–116. Johannsen, L., & Pedersen, K. H. (2012). How to combat corruption: Assessing anti-corruption measures from a civil servant’s perspective. Halduskultuur, 13(2), 130–146. Karhunen, P., & Ledyaeva, S. (2012). Corruption distance, anti-corruption laws and international ownership strategies in Russia. Journal of International Management, 18(2), 196–208. https:// doi.org/10.1016/j.intman.2011.10.002. Kenny, C. (2009). Transport construction, corruption and developing countries. Transport Reviews, 29(1), 21–41. Klinkhammer, J. (2013). On the dark side of the code: Organizational challenges to an effective anti-corruption strategy. Crime, Law and Social Change, 60(2), 191–208. https://doi.org/10.1007/ s10611-013-9453-y. Klitgaard, R. E., & Klitgaard, R. (1988). Controlling corruption.: University of California Press. Le, Y., Shan, M., Chan, A. P., & Hu, Y. (2014a). Investigating the causal relationships between causes of and vulnerabilities to corruption in the Chinese public construction sector. Journal of Construction Engineering and Management, 140(9), 05014007. Le, Y., Shan, M., Chan, A. P. C., & Hu, Y. (2014b). Overview of corruption research in construction. Journal of management in engineering, 30(4), 02514001. https://doi.org/10.1061/(asce)me.19435479.0000300. Lee, S.-H., Oh, K., & Eden, L. (2010). Why do firms bribe? Insights from residual control theory into firms’ exposure and vulnerability to corruption. Management International Review, 50(6), 775–796. https://doi.org/10.1007/s11575-010-0057-9. Legal Weekly, L. (2014). Officials talk about the prohibitions: Rules and regulations become more detailed, and it is more difficult to be a civil servant. Retrieved from http://business.sohu.com/ 20140102/n392791836.shtml.

References

103

Ling, F. Y. Y., Ning, Y., Ke, Y., & Kumaraswamy, M. M. (2013). Modeling relational transaction and relationship quality among team members in public projects in Hong Kong. Automation in Construction, 36, 16–24. https://doi.org/10.1016/j.autcon.2013.08.006. Luo, Y. (2002). Corruption and organization in asian management systems. Asia Pacific Journal of Management, 19(2–3), 405–422. Melgar, N., Rossi, M., & Smith, T. (2009). The perception of corruption. International Journal of Public Opinion Research, 22(1), 120–131. Misangyi, V. F., Weaver, G. R., & Elms, H. (2008). Ending corruption: The interplay among institutional logics, resources, and institutional entrepreneurs. Academy of Management Review, 33(3), 750–770. Mohamed, S. (2002). Safety climate in construction site environments. Journal of Construction Engineering and Management-Asce, 128(5), 375–384. https://doi.org/10.1061/(asce)07339364(2002)128:5(375). Mulder, L. B., Verboon, P., & De Cremer, D. (2009). Sanctions and moral judgments: The moderating effect of sanction severity and trust in authorities. European Journal of Social Psychology, 39(2), 255–269. https://doi.org/10.1002/ejsp.506. Mumford, M. D., Helton, W. B., Decker, B. P., Connelly, M. S., & Van Doorn, J. R. (2003). Values and beliefs related to ethical decisions. Teaching Business Ethics, 7(2), 139–170. National Bureau of Statistics of China, N. (2012). China statistical yearbook, 2012. Retrieved from http://www.stats.gov.cn/tjsj/ndsj/2012/indexch.htm. Ning, Y., & Ling, F. Y. Y. (2013). Reducing hindrances to adoption of relational behaviors in public construction projects. Journal of Construction Engineering and Management, 139(11). https:// doi.org/10.1061/(asce)co.1943-7862.0000745. Norušis, M. J. (2008). SPSS 16.0 advanced statistical procedures companion. New Jersey: Prentice Hall Higher Education. Ofori, G. (2000). Challenges of construction industries in developing countries: Lessons from various countries. In Proceedings of 2nd International Conference of CIB Task Group 29: Challenges Facing the Construction Industry in Developing Countries. Peisakhin, L., & Pinto, P. (2010). Is transparency an effective anti-corruption strategy? Evidence from a field experiment in India. Regulation & Governance, 4(3), 261–280. People Net, P. (2010). The interpretation of issues that are applicable to the Disciplinary Regulations of the Chinese Communist Party, due to the illegal interference on construction projects by leader members of the Party. Retrieved from http://politics.people.com.cn/GB/1026/11636326.html. People’s Liberation Army Daily, P. (2003). Boosting construction of integrity: Megaprojects are enrolled in the investigation list against corruption. Retrieved from http://news.ifeng.com/ mainland/detail_2013_08/09/28429689_0.shtml. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001. Riley, S. P. (1998). The political economy of anti-corruption strategies in Africa. European Journal of Development Research, 10(1), 129–159. https://doi.org/10.1080/09578819808426705. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), III–XIV. Schwartz, A. (2004). Ethics in competitive bidding and contracting. Science and Engineering Ethics, 10(2), 277–282. https://doi.org/10.1007/s11948-004-0023-7. Schwartz, M. S. (2009). Corporate efforts to tackle corruption: An impossible task? The contribution of Thomas Dunfee. Journal of Business Ethics, 88(4), 823–832. Sims, R. R. (2000). Changing an organization’s culture under new leadership. Journal of Business Ethics, 25(1), 65–78. https://doi.org/10.1023/A:1006093713658. Smith, J. H. (2009). The global anticorruption education and training project. Leadership and Management in Engineering, 9(3), 139–143. https://doi.org/10.1061/(ASCE)1532-6748(2009)9: 3(139).

104

5 Effectiveness of Prevailing Anti-corruption Strategies

Sohail, M., & Cavill, S. (2008). Accountability to prevent corruption in construction projects. Journal of Construction Engineering and Management-Asce, 134(9), 729–738. https://doi.org/ 10.1061/(asce)0733-9364(2008)134:9(729). Sööt, M. L. (2012). The role of management in tackling corruption. Baltic Journal of Management, 7(3), 287–301. https://doi.org/10.1108/17465261211245463. Tabish, S., & Jha, K. N. (2011). Analyses and evaluation of irregularities in public procurement in India. Construction Management and Economics, 29(3), 261–274. Tabish, S., & Jha, K. N. (2012). The impact of anti-corruption strategies on corruption free performance in public construction projects. Construction Management and Economics, 30(1), 21–35. Tanzi, V. (1998) Corruption around the world: Causes, consequences, scope, and cures. In IMF staff papers (Vol. 45, pp. 559–594). The State Council of P.R. China, T. (2011). Implementing regulations of the Law of Bidding of People’s Republic of China. Retrieved from http://www.gov.cn/zwgk/2011-12/29/content_2033184. htm. Transparency International, T. (2002). Bribe payers index 2002. Retrieved from https://www. transparency.org/research/bpi/bpi_2002/0. Transparency International, T. (2006). Bribe payers index 2006. Retrieved from https://www. transparency.org/research/bpi/bpi_2006/0. Transparency International, T. (2008). Bribe payers index 2008. Retrieved from https://www. transparency.org/research/bpi/bpi_2008/0. Transparency International, T. (2011). Bribe payers index 2011. Retrieved from https://www. transparency.org/research/bpi/bpi_2011/0. Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195. https://doi.org/10.2307/20650284. Xia, B., Chan, A. P. C., & Yeung, J. F. Y. (2009). Identification of key competences of design-builders in the construction market of the People’s Republic of China (PRC). Construction Management and Economics, 27(11), 1141–1152. https://doi.org/10.1080/01446190903280476. Xinhua Net, X. (2009). The announcement of a campaign against the negative problems in the construction sector of China. Retrieved from http://news.xinhuanet.com/politics/2009-08/19/ content_11912737.htm. Xinhua Net, X. (2011). Mosre efforts will be imposed on the investigation of corruption in public construction sector in China. Retrieved from http://news.xinhuanet.com/legal/2011-05/17/c_ 121426891.htm. Xinhua Net, X. (2014). Irregularities in biddings of three gorges project: Nepotism, and waste of public money. Retrieved from http://news.xinhuanet.com/finance/2014-02/19/c_126159860.htm. Zhao, X., Hwang, B.-G., & Low, S. P. (2013a). Developing fuzzy enterprise risk management maturity model for construction firms. Journal of Construction Engineering and Management, 139(9), 1179–1189. https://doi.org/10.1061/(asce)co.1943-7862.0000712. Zhao, X., Hwang, B. G., & Low, S. P. (2013b). Critical success factors for enterprise risk management in Chinese construction companies. Construction Management and Economics, 31(12), 1199–1214. https://doi.org/10.1080/01446193.2013.867521. Zhao, X., Hwang, B. G., & Low, S. P. (2014). Investigating enterprise risk management maturity in construction firms. Journal of Construction Engineering and Management, 140(8). https://doi. org/10.1061/(asce)co.1943-7862.0000873. Zou, P. X. W. (2006). Strategies for minimizing corruption in the construction industry in China. Journal of Construction in Developing Countries, 11(2), 15–29.

Chapter 6

Measuring Corruption in Public Construction Project: A Case of China

6.1 Introduction Compared with developed countries, those developing countries have more serious corruption problems as they are undergoing the transition of economy and lack mature legislative and administrative system (Ling and Hoang 2010; Ofori 2000). As a typical developing country, China is unexceptional (Shan et al. 2015). The National Bureau of Corruption Prevention reveals that 15,010 persons were prosecuted for corruption in the public construction sector between 2009 and 2011, which caused an estimated loss of CNY 3 billion (approximately USD 490 million) (Xinhua Net 2011). Moreover, people involved in corruption not only include clerks at the bottom but also top leaders at the ministerial level or above. A notorious case is Xilai Bo, who used to be a member of The Political Bureau of the Communist Party of China Central Committee, and also the chief leader of Chongqing City (i.e. a deputy national leader of the country), was found to have grafted CNY 5 million (approximately USD 0.81 million) during the construction of a public project (Xinhua Net 2013b). Another notorious case is Zhijun Liu, the former minister of Ministry of Railways (i.e. minister level), who grafted CNY 64.6 million (approximately USD 10.5 million) within the construction of Chinese railway projects (Xinhua Net 2013a). Although nowadays some anti-corruption measures have been put in place actively by the new leader of the country, President Jinping Xi, and that the positive effects of these measures are emerging, there is still a long way to go for Chinese people in curbing corruption (Beijing Times 2014). Le et al. (2014) has conducted a comprehensive literature review on corruption research in construction in the past two decades, and found that existing research interests of corruption mainly focused on forms of corruption in construction, impacts of corruption in construction, and anti-corruption strategies, but little on the measurement of corruption in construction, which is an important aspect in addressing corruption issues. Therefore, this chapter aims to develop a systematic model to measure the potential corruption in a public construction project. It is envisaged that

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this model can play a vital role in assessing and monitoring corruption within the Chinese public construction projects.

6.2 Literature Review Corruption is a type of dishonest or fraudulent practice conducted by those morally depraved individuals in power, who usually misuse the public power for their private benefit (Gray and Kaufmann 1998; Oxford Dictionaries 2014). This wrong doing distorts markets and the allocation of resources, and is therefore to reduce economic efficiency and growth (Jain 2001; Marquette 2001; Tanzi 1998). Moreover, corruption can give rise to a dirty image of the country and degrade public trust (Ika et al. 2012). With respect to the construction industry, there has even been an increase of corruption within the sector in recent years (Alutu 2007; Ameh and Odusami 2010; Bowen et al. 2012; De Jong et al. 2009; Gunduz and Onder 2013; Le et al. 2014; Sohail and Cavill 2008). In particular, the public construction sector has been consecutively deemed as the most corrupt sector according to the Bribe Payers Index published by Transparency International (1999, 2002, 2006, 2008, 2011). The negative impacts of corruption on the construction sector include unfair resource allocation, waste of public money, low quality of construction work, and foremost, the undermining of free competition in the business (Le et al. 2014; Sohail and Cavill 2008; Tabish and Jha 2011). Measurement of corruption is necessary to achieve progress towards greater integrity, transparency, and accountability in corruption free performance (Andersson and Heywood 2009; Foster et al. 2012; Goel and Nelson 2011; Leon et al. 2013). Only by understanding how much corruption and in what areas, can effective anti-corruption strategies be formulated and then implemented (Sampford et al. 2006). Kaufmann et al. (1999) therefore created an aggregate measure of corruption combining three elements of governance, namely, probity, bureaucratic quality, and rule of law. Hall and Yago (2000) developed an index of opacity, which is the opposite of transparency. Additionally, extensive efforts have been devoted to the measurement of corruption at the country level by many international organizations, such as Business International Corporation, the Political Risk Services Group, World Economic Forum, Political and Economic Risk Consultancy Ltd., Transparency International, and World Bank (Jain 2001; Lambsdorff 1998; Lancaster and Montinola 1997; Mauro 1995; Svensson 2005; Tanzi and Davoodi 1998). However, rare literature was found to focus on the measurement of corruption in the construction sector. Thus, this chapter attempts to bridge this knowledge gap by developing a systematic evaluation model of corruption in construction projects.

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6.3 Data Collection Data source is critical for measuring corruption, which includes perception indicators, judicial system reports, and indirect and outcome indicators (e.g., objective indictors covering financial flows, sector outcomes) (Kenny 2009). Data from judicial system reports can improve the precision of measurement and disclose more significant details of corruption (Della Porta 2001), but those judicial reports are rarely available by the public (Han 2011). Although indirect and outcome indicators can be widely available, the reliability of results derived from these data may be compromised because factors other than corruption might contribute to the final evaluation (Ko and Samajdar 2010). In this chapter, perception indicators were used to solicit perception-based data to measure corruption in public construction projects. This data collection method has also been widely used for the measurement of corruption at a country level (Andersson and Heywood 2009; Foster et al. 2012; Goel and Nelson 2011; Lambsdorff 1998; Lancaster and Montinola 1997; Mauro 1995). Although subjective data collected by such approach can only reflect vague and generic perceptions of corruption rather than specific objective realities and thus sometimes unreliable (Duncan 2006; Golden and Picci 2001; Seligson 2006), perceptions of corruption based on respondents’ actual experiences are, in most cases, the best and the only information the researchers can obtain as corruption is usually carried out clandestinely and leaves no paper trail (Jain 2001). A series of semi structured interviews and a questionnaire survey were employed sequentially in this chapter as tools for data collection because such a combination of methods has been advocated and can overcome inherent limitations of a single method (Zhao et al. 2014).

6.3.1 Semi Structured Interviews To identify measurement items of corruption, semi structured interviews were first conducted between July 2013 and August 2013 with 14 industrial experts and academics. Table 6.1 shows the backgrounds of the interviewees. Apparently, most of interviewees have enough working experience (more than 10 years) and hold senior positions in their organizations. Diversified professional backgrounds and geographic locations of interviewees also help increase the heterogeneity of the interviewee panel and thus improve the validity of interviews. As Tabish and Jha (2011) has already gathered a comprehensive list of 61 measurement items of corruption in Indian public construction projects, this list will be adopted and serve as the basis for the development of measurement items specifically for construction projects in China. Interviewees were requested to evaluate the applicability of each item from Tabish and Jha (2011) to the public construction sector of China, by using a five-point rating system (1 = very inapplicable, 2 = inapplicable, 3 = medium, 4 = applicable, and 5 = very applicable). Interviewees were also encouraged to supplement other measurement items that they had in mind

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Table 6.1 Backgrounds of interviewees No.

Employer

Position

Experience (years)

Largest project ever managed/consulted

Geographic locationsa

A

Government

Director

20

USD$363 million

Eastern China

B

Government

Deputy Director

16

USD$308 million

Central China

C

Client

Project Manager

19

USD$363 million

Western China

D

Client

Project Manager

17

USD$308 million

Eastern China

E

Client

Director

13

USD$167 million

Northeastern China

F

Contractor

General Manager

25

USD$363 million

Eastern China

G

Contractor

Project Manager

20

USD$122 million

Western China

H

Contractor

Director

15

USD$85 million

Central China

I

Consultant

General Manager

20

USD$363 million

Eastern China

J

Consultant

Project Manager

16

USD$122 million

Western China

K

Consultant

Project Manager

15

USD$85 million

Northeastern China

L

Academic

Professor

22

USD$197 million

Central China

M

Academic

Professor

17

USD$73 million

Western China

N

Academic

Associate Professor

13

USD$363 million

Northeastern China

Note a Geographic locations are divided into eastern China with GDP per capita about USD 8600, central China with GDP per capita about USD 4700, western China with GDP per capita about USD 4400, and northeastern China with GDP per capita about USD 6600, according to the National Bureau of Statistics of China (2012)

but had not been included in Tabish and Jha (2011) framework. The mean score of each measurement item was calculated, and a threshold of 2.5 points was established as a cut-off criterion as recommended by Hsueh et al. (2009). Based on the interview results, as shown in Table 6.2, 19 items from Tabish and Jha (2011) framework received evaluation scores above 2.5 points, suggesting that their applicability in the Chinese public construction sector were confirmed. Due to the objective difference between the construction sectors of India and China, other items from Tabish and Jha (2011) framework, for example, “the reimbursement of

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109

service tax, excise duty, etc. is not done after obtaining the actual proof of depositing the same”, and “the recoveries for statutory taxes/duties not made before releasing the payment”, were regarded as inapplicable to measure the corruption in the context of China (with applicability score lower than 2.5 points), and thus were excluded from the list of measurement items. To verify if there is significant difference among the interviewees of different backgrounds (i.e., employer, experience, geographic locations), the Kruskal-Walis test was conducted with the aid of Statistical Package for the Social Sciences (SPSS) 17.0. According to Siegel and Castellan (1988), the significant difference is proved when the asymptotic significance value is lower than 0.05. The testing results in Table 6.2 show that all the asymptotic significance values are greater than 0.05, which indicates that no significant differences exist among the interviewees of different backgrounds. Additionally, a complement of five new measurement items was recommended by the interviewees according to their own experience, as shown in Table 6.3. Therefore, a total of 24 measurement items of corruption were finalized through interviews.

6.3.2 Questionnaire Survey As a systematic data collection method, the questionnaire survey technique has been widely used to collect professional views in construction management research (such as Deng et al. 2014; Hwang et al. 2014; Le et al. 2014; Zhao et al. 2013a, b). Thus, following the semi-structured interviews, a questionnaire survey was conducted to obtain the perception-based data of the measurement items of corruption from two perspectives, namely, probability (i.e., the possibility of occurrence of each measurement item) and severity (i.e., the impact of consequence of each measurement item), using a five-point rating scale (1 = very low, 2 = low, 3 = medium, 4 = high, 5 = very high). The questionnaire was disseminated through three channels between September 2013 and October 2013. First, an online version of the questionnaire was developed and disseminated to experts from government agencies, research institutes, and enterprises involved in public construction projects in China. Second, hard copies of the questionnaire were distributed in a one-to-one interview way to some participants of an industrial forum held in Shanghai, who are required to have experience in Chinese public construction projects. Third, field surveys were conducted in three public construction projects in Shanghai, Jinan (the capital city of Shandong Province), and Zhengzhou (the capital city of Henan Province), respectively. Moreover, two measures were taken to ensure the reliability of the survey feedbacks. First, the questionnaire was administered in an anonymous way. Second, the respondents were asked to evaluate the measurement items of corruption merely based on their knowledge to the industry rather than the projects they were engaging in. The three survey approaches adopted in this chapter are expected to enhance the validity of the survey results. Finally, 188 valid replies were received. Among them, 87 replies were collected from the online survey, 20 from the industrial forum, and 81 from the field surveys.

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Table 6.2 Measurement items refined by the interviewees Code

Measurement item

Applicability evaluation

Asymp. Sig. of KWT Employer

Experiencea

Geographic locations

MI1

Administrative approval and financial sanction not taken to execute the work

2.79

0.274

0.432

0.358

MI2

The provisions are not as per laid down yardstick

3.86

0.352

0.423

0.329

MI3

Work is not executed for the same purpose for which the sanction was accorded

2.93

0.462

0.586

0.497

MI4

The consultant is not appointed after proper publicity and open competition

3.64

0.516

0.607

0.509

MI5

The criteria adopted in prequalification of consultant are restrictive and benefit only few consultants

3.43

0.687

0.723

0.648

MI6

The selection of consultant not done by appropriate authority

3.57

0.414

0.580

0.426

MI7

Adequate and wide publicity is not given to tender

2.71

0.438

0.452

0.379

MI8

Adequate time for submission of tender/offer not given

2.64

0.649

0.765

0.721

MI9

Prequalification criteria for selection of contractor are stringent

3.00

0.649

0.681

0.752

MI10

The evaluation of tenders is not done exactly as per the notified criteria

2.57

0.350

0.308

0.239

(continued)

6.3 Data Collection

111

Table 6.2 (continued) Code

Measurement item

Applicability evaluation

Asymp. Sig. of KWT Employer

Experiencea

Geographic locations

MI11

The negotiation on tender not done as per laid down guidelines

3.00

0.251

0.235

0.189

MI12

The conditions/specifications are relaxed in favour of contractor to whom the work is being awarded

3.50

0.421

0.462

0.473

MI13

The work order/supply order is not placed within justified rates

2.71

0.498

0.502

0.535

MI14

Work is executed without the availability of funds for the said purpose

3.93

0.547

0.640

0.508

MI15

The work is not executed as per original sanction accorded

3.93

0.686

0.703

0.604

MI16

Compliance with conditions regarding obtaining licenses, insurance policies and deployment of technical staff not being followed by contractor

3.71

0.579

0.534

0.406

MI17

The proper record of hindrances is not being maintained from the beginning

2.93

0.663

0.650

0.631

MI18

The deviations, especially in abnormally high rated and high value items are not properly monitored and verified

3.29

0.428

0.460

0.325

(continued)

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Table 6.2 (continued) Code

MI19

Measurement item

Escalation clause is not applied correctly for admissible payment

Applicability evaluation

Asymp. Sig. of KWT Employer

Experiencea

Geographic locations

3.57

0.492

0.431

0.463

Note MI represents for measurement item KWT represents for Kruskal-Walis Test Experiencea is categorized into two groups following the criteria: below, and above 20 years

Table 6.4 shows profile of respondents of the questionnaire survey. The respondents are from diversified organizations (i.e., government, client, contractor, consultant, designer, and academic) involved in public construction projects in China. More than 70% of them had at least 6 years of experience in this sector and held middle managerial positions or above in their organizations. In addition, the respondents were selected from different geographic locations of China in order to provide a more general situation of corruption in the public construction sector across the country.

6.4 Data Analysis To check the reliability of the data collected from the questionnaire survey, Cronbach’s coefficient alpha was tested with the aid of SPSS 17.0, as suggested by Netemeyer et al. (2003). The testing result revealed a Cronbach’s alpha value of 0.902, which indicates a high level of internal consistency among the respondents (Netemeyer et al. 2003). Table 6.5 shows the evaluations of 24 measurement items. The Top5 measurement items in terms of probability are MI17 (3.71 points), MI12 (3.54 points), MI16 (3.52 points), MI15 (3.45 points), and MI4 (3.43 points). The Top5 measurement items in terms of severity are MI23 (4.06 points), MI24 (4.00 points), MI17 (3.80 points), MI22 (3.73 points), and MI21 (3.70 points). The Kruskal-Wallis test was also performed with the aid of SPSS 17.0 to check if there is significant difference among the respondents of different professional backgrounds (i.e. employer, position, experience, and geographic location). Given all the asymptotic significance values are greater than 0.05, there is no such significant difference among the respondents (Siegel and Castellan 1988). Therefore, the data are appropriate to be further analysed. Normally, an evaluation model is developed from a hierarchical framework (Xu et al. 2010). Therefore, to hierarchize the framework of measurement items of corruption, factor analysis was conducted utilizing SPSS 17.0. As recommended by

A large project should have called for bids is split into several small projects and contracted without bidding

Contractors provide false certificates in bidding

Confidential information of bidding is disclosed to a specific bidder

Substitution of unqualified materials in construction

Site supervisor neglects his duties for taking bribe from contractor

MI20

MI21

MI22

MI23

MI24

√

√

√

√

√

D √

√

√

C

√

√

B

√

√

A

Interviewee

Note MI represents for measurement item

Measurement item

Code

Table 6.3 Measurement items supplemented by the interviewees

√

√

E √

F √

√

√

G

√

H √

√

√

√

I

√

√

√

J √

√

√

K √

√

√

√

√

L √

√

√

√

M

√

√

√

N √

3.91

3.54

3.76

3.96

3.40

Applicability evaluation

6.4 Data Analysis 113

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6 Measuring Corruption in Public Construction …

Table 6.4 Profile of respondents Personal attributes

Categories

Number of respondents

Percentage

Employer

Government

20

10.6

Client

43

22.9

Contractor

43

22.9

Consultant

46

24.5

Designer

26

13.8

Academic

10

5.3

Top managerial level (e.g. director, general manager, professor)

49

26.1

Middle managerial level (e.g. project manager)

88

46.8

Professional (e.g. engineer, quantity surveyor)

51

27.1

>20 years

24

12.8

11–20 years

40

21.3

6–10 years

76

40.4

Position

Experience

Geographic locationsa

20

19

20

20

11–20

28

29

49

6–10

37

38

87

20

19

20

20

11–20

28

29

49

6–10

37

38

87