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Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

Purpose – The purpose of this chapter is to present a state of the art of performance measurement and management control systems based on the papers presented in the 6th Conference on Performance Measurement and Management Control in Nice, France, 2011. The chapter is structured around two main topics: performance measurement and control systems.

Methodology/approach – The chapter is based on a thorough review of all the papers presented at the conference. Each paper was classified according to the topic it dealt with and the methodology used in the paper.

Findings – We structure the papers around the following topics: cost measurement and management, mapping the landscape, financial measures, nonfinancial performance frameworks, performance measurement design, sector-specific measures, and marketing measures within performance measurement. The topics identified in management control systems are compensation, management control systems' design, motivational aspects, strategy, risk management and enterprise software, governance, nonprofit organizations, and innovation.

Research limitations/implications – The chapter provides a comprehensive review of these topics. The review is limited to the selection bias implicit in the papers submitted to the conference. However, with almost 200 papers, the conference includes a large set of papers and topics. The analysis indicates that the field is dynamic with close connections to practice and promising research lines.

Practical implications (if applicable) – This state-of-the-art review reveals relevant research findings for companies and organizations. It also indicates that the research community is tackling important issues to society and the evolution of research promises to offer interesting alternatives to tackle these problems.

Social implications – Performance measurement and management control systems are important topics for management. Recent work is extending the implications of these fields beyond companies to include nonprofit organizations and public sector departments. Advances will definitely shape the way societies are designed.

Originality/value of chapter – The chapter provides a review of the field based on the contributions at the conference. As such it portrays the state of the field and gives researchers as well as practitioners a quick way to update their knowledge of the topics that are dominating the field.

Mergers and acquisitions strategies are not risk-free, potential problems in achieving success include integration difficulties, inadequate evaluation of target, inability to achieve synergy, and complexity. Such strategies can fail for many reasons including inadequate evaluation of targets or inadequate pre-decision control mechanisms. Mergers and acquisitions are reviewed in this chapter as strategic investment decision-making perspective. Established financial analyses remain important in appraising investment choices, despite their limiting assumptions and their recognised shortcomings in capturing strategic project dimensions. However, managers balance these economic analyses with less-structured, strategic analyses underpinned by informed judgement. The fact that empirical studies reveal a continued reliance on judgement by investment decision-makers does not mean that rational economic analysis is a futile exercise. What studies of practice do seem to suggest is that the theory and practice of strategic investment decision-making need to take into account both economically rational and intuitive decision processes. Reflecting on the research evidence, we conclude that strategic investment appraisal will be best supported by approaches that (i) couple sound economic analysis with the development of managerial judgement and (ii) take account of the broader decision-making context within which both economic and strategic analyses are used.

This paper provides evidence on the impact of regulatory environment on financial reporting quality of transitional economies. This study compares the financial reporting quality of Hong Kong firms which are cross-listed in mainland China with those of Hong Kong firms cross-listed in China using specific earnings management metrics (earnings smoothing, timely loss recognition, value relevance and managing towards earnings targets) under pre- and post-IFRS regimes.

The financial reporting quality of Chinese A-share companies and Hong Kong listed companies are examined using earnings management measures. Using 2007 as base year, the study used a cumulative of −5 and +5 years of convergence experience which provide a total of 3,000 firm-year observations. In addition to regression analyses, we used the difference-in-difference analysis to check for the impact of regulatory environments on earnings management.

Through the lens of contingency theory, our results indicate that the adoption of the new substantially IFRS-convergent accounting standards in China results in better financial reporting quality evidenced by less earning management. The empirical results further shows that accounting data are more value relevant for Hong Kong listed firms, and that firms listed in China are more likely to engage in accrual-based earnings management than in real earnings management activities. We established that different earnings management practices that are seemingly tolerable in one country may not be tolerable in another due to level of differences in the regulatory environments.

The findings show that Hong Kong listed companies’ exhibit higher level of financial reporting quality than Chinese listed companies, which implies that the financial reporting quality under IFRS can be significantly different in regions with different institutional, economic and regulatory environments. The results imply that contingent factors such as country’s institutional structures, its extent of regulation and the strength of its investor protection environments impact on financial reporting quality particularly in transitional and emerging economies. As such, these factors need to be given appropriate considerations by financial reporting regulators and policy-makers interested in controlling earnings management practices among their corporations.

This study is a high impact study considering that China plays a significant role in today’s globalised economy. This study is unique as it the first, that we are aware of, to compare real earnings activities against accrual-based earnings management in pre- and post-IFRS adoption periods within the Chinese and Hong Kong financial reporting environments, distinguishing between cross-listed and non-cross-listed firms.

A one-year-ahead price change forecasting model is proposed based on the fundamental analysis to examine the relationship between equity market value and financial performance measures. By including book value and six financial statement items in the valuation model, current firm value can be determined and the estimation error can predict the direction and magnitude of future returns of a given portfolio. The six financial performance measures represent both cash flows – cash flows from operations (CFO), cash flows from investing (CFI), and cash flows from financing (CFF) – as well as net income – R&D expenditures (R&D), operating income (OI), and adjusted nonoperating income (ANOI). This study uses a 10-year sample of the Taiwan information electronic industry (1995–2004 with 2,465 firm-year observations). We find hedge portfolios (consisting of a long position in the most underpriced portfolio and an offsetting short position in the most overpriced portfolio) provide an average annual return of 43%, more than three times the average annual stock return of 12.6%. The result shows the estimation error can be a good stock return predictor; however, the return of hedge portfolios generally decreases as the market matures.

Investors frequently make judgments and decisions in the presence of affect (i.e., mood or emotion). Investors' moods may influence the extent to which they incorporate available financial information in their investment judgments. I propose that investors interpret their moods as signals of the extent to which financial information should be processed to make investment judgments, but only when other, more direct signals regarding the need for in-depth processing are unavailable. Consistent with research in psychology, my experimental results suggest that investors experiencing positive mood exert less effort to process available financial information than investors experiencing negative mood. Consequently, positive mood results in lower-quality financial judgments in my setting. However, when investors receive cues suggesting that initially received information is subjective, the effect of mood on effort to process financial information is mitigated. Overall, my results suggest that factors associated with positive investor mood (e.g., positive market sentiment) reduce the depth of investor analysis and lower judgment quality absent signals regarding the subjectivity of financial information.