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The International Ergonomics Association (IEA)'s definition of HF/E includes the following:Ergonomics (or human factors) is the scientific discipline concerned with the understanding of interactions among humans and other elements of a system, and the profession that applies theory, principles, data and methods to design in order to optimize human well-being and overall system performance … Organizational ergonomics is concerned with the optimization of sociotechnical systems, including their organizational structures, policies, and processes. (IEA Council, 2000)

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

In this paper, I propose an algorithm combining adaptive sampling and Reversible Jump MCMC to deal with the problem of variable selection in time-varying linear model. These types of model arise naturally in financial application as illustrated by a motivational example. The methodology proposed here, dubbed adaptive reversible jump variable selection, differs from typical approaches by avoiding estimation of the factors and the difficulties stemming from the presence of the documented single factor bias. Illustrated by several simulated examples, the algorithm is shown to select the appropriate variables among a large set of candidates.

We analyze the sizes of standard cointegration tests applied to data subject to linear interpolation, discovering evidence of substantial size distortions induced by the interpolation. We propose modifications to these tests to effectively eliminate size distortions from such tests conducted on data interpolated from end-of-period sampled low-frequency series. Our results generally do not support linear interpolation when alternatives such as aggregation or mixed-frequency-modified tests are possible.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Since much of the design work for vessels and offshore installations occurs in countries other than where the vessel may operate or where the installation may be located, it is particularly important that the expected user be considered and accommodated through the design and operational phases of a project.

Within the framework of engineering design and marine operations, this chapter will discuss “soft” issues, such as organizational and line management decisions and personnel selection procedures, as well as “hardware” issues related to design of living and working environments. In particular, the chapter will address how culture should be considered while identifying “user” needs and requirements.

This study investigates the stability of the “hospital effect” on performance over time by administrative health data as a source of evidence. Using 78,907 heart failure adult records from 117 hospitals in the Lombardy Region (Northern Italy) over three years (2010–2012), we analyzed hospital performance in terms of 30-day mortality and 30-day unplanned readmissions to gather evidence about the stability of the “hospital effect.” Best/worst performers were identified through multi-level models that combine both patient and hospital covariates. Our results confirm that managerial choices affect hospital performance, and that the “hospital effect” is not, contrary to expectations, stable over the short term. Performance improvement/worsening over the three years has been also analyzed.

The current study examines the performance effects of financial incentives for a simple, recurrent task designed to simulate an assembly-line setting. The study looks at early performance, improvement and overall performance. For a new task, performance-based incentives appear to improve the initial performance of the task but not subsequent improvement rate (Bailey et al., 1998). The current paper reports on a laboratory experiment whose results confirm the findings of Bailey et al. (1998) but also indicates that for both performance-based and fixed incentives, significant performance improvement takes place well beyond the initial performance of the task, declining gradually over time. This is in contrast with the suggestion of Bailey et al. (1998) that workers with performance-based incentives will choose to improve initial performance rather than subsequent performance. Findings also suggest that improvement peaks earlier for performance-based incentives than for a fixed incentive. Improvement persisted longer and there was better overall performance with the high fixed component quota and piece rate incentives than with the low fixed component quota implying that incentives that impose higher risk (e.g. a low fixed component quota incentive) on workers result in de-motivation and lower performance.

There has been a call for additional managerial accounting research that examines the effect of non-pecuniary preferences (such as those for honesty and fairness) on managerial reporting decisions.

Drawing from trait theory, agency theory, and psychological contracts theory, Kidder (2005) suggests that personality traits and perceived unfairness in the workplace both help predict detrimental workplace behaviors, with perceived fairness affecting the honesty in reporting of some individuals but not others. We test Kidder’s (2005) theory in an experimental setting where participants have opportunity and incentive to report dishonestly.

Participants’ honesty preferences and ethical values (idealism and relativism) were measured, and the fairness of the participants’ employment contracts was manipulated. As predicted, higher preferences for honesty are significantly associated with honesty in reporting, suggesting that participants make trade-offs between increasing their own wealth and acting honestly. Additionally, the perceived fairness of compensation interacted with honesty preferences and relativism to affect honesty in reporting.

The implication for practice is that while a small number of employees are likely to consistently behave in honest or self-interested ways, firms may be able to positively influence the behavior of the majority of employees by enacting policies and procedures that contribute to perceptions that compensation is fair.

These findings contribute to our understanding of non-pecuniary preferences on managerial reporting decisions.