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This synthesis covers academic research on the use of valuation, tax, information technology (IT), and forensic specialists on audit engagements. The importance and role of specialists on audit engagements have recently increased, and specialist use has garnered significant attention from regulators and academics. Given the PCAOB’s (2017b) recent proposal to revise auditing standards regarding specialists’ involvement, it is important to review the specialist literature as a whole. By integrating research across these four domains, I identify commonalities and differences related to: (1) factors associated with the use of specialists on audit engagements (including the nature, timing, and extent of use); (2) factors impacting auditors’ interactions with specialists (including specialists contracted by the auditor or management); and (3) outcomes associated with the use of specialists. This integrated analysis of the specialist literatures shows variation in the use of specialists, and various factors affecting both if and how they are involved and whether auditors use specialists internal or external to the audit firm. Additionally, research has sometimes (but not always) linked specialist involvement to higher audit quality. The commonalities and areas of variation identified are informative to audit research and practice, particularly as regulators and audit firms look to improve the quality of audits using specialists. Throughout the synthesis, I also provide a number of directions for future research.

There is significant amount of literature tackling different issues related to the port industry. The present chapter focuses on a single business unit of seaports aiming at the documentation of works related to container terminals.

An effort to review, collect and present the majority of the works present in the last 30 years, between 1980 and 2010, has been made in order to picture the problems dealt and methods used by the authors in the specific research field. To facilitate the reader, studies have been grouped under five categories of addressed problems (productivity and competitiveness, yard and equipment utilization, equipment scheduling, berth planning, loading/unloading) and four modelling methodologies (mathematics and operations research, management and economics, simulation, stochastic modelling).

The analysis shows that most works focus on productivity and competitiveness issues followed by yard and equipment utilisation and equipment scheduling. In reference to the methodologies used managerial and economic approaches lead, followed by mathematics and operations research.

In reference to future research, two fields have been identified where there is scope of significant contribution by the academic community: container terminal security and container terminal supply chain integration.

The present chapter provides the framework for researchers in the field of port container terminals to picture the so far works in this research area and enables the identification of gaps at both research question and methodology level for further research.

The purpose of this paper is to identify the dimensions of experiential quality and examining the interrelationships among experiential quality, experiential satisfaction, perceived value, experiential trust and experiential loyalty using a multi-dimensional and hierarchical model as a framework perceived by coffee chain customers.

The data used in this study were based on a sample of 428 customers at Starbucks in Taipei City of Taiwan. Data were analyzed using exploratory factor analysis and confirmatory factor analysis.

The findings reveal that there are four primary dimensions and 13 sub-dimensions of experiential quality in a coffee chain. In addition, the results indicate that affective quality is identified as the most primary dimension of experiential quality perceived by coffee chain customers. Experiential quality significantly influences perceived value and experiential trust, respectively. Also, experiential satisfaction is influenced by perceived value, experiential quality and experiential trust. Furthermore, experiential satisfaction and experiential trust are determinants of experiential loyalty.

This is the first study identifying experiential quality, experiential satisfaction, perceived value, experiential trust and experiential loyalty in the context of coffee chains.

The representation of Heterogeneous Object (HO) is divided into two categories: Data model (DM) and material evaluation paradigm (MEP). A hybrid methodology with geometry model and volumetric dataset to represent heterogeneous properties is proposed in this paper. Geometry model of an object can guarantee the accuracy of the final HO slices; and volumetric dataset lends the flexible manipulability and other advantages to HO representation. Two MEPs, namely distance field (DF) based and Fixed Reference Features & Active Grading Source(s) (FRF&AGS) are presented to facilitate the process of HO representation according to the designer)s input parameters. The DM can be modified interactively with users until the final satisfactory result is obtained. In this paper, a scheme of HO slicing is described. In this method, we utilize the slices contour of geometrical model as constraint to reconstruct the HO slices, which can theoretically achieve the same accuracy with the geometrical shape. Some examples of Heterogeneous object represented with our scheme are provided.

This research examines the bidirectional relationship between Environmental, Social and Governance (ESG) voluntary disclosure engagement and financial performance of a panel of banks extracted from the Gulf Cooperation Council (GCC) banking industry, covering a period of 11 years (2007–2017). We find that GCC banks, and in particular Islamic banks, voluntarily disclose low level of information related to ESG activities. Using system GMM methodology, we provide evidence that ESG disclosure adversely affects bank performance, regardless of the bank performance measure used. Thus spending on ESG turns out to be costly for GCC banks, a result that is consistent with the agency problem, where managers are likely to reduce long-term expenditures related to ESG actions in order to boost short-term profits. As managers' compensations often relate to short-term financial performance, managers tend to reduce their spending on ESG activities. Furthermore, contrary to previous research, our results indicate that the relationship between ESG and financial performance is bidirectional and dynamic. We also find evidence that ESG disclosure positively affects performance only for well-diversified banks. Finally, although conventional banks disclose significantly more information related to ESG activities, we do not find any significant differences between the two types of banks in the relationship between ESG disclosure and performance. Our suggestion is that these results are consistent with what we call “clientele” and “gravitation” effects, where a customer tends to choose to deal with the bank that reflects his religious beliefs (gravitation effect) and with the bank that provides him with the best services (clientele effect) regardless of its ESG disclosure.

The purpose of this paper is to examine the relationships among the experiential quality (EQ) dimensions, experiential satisfaction (ES), experiential involvement (EI), host city image (HCI), experiential equity (EE), spectator affection (SA) and experiential loyalty (EL) in the sport context.

The data used in this study were based on a sample of 674 spectators from the finals of the men’s and women’s football tournaments held at the Maracanã Stadium in Rio, indicating that the proposed model fitted the data.

Findings show that the six EQ dimensions physical environment quality, outcome quality, access quality (AQ), game quality, trip quality (TQ) and security quality (SQ), EI, EE, HCI and SA positively influence ES. Also, EI moderates the relationship between HCI and EL. Moreover, EL is influenced by EI, HCI, SA and ES.

Results will assist sport management in developing and implementing market-orientated service strategies to increase the EQ dimensions, ES, EI, EE, HCI and SA in order to increase EL.

This study provides data that result in a better understanding of the relationships among the EQ dimensions, ES, EI, HCI, EE, SA and EL in the sporting context.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem.

The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively.

The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units.

This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.