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The main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.

11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.

The results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.

By encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.

This study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.

This study examines how structured procedures and psychological safety can enhance six sigma processes’ effectiveness and foster individual creativity within an organization.

This study employs a novel methodological approach, combining structural equation modeling (SEM) and multi-value qualitative comparative analysis (mvQCA).This combination gave us a unique lens through which to view the complex connections between the constructs that were the subject of the investigation.

The results substantiate that a well-structured six sigma approach, paired with a psychologically safe environment, fosters knowledge exploration and exploitation as well as triggering conducive learning behaviors. These elements, in turn, augment individual creativity, which is found to be instrumental in boosting six sigma performance.

This study is one of the first to combine mvQCA and SEM to investigate the complex interplay between structured methods, psychological safety and individual creativity in enhancing six sigma performance. This research highlights the importance of fostering creativity in strategic management processes and the unrealized potential of combining these methods.

For those who plan research funds and assess the research performance from the funds, it is necessary to overcome the limitations of the conventional classification of evaluated papers published by the research funds. Besides, they need to promote the objective, fair clustering of papers, and analysis of research performance. Therefore, the purpose of this paper is to find the optimum clustering algorithm using the MATLAB tools by comparing the performances of and the hybrid particle swarm optimization algorithms using the particle swarm optimization (PSO) algorithm and the conventional K-means clustering method.

The clustering analysis experiment for each of the three fields of study – health and medicine, physics, and chemistry – used the following three algorithms: “K-means+Simulated annealing (SA)+Adjustment of parameters+PSO” (KASA-PSO clustering), “K-means+SA+PSO” clustering, “K-means+PSO” clustering.

The clustering analyses of all the three fields showed that KASA-PSO is the best method for the minimization of fitness value. Furthermore, this study administered the surveys intended for the “performance measurement of decision-making process” with 13 members of the research fund organization to compare the group clustering by the clustering analysis method of KASA-PSO algorithm and the group clustering by research funds. The results statistically demonstrated that the group clustering by the clustering analysis method of KASA-PSO algorithm was better than the group clustering by research funds.

This study examined the impact of bibliometric indicators on research impact of papers. The results showed that research period, the number of authors, and the number of participating researchers had positive effects on the impact factor (IF) of the papers; the IF that indicates the qualitative level of papers had a positive effect on the primary times cited; and the primary times cited had a positive effect on the secondary times cited. Furthermore, this study clearly showed the decision quality perceived by those who are working for the research fund organization.

There are still too few studies that assess the research project evaluation mechanisms and its effectiveness perceived by the research fund managers. To fill the research void like this, this study aims to propose PSO and successfully proves validity of the proposed approach.

The paper aims to estimate abnormal audit fees more precisely than the traditional audit fee model by applying an artificial intelligence (AI) method.

The AI technique employed in this paper is the deep neural network (DNN) model, which has been successfully applied to a wide variety of decision-making tasks. The authors examine the ability of the classic ordinary least squares (OLS) and the DNN models to describe the effects of abnormal audit fees on accounting quality based on recent research that demonstrates a systematic link between accruals-based earnings management and abnormal audit fees. Thus, the authors seek to imply that their new method provides a more precise estimate of abnormal audit fees.

The findings indicate that abnormal audit fees projected using the DNN model are substantially more accurate than those estimated using the classic OLS model in terms of their association with earnings management. Specifically, when abnormal audit fees predicted using the DNN rather than the OLS are incorporated in the accruals-based earnings management model, the adjusted R²s are larger. Additionally, the DNN-estimated coefficient of abnormal audit fees is more favorably associated to earnings management than the classic OLS-estimated coefficient. Additionally, the authors demonstrate that the DNN outperforms OLS in terms of explanatory power in a negative discretionary accruals subsample and a Big 4 auditor subsample. Finally, abnormal audit fees projected using the DNN method provide a better explanation for audit hours than those estimated using the OLS model.

This is the first approach that utilized a machine learning technology to estimate abnormal audit fees. Because more precise measurement yields more credible research results, the findings of this study imply a significant advancement in calculating unusually higher audit fees.

The purpose of this study is to examine a mobile delivery system as a working ubiquitous decision support system (UDSS) and determine whether it would improve decision quality.

Ubiquitous mobility and context awareness are the two core functions of the UDSS. Hence the authors examined how they might influence individual absorptive capacity and perceived usefulness. Moreover the authors investigated how individual absorptive capacity and perceived usefulness might be related to decision quality. A total of 174 completed questionnaires were collected from delivery workers, and a financial incentive was provided to participants. To test the hypotheses the research model was analysed with the partial least square method.

The results reveal that all paths are statistically valid. Individual absorptive capacity and perceived usefulness were positively influenced by ubiquitous mobility and context awareness. In addition individual absorptive capacity and perceived usefulness have positive effects on decision quality.

This research model did not consider all the capabilities enabled by the UDSS. Future study should pay attention to nomadicity, proactiveness, invisibility, and portability as relevant antecedents within the model.

In the field of IS studies the impact of the UDSS on users' decision quality has remained unclear to date. The authors adopted a mobile delivery system as a working UDSS and applied it in their study. Thereby the authors found the mediating effects of perceived usefulness and absorptive capacity under a ubiquitous environment.

The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

The purpose of this paper is to investigate how employing human brands affects consumers' decision quality in an online shopping environment by analysing visual attention using an eye‐tracking technique.

The experimental design used to examine the effect of human brands in an online shopping environment was a two‐factor repeated measure with two levels for each factor. The first factor of the design was a within‐factor measure of the product type, and the second was a between‐factor measure of the level of perceived decision quality. For this experiment 38 healthy participants were recruited at a university in South Korea.

First employing human brands in an online shop influences consumers' perceived decision quality. Second the results support a significant difference in perceived product trust between the two perceived decision quality levels. Finally the product type influences consumers' perceived trust towards the product.

This research has the limitations of a relatively small sample size and the use of a sample of university students, which may not be representative of the general population. Future researchers could utilise experiments to analyse the message area of the screen for detailed product descriptions and include various samples, which could result in additional insights and generalised experimental results.

Employing human brands can improve consumers' decision making processes and enhance the quality of their decisions by reducing cognitive effort and appealing to consumers emotionally through heuristic choices. In addition it can increase trust towards products and, furthermore, lead consumers to think positively about the quality of the decisions they make. The paper suggests that companies apply human brands in online shops to improve consumers' decision quality and obtain competitive advantage.

One of this paper's contributions is employing a multi‐method approach, a self‐reported questionnaire and eye‐movement data, to gain a deeper understanding of the data when observing a complex phenomenon, as consumers themselves may not be aware of their reactions in such situations.

This study aims to examine the effects of system and information characteristics in developing users' perceptions towards ubiquitous decision support systems (UDSS).

The research model is empirically examined with survey data from 218 mobile users who have adopted a UDSS, i.e. mobile Dianping.com. A structural equation modelling approach is employed to assess the hypotheses.

The findings demonstrate that system characteristics of wireless networks, mobile devices and mobile applications significantly predicted system quality, which in turn determined system usefulness. Localisation, immediacy and customisation of mobile word‐of‐mouth were the major predictors of information quality, which in turn determined information usefulness.

This study contributes to our current understanding of ubiquitous commerce, especially mobile word‐of‐mouth, by presenting an integrated research framework, identifying system and information characteristics that are specific to the ubiquitous era, extending system quality and system usefulness from a single system to a combination of systems, and empirically examining the crossover effects between system and information factors.

This study proposes a new approach to an adaptive web site design using the cognitive map (CM).

The responses from 134 questionnaires were used in order to compute the causality coefficients of the CM objectively. In addition, using the evaluations of 64 college students with regard to three typical web sites, reference criteria were built that could be used by web site designers to determine the best web site design, given constraints and requirements. On the basis of the CM and two scenarios, a web site design simulation was performed.

The results showed that the proposed approach could be used effectively to analyze web site design in an adaptive and practical manner when the market situation is constantly changing.

The implications of this article will help decision makers predict the chain of effects that could result from changes in part of the web site design factors, before actually making those changes to a web site. Also, since the CM permits all related web site design factors to be viewed in a single interrelated diagram and organized into an adjacency matrix, decision makers can perform a number of what‐if simulations in accordance with changes in the intended design factors to see whether such changes would lead to maximal objectives (or target nodes).