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The main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.

Based on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.

With zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.

As the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

This study aims to investigate whether mandated disclosure of engagement quality review hours provides new information that affects investors’ decision-making.

In 2014, Korean authorities mandated that audit engagement quality review hours must be disclosed in their audit reports. Using this unique field setting in Korea, this study presents empirical evidence of the policy initiative’s effect on earnings reliability by examining both pre- and post-implementation periods.

Following the initial disclosure of engagement quality review hours in 2014, the authors observe that the capital market’s valuation of quarterly earnings surprises, measured by earnings response coefficients (ERCs), was significantly lower for firms with high levels of abnormal engagement quality review hours than for other firms. This paper also finds that the observed association between engagement quality review hours and ERCs in the postregulation period hinges on the probability of earnings management, proxied by discretionary accruals and just meeting or beating analyst earnings forecast.

This paper suggests that the policy mandating disclosure of engagement quality review hours provides original information that the market considers relevant for appraising the reliability of reported earnings.

This study investigated the impacts of the technology acceptance model (TAM) determinants and image congruence on attitude and, in turn, behavioral intentions. In addition, the differences between the US and Korean consumers in terms of the predictors of attitude were assessed.

The data were collected via an online survey from 342 South Korean and 353 American consumers who patronized a restaurant within a three-month timeframe.

The findings revealed that two dimensions of TAM and three sub-factors of self-image congruence positively affected customer attitude toward face recognition (FR) payment. Customer attitude also significantly influenced behavioral intentions toward FR payment. Lastly, the cultural differences between the Korean and American consumers played significant moderating roles in the relationships between perceived usefulness and attitude as well as between actual self-image congruence and attitude.

No prior empirical research has incorporated cultural differences into the FR payment acceptance model. Unlike previous research, the current study included cultural differences as a moderator of the relationships between the five predictors (i.e. two predictors from TAM and three dimensions of self-image congruence) and attitude toward FR payment in the research model.

We conducted this study to examine the relationship between revenue concentration and a firm's financial leverage. We aimed to analyze whether revenue concentration influences a firm's capital structure decisions and whether this relationship is driven by customer-specific investments or the direct effect of revenue concentration itself. Additionally, we investigated the role of asset redeployability in mediating or moderating the relationship between revenue concentration and financial leverage.

The paper investigates the relationship between revenue concentration and a firm's financial leverage. The results indicate a negative association between revenue concentration and financial leverage. This finding holds across various regression models and is statistically significant. Furthermore, the paper explores the potential role of asset redeployability in explaining the relationship between revenue concentration and financial leverage. The results indicate that even after controlling for asset redeployability, the negative relationship between revenue concentration and leverage remains significant, suggesting that revenue concentration affects capital structure decisions independently of the risks associated with relationship-specific investments. Robustness tests are conducted using a three-stage least squares approach to account for the simultaneity between revenue concentration, asset redeployability and capital structure.

Our findings demonstrate that revenue concentration is negatively associated with financial leverage, even after accounting for asset redeployability. This suggests that revenue concentration affects capital structure decisions independently of the risks associated with customer-specific investments. Furthermore, we performed robustness tests to address potential simultaneity issues between revenue concentration, asset redeployability and capital structure.

The study relies on available data sources, which may have inherent limitations in terms of accuracy, completeness or consistency. The quality of the data used in the analysis could impact the robustness of the findings. Time Period: The study focuses on more recent years, which might limit the ability to compare the findings with studies conducted over different time periods. Historical trends or structural changes that could impact the relationship between revenue concentration and financial leverage might not be fully captured.

Firms with higher revenue concentration tend to have lower financial leverage. Recent years show a negative relationship between profitability and market leverage compared to earlier periods. Revenue concentration has a distinct effect on financial leverage, not fully explained by risks from relationship-specific investments or asset redeployability. Insights for firms in managing capital structure decisions, considering revenue concentration and its implications for leverage.

This research is one of the first papers that investigates the impact of revenue concentration on the capital structure choices of firms. By exploring the relationship between revenue concentration and financial leverage, the study contributes to the existing literature by shedding light on an underexplored area. Thus, this study adds originality to the field by addressing a research gap and contributing to the understanding of the relationship between revenue concentration and capital structure choices.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to generate generalized conclusions about its effects, set directions for future lifelong vocational education and identify implementation measures.

To conduct a meta-analysis on research results that have a heterogeneous distribution, it is important to specify the analysis category for examining the effects of research variables.

First, lifelong vocational education has an effect on dependent variables. And action appears to have the highest effect size on dependent variables. Next, when calculating the size of variables that had an effect on lifelong vocational education by educational type, the effect size of informal education was found to be larger than that of formal education. Finally, regarding the effect on the participants, office workers were influenced most, followed by university students, North Korean defectors, job seekers and foreigners.

Although this study attempted to conduct an in-depth analysis of subcomponents, it was not possible to analyze variables at a more detailed level. Therefore, future studies should aim to conduct a more comprehensive analysis of different variables based on a wider composition. Because lifelong vocational education is relevant to people’s daily lives, it should be investigated in the context of their personal characteristics and social backgrounds.

This research was designed to uncover general effects of lifelong vocational education and discover relevant variables affecting lifelong vocational education in South Korea. A meta-analysis of 15 studies with 67 subgroups examining lifelong vocational education was conducted.

In the current era of VUCA (Volatility, Uncertainty, Complexity and Ambiguity), lifelong vocational education needs to be organized systematically, unlike in the past. With the rapid advancements in technology influenced by artificial intelligence and the fourth industrial revolution, there is a surge in social demands for continued reeducation and redevelopment of employees to prepare for talent development paradigm innovation, increasing unemployment among unskilled workers and competence enhancement needs among job seekers and employed individuals.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to draw generalized conclusions on its effectiveness and discuss its implications for implementation measures. Specifically, this study will analyze the general effect size; differences in the effect size among different dependent variable groups; and the effect size based on lifelong vocational education participants.