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We employ an experiment with experienced Chinese auditors to examine how family firm structures influence auditors' reliance on management's explanations for evidence and their assessments of fraud risk. Our findings indicate that for firms with family ownership, high levels of family managerial control cause auditors to rely less on management's explanations and assess higher levels of fraud risk when a firm's control environment is strong. However, when the control environment is weak, auditors' judgments are not influenced by family firm structure.

Corporate donation behavior sends two financial-related signals, i.e. sufficient cash flow and self-confidence in future earnings. This paper aims to investigate whether these financial-related signals released by corporate donation drive investors to make more optimistic forecasts about the firm’s future earnings per share (EPS) and whether this effect varies across different historical earnings trends.

This study is based on a controlled online experiment with 553 MBA students.

The results demonstrate that a financial signaling mechanism works, but it is moderated by historical earnings trends. When the earnings trend is always increasing, the more the number of financial signals received, the higher the investors’ EPS forecast; when the earnings trend is fluctuating (down then up or up then down), investors’ EPS forecast is higher when they receive financial signal(s) than when they do not, but no additive effect occurs from receiving one signal to two signals; when the earnings trend is always decreasing, investors’ EPS forecast is irrelevant to the number of financial signals received.

To the best of the authors’ knowledge, this study is the first to experimentally investigate a possible mechanism to explain investors’ positive response to corporate social responsibility (CSR) (specifically, corporate donation) disclosures – the financial signaling mechanism. This study also extends the research on the impact of financial information on investors’ use of nonfinancial information by investigating the moderating role of historical earnings trends on the financial signaling mechanism of the CSR effect.

An individual's capacity to manage finances has become critical in today's environment. The availability of various sophisticated financial instruments, combined with the economy's complexity and rising uncertainty, has prompted a significant push to analyse from where the youth learn about managing their money. This study intends to investigate the differences in the selected social predictors (Parents, Friends, School, Books, Job Experiences, Life experiences and Media) that influence the money management behaviour of emerging adults. The data was collected through a structured questionnaire from 230 undergraduates in the age group of 18–22 years. To test the normality of data, Kolmogorov–Smirnov (KS) test was applied and further Kruskal–Wallis test was found to be the appropriate method based on the identification of statistically significant deviations. The results show that parents have been considered as the most influential predictor (X = 3.565) of money management behaviour among emerging adults. followed by Life Experiences (X = 3.526). Whereas School and Job Experience were the least influential social predictors with mean value of 2.278 and 2.130 respectively. The study provides insights to the regulators, academicians and policymakers to initiate innovative strategies and processes for helping emerging adults for effective money management to increase their academic performance in a stress-free environment. Further, this paper contributes towards effective money management advice by recommending implementation of tools, apps and programs relating to Financial Literacy for better Financial Behaviour. Lastly, the paper provides implications that focus on enhancing the financial literacy of the parents as they act as role models for their children by teaching them skills to manage money.

Several African American educators served as an inspiration in the development and scholarship of an African American female who teaches at a Predominantly White Institution (PWI) of higher learning. This chapter shares the author's foundational beginnings and persistence in academe while teaching and leading in a race-conscious society. She shares some of her upbringing, education, and early teaching experiences. She also shares her motivation to learn and serve (Bethune, 1950, 1963), while walking in circles. Sizemore (1973, 2008) to provide a roadmap of her journey to support new and developing African American female professors. She uses poetry and the dimensions of African American culture (Boykin, 1983) to guide her sharing. The author uses her exploration of identity development as an African American womanist who advocates as an African American first, to share how she has developed as a scholar whose renewal of purpose targets becoming a full professor.

This study examines whether digital streaming and observation technologies can serve as an alternative approach for collecting inventory audit evidence, the challenges faced in their adoption and the factors contributing to their non-adoption.

This study adopts a two-stage, mixed-method approach, beginning with pilot study interviews that informed the comprehensive survey with qualitative and quantitative data. Quantitative data were analyzed using descriptive statistics, t-tests and Pearson’s correlation coefficient, while qualitative data were analyzed using qualitative content analysis.

Our findings revealed a positive perspective concerning the effectiveness and reliability of these technologies for inventory audits and the efficiency of internal controls within them, despite challenges such as obtaining a holistic view of the warehouse, observing obsolescence, ensuring inventory completeness and general reliability concerns. Additionally, preferences for physical inventory audits and skepticism about these technologies’ potential to enhance audit quality were identified as factors contributing to their non-adoption.

These findings have important implications for cost-conscious firms because they reveal that carefully adopting intermediate technologies can enhance the audit process. Our findings are relevant to audit regulators and firms interested in determining whether such technologies enhance audit efficiency and quality. This study highlights the need for updated auditing standards and directives and technologies that meet auditing requirements.

This study contributes to the literature by uncovering whether less advanced technologies can be used as an alternative approach to collect audit evidence. Consequently, the finding adds to the growing body of literature underscoring the potential of technologies, even less sophisticated ones, to enhance the efficiency and effectiveness of audits, despite their challenges. Additionally, it underscores the need for clear regulatory standards, suggests that auditors embrace emerging technologies and acquire relevant skills and offers insights for technology developers on audit firms’ concerns regarding digital technologies.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

This study examines how asking employees to self-assess their performance during the compensation setting process, when they are unaware of their marginal contribution to firm profit, affects employer welfare. Previous research suggests that giving employees a voice in the compensation setting process can positively affect employee performance and firm profit (Jenkins & Lawler, 1981; Roberts, 2003). However, the study proposes that asking employees to assess their own performance as part of the compensation setting process can have unintended consequences that ultimately lead to higher employee compensation demands. This is because asking employees to assess their performance increases their overconfidence in their own performance and their compensation demands. As a result, employers may face the dilemma of whether to meet these higher compensation demands or risk economic losses due to employee retaliation if their demands are not met. Through experimental evidence comparing a control condition without self-assessments and three self-assessment reporting conditions, the study provides evidence that supports the notion that eliciting employee self-assessments as part of the compensation process reduces employer welfare. Data on employee perceptions of performance further support the notion that asking employees to evaluate their performance leads to an inflated perception of their performance. These findings provide a theory-based explanation of why, in practice, many companies disentangle employee performance assessments from the compensation setting process and that companies are well advised in doing so.

The purpose of this study is to examine the direct and indirect effects of motivating language on organizational commitment, as this phenomenon has drawn the attention of researchers.

This study employs social exchange theory and motivating language theory to examine data collected from 217 full-time employees across various US-based companies. The proposed hypotheses were analyzed using the PLS-SEM method.

This study’s findings demonstrate that motivating language positively affects employees’ organizational commitment and affective trust but not cognitive trust, which mediates the relationship between motivating language and organizational commitment.

There are some limitations of our study that need to be mentioned. First, there are concerns about survey data collection via M-Turk (Shapiro et al., 2013). We attempted to overcome some of these problems by including questions to identify careless respondents. Also, we eliminated many respondents who completed the surveys in unreasonably short periods of time. Hence, we believe we accounted for response bias with these check points. Also, while we believe our final sample is a representative sample due to the significant amount of data elimination during the data collection, we believe that checking for non-response bias, as Armstrong and Overton (1977) suggest, is imperative. Unfortunately, due to the nature of M-Turk, that is impossible. However, M-Turk recruits respondents based on the parameters provided by the researchers, so we expect the non-respondents to be not significantly different from the respondents. In parallel to that, we acknowledge the limitations of our study sample. Due to that reason, our findings must be considered within the context of our sample parameters. We urge future researchers of this area to further validate our findings in different types of samples.

To the best of the authors’ knowledge, they are the first to analyze the impact of motivating language on organizational commitment and the mediating role of trust (cognitive and affective) in this relationship.