This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

This study uses the UTAUT2 (Unified theory of acceptance and use of technology) model as well as adding other factors such as Platform Usability, User Autonomy to determine the behavioral intention and behavior of online shoppers using e-commerce applications (ECAs) in Vietnam. Using the analysis results from structural equation modeling, it was shown that Social Influence, Use Proficiency, Hedonic Motivation, User Skill, Effort Expectancy positively affect Behavioral Intention. At the same time, Behavioral Intention is negatively affected by Performance Expectancy. Behavioral Intention and Facilitating Conditions are two factors that positively affect Use Behavior. Besides, User Autonomy negatively affects Use Behavior. The research results are an important basis for ECAs providers, managers and stakeholders to apply in assessing the behavioral intentions and behaviors of online shopping customers using ECAs in Vietnam to promote the use of ECAs in online shopping.

With the popularity of smartphones, consumers’ daily lives and consumption patterns have been changed by using multi-functional apps. Convenience store operators have developed membership apps as a platform to promote their brands to consumers to create the benefits of “membership economy”. This study examined consumer behavior towards convenience store membership apps using UTAUT2. Consumers who have installed the convenience store membership apps were recruited as the target population. SPSS 23.0 was used to conduct item analysis and reliability analysis in the pretest questionnaires. The formal questionnaires were distributed online by convenience sampling method, with 375 valid questionnaires collected. Smart PLS 3.0 was conducted by analyzing the confirmatory factor analysis and structural equation model analysis. The results of the study, “performance expectancy”, “social influence”, “price value” and “habit” of convenience store member app users showed positive and significant effects on “behavioral intention”. “Facilitating conditions”, “habit” and “behavioral intention” have positive and significant effects on “actual use behavior”. “Gender” affects “habit” to have a significant moderating effect on “use behavior”. “Use experience” affects “habit” to have a significant moderating effect on “behavioral intention”. Based on the study results, the further suggestions of marketing management implications and feasible recommendations are proposed for convenience store operators to refer to in the implementation of membership app marketing management.

In this paper, we explore the static and dynamic effects of oil rent on competitiveness in Saudi Arabia’s economy during the period 1970–2022. In addition, we examined the short-run, strong and long-run relationships between exports and industry, inflation, energy use (oil rents) and agriculture using the Autoregressive Distributed Lag (ARDL) approach developed. The analysis showed that government spending will contribute to enhancing the competitive environment with a difference of one year. Moreover, the industry will contribute to increasing competitiveness for a positive relationship in the long term. The results stated that there is an insignificant relationship between competitiveness, inflation, and oil rents. The analysis also shows that inflation has a negative impact with statistical significance in the short term. In addition, the error correction model (ECM) coefficient is negative and has statistical significance at 0.76 at a 1% significant level, which indicates the existence of an error correction mechanism and thus the existence of a long-term relationship between the variables.