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According to the management obfuscation hypothesis, managers have incentives to influence the audit reports’ communicative value. This study aims to examine the relationship between corporate earnings management and the readability of Chinese-text audit reports and the impact of key audit matter (KAM) disclosure requirements on this relationship.

This research adopts Taiwanese firms from 2010 to 2019 to investigate the association between earnings management and readability of Chinese-text audit reports within the framework of the KAM disclosure requirements implemented in 2016.

The findings show that auditors tend to issue less readable audit reports to firms undertaking earnings management, particularly after introducing KAM disclosures. Additional analyses indicate that such adverse impacts of client earnings management on audit report readability have become more pronounced for firms audited by a newly pointed or long-tenure lead audit partner, with high business risk, poor monitoring of governance mechanisms or a large amount of nonaudit services. These results suggest that auditor partners may compromise auditor independence and use flexible narratives in audit reports as a form of moral insurance.

As auditors may manage audit report readability to reduce audit liability, authorities must formulate policies concerning audit report disclosure to strengthen its communicative value and simplify language usage. Additionally, authorities should strengthen quality control standards concerning auditor independence to reduce auditor pressure from clients’ economic importance.

This study provides valuable insights into auditors' responses to corporate earnings management behavior, particularly regarding the interplay between earnings management, audit report quality and regulatory changes, thus expanding our understanding of the dynamics within the auditing profession.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.

This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.

This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.

The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.

This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.

As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

Researchers, business professionals, and policymakers are now focusing on how disruptive innovations affect markets and business performance in the quickly changing business landscape of today. This study investigates the complexity of disruptive innovations and how they alter established organizational strategies and market structures. The study explores the core traits of disruptive innovations and looks at how they upend established business models and alter the structure of entire industries. This study employs a comprehensive literature review to identify the primary motivators behind disruptive innovations and their diverse impacts on market dynamics. It looks into how disruptive change is sparked by evolving consumer behavior, flexible business models, and emerging technologies. Additionally, research examines how businesses use internal reorganizations and strategic alliances among other tactics to capitalize on and adjust to disruptive innovations. It also examines how market leaders deal with these difficulties in an effort to stay competitive, shedding light on the possible risks and uncertainties related to disruptive innovations. It also highlights how crucial it is for organizations to have innovative cultures and proactive adaptation in order to prosper in a time of swift technological advancements.

In recent decades, organizations globally have faced challenges in adopting disruptive innovation. This chapter explores the relationship between change management and adopting disruptive innovations. Through a systematic analysis of existing literature, common themes and trends in this field are identified. The present study employs a bibliometric and content analysis of 77 publications to analyze the role of change management in the adoption of disruptive innovation. Additionally, a framework is presented to facilitate the understanding of how change management influences the adoption of disruptive innovations within organizations. The research highlights that embracing disruptive innovation is not straightforward; it demands a well-thought-out change management approach. This approach should emphasize cultivating an environment that encourages and nurtures innovation. Creating a culture that empowers employees to take risks and explore new ideas is crucial. Additionally, organizational culture, leadership, communication, and employee engagement emerge as critical factors for the successful adoption of disruptive innovations in the workplace. In summary, it is important to recognize that the successful adoption of disruptive innovations within organizations relies on the successful implementation of change management initiatives.

By integrating the Uses and Gratifications Theory and Flow Theory, this research seeks to untwine the veiled effects of interactive commerce enhancements (ICEs), specifically haptic imagery and social presence, in promoting user immersion and sustaining social commerce (SC) users' usage intention, considering the moderating role of autotelic personality.

The research utilized purposive sampling of Malaysian SC app users with recent transactions. A dual-source data collection approach, encompassing offline and online channels, was employed to ensure a broad and diverse respondent pool. Partial least squares–structural equation modeling was chosen for its adeptness in analyzing complex relationships in predictive studies.

The findings revealed the significant positive effects of haptic imagery and social presence on user immersion and continuance intention within SC apps. Social presence and immersion were found to mediate the proposed paths. Additionally, autotelic personality traits were identified as moderators, influencing the strength of these relationships.

This research makes a unique contribution by addressing critical gaps in SC environments, extending the concept of ICEs, understanding the impacts of underlying mediators and pioneering the examination of autotelic personality traits' moderating effects. It introduces a fresh perspective on how individual differences impact user engagement. This groundbreaking study benefits social media and interactive marketing literature by comprehensively understanding how ICEs elevate SC, fostering innovation and heightened engagement.

In the vibrant world of social commerce (SC), where information flows freely, interactions thrive and online purchases abound, there is an escalating challenge. Users are uninstalling and disengaging due to approach and avoidance stimuli, a trend mirroring the approach-avoidance motivation model (AAMM). Our study, anchored in AAMM and the stimulus-organism-response (SOR) model, aims to dive into the complex dynamics of these factors that shape users' SC continuance intentions.

Our findings, drawn from 472 SC users in Malaysia, paint an intriguing research framework via PLS-SEM analysis by testing the proposed hypotheses. A purposive sampling technique was utilized, deliberately selecting respondents based on specific criteria. Subsequently, data were gathered through the distribution of face-to-face questionnaires at selected shopping malls, facilitating a focused and comprehensive exploration of consumer perspectives.

The empirical results demonstrate the following: (1) Users' determination to stay engaged on SC platforms hinges on approach factors, like emotional support, surveillance gratification and multisensory gratification. (2) Simultaneously, avoidance factors such as technostress and perceived deception exert their negative influence. (3) Flow experience, rooted in flow theory, emerges as the underlying mechanism connecting these duality stimuli, influencing the continuance intention.

In a departure from conventional research, our study pioneers a comprehensive approach and boldly confronts the research gap by introducing a rich tapestry of antecedents, embracing both the appeal of approach factors and the deterrence of avoidance ones, using the AAMM that sheds light on how individuals navigate between embracing opportunities and avoiding pitfalls based on perceived gains and losses. This holistic approach enables us to redefine our understanding of digital engagement dynamics, offering a captivating journey into the realm of user experience and intention that transcends the ordinary.

This study aims to proposes a theoretical model to understand the intention of Indonesian traditional micro businesses (MB) to continue using an e-marketplace.

This research involved the utilization and combination of habit formation and social impact theories to develop the model. A total of 162 responses were retrieved from the owners of traditional MB and used to validate the proposed model.

The results showed subjective norms to be the most substantial direct predictor of continuous usage, followed by usefulness, satisfaction and promotion proneness. Meanwhile, compatibility was found to have only an indirect effect through usefulness as a mediator. Moreover, moderating effect analysis also showed that the impact of herd behaviour was only significant for male and younger traditional MB owners.

The emphasis on the seller perspective using habit formation as well as the social impact theory to explain continuance behaviour is an alternative approach observed not to have been widely explored, especially for traditional businesses such as indigenous industry rather than organizations.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

Based on CFD numerical simulation and analysis, this study mainly uses LCS and entropy production theory to visualize the top vortex of the impeller. Through the combination of the two methods, the accumulation problem of the top vortex of the impeller and the location of the energy loss caused by the vortex can be well revealed.

(1) The CFD numerical simulation analysis of the high-speed fuel pump is carried out, and the test is conducted to verify the numerical simulation results. The inlet and outlet pressure difference? P is used as the validation index, and the error analysis shows that the error between numerical simulation and test results is within 10%, which meets our requirements. Therefore, we carry out the next analysis with the help of CFD numerical simulation. By analyzing the full working condition simulation, its inlet and outlet differential pressure? P and efficiency? Are evaluated. It is found that its differential pressure decreases with the flow rate and its efficiency reaches its maximum at Qv = 9.87 L/s with a maximum efficiency of 78.32%. (2) We used the LCS in the analysis of vortices at the top of the impeller blades of a high-speed fuel pump. One of the metrics used to describe the LCS in fluid dynamics is the FTLE. The high FTLE region represents the region with the highest and fastest particle trajectory stretching velocity in the fluid flow. We performed a cross-sectional analysis of the FTLE field on the different height surfaces of the impeller on 25% Plane, 50% Plane, and 75% Plane, respectively. And a quarter turn of the rotor rotation was analyzed as a cycle divided into 8 moments. It is found that on 25% Plane, the vortex at the top of the lobe is not obvious, but there are high FTLE values on the shroud surface. On 50% Plane, the lobe top vortex is relatively obvious and the number of vortices is three. The vortex pattern remains stable with the rotating motion of the rotor. At 75% Plane, the lobe top vortex is more visible and its number of vortices increases to about 5 and the vortex morphology is relatively stable. The FTLE ridges visualize the vortex profile. This is a good guide for fluid dynamics analysis. (3) At the same time, we use the entropy production theory to quantitatively analyze the energy loss, and define the entropy production rate Ep. Through the entropy production analysis of the impeller shroud surface and the suction surface of the pressure surface of the blades at eight moments, we find that the areas of high energy loss are mainly concentrated in the leading and trailing edges of the blades as well as in the shroud surface close to the leading edge of the blades, and the value of the entropy production rate is up to 106 W/m3/K. The areas of high energy loss in the leading edge of the blades as well as the trailing edge show a curved arc, and the energy loss is decreasing as it moves away from the shroud surface and closer to the hub surface. The high energy loss areas at the leading and trailing edges of the blades are curved, and the energy loss decreases as they move away from the shroud surface and closer to the hub surface. The energy loss at the pressure surface of the blade is relatively small, about 5 × 105 W/m3/K, which is mainly concentrated near the leading edge of the blade near the shroud surface and the trailing edge of the blade near the hub surface. Such energy loss corresponds to the vortex LCS at the top of the impeller, and the two mirror each other.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

Using the upper echelons theory, this study aims to investigate the moderating effect of managerial discretion (MD) on the impact of dynamic managerial capabilities (DMCs) on established firms’ (EFs) response strategies to disruptive innovation (RStDI).

A cross-sectional study was conducted using an online questionnaire to collect data from senior management of sample firms, targeting the population of professional service firms (PSFs) operating in the Emirate of Dubai. After receiving 491 responses, data was analyzed using IBM packages (SPSS and Amos) through a covariance-based structural equation modeling technique.

As proposed, the underpinnings of DMCs (managerial human capital, managerial social capital and managerial cognitive perceptions) were associated with EFs’ strategies for responding to DIs. Surprisingly, despite theoretical predictions, MD did not moderate the relationship. These findings provided support to the main propositions of the upper echelons theory, however, not for its contextual moderator (MD).

The cross-sectional approach to testing the research model limits the identified significant effects that should be further investigated. The research sample was restricted to PSFs operating in Dubai, UAE, thus limiting the generalizability of the findings to the examined context.

The findings of this investigation are valuable to managers and hiring teams. They provide empirically supported insights on the critical role of managerial dynamic capabilities underpinnings (human capital, social capital and cognitive perceptions) in facilitating organizational RStDI. The findings also provide significant insights to policymakers, notably on the importance of innovative and well-crafted policies and regulative frameworks that enhance MD.

This study provides one of the first empirical quantitative analysis to assess MD and test its effects as a moderator, thus contributing significantly to the existing theoretical arguments on MD. To the best of the authors’ knowledge, this study is among the first to quantify the relationship between DMCs and organizational RStDI.