Valuation of Exit Strategy under Decaying Abandonment Value

We examine the valuation of abandonment decision in a contingent claims model with uncertainty in future market conditions and analyze the effect of determinants on the abandonment value. We find the abandonment value is positively related with the number of abandonment opportunities. The increase in the volatility, variable cost, and facility value increases the expected abandonment value, whereas the increase in the growth rate and depreciation rate reduces the expected abandonment value. The volatility, growth rate, and depreciation rate are negatively related with the exit threshold, whereas the variable cost and facility value are positively related with the exit threshold

Flow Experience and Challenge-Skill Balance in E-Learning

Flow is an optimal experience resulting in intense engagement in the activity. People achieved flow state when they perceived balance between challenge of the activity and their skill to the activity. The concept of flow can be used to explore students’ learning performance in e-learning environment. The current research aims to empirically explore the influence of challenge-skill balance on the flow experience and the influence of flow experience on learning satisfaction and learning performance in e-learning environment. The current research conducted a quasi-experimental design with questionnaire survey and carried out an electroencephalography (EEG) analysis, a psychophysiological method. The empirical survey results have shown that challenge-skill balance is an antecedent factor affecting learners’ flow experience. Once learners reach flow experience, their learning performance and learning satisfaction would get improved. Besides, the current research also found that flow experience is relative with learners’ attention measured by EEG brainwave signal. Learners’ perception of challenge-skill balance would influence their attention in e- learning activities. The current research is also in the pioneering position that using non-medical purpose EEG device in e-learning research

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm

Accounting harmonization in China - A comparison of A-share and H-share reported earnings

This study provides a comprehensive assessment of the harmonization of Chinese GAAP (2006) with IAS, and focuses on the earning gap differences based on industry categorization. We summary the main findings in our researches: (1)The research result of 2006 was consistent with the prior study that Chinese GAAP based net income was lower than IFRS based net income. (2)Our research finding for 2007 that the earning gaps were eliminated notably as the advent of new Chinese GAAP was supported by our descriptive statistics (3)Under the industry classification, our findings are both supported by the figure description and descriptive statistics with the conclusions that different industry has different level of convergence with IAS. (4)Under the research of accounting items in each industry for data analysis, we found out that the earning gaps between Chinese GAAP and IAS are caused form the environmental factors such as government regulation and industry characteristics in China. We would conclude that the new Chinese GAAP has been converged with IAS in a certain high degree, even though there might be some convergence spaces between these two standards, the accounting differences would not be fully erased due to the national regulations and industry features

The Influence of the privacy concern and social advertising type on the attitude and behavior

Nowadays social media can collect consumers' online behavior. The enterprises make the customized advertisement to achieve targeting marketing and close consumers' needs. With the right of privacy, the consumers pay attention to this kind of advertisement. In this study, we made the online questionnaire. Asking the privacy concern, and analyzing the advertising attitude and behavior in a different advertising situation. The result we found that customized advertising made consumers increase positive attitude, but made negative attitude on advertising behavior like click, share, etc. In addition, both male and female have different responses to customized advertisements and intimate products advertisements. The result can serve a reference for manufacturers to make advertising strategies in the future

LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines

Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) excel. However, current LLMs are largely designed for text-based interactions, thus making it unclear how user interactions will evolve if their modality is changed to voice. In this work, we investigate whether LLMs can enrich VA interactions via an exploratory study with participants (N=20) using a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative planning, and debate) with varied constraints, stakes, and objectivity. We observe that LLM-powered VA elicits richer interaction patterns that vary across tasks, showing its versatility. Notably, LLMs absorb the majority of VA intent recognition failures. We additionally discuss the potential of harnessing LLMs for more resilient and fluid user-VA interactions and provide design guidelines for tailoring LLMs for voice assistance