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Following OpenAI's introduction of GPTs, a surge in GPT apps has led to the launch of dedicated LLM app stores. Nevertheless, given its debut, there is a lack of sufficient understanding of this new ecosystem. To fill this gap, this paper presents a first comprehensive longitudinal (5-month) study of the evolution, landscape, and vulnerability of the emerging LLM app ecosystem, focusing on two GPT app stores: \textit{GPTStore.AI} and the official \textit{OpenAI GPT Store}. Specifically, we develop two automated tools and a TriLevel configuration extraction strategy to efficiently gather metadata (\ie names, creators, descriptions, \etc) and user feedback for all GPT apps across these two stores, as well as configurations (\ie system prompts, knowledge files, and APIs) for the top 10,000 popular apps. Our extensive analysis reveals: (1) the user enthusiasm for GPT apps consistently rises, whereas creator interest plateaus within three months of GPTs' launch; (2) nearly 90\% system prompts can be easily accessed due to widespread failure to secure GPT app configurations, leading to considerable plagiarism and duplication among apps. Our findings highlight the necessity of enhancing the LLM app ecosystem by the app stores, creators, and users.

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars. © 2019 IEEE.

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars. © 2019 IEEE.

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars.

Chinese multinational enterprises are facing almost continuous negative media coverage in many Western countries, given the rising geopolitical tensions between the China and the West. This thesis aims to scrutinize this phenomenon to examine why and how Chinese multinationals are suffering from negative media coverage, and how firms can deal with such challenges when facing geopolitical complexities. To examine the phenomenon of interest, this thesis takes a cross-disciplinary approach by combining the subjects of international business, mass communication, and international relations. The research problem that this thesis aims to address is: How can Chinese multinationals interpret and respond to media-framed legitimacy challenges when facing intensive geopolitical tensions? This research problem leads the author to develop three concrete research questions: How are liabilities of origin framed by the media, thereby posing legitimacy challenges for Chinese multinationals? How are the voices of different stakeholders framed by the media in constructing the legitimacy of Chinese multinationals? How can Chinese multinationals form voice strategies to mitigate legitimacy challenges in the geopolitical context? The research design in this thesis contains three independent yet related studies in dealing with the general research problem. The first case study focuses on the legitimacy challenges of Huawei in the UK. The second case study focuses on the legitimacy challenges of TikTok in the US. The third study supplements the previous two case studies and explores Chinese multinationals’ voice strategies through semi-structured interviews with public relations managers. This thesis extends knowledge and makes contributions to three theoretical gaps regarding multinationals’ legitimacy challenges in the geopolitical rivalry, media framing of multinationals’ legitimacy, and multinationals’ voice strategies as part of the nonmarket strategy. Besides, it responds to the call

Chinese multinational enterprises are facing almost continuous negative media coverage in many Western countries, given the rising geopolitical tensions between the China and the West. This thesis aims to scrutinize this phenomenon to examine why and how Chinese multinationals are suffering from negative media coverage, and how firms can deal with such challenges when facing geopolitical complexities. To examine the phenomenon of interest, this thesis takes a cross-disciplinary approach by combining the subjects of international business, mass communication, and international relations. The research problem that this thesis aims to address is: How can Chinese multinationals interpret and respond to media-framed legitimacy challenges when facing intensive geopolitical tensions? This research problem leads the author to develop three concrete research questions: How are liabilities of origin framed by the media, thereby posing legitimacy challenges for Chinese multinationals? How are the voices of different stakeholders framed by the media in constructing the legitimacy of Chinese multinationals? How can Chinese multinationals form voice strategies to mitigate legitimacy challenges in the geopolitical context? The research design in this thesis contains three independent yet related studies in dealing with the general research problem. The first case study focuses on the legitimacy challenges of Huawei in the UK. The second case study focuses on the legitimacy challenges of TikTok in the US. The third study supplements the previous two case studies and explores Chinese multinationals’ voice strategies through semi-structured interviews with public relations managers. This thesis extends knowledge and makes contributions to three theoretical gaps regarding multinationals’ legitimacy challenges in the geopolitical rivalry, media framing of multinationals’ legitimacy, and multinationals’ voice strategies as part of the nonmarket strategy. Besides, it responds to the call