Forecasting Popularity of Videos using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards

Mercury: using the QuPreSS reference model to evaluate predictive services

Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft

Penalty and reward contracts between a manufacturer and its logistics service provider

Contracts are used to coordinate disparate but interdependent members of the supply chain. Conflicting objectives of these members and lack of coordination among the members lead to inefficiencies in matching supply with demand. This study reviews different types of contracts and proposes a methodology to be used by companies for analyzing coordinating contracts with their business partners. Efficiency of the contract is determined by comparing the performance of independent companies under the contract to the supply chain performance under the central decision maker assumption. We propose a penalty and reward contract between a manufacturer and its logistics service provider that distributes the manufacturer’s products on its retail network. The proposed contract analysis methodology is empirically tested with transportation data of a consumer durable goods company (CDG) and its logistics service provider (LSP). The results of this case study suggest a penalty and reward contract between the CDG and its LSP that improves not only the individual firm’s objective functions but also the supply chain costs. Compared to the existing situation, the coordination efficiency of the penalty and reward contract is 96.1 %, proving that optimizing contract parameters improves coordination and leads to higher efficiencies

Entrepreneurs'' attitude towards the computer and its effect on e-business adoption

This paper presents research exploring further the concept that many SMEs do not adopt computer based technologies due to decision maker's negative attitudes towards computers generally. Importantly, by assessing the entrepreneur's belief structure, we provide quantitative evidence how SMEs, particularly micros, are affected. Earlier research that addresses technology acceptance model (TAM) suggests that TAM parameters are particularly influential factors of e-commerce adoption, as perceived by top managers of SMEs. The model we develop is tested using a sample of 655 enterprises. The information was gathered, via a telephone survey of UK SMEs, from decisions makers in the enterprise. Technically, the paper uses k-means cluster analysis to segment respondents using the TAM perceptions, ease of use, usefulness and enjoyment. Based on two determined segments we look at the differential rate of adoption of internet, and the potential adoption of new e-collaborative technologies like video conferencing and electronic whiteboards. The diffusion of internet for low IT utility (LIT) segments was considerably slower than in the high utility segment (HIT). Similarly, the anticipated adoption of e-collaboration technologies was much lower for LIT than HIT. Interestingly, we find that LIT is populated by more micro SMEs than HIT. The results we present are limited however as our sample is considerably underweight in micro SMEs, suggesting that the problem may be much larger in the economy than our model predicts. For policy makers, this research confirms the value of knowledge transfer programs to SMEs in the form of technology support. Our research shows that organisations which have dedicated IT support will tend to be more advanced technologically than those that do not. The implication for entrepreneurs is if they can be persuaded that a technological route is beneficial to them, and that suitable support can be provided via KT, then operational efficiency gains could be made. This paper contributes to knowle