Abstract: Hedonic modeling can be used to examine the impacts of housing characteristics on selling prices. This paper estimates a hedonic price function for single-family houses in Savannah, GA, for the period 2007–2016. Digressing from conventional approaches of modeling a reduced-form hedonic price function, we estimate a structural function whereby the house sale price is directly affected by the usual house attributes and marketing time. Both the home sale price and time on the market, however, are endogenously determined. To account for endogeneity, we estimate the structural hedonic function using a control function approach. The control-function estimator utilizes conditional heteroskedasticity of structural errors in the triangular model. Using this approach, we identify the relationship between the house price and its time on the market solely based on nonlinearities in the control function without looking for excludable instrumental variables for the latter endogenous variable. Our findings suggest that housing prices increase with marketing time.
Keywords: Control function, endogeneity, hedonic pricing, marketing time

Abstract: Sustainable Assessment Tools (SATs) are generally designed for medium and large enterprises with structured and available information, while small producers are generally excluded from the evaluation process. The present work fills this gap by analysing how new technologies, such as SATs developed using mobile applications (apps), can promote the sustainability management of small agri-producers in Ecuador. In this regard, the SAFA (Sustainability Assessment of Food and Agriculture) App is the first SAT specifically designed to evaluate sustainability for small and micro-producers. To operationalise the process, it implements a one hundred-item questionnaire. To answer the questions, the interviewee does not need to review documents. Considering that interviews may be held in areas with no internet service, the answers are processed using an offline mobile application that registers the data immediately. The results show that the good governance is the dimension that achieves the best result and associations are key drivers for the development of sustainable practices. Additionally, this study highlights that SAFA App is useful in catching the specific features of small producers. However, this SAT should be improved in terms of its versatility and the depth of its analysis in order to be taken as a benchmark for sustainability policies.
Keywords: small producers, sustainable development, natural resource management systems, SAFA App, agriculture, rural.

Abstract: Set in the context of the French traditional restaurant sector, this study contributes to prior research on Big Data tools. It focuses on barriers (real and perceived) that can prevent micro-firms (fewer than 10 employees) from integrating digital solutions. By means of focus group interviews followed by survey methodology, the authors examine perceptions and usage of Big Data, from the perspective of micro-firm managers/owners. The results suggest that a combination of factors affect how micro-firms adopt/accept Big Data technologies including: perception of Big Data as a source for developing the business, uncertainty regarding return-on-investment, and awareness of the opportunities that Big Data can deliver. This study provides a brick-in-the-wall insight in to culture as a hindrance to digital transformation. It extends the literature on Big Data by offering a contemporary perspective of micro-firm managers/owners who face the challenge of assessing how and where they could innovate their business model with regard to Big Data.
Keywords: Big Data; perception and usage; micro-firms; French restaurant trade

Abstract: According to different application scenarios of blockchain system, it is generally divided into public chain, private chain and consortium chain. Consortium chain is a typical multi-center blockchain, because it has better landing, it is supported by more and more enterprises and governments. This paper analyzes the advantages and problems of Practical Byzantine Fault Tolerance (PBFT) algorithm for the application scenarios of the consortium chain. In order to be more suitable for consortium chains, this paper proposes a new optimized consensus algorithm based on PBFT. Aiming at the shortcomings of PBFT, such as the inability to dynamically join nodes, low multi-node consensus efficiency, and primary master node selection, our optimized algorithm has designed a hierarchical structure to increase scalability and improve consensus efficiency. The simulation results show that compared with PBFT and RAFT, our new consensus algorithm increases the data throughput while supporting more nodes, and effectively reducing the consensus delay and the number of communication times between nodes.
Keywords: Blockchain, PBFT, Consensus Algorithm, Consortium Blockchain

Abstract: Recently, data analysis and its application have given a chance for various researchers to utilize it for decision making process. In this process, most of the researchers addressed the issue of data analysis, its representation as well as graphical structure visualization. Most of the time spent on understanding and categorization of the data in form of static, dynamic, complete, incomplete or uncertain due to its large veracity. Some time it may happen that the given data set is unstructured or semi-structured. Due to that, a problem arises in precise representation of these data and finding some useful information for knowledge processing tasks. Another problem arises with time complexity evaluation of the given research problem as basic. This talk will be focused on handling large and static data set for knowledge processing tasks. The glimpses will be given on unstructured data representation, its pre-processing and its graphical visualization using one of the algorithms. The step by step demonstration will be shown with an illustrative example. The analysis derived from the given data set is also discussed for decision making process. The comparative study of the obtained results will be also discussed. This talk will be helpful for those scholars who work in data analysis, data visualization, and knowledge processing tasks, decision making or other areas. In addition some useful information will be given for further extension of the research activities.
Keywords: Data Visualization, Knowledge processing data, Static Data, Decision Making

Abstract: Lockdown measures due to the pandemic have caused unprecedented disruption to the worldwide education system. In view of COVID-19 spread, many educational institutions have been determining what procedures to adopt so as to fulfill sanitary protocol, which has meant on many occasions the limit of face-to-face contacts. The social media role has gained popularity during these breakouts, not just because of its significance as an entertainment tool but rather due to its socializing function. Twitter has been identified as the most popular microblog platform and a reliable source for examining and studying society's behavior. It is yet unknown how the use of User-Generated Content (UGC) will impact the design and performance of future educational programs. The main goal of this research is to explore the future of data-driven decision-making, based on UGC and the students’ performance presented in their learning analytics.