Biogas production by co-ensiling catch crops and straw, effect of substrate blend and microbial communities

The combination of catch crop (CC) and barley straw(S) for biogas production was investigated in order to evaluate the ensiling process in batch assay and in continuous process. Based on two new agriculture strategies designed to produce energy and improve nutrient cycling in organic farming are being evaluated, one of them consisting on the harvest of straw and catch crop in different periods whereas the other strategy consists on harvesting them at the same time. Catch crops is promoted to reduce nutrient leaching during rainy season and straw that is not used for animal feeding or bedding is generally left in the field. Mixtures of CC and S provides several advantages: 1) Provides adequate TS for silage, 2) Absorbs the silage effluent, 3) Produces high LAB activity, and 4) Provides an optimal C/N for anaerobic digestion (AD). The effect of feeding compositions (straw or manurea ddition) on the microbial community structures were also investigated

Urban agriculture: a global analysis of the space constraint to meet urban vegetable demand

Urban agriculture (UA) has been drawing a lot of attention recently for several reasons: the majority of the world population has shifted from living in rural to urban areas; the environmental impact of agriculture is a matter of rising concern; and food insecurity, especially the accessibility of food, remains a major challenge. UA has often been proposed as a solution to some of these issues, for example by producing food in places where population density is highest, reducing transportation costs, connecting people directly to food systems and using urban areas efficiently. However, to date no study has examined how much food could actually be produced in urban areas at the global scale. Here we use a simple approach, based on different global-scale datasets, to assess to what extent UA is constrained by the existing amount of urban space. Our results suggest that UA would require roughly one third of the total global urban area to meet the global vegetable consumption of urban dwellers. This estimate does not consider how much urban area may actually be suitable and available for UA, which likely varies substantially around the world and according to the type of UA performed. Further, this global average value masks variations of more than two orders of magnitude among individual countries. The variations in the space required across countries derive mostly from variations in urban population density, and much less from variations in yields or per capita consumption. Overall, the space required is regrettably the highest where UA is most needed, i.e., in more food insecure countries. We also show that smaller urban clusters (i.e., <100 km2 each) together represent about two thirds of the global urban extent; thus UA discourse and policies should not focus on large cities exclusively, but should also target smaller urban areas that offer the greatest potential in terms of physical space

A Synthesis of Global Urbanization Projections

This chapter reviews recent literature on global projections of future urbanization, covering the population, economic and physical extent perspectives. We report on several recent findings based on studies and reports on global patterns of urbanization. Specifically, we review new literature that makes projections about the spatial pattern, rate, and magnitude of urbanization change in the next 30–50 years. While projections should be viewed and utilized with caution, the chapter synthesis reports on several major findings that will have significant socioeconomic and environmental impacts including the following: By 2030, world urban population is expected to increase from the current 3.4 billion to almost 5 billion; Urban areas dominate the global economy – urban economies currently generate more than 90 % of global Gross Value Added; From 2000 to 2030, the percent increase in global urban land cover will be over 200 % whereas the global urban population will only grow by a little over 70 %. Our synthesis of recent projections suggest that between 50%–60% of the total urban land in existence in 2030 will be built in the first three decades of the 21st century. Challenges and limitations of urban dynamic projections are discussed, as well as possible innovative applications and potential pathways towards sustainable urban futures

How mobile technologies support business models: Case study-based empirical analysis

[Otros] Les technologies mobiles ont poussé la connectivité des systèmes informatiques à la limite, permettant aux personnes et aux objets de se connecter les uns aux autres à tout moment. La quantité d'informations dont disposent les entreprises a augmenté de façon exponentielle, en grande partie grâce à la géolocalisation et à la vaste gamme de capteurs intégrés dans les appareils mobiles. Ces informations peuvent être utilisées pour améliorer les activités et les processus métier, mais également pour créer de nouveaux modèles d'affaires. En nous concentrant sur les modèles d'affaires, nous analysons les technologies mobiles comme catalyseurs des changements d'activité. Nous examinons les caractéristiques distinctives des technologies mobiles et examinons comment celles¿ci peuvent supporter différentes fonctions de l'entreprise. Une étude basée sur une analyse qualitative comparée d'ensemble floue (fsQCA) de 30 cas, de différents secteurs, a permis d'identifier les facteurs de succès de la technologie mobile pour différentes activités du cœur de métier des firmes. Les résultats montrent que plusieurs combinaisons de technologie mobile procurent un avantage concurrentiel lorsqu'elles correspondent au modèle d'affaire.[EN] Mobile technologies have pushed the connectivity of IT systems to the limit, enabling people and things to connect to one another at all times. The amount of information companies have at their disposal has increased exponentially, thanks largely to geolocation and to the vast array of sensors that have been integrated into mobile devices. This information can be used to enhance business activities and processes, but it can also be used to create new business models. Focusing on business models, we analyze mobile technologies as enablers of activity changes. We consider the differentiating characteristics of mobile technologies and examine how these can support different business functions. A study based on fuzzy-set qualitative comparative analysis (fsQCA) of 30 cases across different industries allows us to identify mobile technology success factors for different core activities. The results show that several combinations of mobile technology initiatives provide a competitive advantage when these initiatives match the business model.Peris-Ortiz, M.; Devece Carañana, CA.; Hikkerova, L. (2020). How mobile technologies support business models: Case study-based empirical analysis. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration. 37(1):95-105. https://doi.org/10.1002/cjas.1550S95105371Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19(3), 359-376. doi:10.1057/ejis.2010.21Arlotto, J., Sahut, J.-M., & Teulon, F. (2011). Le concept de Business Model au travers de la littérature. Gestion 2000, 28(4), 33. doi:10.3917/g2000.284.0033Clemons, E. K. (2009). Business Models for Monetizing Internet Applications and Web Sites: Experience, Theory, and Predictions. Journal of Management Information Systems, 26(2), 15-41. doi:10.2753/mis0742-1222260202Comberg, C., & Velamuri, V. K. (2017). The introduction of a competing business model: the case of eBay. International Journal of Technology Management, 73(1/2/3), 39. doi:10.1504/ijtm.2017.082356Coursaris C. Hassanein H. &Head M. (2006).Mobile technologies and the value chain: Participants activities and value creation(p. 8) sInternational Conference on Mobile Business Copenhagen Denmark.Ehrenhard, M., Wijnhoven, F., van den Broek, T., & Zinck Stagno, M. (2017). Unlocking how start-ups create business value with mobile applications: Development of an App-enabled Business Innovation Cycle. Technological Forecasting and Social Change, 115, 26-36. doi:10.1016/j.techfore.2016.09.011European Parliament(2015).The Internet of things: Opportunities and challenges. Retrieved fromwww.europarl.europa.eu/RegData/etudes/BRIE/2015/557012/EPRS_BRI(2015)557012_EN.pdfGurrin, C., Smeaton, A. F., & Doherty, A. R. (2014). LifeLogging: Personal Big Data. Foundations and Trends® in Information Retrieval, 8(1), 1-125. doi:10.1561/1500000033Hübner, A. H., Kuhn, H., & Wollenburg, J. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework. International Journal of Retail & Distribution Management, 44(3). doi:10.1108/ijrdm-11-2014-0154Kauffman, R. J., & Wang, B. (2008). Tuning into the digital channel: evaluating business model characteristics for Internet firm survival. Information Technology and Management, 9(3), 215-232. doi:10.1007/s10799-008-0040-3Liang, T., Huang, C., Yeh, Y., & Lin, B. (2007). Adoption of mobile technology in business: a fit‐viability model. Industrial Management & Data Systems, 107(8), 1154-1169. doi:10.1108/02635570710822796Martinez-Simarro, D., Devece, C., & Llopis-Albert, C. (2015). How information systems strategy moderates the relationship between business strategy and performance. Journal of Business Research, 68(7), 1592-1594. doi:10.1016/j.jbusres.2015.01.057Mello P.A.(2012).A critical review of applications in QCA and fuzzy‐set analysis and a ‘toolbox' of proven solutions to frequently encountered problems APSA Annual Meeting Paper. Retrieved fromhttps://ssrn.com/abstract=2105539Melville, Kraemer, & Gurbaxani. (2004). Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value. MIS Quarterly, 28(2), 283. doi:10.2307/25148636Ngai, E. W. T., & Gunasekaran, A. (2007). Mobile commerce: Strategies, technologies, and applications. Decision Support Systems, 43(1), 1-2. doi:10.1016/j.dss.2005.05.002Palattella, M. R., Dohler, M., Grieco, A., Rizzo, G., Torsner, J., Engel, T., & Ladid, L. (2016). Internet of Things in the 5G Era: Enablers, Architecture, and Business Models. IEEE Journal on Selected Areas in Communications, 34(3), 510-527. doi:10.1109/jsac.2016.2525418Pateli, A. G., & Giaglis, G. M. (2005). Technology innovation‐induced business model change: a contingency approach. Journal of Organizational Change Management, 18(2), 167-183. doi:10.1108/09534810510589589Piccoli, & Ives. (2005). Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature. MIS Quarterly, 29(4), 747. doi:10.2307/25148708Porter M. E.(2001).Strategy and the Internet. Harvard Business Review March 63–78.Ragin C. C.(2008).User's Guide to Fuzzy‐Set/Qualitative Comparative Analysis. Working Paper University of Arizona Arizona.Ray, G., Barney, J. B., & Muhanna, W. A. (2003). Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource-based view. Strategic Management Journal, 25(1), 23-37. doi:10.1002/smj.366Richter, C., Kraus, S., & Syrjä, P. (2015). The shareconomy as a precursor for digital entrepreneurship business models. International Journal of Entrepreneurship and Small Business, 25(1), 18. doi:10.1504/ijesb.2015.068773Schneider, M. R., Schulze-Bentrop, C., & Paunescu, M. (2009). Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance. Journal of International Business Studies, 41(2), 246-266. doi:10.1057/jibs.2009.36Sheng, H., Nah, F. F.-H., & Siau, K. (2005). Strategic implications of mobile technology: A case study using Value-Focused Thinking. The Journal of Strategic Information Systems, 14(3), 269-290. doi:10.1016/j.jsis.2005.07.004Sorescu, A. (2017). Data-Driven Business Model Innovation. Journal of Product Innovation Management, 34(5), 691-696. doi:10.1111/jpim.12398Tallon, P. P. (2007). A Process-Oriented Perspective on the Alignment of Information Technology and Business Strategy. Journal of Management Information Systems, 24(3), 227-268. doi:10.2753/mis0742-1222240308Tjaden, G. S. (1996). Measuring the information age business. Technology Analysis & Strategic Management, 8(3), 233-246. doi:10.1080/09537329608524248Vilmos A. Kovacs K. &Kutor L. (2007).NFC applications and business model of the ecosystem(pp.1469–1473) 16th IST Mobile and Wireless Communications Summit Budapest Hungary. doi:https://doi.org/10.1109/ISTMWC.2007.4299324.Wirtz, B. W., Schilke, O., & Ullrich, S. (2010). Strategic Development of Business Models. Long Range Planning, 43(2-3), 272-290. doi:10.1016/j.lrp.2010.01.005Woodbridge R.(2010).9 mobile business models that you can use right now to generate revenue. Tether. Retrieved February 2 2019 fromhttp://untether.tv/2010/8‐mobile‐business‐models‐that‐you‐can‐use‐right‐now‐to‐generate‐revenue/Woodside, A. G., & Zhang, M. (2011). Identifying X-Consumers Using Causal Recipes: «Whales» and «Jumbo Shrimps» Casino Gamblers. Journal of Gambling Studies, 28(1), 13-26. doi:10.1007/s10899-011-9241-5Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463-472. doi:10.1016/j.jbusres.2012.12.02

Designing Chatbots for Crises: A Case Study Contrasting Potential and Reality

Chatbots are becoming ubiquitous technologies, and their popularity and adoption are rapidly spreading. The potential of chatbots in engaging people with digital services is fully recognised. However, the reputation of this technology with regards to usefulness and real impact remains rather questionable. Studies that evaluate how people perceive and utilise chatbots are generally lacking. During the last Kenyan elections, we deployed a chatbot on Facebook Messenger to help people submit reports of violence and misconduct experienced in the polling stations. Even though the chatbot was visited by more than 3,000 times, there was a clear mismatch between the users’ perception of the technology and its design. In this paper, we analyse the user interactions and content generated through this application and discuss the challenges and directions for designing more effective chatbots

Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table