A data based model as a metropolitan management tool: The Bogotá-Sabana region case study in Colombia

Del-Busto, Felipe; Souza, Fábio Teodoro de

doi:10.1016/j.landusepol.2016.02.019

Cited by 9 publications

(7 citation statements)

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“…The main problem with big data is its composition. Big data is composed of a fast-flowing stream and it demands continuous management of the data gathered [19,69]. This can be done through the use of analytics.…”

Section: Big Datamentioning

confidence: 99%

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

et al. 2019

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Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company's business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.Sustainability 2020, 12, 277 2 of 29 to frequent unsuccessful results about the underlying business logic and, therefore, perpetuating failures that slow down the process of knowledge creation and, thus, undermining possibilities for growth. In this regard, there is a gap between what has been studied in academia and what can be potentially useful for managers willing to innovate their BM [8,9].Thus, BM innovation, validation, and experimentation processes need to be structured and implemented. This is relevant in several industries as the greater frequency of disruption and dislocation are shortening business model life cycles and the sustainability of any specific business model is unclear [10,11]. In a scenario of constant change and need for a better structured management process for BMI, it is important to consider alternatives for testing and experimenting with new alternatives of BM. However, it is not clear in the BMI literature how to do this in the face of the recognized complexity of this process [12,13].Christensen, Bartman, and van Bever [14] argue that there is the need for a systematic mechanism to evaluate the relationship between opportunities and BM. Besides, it is necessary to consider that innovation in BM cannot be generated randomly through guesswork. Nonetheless, Laudien and Daxböck [15] show that, in many cases, innovations in BM are not deliberate strategic options but rather a result of an emergent process, often unplanned. Strategic options, however, are usually related to a company's performance management which means the measurement of a set of indicators on various perspectives of the company [16,17]. In this sense, it seems natura...

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Section: Big Datamentioning

confidence: 99%

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

et al. 2019

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“…The study by Del Busto Pinzon and Souza [52] aimed to apply data mining techniques as a tool for metropolitan management. An autocorrelation matrix (ACM), PCA, clustering, and a hierarchical tree were used to study the structure of the variables employed and to analyze the evolution of growth over time.…”

Section: Bibliographic Portfoliomentioning

confidence: 99%

“…The studies also exhibit that the proposed studies are motivated to find solutions for specific domains of the city (e.g., traffic, mobility, environment, and so on). Clustering analyses were the most used approaches in the studies [22,25,30,39,44,46,50,52,54,57] and later, supported vector machine [23,31,38,40,43,49,57].…”

Section: Bibliographic Portfoliomentioning

confidence: 99%

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Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

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Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

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“…Além disso, a mineração de dados pode lidar com grandes bases de dados complexos e ser adequada para enfrentar dinâmicas multidimensionais, como o crescimento urbano (e.g., Pinzón & Souza, 2016).…”

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A distância como variável em modelos configuracionais no estudo da distribuição de atividades econômicas urbanas

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2017

urbe, Rev. Bras. Gest. Urbana

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Resumo Este trabalho demonstra que diferentes modelos configuracionais urbanos podem gerar resultados similares quando se alteram parâmetros de distância imputados nos processamentos. Assim, demonstra-se uma maneira alternativa de se regular modelos configuracionais para a compreensão de fenômenos urbanos: a utilização da distância como variável ajustável. Empiricamente, o fenômeno da distribuição de atividades econômicas urbanas é analisado linearmente comparando as suas localizações com os resultados obtidos pela aplicação de modelos configuracionais sobre redes espaciais urbanas. Para teste da hipótese, as atividades econômicas de três cidades são espacializadas, e, sobre a rede espacial formada por trechos de ruas dessas cidades, são aplicados modelos configuracionais urbanos com variação em seus raios de processamento. Os valores de centralidade resultantes do processamento de cada modelo são correlacionados à distribuição das atividades econômicas nas cidades por meio do coeficiente de correlação de Pearson (r). Resultados similares foram obtidos com a aplicação dos mesmos modelos configuracionais urbanos para um conjunto de diferentes cidades, alterando-se apenas a distância considerada no processamento dos modelos. Portanto, mostra-se mais importante ajustar o parâmetro distância do que descobrir qual modelo configuracional consegue descrever como o fenômeno da distribuição de atividades econômicas ocorre. Em outras palavras, dependendo de cada caso, existiriam distâncias que, associadas a determinados modelos, estariam mais ou menos correlacionadas com a maneira como as atividades econômicas se distribuem pela malha urbana. Dessa forma, este trabalho aponta que os modelos configuracionais podem ser ajustados em função de uma distância otimizada, aumentando a correlação com o fenômeno urbano estudado.

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A data based model as a metropolitan management tool: The Bogotá-Sabana region case study in Colombia

Cited by 9 publications

References 10 publications

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

A distância como variável em modelos configuracionais no estudo da distribuição de atividades econômicas urbanas

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