Big Data-Driven Innovation: An Empirical Research on Sustainable Development of Business Model

2018 ◽  
pp. 907-914 ◽  
Author(s):  
Yucong You
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Empirical Research ◽  
Business Model ◽  
Data Driven

Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

2019 ◽  
pp. 260-291
Author(s):  
Soraya Sedkaoui ◽  
Mounia Khelfaoui
Keyword(s):  
Big Data ◽  
Small Business ◽  
Business Model ◽  
Business Process ◽  
Data Analytics ◽  
Data Driven ◽  
Business And Society ◽  
Crucial Importance

This chapter treats the movement that marks, affects, and transforms any part of business and society. It is about big data that is creating, and the value generating that companies, startups, and entrepreneurs have to derive through sophisticated methods and advanced tools. This chapter suggests that analytics can be of crucial importance for business and entrepreneurial practices if correctly aligned with business process needs and can also lead to significant improvement of their performance and quality of the decisions they make. So, the main purpose of this chapter are exploring why small business, entrepreneur, and startups have to use data analytics and how they can integrate, operationally, analytics methods to extract value and create new opportunities.

scholarly journals Big Data for the Sustainability of Healthcare Project Financing

2019 ◽  
Vol 11 (13) ◽  
pp. 3748 ◽  
Author(s):  
Roberto Moro Visconti ◽  
Donato Morea
Keyword(s):  
Big Data ◽  
Business Model ◽  
Predictive Analytics ◽  
Interdisciplinary Approach ◽  
Data Driven ◽  
Project Finance ◽  
Public And Private ◽  
Smart Healthcare ◽  
Economic Marginality ◽  
Sustainable Planning

This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment.

Big Data Driven Smart Agriculture: Pathway for Sustainable Development

2019 ◽  
Author(s):  
Md Nazirul Islam Sarker ◽  
Min Wu ◽  
Bouasone Chanthamith ◽  
Shaheen Yusufzada ◽  
Dan Li ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Data Driven ◽  
Smart Agriculture

Big data-driven business model innovation by traditional industries in the Chinese economy

2017 ◽  
Vol 10 (3) ◽  
pp. 229-251 ◽  
Author(s):  
Sarah Cheah ◽  
Shenghui Wang
Keyword(s):  
Big Data ◽  
Business Model ◽  
Deductive Reasoning ◽  
Production Systems ◽  
Business Model Innovation ◽  
Chinese Economy ◽  
Data Driven ◽  
Economic Resource ◽  
Content Type ◽  
Integrated Framework

Purpose This study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation. Design/methodology/approach The authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms. Findings The authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process. Research limitations/implications The case sample is relatively small, which is a typical trade-off in qualitative research. Practical implications A robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation. Social implications When provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience. Originality/value Extant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.

scholarly journals Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

2016 ◽  
Vol 36 (10) ◽  
pp. 1382-1406 ◽  
Author(s):  
Philipp Max Hartmann ◽  
Mohamed Zaki ◽  
Niels Feldmann ◽  
Andy Neely
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Models ◽  
Clustering Algorithms ◽  
Reference Points ◽  
Data Driven ◽  
Content Type ◽  
Start Up ◽  
Different Types ◽  
Start Ups

Purpose The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample. Findings The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. Practical implications A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. Originality/value This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Scientific methodological approaches to the developmentof strategy of sustainable development of territorial communities

2019 ◽  
pp. 71-80
Author(s):  
Olexander Martynyuk ◽  
Inna Studennikova ◽  
Volodymyr Hromozdov ◽  
Victor Maliarevsky
Keyword(s):  
Sustainable Development ◽  
Comparative Analysis ◽  
Empirical Research ◽  
Working Group ◽  
Swot Analysis ◽  
Expert Evaluation ◽  
Analysis Methods ◽  
Methodological Approaches ◽  
Structured Approach ◽  
Developing Strategies

The purpose of the article is to propose methodological assistance to the selfgovernment system in developing strategies for sustainable development of territorialcommunities. Methodology. The research used the works of foreign and domesticspecialists involved in the development of sustainable development strategies.Authors applied methods of scientific comparative analysis, general scientificmethods of theoretical, empirical research and a method of expert evaluation. Scientificnovelty includes clear well-structured approach to identification of what is neededto develop a strategy of territorial communities’ development, important tasks fora working group, SWOT analysis methods, what should be analysed to achievestrategic goals and consistency of organizational details. Conclusions. Complianceto the proposed scientific methodological approaches to the development of thestrategies of territorial communities’ sustainable development enables the communityto obtain a strategic algorithm for its sustainable development.

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