The field of Business Intelligence and Big Data is currently regarded as one of the most dynamically developing research areas in the world. The importance of Business Intelligence and Big Data potential is noticed both by representatives of academic centers and business practice. The various studies claim that the challenge for the coming years and, at the same time, one of the greatest needs of modern organizations is intelligent analytics allowing the discovery of business value from large-scale data. The capability to process and use large-scale data is recognized as the main driver of an organization’s development, as well as the basis for market survival, innovative success, improved competitiveness and more effective decision-making.
The development of the Internet, social media, distributed databases, and a variety of mobile devices has caused a huge increase in data. Much of this diverse data, in unstructured and structured forms, has a high business value and, if properly utilized, can become an important organizational asset. It contains various information about customers, competition, labor market, and development trends for industries, products and services, as well as the public and political mood. For innovative and sustainable development, it is essential for organizations to utilize this data to increase sales, identify future opportunities and new markets, outperform the competition, enhance products and services, recruit talent, improve operations, perform forecasting, protect the brand, and identify areas for improvement, to name a few.
This special issue of the Information Systems Management journal titled Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations presents the five following articles.
The first article titled Data Philanthropy: Corporate Responsibility with Strategic Value? by Jordana J. George, Jie (Kevin) Yan, and Dorothy E. Leidner explores a new and important topic of data philanthropy and how it can benefit both donor firms and society through a model and several propositions. The authors argue that data philanthropy is dissimilar to other corporate philanthropy because data assets are not subject to rivalry and can be more valuable through sharing and collaboration. They describe three illustrations (United Parcel Service, MasterCard, and the United Nations Global Pulse Data for Climate Action Innovation Challenge) that demonstrate the data philanthropy followed by a cross-illustration comparison and develop a conceptual framework of data philanthropy activity.
In the second article, entitled Data-Driven Intelligence on Innovation and Competition: Patent Overlay Network Visualization and Analytics, Serhad Sarica, Bowen Yan, and Jianxi Luo present a new data-driven network visualization and analysis methodology to assess and compare the technology positions of firms in the total technology space for competitive intelligence analytics based on patent data. The presented methodology is based on the synthesis of innovation theories, network analysis and visualization, information sciences, and patent data mining. The implementation of a case study in the cloud-based InnoGPS system provides firms and managers with agile, data-driven, and visually informed innovation and competitive intelligence, as well as contributes to the developments of data-driven innovation decision support systems.
The third paper, titled Toward an Understanding of Participants’ sustained participation in Crowdsourcing Contests by Xuan Wang, Hanieh Javadi Khasraghi, and Helmut Schneider, addresses a little known issue regarding the factors that influence individuals’ continued participation in crowdsourcing contests. The authors conduct an empirical study using data from an online crowdsourcing contest platform, Kaggle, which delivers data science and machine learning solutions and models to its clients. The analysis finds that both collaborative and competitive components such as tenure, performance, price amount, number of competitors, and competition duration have a statistically significantly effect on continued participation of individuals in crowdsourcing contests.
The fourth paper, entitled Turning Data into Value – Exploring the Role of Synergy in Leveraging Value among Data by Johannes Weibl and Thomas Hess, explores the role of synergy in the value generation path from heterogeneous data. The authors contribute to the body of knowledge on synergistic value generation by identifying contextual relatedness, data quality, and storage infrastructure as new enabling conditions in organizations. They refine and contextualize two types of realized synergy outcomes among data: super-additive informational values and super-additive transactional values. The paper, based on the qualitative-empirical research, revises an initial conceptual model of data synergy, identifying additional enabling conditions and shedding light on two types of realized data synergies. The authors summarize their findings in the following propositions: (i) the synergistic value generation among data creates more value than the sum of the value created by each data set in isolation and (ii) the synergistic value generation among data leads to super-additive information value (intangible benefit) and creates super-additive transactional values based on automated decision-making and efficiency gains (tangible benefits) in organizations.
The fifth and final article, titled Big Data in Capturing Business Value by Celina M. Olszak and Jozef Zurada, investigates an issue of Big Data and elements shaping Big Data-based business value creation. The research resulted in the proposal to outline a Big Data-driven value creation framework in organizations. The framework distinguishes three main constructs: the organization’s dynamic capabilities, the integrated process of Big Data resource exploration and exploitation as well as the identification and measurement of value. The developed model has been subject to initial verification. This verification was carried out in selected organizations using an in-depth interview method. The conclusions from the conducted research may prove to be helpful for all organizations that intend to use Big Data resources in their activities.
We hope you find the topics in this special related to Business Intelligence and Big Data to be current and informative.
Notes
P.S. The initial versions of the above articles were presented at the 52nd Hawaii International Conference on System Sciences (HICSS-52), Maui, Hawaii, USA, Jan 8–11, 2019, published in the HICSS-52 Proceedings, and released in ScholarSpace and eLibrary under the Creative Commons license (CC-BY-NC-ND). Authors own the copyright of their work and they can disseminate the paper freely after the conference has taken place. The five papers published in this special issue constitute the enhanced and extended versions of the initial articles and were subject to a thorough review process required by the Information Systems Management journal.