Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations

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    Big Data-driven Value Creation for Organizations
    ( 2019-01-08) Olszak, Celina ; Zurada, Jozef
    The past few years have been characterized by an enormous increase in data coming from different sources in real time and in many diverse forms. The term commonly used for such data is Big Data (BD). Much of this BD has a high business value and, if properly utilized, can become an important organizational asset helping the organization to achieve competitive advantage. However, many organizations make a limited use of BD because they lack necessary tools and/or do not understand the value of this data. The main contribution of this study is to investigate an issue of Big Data and elements shaping creation of BD-based business value. In particular, the outcome of this research is to build and verify a framework to provide business value based on BD. The proposed framework is distinguished by three components: (1) dynamic capabilities of organizations, (2) integrated process of BD resource exploration and exploitation, and (3) identification and measurement of business value creation based on BD.
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    Reinforcement Learning for Extended Reality: Designing Self-Play Scenarios
    ( 2019-01-08) Espinosa Leal, Leonardo ; Chapman, Anthony ; Westerlund, Magnus
    A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization. In this review, we look at extended reality, a promising but often overlooked field, for training agents using reinforcement learning. We review several techniques from the literature and then synthesize the information in order to propose a recommended design. Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful AI. Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction.
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    Effects of Quantitative Measures on Understanding Inconsistencies in Business Rules
    ( 2019-01-08) Nagel, Sabine ; Corea, Carl ; Delfmann, Patrick
    Business Rules have matured to an important aspect in the development of organizations, encoding company knowledge as declarative constraints, aimed to ensure compliant business. The management of business rules is widely acknowledged as a challenging task. A problem here is a potential inconsistency ofbusiness rules, as business rules are often created collaboratively. To support companies in managing inconsistency, many works have suggested that a quantification of inconsistencies could provide valuable insights. However, the actual effects of quantitative insights in business rules management have not yet been evaluated. In this work, we present the results of an empirical experiment using eye-tracking and other performance measures to analyze the effects of quantitative measures on understanding inconsistencies in business rules. Our results indicate that quantitative measures are associated with better understanding accuracy, understanding efficiency and less mental effort in business rules management.
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    What Sustains Individuals’ Participation in Crowdsourcing Contests?
    ( 2019-01-08) Wang, Xuan ; Javadi Khasraghi, Hanieh ; Schneider, Helmut
    Crowdsourcing contests have become widely adopted for idea generation and problem-solving in various companies in different industries. The success of crowdsourcing depends on the sustained participation and quality-submissions of the individuals. Yet, little is known about the factors that influence individuals’ continued participation in these contests. We address this issue, by conducting 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 findings show that the community activities and team activities do not contribute to motivating the continued participation, but tenure does significantly affect the continued participation. We also found statistically significant effects of amount of prize, number of competitions, previous team performance, and competition duration on individuals sustained participation in crowdsourcing contests. This research contributes to the literature by identifying the factors influencing individuals’ sustained participation in crowdsourcing contests.
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    Data-Driven Network Visualization for Innovation and Competitive Intelligence
    ( 2019-01-08) Sarica, Serhad ; Yan, Bowen ; Bulato, Gerardo ; Jaipurkar, Pratik ; Luo, Jianxi
    Technology positions of a firm may determine its competitive advantages and innovation opportunities. While a tangible understanding of the technology positions of a firm, i.e., the set of technologies the firm has mastered, can inform innovation and competitive intelligence, yet such positions are heterogeneous, intangible and difficult to analyze. Herein, we present a data-driven network visualization methodology to locate the knowledge positions of a firm as a subspace of the total technology space for innovation and competitive intelligence analytics. The total technology space is empirically constructed as a network map of all patent technology classes and can be overlaid with the knowledge positions of a firm according to its patent records. This paper demonstrates how to use the system to conduct historical, comparative and predictive analyses of the technology positions of individual and different firms. The methodology has been implemented into a cloud-based data-driven visual analytics system – InnoGPS.
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