Open Source Collaboration in Digital Entrepreneurship

2022 | Organization Science | Citations: 3

Authors: Lin, Yu-Kai; Maruping, Likoebe M.

Abstract: Emerging digital technologies give rise to digital entrepreneurship and the wide ... Expand

Abstract: Emerging digital technologies give rise to digital entrepreneurship and the widespread phenomenon of open source collaboration (OSC) on GitHub for entrepreneurial pursuits. Although openness is a common theme in digital entrepreneurship, it is unclear how digital startups—that is, startups that that have digital artifacts at the core of their business model for value creation and capture—actually realize value from their OSC engagement. We develop a theoretical framework to explain how the engagement in OSC may affect the value of digital startups and how the effect is contingent on the stage of venture maturity (conception, commercialization, or growth) and the mode of OSC engagement (inbound or outbound). In analyses that pool 17,552 matched digital startups with monthly panel observations between 2008 and 2017, we find digital startups in the conception and commercialization stages benefit more from inbound OSC whereas the ones in the growth stage benefit more from outbound OSC. As digital startups increasingly use OSC for ideation, experimentation, and scaling, our contribution is to show whether, when, and how knowledge flows through OSC might affect the value of digital startups. We discuss implications for research on organizing for digital entrepreneurship as well as open innovation. Collapse

Topics: digital entrepreneurship open innovation innovation management value creation open source

Methods: theory development longitudinal research experimental group descriptive statistic regression analysis method

Theories: organizational theory

Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks

2021 | Information Systems Frontiers | Citations: 0

Authors: Michiardi, Pietro; Carra, Damiano; Migliorini, Sara

Abstract: In modern large-scale distributed systems, analytics jobs submitted by various u ... Expand

Abstract: In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks. Collapse

Topics: Apache Spark distributed computing application programming interface database system operating system

Methods: experiment computational algorithm design artifact optimization model knapsack problem