01888nas a2200217   4500008004100000022001400041245006900055210006800124260004200192300000900234490000700243520123700250653003601487653002601523653001701549653001401566653002401580100001801604700001801622856003001640       2021                        eng d  a1927-032100aCoping with the Double-Edged Sword of Data Sharing in Ecosystems0 aCoping with the DoubleEdged Sword of Data Sharing in Ecosystems  aOttawabTalent First Networkc12/2021  a5-160 v113 aResearchers and practitioners agree upon the huge potential of Big Data Analytics (BDA) for firms' competitive advantage. Capitalizing on Big Data (BD) often requires sharing firms' data with their stakeholders in an ecosystem. Sharing data, however, is a double-edged sword, because firms might also risk losing their competitive advantage by doing so. This conceptual paper uses extant literature on data analytics to introduce a comprehensive data sharing strategy framework that helps firms decide which data can be shared in an ecosystem, which should be kept secret, or if additional rules and regulations are required for sharing the data. The framework combines two distinct categorizations of data, and it relates the data categories to firms' strategic competitive advantage by employing the Resource-Based View (RBV). Firstly, the framework is grounded in the stages of the data analytics process and secondly, it distinguishes between operative, strategic and monetizable data, a new categorization introduced by this paper. Depending on the categories of data a company intends to share, the framework recommends five distinct data sharing strategies that help mitigating the risk of losing their competitive advantage.10aBig Data Analytics Capabilities10acompetitive advantage10adata sharing10aecosystem10aResource-Based View1 aKugler, Petra1 aPlank, Thomas  utimreview.ca/article/147001690nas a2200217   4500008004100000022001400041245003800055210003700093260004200130300001000172490000700182520109900189653001701288653002401305653001901329653002001348653004701368653000901415100001801424856003001442       2020                        eng d  a1927-032100aApproaching a Data-Dominant Logic0 aApproaching a DataDominant Logic  aOttawabTalent First Networkc10/2020  a16-280 v103 aThis paper introduces the construct of "data-dominant logic". The findings of a multi-step exploratory study indicate that SME have an established mindset (dominant logic) that often hinders these firms from turning data in innovative products, services, and business models. The availability of large amounts of data and the use of this data through data science-driven practices has reached a stage when it now enables new and promising possibilities for firms to innovate. However, the actual use of data and data science insights has proven to be difficult for many companies. The firms under consideration in this paper recognize that the availability of data fundamentally changes their businesses. But also, they lack the appropriate culture, mindset, and business repertoire that would enable them to act by turning data into innovation. The paper concludes that firms first need to establish a new mindset in which data plays a central role. Here I term this mindset "data-dominant logic" (DDL). Future research is required to further concretize the construct beyond this introduction.10aData science10adata-dominant logic10adominant logic10aempirical study10aorganizational and managerial requirements10aSMEs1 aKugler, Petra  utimreview.ca/article/1393