Structured Sensemaking of Videographic Information within Dataphoric Space

Attempts to create a structured sensemaking model have proven difficult. Much of the research today has evolved into a cacophony of conceptual models. Many of these sensemaking models have been proposed but not tested. Using structural equations, a unified model of sensemaking was developed and tested. This structured sensemaking model contains five sensemaking constructs: chaos, anchoring, articulation, retrospection, and identity. This model was tested using data collected from 224 educationally focused YouTube videos. The confirmatory factor model developed for this research has a measured Comparative Fit Index of 0.979, a measured Standardized Root Mean Square Residual of 0.078, and a measured Akaike’s Information Criterion of 182.892. The associated structural model has a measured Comparative Fit Index of 0.991, a measured Standardized Root Mean Square Residual of 0.047, and a measured Akaike’s Information Criterion of 131.680. This theory of structured sensemaking supports a) the unification of five sensemaking constructs b) a structured sensemaking framework c) the integration of information theory and d) a reusable sensemaking method. This structured sensemaking framework is the first of its kind

Theory of Dataphoric Space: A Dataphoric Systems Theory

A dataphore is akin to a species of animal within a biological biome. Humans facilitate the movement of information between dataphores by taking knowledge and insights from around us and transposing that knowledge into other dataphoric forms (Vicente and Rasmussen, 1990). Like the relationship between bees and flowers - pollen is the information that is exchanged between us (Margalef, 1957). We cultivate and mediate the flow of data within dataphoric space. Yet, our role as the predominant content mediator for our portion of dataphoric space is not a singular role as we have created artificial dataphores to assist in the cultivation of data (e.g., data crawlers, data recognizers, data scrapers, data cleansers). Like data contained within a genetic sequence; its topology drives its expression (Kay, 1998). From tiny cellular data forms up to the most complex of dataphora, the topology of the data contained within a dataphora also drives its expression (Fath, Cabezas, and Pawlowski, 2003). Using this ontology, information systems researchers will be able to create, observe and analyze species of dataphores within a dataphora across multiple domains of scientific inquiry (e.g., sociological, anthropological, biological, medical, legal…). As we move forward, we expect that our dataphoric terminology will expand over time to encompass more constructs (Zhongguo, Hongqi, Ali, and Yile, 2017). Physics points us towards a “science of information†(Brukner and Zeilinger, 2005; Shannon, 1948; Susskind, 2007). Biological information systems points us towards an evolutionary perspective of data (Hirata and Ulanowicz, 1984; Ulanowicz and Abarca-Arenas, 1997). Another path of inquiry becomes available if we allow ourselves to view information systems as biologically styled entities. Ultimately, we see our creation of dataphoric space as a stimulating development for information systems research

Theory of Dataphoric Space

A dataphore is akin to a species of animal within a biological biome. Humans facilitate the movement of information between dataphores by taking knowledge and insights from around us and transposing that knowledge into other dataphoric forms. Like the relationship between bees and flowers - information is the pollen that is exchanged between us. Humans cultivate and mediate the flow of data within dataphoric space. Our role as the predominant content mediator is not a singular role as we have created artificial dataphores to assist in the cultivation of data (e.g., data crawlers, data recognizers, data scrapers, data cleansers). Like data contained within a genetic sequence; its topology drives its expression. From tiny cellular data forms up to complex of dataphores, the topology of the data contained within a dataphora also drives its expression. We explore our creation of the dataphoric ontology and the associated research constructs that operate within dataphoric space