Using scenarios to understand the frontiers of IS: Fifteen years later (a postscript)

Hovav, Anat

doi:10.1007/s10796-014-9501-x

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Although big data technology is widely used in the integration of government information resources, there is still a lack of big data awareness in the concrete implementation [8]. Although claiming to use big data to solve problems, the original approach is still used in problem-solving.…”

Section: Introductionmentioning

confidence: 99%

The Construction of Big Data Computational Intelligence System for E-Government in Cloud Computing Environment and Its Development Impact

Hao

Lin

et al. 2022

Computational Intelligence and Neuroscience

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The traditional E-government big data system fills and classifies algorithms with low accuracy and poor work efficiency. With the development and wide application of big data, the internet of things, and other technologies, the integration of information resources has become the key to information construction. In the process of information resource integration, there are still outstanding problems such as incomplete government information resource system, different standards of government information resource management system construction, and serious threats to network and information security. In order to solve this problem, a new E-government big data system filling and classification algorithm is studied in the cloud computing environment; E-government big data filling is carried out on the basis of complete compatibility theory; and the E-government big data computing intelligence system in the cloud computing environment is constructed and its development impact, so as to parallelize the data, classify the data through decision trees, and realize incremental update decision forest parallelization processing. To verify the effectiveness of the method, comparative experiments are set, and the results demonstrate that experiment one is randomly built into the classification model, and according to the decision forest algorithm, the optimal number of decision trees is 24.

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Section: Introductionmentioning

confidence: 99%

The Construction of Big Data Computational Intelligence System for E-Government in Cloud Computing Environment and Its Development Impact

Hao

Lin

et al. 2022

Computational Intelligence and Neuroscience

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“…For example, the World Economic Forum (2018), reporting on the “Eight Futures of Work,” takes an approach that is similar to that which we use in this article: to choose the most meaningful dimensions, which they termed the “most impactful and most uncertain variables.” In a very real way, these routine and careful analytic summaries of scenario-building serve as the basis for generating new scenarios – which is what we do, below. The scholarship that explicitly relies on Futurist perspectives has the least overlap with IS scholars (with the work of IS scholar Hovav (2014), Gray and Hovav (2007) a notable exception as they draw explicitly on the structured scenario developments to explore possible future arrangements).…”

Section: Writings On the Futures Of Workmentioning

confidence: 99%

The multi-dimensional space of the futures of work

Sawyer

Carmel

2022

ITP

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Purpose The authors present nine dimensions to provide structure for the many Futures of Work (FoW). This is done to advance a more sociotechnical and nuanced approach to the FoW, which is too-often articulated as singular and unidimensional. Futurists emphasize they do not predict the future, but rather, build a number of possible futures – in plural – often in the form of scenarios constructed based on key dimensions. Such scenarios help decision-makers consider alternative actions by providing structured frames for careful analyses. It is useful that the dimensions be dichotomous. Here, the authors focus specifically on the futures of knowledge work.Design/methodology/approach Building from a sustained review of the FoW literature, from a variety of disciplines, this study derives the nine dimensions.Findings The nine FoW dimensions are: Locus of Place, Locus of Decision-making, Structure of Work, Technologies’ Roles, Work–Life, Worker Expectations, Leadership Model, Firm’s Value Creation and Labor Market Structure. Use of the dimensions is illustrated by constructing sample scenarios.Originality/value While FoW is multi-dimensional, most FoW writing has focused on one or two dimensions, often highlighting positive or negative possibilities. Empirical papers, by their nature, are focused on just one dimension that is supported by data. However, future-oriented policy reports tend are more often multi-faceted analyses and serve here as the model for what we present.

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