Taking the good with the bad: The impact of forecasting timing and valence on idea evaluation and creativity.

McIntosh, Tristan; Mulhearn, Tyler J.; Mumford, Michael D.

doi:10.1037/aca0000237

Cited by 22 publications

(10 citation statements)

References 53 publications

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“…Rather, people move back and forth between the actions taken, often for reasons yet unknown (Mumford et al, 2012). Interestingly, forecasting, the deliberate anticipation of potential consequences of the implementation of an idea, is studied as a technique aimed to support the execution of the creative process (McIntosh et al, 2019; Todd et al, 2019). Specifically, forecasting has been studied as a technique to support idea evaluation, where forecasting the consequences of implementing ideas is used to gather information in order to make decisions about what ideas can be implemented, should be revised, or not pursued at all (Bruijninckx and de Rooij, unpublished).…”

Section: Introductionmentioning

confidence: 99%

The Predictive Creative Mind: A First Look at Spontaneous Predictions and Evaluations During Idea Generation

Valtulina

Rooij

2019

Front. Psychol.

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Idea generation, the process of creating and developing candidate solutions that when implemented can solve ill-defined and complex problems, plays a pivotal role in creativity and innovation. The algorithms that underlie classical evolutionary, cognitive, and process models of idea generation, however, appear too inefficient to effectively help solve the ill-defined and complex problems for which one would engage in idea generation. To address this, these classical models have recently been redesigned as forward models, drawing heavily on the “predictive mind” literature. These pose that more efficiency can be achieved by making predictions based on heuristics, previous experiences, and domain knowledge about what material to use to generate ideas with, and evaluate these subsequently generated ideas based on whether they indeed match the initial prediction. When a discrepancy occurs between prediction and evaluation, new predictions are made, and thus shaping what actions, and how these actions, are undertaken. Although promising, forward models of idea generation remain theoretical and thus no empirical evidence exists about whether such predictions and evaluations indeed form part of the idea generation process. To take a first empirical look at this, a mixed-methods study was conducted by analyzing people’s self-reports for the reasons of the actions that they take during an idea generation task. The results showed that predictions and evaluations are pervasive in the idea generation process. Specifically, switching between concept selection and conceptual combination and idea generation, as well as repeating idea generation based on earlier selected conceptual combination, and possibly (but to a lesser extent) concept selection and the repetition thereof, are likely to be driven by predictions and evaluations. Moreover, the frequencies of the predictions and evaluations that drive these actions influenced the amount of ideas generated, amount of concepts used, and within-concept fluency (the ratio of the amount of ideas generated per concept used). Therefore, the contribution of this paper is the first empirical evidence that indicates that the idea generation process is driven by both predictions and evaluations.

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Section: Introductionmentioning

confidence: 99%

The Predictive Creative Mind: A First Look at Spontaneous Predictions and Evaluations During Idea Generation

Valtulina

Rooij

2019

Front. Psychol.

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“…Topic modeling also enables the identification of valuable insights [26]. Forecasting improves creative performance as part of idea generation [53]. Moreover, it is possible to use scientometric, machine learning, and visual analytics to elicit trends and temporal patterns [54].…”

Section: How Can the Activities In This Phase Be Done?mentioning

confidence: 99%

“…Evolving trends identified from topics can be as inputs for decision making in research and real-world technology [28]. Analysis of trends results in forecasting trends in technology [29], forecasting while idea generation improves idea evaluation [53].…”

Section: Running Correlation Tests On Candidate Terms -mentioning

confidence: 99%

Adapting CRISP-DM for Idea Mining

Ayele¹

2020

IJACSA

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Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

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“…Idea generation could be done through collaborating of persons aided by machine learning techniques [12], by finding solutions to analogous problems using machine learning on textual data [1,2], network analysis and text mining [12], algorithms of recommendation systems [25]. Also, idea generation improves idea evaluation as part of forecasting [28].…”

Section: Background and Previous Researchmentioning

confidence: 99%

“…Emerging trends elicited from topics are used for decision making in science and technology [47]. Analysis of trends results in forecasting trends in technology [48], forecasting while idea generation improves idea evaluation [28]. Machine learning, AI, and analytics can be used to elicit ideas by forecasting relevance and extracting valuable Fig.…”

Section: Phase 4: Generating and Evaluating Ideasmentioning

confidence: 99%

A Process Model for Generating and Evaluating Ideas: The Use of Machine Learning and Visual Analytics to Support Idea Mining

Ayele

Juell-Skielse

2020

Electronic Government and the Information Systems Perspective

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The significance and possibilities of idea generation and evaluation are increasing due to the increasing demands for digital innovation and the abundance of textual data. Textual data such as social media, publications, patents, documents, etc. are used to generate ideas, yet manual analysis is affected by bias and subjectivity. Machine learning and visual analytics tools could be used to support idea generation and evaluation, referred to as idea mining, to unlock the potential of voluminous textual data. Idea mining is applied to support the extraction of useful information from textual data. However, existing literature merely focuses on the outcome and overlooks structuring and standardizing the process itself. In this paper, to support idea mining, we designed a model following design science research, which overlaps with the Cross-Industry-Standard-Process for Data Mining (CRISP-DM) process and adapts well-established models for technology scouting. The proposed model separates the duties of actors in idea mining into two layers. The first layer presents the business layer, where tasks performed by technology scouts, incubators, accelerators, consultants, and contest managers are detailed. The second layer presents the technical layer where tasks performed by data scientists, data engineers, and similar experts are detailed overlapping with CRISP-DM. For future research, we suggest an ex-post evaluation and customization of the model to other techniques of idea mining.

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Taking the good with the bad: The impact of forecasting timing and valence on idea evaluation and creativity.

Cited by 22 publications

References 53 publications

The Predictive Creative Mind: A First Look at Spontaneous Predictions and Evaluations During Idea Generation

The Predictive Creative Mind: A First Look at Spontaneous Predictions and Evaluations During Idea Generation

Adapting CRISP-DM for Idea Mining

A Process Model for Generating and Evaluating Ideas: The Use of Machine Learning and Visual Analytics to Support Idea Mining

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