Combining Ideas in Crowdsourced Idea Generation

Wang, Kai; Wang, Hui; Tao, Yu

doi:10.1515/fman-2017-0016

Cited by 7 publications

(8 citation statements)

References 51 publications

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“…Following a previous study (Wang, Wang, & Tao, 2017), we computed the inter-rater reliability of evaluations, using interclass correlation coefficient (ICC) for originality and practicality for each group separately.…”

Section: Stage 3: Computing Inter-class Correlationmentioning

confidence: 99%

“…In other words, we considered a scenario as creative if the scores for both originality and practicality were greater than 4.0. We then computed a new variable 'creativity' by calculating the mean of originality and practicality to get the overall creativity scores Wang et al (2017). Further, we created a dichotomous variable 'creative', which we encoded as 1 if the scores for both the originality and practicality were greater than 4.0 (on a seven-point scale) and 0 otherwise.…”

Section: Stage 4: Preparation Of Data Setmentioning

confidence: 99%

“…Furthermore, current crowdsourcing quality mechanisms usually improve the quality of ideas by first generating a large quantity of ideas and then employing a second unit of crowdworkers to evaluate their quality on different parameters Yu and Nickerson (2011). Another way to improve the quality is to combine the ideas generated from other crowd-workers to create more novel ideas Wang et al (2017). Although these mechanisms help to improve the quality, they require extra time and costs.…”

Section: Text Metrics and Smart Home Devicesmentioning

confidence: 99%

“…Existing work in the realm of crowd creativity has mainly focused on creative writing (Siangliulue, Arnold, Gajos, & Dow, 2015), product design (Jang, 2014;Yu, Kittur, & Kraut, 2014;Yu & Nickerson, 2011), planning (Wang et al, 2017), problem solving and mind mapping techniques (Andolina et al, 2017) (Table 12). In the IoT domain, researchers have developed participatory design techniques and card-based tools to foster creativity.…”

Section: Differences With Other Crowdsourced Ideation Techniquesmentioning

confidence: 99%

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Investigating the Crowd’s Creativity for Creating On-Demand IoT Scenarios

Abbas¹,

Markopoulos²,

Khan³

2020

International Journal of Human–Computer Interaction

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The IoT industry supplies a plethora of Internet connected devices and services supporting smart home automation. However, end-users having little knowledge of the features and possibilities of such technologies, face difficulties in conjuring up useful application scenarios combining such devices and services, thus missing out on potential applications outside those provided by vendors. A remedy for such end-users can potentially be found in crowdsourcing IoT scenario creation. For such an enterprise to be viable it is essential to assess whether crowdsourcing can result in practical and original scenarios. This article reports two studies aiming to establish the practicality and originality of crowdsourced IoT scenarios for smart homes. In the first study, we recruited 102 crowd workers who created 306 scenarios in various categories. We then recruited a second cohort of 620 crowd workers to rate the scenarios' creativity. In the second study, we evaluated the corpus of IoT scenarios by 20-experienced smart home users recruited through a screening survey. Our results show that the crowd evaluations of originality and creativity are strongly correlated with those of smart home users. Our major IoT-specific findings in relation to creativity are: a) The number of IoT devices and the number of combination of devices impact how creative the scenarios are perceived; b) Workers with self-reported intermediate programming knowledge wrote more creative scenarios when compared to workers having expert knowledge; c) Computational metrics such as text metrics can provide the basis for automated assessment of the scenarios' creativity. Finally, an inductive thematic analysis of the scenarios revealed interesting themes (e.g., types of rules, automation styles and novel operators) which can serve as a guide for designing more expressive and intuitive end-user development solutions, in the context of IoT.

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Section: Stage 3: Computing Inter-class Correlationmentioning

confidence: 99%

Section: Stage 4: Preparation Of Data Setmentioning

confidence: 99%

Section: Text Metrics and Smart Home Devicesmentioning

confidence: 99%

Section: Differences With Other Crowdsourced Ideation Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Crowd’s Creativity for Creating On-Demand IoT Scenarios

Abbas¹,

Markopoulos²,

Khan³

2020

International Journal of Human–Computer Interaction

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“…In particular, these studies have looked at the crowdsourcing marketplace, Amazon Mechanical Turk (www.mturk.com) and the internal crowdsourcing website IdeaStorm (www.ideastorm.com) (Bayus, 2013;Elerud-Tryde & Hooge, 2014;Hutter et al, 2011;Kittur, 2010;Ren, Nickerson, Mason, Sakamoto, & Graber, 2014;Sakamoto & Bao, 2011). Even though crowd ideation can be organized with explicit roles and rules, such as idea combination (Malhotra & Majchrzak, 2014;Wang, Wang, & Tao, 2017;Yu & Nickerson, 2011), in practice there are often few instructions for crowd interactions in ideation so that being exposed to others' ideas is a major interaction mechanism (Bayus, 2013;Luo & Toubia, 2015;Majchrzak & Malhotra, 2013;Moradian, Nasir, Lyons, Leung, & Sim, 2014;Siangliulue, Arnold, Gajos, & Dow, 2015). In addition, some companies deliberately provide idea examples to guide crowd members (e.g., mystarbucksidea.com).…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced idea generation: The effect of exposure to an original idea

2018

Self Cite

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Crowdsourcing is an increasingly important approach to pursuing innovation. In crowdsourcing ideation websites, crowd members are often exposed to some stimulus ideas, such as examples provided by companies or peers' ideas. Understanding the effect of being exposed to original stimulus ideas in this context may inform the design of the crowdsourcing process. To test this effect, an experiment was conducted where crowd workers were asked to design a public service advertisement. Depending on the experimental condition, the participants were exposed to an original idea, or a common idea, or no idea. As compared to the absence of exposure, exposure to an original idea decreased fluency, defined as the number of ideas generated by each person, and increased the average originality of ideas generated by each person. By contrast, exposure to a common idea had no effect on either idea originality or fluency. The semantic similarity between the stimulus idea and the first idea generated was higher when the stimulus was common versus original as measured by latent semantic analysis. The implications of these results for research and practice are discussed.

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Testing Computational Assessment of Idea Novelty in Crowdsourcing

Wang

Dong

2023

Creativity Research Journal

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Combining Ideas in Crowdsourced Idea Generation

Cited by 7 publications

References 51 publications

Investigating the Crowd’s Creativity for Creating On-Demand IoT Scenarios

Investigating the Crowd’s Creativity for Creating On-Demand IoT Scenarios

Crowdsourced idea generation: The effect of exposure to an original idea

Testing Computational Assessment of Idea Novelty in Crowdsourcing

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