Ideation in the digital age: literature review and integrative model for electronic brainstorming

Can artificial intelligence’s limitations drive innovative work behaviour?

Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.

Can artificial intelligence’s limitations drive innovative work behaviour?

Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.

Translating Member Ability Into Group Brainstorming Performance: The Role of Collective Intelligence

In investigating how member ability is translated into group brainstorming performance, it was predicted that a group’s collective intelligence (CI) would enable it to capitalize on member ability while maximizing process gains and mitigating process losses. Ninety-nine groups were randomly assigned to complete a short brainstorming task using a hybrid (individual-group work) or collective (only group work) task structure. High CI groups were better than low CI groups at translating member ability into group brainstorming performance. Additionally, this hybrid structure was more beneficial for low CI groups than for high CI groups in generating total ideas.

Revisiting the Effects of Gender Diversity in Small Groups on Divergent Thinking: A Large-Scale Study Using Synchronous Electronic Brainstorming

Numerous studies have examined the effects of gender diversity in groups on creative performance, and no clear effect has been identified. Findings depend on situational cues making gender diversity more or less salient in groups. A large-scale study on two cohorts (N = 2,261) was conducted among business students to examine the impact of the gender diversity in small groups on divergent thinking in an idea-generation task performed by synchronous electronic brainstorming. Participants were automatically randomized in three- or four-member groups to generate ideas during 10 min on a gendered or neutral task. Then, five categories of groups where the proportion of men/women in groups varied from three/four men to three/four women were compared to examine creative performance on three divergent thinking measures (fluency, flexibility, and originality). A Multivariate Generalized Linear Mixed Model (mGLMM) showed greater fluency in all-women groups than in other groups (except mixed-gender groups composed of two men and two women), and more specifically "solo" groups composed of a single woman/man among a majority of men/women. For flexibility and originality, the superiority of all-women groups was found only in comparison to "solo" groups composed of a single woman. As gender differences are more salient in "solo" groups than in other groups faultlines may appear in groups, leading to a deleterious impact on creative performance.

Climate change adaptation networks for small and medium-sized cities

Cities are particularly vulnerable to the impacts of climate change. Many larger cities have identified the potential impacts of different climate change adaptation scenarios. However, their smaller and medium-sized counterparts are often not able to address climate risks effectively due to a lack of necessary resources. Since a large number of cities worldwide are indeed small and medium-sized, this lack of preparedness represents a crucial weakness in global response systems. A promising approach to tackling this issue is to establish regional municipal networks. Yet, how might a regional network for small and medium-sized cities be systematically designed and further developed? Focussing on the German federal state of Baden-Wuerttemberg, we have explored this question by applying a participatory action research approach. As part of our research, we established a regional network framework for small and medium-sized cities. The framework supports small and medium-sized cities in identifying key regional actors, while taking local and regional contextual factors into account. Based on our findings, we suggest that other small and medium sized cities follow these steps: develop the knowledge base; build the network; and transfer and consolidate knowledge.

Enhanced Motivation and Decision Making from Hybrid Creative Problem Solving

Hybrid brainstorming is ecologically more valid than all-interactive or all-noninteractive brainstorming, yet understudied. Although ideational benefits of hybrid groups have been found, studies have rarely focused on its affective/motivational contributions or ability to select ideas. In a randomized experiment, noninteractive-then-interactive (hybrid) groups perceived (1) higher goal clarity, engagement, and task attractiveness, and (2) chose more quality ideas than all-noninteractive groups. Additionally, (3) given the instruction for both hybrid and all-noninteractive conditions to be critical in idea selection, participants individually selected ideas that were more useful, thus overall higher quality, than the nonselected.