An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation.

The challenging meet between human and artificial knowledge. A systems-based view of its influences on firms-customers interaction

Purpose

This paper aims to recall the attention on a key challenge for customer relationship management related to the role of human agents in the management of the “switch point” for ensuring the effectiveness and efficiency in a customer-machine conversation.

Design/methodology/approach

This study contributes to the discussion about the firms’ approach to artificial intelligence (AI) in frontline interactions under the conceptual umbrella provided by knowledge management studies.

Findings

This paper provides a theoretical model for clarifying the role of human intelligence (HI) in AI-based frontline interactions by highlighting the relevance of the actors’ subjectivity in the dynamics and perceptions of customer-machine conversations.

Originality/value

An AI-HI complementarity matrix is proposed in spite of the still dominant replacement view.

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.

The effects of the COVID-19 pandemic for artificial intelligence practitioners: the decrease in tacit knowledge sharing

Purpose

This study aims to contribute by showing that although artificial intelligence (AI) practitioners have been faster to adapt, redefine and improve their remote working performance for routine tasks, they have instead decreased their tacit knowledge sharing and ability to perform extra tasks and manage the diverse time allocation.

Design/methodology/approach

Based on a grounded theory study of 57 in-depth interviews, conducted before the outbreak of the pandemic and after, this study investigates how remote work as a pandemic response measure affected AI practitioners.

Findings

Although remote working was a reality for AI practitioners before the COVID-19 pandemic, the overall remote working restrictions appear to have affected tacit knowledge sharing between AI practitioners, with a consequent negative impact on AI project output diversity.

Originality/value

The interactions of AI practitioners are partly embedded in AI tools and partly in human exchange. During the COVID-19 pandemic, these interactions appear to have become more obvious, even if the consequences have been unforeseen.

Case Study on the Diffusion of Digital Dental Technology Innovation–Double Diamond Model Perspective

This research employs a case study of a professional dental medical equipment company in Taiwan, cites the theoretical basis of innovation diffusion, and uses the design-thinking double-diamond model to review the company's history of promoting digital IOSs in the market from ground zero. Integrating the theoretical foundations of medical institution procurement and innovation diffusion, this study finds that different target customers have very different perceptions and acceptance of new technologies. The knowledge and attitude of medical personnel are the primary factor affecting new technology procurement, only then will decision-making factors such as financial, functional, and service aspects be further considered.

Product Creativity as an Identity Issue: Through the Eyes of New Product Development Team Members

In this study, we introduce a concept of product identification that denotes the overlap between identities of a new product and its developer. As creativity is the most important identity dimension in the new product, we draw on two dimensions of creativity: novelty and meaningfulness. According to the argument that novelty represents exploration, whereas meaningfulness represents exploitation, we hypothesize that product novelty is associated with an explorative behavior of new product team members, while product meaningfulness is associated with exploitative behavior. More importantly, product identification is proposed as the mechanism that explains the amplification effect of product identity on team members. Based on survey data collected from 200 Japanese new product development (NPD) team members, we conduct a statistical analysis to test the hypotheses. The findings demonstrate the alignment between the identity of a new product and the behaviors of the NPD members, which is amplified by product identification but not by organizational identification.