An empirical study of displaceable job skills in the age of robots

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.

Finger dexterity in well-functioning cohort of office workers in Macau

AIM

The study aimed to describe the finger dexterity in office workers of an Asian population.

METHODS

One hundred twenty-seven right-handed office workers, aged 21-50 with a similar split of male and female, were recruited with finger dexterity measured by the O'Connor Finger Dexterity Test. The grip strength, tip and lateral pinch strength of both hands were also measured.

RESULTS

This study provided the percentile score of the O'Connor Finger Dexterity Test of both males and females in the Asian population. Raw scores of below 218 and 213 seconds in male and female participants respectively reach the 90th percentile, and above 237 and 235 seconds in male and female below the 10th percentile. Results showed no significant difference in local mean scores across different age groups, between male and female and with varying hours of working in typing, filing, and writing. A significant difference was only found in finger dexterity and years of working as office workers. No significant correlation was found between the finger dexterity with grip strength, tip and lateral pinch of the dominant right hand. The results were similar to the original normative score with similar work skills and demands.

CONCLUSION

The mean scores could be used as a valid reference for local occupational therapists to evaluate the finger dexterity of office workers. However, caution has to be taken that conclusions drawn can be biased because of the relatively small sample size, and the results cannot be generalized to represent a wider Asian population.

Working With Robots: Human Resource Development Considerations in Human–Robot Interaction

Advancements in robotic technology have accelerated the adoption of collaborative robots in the workplace. The role of humans is not reduced, but robotic technology requires different high-level responsibilities in human–robot interaction (HRI). Based on a human-centered perspective, this literature review is to explore current knowledge on HRI through the lens of HRD and propose the roles of HRD in this realm. The review identifies HRD considerations that help implement effective HRI in three human-centered domains: human capabilities, collaboration configuration, and attributes related to contact. The eight HRD considerations include employees’ attitudes toward robots, their readiness for robot technology, communication with robots, human–robot team building, leading multiple robots, systemwide collaboration, safety interventions, and ethical issues. Theoretical implications, practical implications, and limitations are discussed. This paper contributes to HRD by introducing potential areas of multidisciplinary collaborations to help organizations implement robotic systems.