A Framework to Guide the Assessment of Human-Machine Systems

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

AIMS

To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.

DESIGN

A cross-sectional questionnaire study.

METHODS

Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.

RESULTS

ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.

CONCLUSION

ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.

IMPACT

This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

On the Role of Beliefs and Trust for the Intention to Use Service Robots: An Integrated Trustworthiness Beliefs Model for Robot Acceptance

With the increasing abilities of robots, the prediction of user decisions needs to go beyond the usability perspective, for example, by integrating distinctive beliefs and trust. In an online study (N = 400), first, the relationship between general trust in service robots and trust in a specific robot was investigated, supporting the role of general trust as a starting point for trust formation. On this basis, it was explored—both for general acceptance of service robots and acceptance of a specific robot—if technology acceptance models can be meaningfully complemented by specific beliefs from the theory of planned behavior (TPB) and trust literature to enhance understanding of robot adoption. First, models integrating all belief groups were fitted, providing essential variance predictions at both levels (general and specific) and a mediation of beliefs via trust to the intention to use. The omission of the performance expectancy and reliability belief was compensated for by more distinctive beliefs. In the final model (TB-RAM), effort expectancy and competence predicted trust at the general level. For a specific robot, competence and social influence predicted trust. Moreover, the effect of social influence on trust was moderated by the robot's application area (public > private), supporting situation-specific belief relevance in robot adoption. Taken together, in line with the TPB, these findings support a mediation cascade from beliefs via trust to the intention to use. Furthermore, an incorporation of distinctive instead of broad beliefs is promising for increasing the explanatory and practical value of acceptance modeling.

Using the ISM-ANP-SD combination model to explore the mechanism and intervention strategies of influencing factors of coal mine safety system

Background

With the intelligent construction of coal mines, the number of coal mine accidents is gradually decreasing, but the complexity of accidents is increasing. Understanding the interaction mechanism among the influencing factors of the coal mine safety system is an essential part of improving and enhancing the safety of the coal mine system.

Methods

The descriptive, structural model-network hierarchical analysis (ISM-ANP) was used to explore the interaction between the factors influencing the coal mine safety system and determine each factor's importance. A system dynamics simulation model was constructed to clarify the mechanism of each factor's effect on the safety system.

Results

The results show that Individual miners' factors directly influence coal mine system safety, organizational management factors, and group factors indirectly influence system safety and play the role of macro regulation. The intelligent system is the most profound factor influencing system safety. There are apparent differences in the influence of different subsystems on system safety, with organizational management having the most significant influence on system safety, followed by individual miners and group factors, and intelligent system factors and external environmental factors having a more negligible influence on system safety.

Conclusion

There is a complex interaction between the factors affecting the safety of the coal mine system, and there are apparent differences in the influence of different subsystems on the safety level of the coal mine system. This study puts forward the intervention strategy to improve the safety of the coal mine system, which provides theoretical support and method guidance for preventing coal mine accidents and improving the safety level of the coal mine system.

Employee-(ro)bot collaboration in service: an interdependence perspective

Purpose

Collaboration between frontline employees (FLEs) and frontline robots (FLRs) is expected to play a vital role in service delivery in these increasingly disrupted times. Firms are facing the challenge of designing effective FLE-FLR collaborations to enhance customer experience. This paper develops a framework to explore the potential of FLE-FLR collaboration through the lens of interdependence in customer service experience and advances research that specifically focuses on employee-robot team development.

Design/methodology/approach

This paper uses a conceptual approach rooted in the interdependence theory, team design, management, robotics and automation literature.

Findings

This paper proposes and defines the Frontline employee – Frontline robot interdependence (FLERI) concept based on three structural components of an interdependent relationship – joint goal, joint workflow and joint decision-making authority. It also provides propositions that outline the potential impact of FLERI on customer experience and employee performance, and outline several boundary conditions that could enhance or inhibit those effects.

Practical implications

Managerial insights into designing an employee-robot team in service delivery are provided.

Originality/value

This study is the first to propose a novel conceptual framework (FLERI) that focuses on the notion of human-robot collaboration in service settings.

Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses

OBJECTIVE

The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area.

BACKGROUND

Human-robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs.

METHOD

The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis.

RESULTS

Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI.

CONCLUSION

The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered.

APPLICATION

The identified trust factors can be used in order to promote appropriate levels of trust in robots.

Improving Teamwork Competencies in Human-Machine Teams: Perspectives From Team Science

In response to calls for research to improve human-machine teaming (HMT), we present a “perspective” paper that explores techniques from computer science that can enhance machine agents for human-machine teams. As part of this paper, we (1) summarize the state of the science on critical team competencies identified for effective HMT, (2) discuss technological gaps preventing machines from fully realizing these competencies, and (3) identify ways that emerging artificial intelligence (AI) capabilities may address these gaps and enhance performance in HMT. We extend beyond extant literature by incorporating recent technologies and techniques and describing their potential for contributing to the advancement of HMT.

Occupation-level automation probability is associated with psychosocial work conditions and workers' health: A multilevel study

OBJECTIVE

Work automation is increasing worldwide, and the probability of job automation has been associated with workers' adverse health outcomes. This study aimed to examine the association of occupation-level automation probability with work stress and workers' health.

METHODS

We used data from a national survey of 14,948 randomly selected general workers conducted in 2016. Job control and job demand were assessed by the Job Content Questionnaire, and working hours and job insecurity were self-reported. Health outcomes were measured according to burnout and work-related injury or disease. We derived automation probabilities for 38 occupational groups and conducted multilevel analyses to examine the associations between occupation-level automation probability and workers' safety and health after adjusting for psychosocial work conditions.

RESULTS

Participants working in jobs with a high probability of automation were more likely to have low job control, higher job insecurity, and work-related injury and disease prevalence; whereas workers in jobs with a low automation probability had higher psychological and physical demands and burnout prevalence. Furthermore, automation probability significantly predicted workers' health after adjustment for demographic characteristics and psychosocial work conditions.

CONCLUSIONS

Workers with low automation probability jobs may experience work stress other than that captured by traditional measures of job strain. Organizational approaches to improve employment security and psychosocial conditions are essential for workers' safety and health in the context of increasing job automation.

Development of a Change Management Instrument for the Implementation of Technologies

The manufacturing industry is increasingly being dominated by information and communication technology, leading to the development of cyber-physical systems. Most existing frameworks on the assessment of such technological advancements see the technology as a solitary system. However, research has shown that other environmental factors like organizational processes or human factors are also affected. Drawing on the sociotechnical systems approach, future technologies could be evaluated using scenarios of digitized work. These scenarios can help classify new technologies and uncover their advantages and constraints in order to provide guidance for the digital development of organizations. We developed an instrument for evaluating scenarios of digitized work on the relevant dimensions ‘technology’, ‘human’ and ‘organization’ and conducted a quantitative study applying this instrument on three different scenarios (N = 24 subject matter experts). Results show that our instrument is capable of measuring technological, human and organizational aspects of technology implementations and detecting differences in the scenarios under investigation. The instrument’s practical value is significant as it enables the user to compare and quantify scenarios and helps companies to decide which technology they should implement.