Cognition Technology & Work最新文献

Background: Older drivers are a rapidly growing demographic group worldwide; many have visual processing impairments. Little is known about their preferences about vehicle instrument cluster design.

Methods: We evaluated the psychometric properties of a questionnaire on "dashboard" design for a population-based sample of 1000 older drivers. Topics included gauges, knobs/switches, and interior lighting; items were statements about their visual design. Response options used a Likert-scale ("Definitely True" to "Definitely False"). Factor and Rasch analyses identified underlying subscales.

Results: Driver responses revealed four thematic subscales fitting the Rasch model: cognitive processing, lighting, pattern recognition, and obstructions. Internal consistency of subscales was acceptable (0.70-0.87); all possessed a sufficiently unidimensional structure. Opportunities for improvement were identified (item scope, category ordering, discrimination of respondents' perception levels).

Conclusions: Assessment of motor vehicle dashboard preferences indicated cognitive processing, lighting, pattern recognition, and obstructions are areas relevant to older drivers. Future work will examine the relationship between older drivers' visual function (e.g., contrast sensitivity, visual processing speed) and their design preferences as revealed by the Dashboard Questionnaire, with the aim to optimize instrument cluster displays for older drivers.

The workplace is an important setting for health protection, health promotion and disease prevention. Currently, health and wellbeing approaches at an aviation organisational level are not addressing both human and safety needs. This issue has been intensified since the COVID 19 pandemic. This paper reports on the findings of a survey pertaining to aviation worker wellbeing and organisational approaches to managing wellbeing and mental health. The survey was administered at two different time periods during the COVID 19 pandemic (2020 and 2021). Collectively, feedback was obtained from over 3000 aviation workers. Survey feedback indicates that aviation workers are experiencing considerable challenges in relation to their health and wellbeing. These challenges are not being adequately addressed at an organisational level, which creates risk both from an individual and flight safety perspective. The descriptive findings of both surveys along with a regression analysis is used to make a principled case for augmenting the existing approach to managing aviation worker wellbeing (including mental health), at both an organisational and regulatory level. It is argued that aviation organisations, with the support of the regulator should implement a preventative, ethical and evidence-based strategy to managing wellbeing and mental health risk. Critically, aviation organisations need to advance and integrated health, wellbeing, and safety culture. This necessitates an alignment of human, business, and safety objectives, as articulated in concepts of corporate social responsibility (CSR) and responsible work. Critically, this approach depends on trust and the specification of appropriate protections, so that aviation workers feel safe to routinely report wellbeing levels and challenges, and their impact on operational safety.

Contributing to the scarce literature on how companies can deal with their business model of digital transition, this work explores the digital transformation (DT) process in small and medium enterprises (SME), investigating how organizational culture, structure, and leadership influence it. While such three factors are deemed essential components to facilitate DT, how they operate and how they relate to each other are still not very well-defined issues in need of in-depth investigation. This study employed a mixed-methods approach, following an exploratory sequential design. First, a conceptual model was developed based on qualitative data collected from expert interviews and analyzed through grounded theory. This stage uncovered 25 first-order concepts about culture, structure, and leadership, further organized into 6 constructs and hypothesis paths. Then, with a sample of 192 SMEs, the structural model was measured and validated using exploratory factor analysis and PLS-SEM. As a result, our study offers robust and timely research, whose conceptual model condenses a knowledge corpus that future research can benefit from, and it provides statistical extrapolations about how and how much those factors relate to each other in SME context; moreover, given the traditional scarce resources and lack of flexibility in SMEs, it provides orientation and guidelines to managers facing DT and needing to understand the organizational factors they should be aware of, where to focus energy, and what to expect as results. From a large-scale perspective, this study carries an impactful contribution to the many countries where SMEs play a major economic and social role.

In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice.