The impact of AI-enabled service attributes on service hospitableness: the role of employee physical and psychological workload

Purpose Hospitableness lies in the center of hospitality services. With the infusion of artificial intelligence (AI) technology in the hospitality industry, managers are concerned about how AI influences service hospitableness. Previous research has examined the consequences of AI technology based on customers’ assessment while ignoring the key players in service hospitableness – frontline employees (FLEs). This study aims to reveal how AI technology empowers FLEs physically, mentally and emotionally, facilitating hospitableness provision. Design/methodology/approach As the starting point, the instrument for AI-enabled service attributes was designed based on previous literature, hotel FLE interviews, expert panel and a pilot survey, and then validated using survey data. After that, a paired supervisor-employee sample was recruited in 15 hotels, and 342 valid questionnaires covering the constructs were obtained. Findings Factor analyses and measurement model evaluation suggest that the four factors, including anthropomorphic, entertainment, functional and information attributes, explain the construct of AI-enabled service attributes well, with high reliability and validity. Additionally, anthropomorphic, functional and information attributes of AI technology have been found to enable FLEs physically, mentally and emotionally, which further lead to increased service hospitableness. The entertainment attributes do not significantly reduce physical and mental fatigue but lead to positive emotions of FLEs significantly. Additionally, psychological job demand moderates the effects of AI-enabled service attributes on physical fatigue. Practical implications Practical implications can be made for AI technology application and hospitableness provision, in terms of AI technology analysis, job design and employee workload management. Originality/value This research contributes to understanding AI-enabled service attributes and their consequences, extends the conservation of resources theory to AI application context and promotes the research on service hospitableness.

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach

With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

The art of delegation: integration of the veterinary nursing assistant into New Zealand veterinary practice

Background: In 2016 veterinary nursing assistants (VNAs) were introduced as an additional tier to New Zealand veterinary practice. Aim: This study explores the utilisation of VNAs in New Zealand veterinary practices to ascertain the impact of an additional staffing layer to patient outcomes, workload management and staff wellness. Method: Through focus groups and semi-structured interviews with 30 participants, three themes emerged allowing evaluation of the Allied Veterinary Professionals Regulatory Council (AVPRC) Scope of Practice (SP) (AVPRC, 2020) and development of delegation guidelines (DG). Results: Analysis identified weak processes in delegation. The practice-based perspectives of VNA staff utilisation supports the AVPRC SP. Conclusion: Effective communication of the SP and DG for veterinary practice utilisation could contribute to reducing workload pressure. Additionally, individual practice staff discussions regarding own and colleague job expectations, along with review of contractual job descriptions, could further evolution of multi-tiered practices leading to improved patient outcomes, team wellness and business success.

Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques

Objectives Nurses are at a high risk of developing mental health problems due to exposure to work environment risk factors. Previous research in this area has only examined a few factors within nurses’ work environments, and those factors were not conceptualized with the goal of improving workplace mental health. The purpose of this study is to identify the most important work environment predictors of nurse mental health using a comprehensive and theoretically grounded measure based on the National Standard of Psychological Health and Safety in the Workplace. Methods This is an exploratory cross-sectional survey study of nurses in British Columbia, Canada. For this study, responses from a convenience sample of 4029 actively working direct care nurses were analyzed using random forest regression methods. Key predictors include 13 work environment factors. Study outcomes include depression, anxiety, post-traumatic stress disorder (PTSD), burnout and life satisfaction. Results Overall, healthier reports of work environment conditions were associated with better nurse mental health. More specifically balance, psychological protection and workload management were the most important predictors of depression, anxiety, PTSD and emotional exhaustion. While engagement, workload management, psychological protection and balance were the most important predictors of depersonalization, engagement was the most important predictor of personal accomplishment. Balance, psychological protection and engagement were the most important predictors of life satisfaction. Conclusions Routine assessment with standardized tools of nurses’ work environment conditions and mental health is an important, evidence-based organizational intervention. This study suggests nurses’ mental health is particularly influenced by worklife balance, psychological protection and workload management.

Results of the study of information competence formation of future specialists in navigation and ship handling

The article presents the results of experimental testing of pedagogical conditions for forming information competence of future specialists in navigation and ship handling. While teaching the disciplines «Ocean Routes of the World», «Navigation Bridge Resource Management», «Actions in Accidents, Search and Rescue at Sea», «Navigation Information Systems» it is suggested to use interactive methods to form the cadets’ ability to generalize, analyze and use information during professional interaction, develop the ability to spread information about vessel handling, workload management, to share their experience in navigation with other people, make requests, give suggestions on solving pressing problems. To develop cadets' skills in information support of navigation information systems, it is proposed to use electronic educational resources, including educational, information, scientific, reference materials presented in electronic form or stored in computer networks. The importance of modern technologies, including information resources of the Internet, such as blogs, web quests, blog quests, as well as simulation technologies with augmented and virtual reality has been highlighted.

Comparison of Elbow Varus Torque During Interval Throwing Programs and Gameday Pitching in High School Baseball Pitchers (125)

Objectives: Completion of an interval throwing program (ITP) is a common benchmark for return to full competition following an injury to the dominant extremity of an overhead throwing athlete. While workload management for overhead athletes has evolved, the general structure of the ITP remains relatively unexamined. Furthermore, the daily and cumulative workload of ITPs is generally unknown. An ideal ITP would allow for a gradual increase in workload that eventually approximates, but does not exceed, workload measurements attained during competition. It is currently unknown if ITPs achieve this critically important objective. Therefore, the current study sought to 1) determine the daily and cumulative workload for common ITPs using elbow varus torque (EVT), and 2) compare EVT experienced during completion of ITPs to game pitching EVT values. Methods: A retrospective review identified high school pitchers with at least 50 throws at distances of 90, 120, 150, and 180 feet plus game pitches while wearing a MotusBASEBALL sensor. Averages for EVT per throw and torque per minute were calculated at each distance. Three throwing programs were created using a template of one phase at each distance with two steps per phase (Table 1). Programs varied only by number of throws per set (20, 25, and 30 throws for Programs A, B, and C, respectively). Total EVT for each step, phase, and program were calculated using average EVT values for each distance. Total torque for each step and program was converted to an average inning pitched equivalent (IPE) and maximum pitch count equivalent (MPE), respectively, using pitching EVT values and expected average pitch counts (16 pitches/inning and maximum 105 pitches/game). Results: 3,447 throws were analyzed from 7 pitchers with an average age of 16.7 yrs (0.8 yrs SD). EVT progressively increased with distance (range 36.9-45.5 N·m), comparable to game pitching (45.7 N·m). Average torque per minute was highest for 90 ft throws (193.4 N·m/min) and lowest for game pitches (125.0 N·m/min). Program A demonstrated the lowest range of IPE per step (2.0-3.7), and Program C had the highest range (3.0-5.6) (Figure 1). The phases of Program A never exceeded 1MPE. Program B exceeded this threshold after phase 1, and Program C exceeded 1MPE at every phase (Figure 2). Total program MPE ranged from 3.5 (Program A) to 5.2 (Program C). Conclusions: Performing long-toss throwing led to greater torque per minute compared to gameday pitching. Additionally, ITPs requiring 25 or more throws per set led to increased cumulative EVT, especially at distances greater than 150 ft, which can exceed typical values from gameday pitching. ITPs should be adjusted accordingly to encourage a slower pace of long-toss throws and less than 25 throws per set, especially at distances greater than 120 ft. Most ITPs currently recommend one rest day between steps. However, cumulative EVT at longer distances can exceed 5 IPE. Most pitch count rules require more than one rest day after a pitching outing that exceeds multiple innings pitched. Therefore, days off between steps and phases of an ITP should reflect these demands. We advocate for multiple days off between steps that require more than 3 IPE. Table 2 presents a novel ITP based on the findings of the current study.