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Capable service employees are increasingly scarce and costly. Many organizations opt to partially replace, support or augment human employees with AI systems. This study builds a framework to help managers map and understand the challenges of crafting a service climate that fosters synergies between AI and human employees, where customers require value-added, personalized and excellent service.

This conceptual article identifies barriers and facilitators of building a service climate for organizations using both human and AI-based employees through an eclectic review of relevant literature.

A conceptual framework is built, and a future research agenda is brought forth.

By identifying barriers and facilitators for AI–human synergies in service settings, this article clarifies how AI can be made to complement human employees, especially in delivering personalized, value-added services, while also highlighting knowledge gaps.

This study provides a practical framework for integrating AI into the workforce. It offers insights into addressing challenges in creating a service climate that combines human and AI capabilities to maintain service excellence. Identifying key barriers and facilitators, the framework guides managers to improve efficiency and customer satisfaction in a rapidly changing service landscape.

This research offers insights on incorporating AI to address labor shortages while maintaining high-quality, personalized service. It provides a pathway to improving service experiences, especially in sectors facing staffing challenges from an aging population.

This research builds on Bowen and Schneider’s (2014) seminal service climate framework to account for a mix of human and AI-based employees.

The purpose of this paper is to present an improved winding function theory (IWFT) for performance analysis of surface mounted permanent magnet (SMPM) motors, which can precisely and simultaneously consider the impacts of stator slotting, the winding distribution, the magnetic flux density within PMs because of the armature reaction, the PM magnetization angle and the magnetic saturation,.

To obtain this improved analytical model, the conformal mappings (CMs) are introduced to calculate the relative complex permeance of slotted air-gap, which is used to obtain the function of slotted air-gap length. The equivalent magnetizing current model is used to extract the equivalent winding function for each PM pole. For retaining the basic assumption of WFT, the magnetic saturation is also considered by a proper increase in the air-gap length in the front of the stator teeth.

A new hybrid analytical model (HAM) based on WFT is presented in this paper, which can simultaneously and accurately consider the effects of slotting, the magnetic saturation, the variation of PM operating point and the winding distribution. In fact, IWFT removes all the drawbacks of the conventional WFT. Moreover, IWFT is more user-friendly and faster than other analytical and numerical techniques.

The obtained HAM can be used for design, optimization and fault diagnosis in electric machines.

This paper presents a new HAM for accurate modeling the SMPM motors, which includes different considerations of electromagnetic modeling. This new HAM can also be used for modeling the other electric motors.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.