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The study aims to examine the joint effect of service design (SD), information and analysis (IA) and organizational performance through the mechanism of organizational excellence.

To achieve the goal of this study through the hypothesized model, a survey questionnaire research design was employed. The data were collected from Dubai Police organization. Out of 550 questionnaires, 320 questionnaires were returned. The partial least square structural equation modeling (PLS-SEM) approach was used to analyze the data for measurement and structural models.

The statistical results confirmed the positive and significant effects of IA on organizational excellence and excellence on organizational performance. The mediation role of organizational excellence between IA and organizational performance was also confirmed.

Throughout this study, further details and valuable implications have been discussed. Findings provide several practical implications. Findings also help practitioners and managers make proper decisions when implementing SD, IA and excellence practices in their organizations. With the joint effect of SD, IA and organizational excellence, organizations can achieve maximum strong performance and remain in a competitive market.

This study is a unique empirical research study that examines the joint effect of SD, IA and excellence on performance relationships within the public sector in general and police organization in particular, which is limited especially in research of Middle East countries.

With the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective.

With 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility.

Results show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization.

The findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others.

This study differs from previous works in a number of respects, particularly, in that to the best of the authors’ knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.