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The purpose of this article is to examine the impact of digital technology (specifically mobile applications) use on employees' perceptions of motivation at work (MW) and job performance (JP).

Survey data were collected from 4,089 employees working in small and medium-sized enterprises (SMEs) registered to Small and Medium Enterprises Development Organization (SMEDO) in Turkey. The relationships were assessed through structural equation modeling with bootstrap estimation.

The results support the proposed framework illustrating the positive effect of perceived usefulness (PU) and perceived ease of use (PEOU) of mobile applications on employees' perceived JP. Findings indicate that MW has exhibited a mediating effect between both PU and JP and PEOU and JP.

This article discusses the accelerating role of coronavirus disease 2019 (COVID-19) pandemic on SMEs' technology acceptance and the acceptance's positive impact on employees' motivation and performance. This article adds to the literature on SMEs by enabling researchers and practitioners to understand the issues in digital technologies acceptance by SME employees and contributes towards enriching the knowledge on technology acceptance perceptions' role in SMEs coping strategies during the COVID-19.

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.