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This study aims to report the descriptive statistics of cost-system obsolescence (CSO) in the Turkish hotel industry and contingent factors that affect the CSO.

To validate the reflective measurement model, first, an exploratory factor analysis in statistical package for social sciences was conducted. Then, a confirmatory factor analysis using maximum likelihood in analysis of moment structures was performed to establish the reliability and validity of the construct measurements.

The results demonstrate that CSO symptoms did not occur frequently in sample Turkish hotels. Turkish hotels were found to operate in intense competition and a significant relationship between competition intensity (CI) and CSO takes place. The hospitability industry has been perceived as rather uncertain. However, perceived environmental uncertainty (PEU) had no significant effect on CSO, and there was no mediation effect on the relationship between CI and CSO. Turkish hotels were found to perform market-orientated activities at a mediocre level. However, no significant relationship was found between market orientation (MO) and CSO. Additionally, Turkish hotels with decentralized decision-making structures and “prospector-type” strategies were found to have less CSO. On average, Turkish hotels have centralized decision-making structures and pursue “analyzer-type” strategies.

This study contributes to previous literature related to accounting information in the hotel industry. It also aims to give additional insight into Turkish hotels’ competitive environment, MO activities, decision-making structures and strategies and their association with CSO.

The study examined CSO in the Turkish hotel industry and factors that affect CSO. The study results provide additional insight into Turkish hotels’ competitive environment, MO activities, hotel structure and strategy. A new model to test CSO was created and validated using structural equation modeling (SEM).

The purpose of this paper is to show how intelligence techniques have been used in information management systems.

The results of a literature review on intelligence decision systems used in enterprise information management are analyzed. The intelligence techniques used in enterprise information management are briefly summarized.

Intelligence techniques are rapidly emerging as new tools in information management systems. Especially, intelligence techniques can be used to utilize the decision process of enterprises information management. These techniques can increase sensitiveness, flexibility and accuracy of information management systems. The hybrid systems that contain two or more intelligence techniques will be more used in the future.

The intelligence decision systems are briefly introduced and then a literature review is given to show how intelligence techniques have been used in information management systems.

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.

Along with accounting scandals in the past, academics, researchers, and legislators have focused on fraud. The purpose of this study is to examine postgraduate and doctoral studies, articles, and books about forensic accounting and fraud audit published between the years 2008 and 2018 in Turkey. For this purpose, a total of 96 studies have been examined and 35 of these are master’s theses, 10 of them are PhD theses, 45 of them are articles, and six of them are books. These studies were presented in tables as classified. The studies examined in our research are summarized as year they were published, the author, and the scope of the topic and in terms of results. The conclusions of this study can be summarized as follows: (a) the majority of thesis published about forensic accounting and fraud audit are in 2011 and following years. In addition, most of the theses are focused on forensic accounting review rather than fraud audit. (b) Results in the articles reviewed are in the same direction with theses. (c) There are very few books about fraud audit and forensic accounting. One of them is related to fraud audit, while the rest of them are related to forensic accounting and forensic accounting profession. We suggest extending the scope of the study and making to other countries.