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This study aims to evaluate the smart destination potential of the Şile district by identifying its strengths, weaknesses, opportunities and threats related to smart city applications.

A comprehensive SWOT analysis was conducted using data from participants and relevant documentation to assess the feasibility of smart tourism initiatives in Şile.

The analysis reveals that Şile, due to its proximity to Istanbul, has significant potential to develop into a smart tourism destination. Key strengths include its natural attractions, while challenges revolve around infrastructure limitations.

The study is limited to the Şile district and may not be generalizable to other regions. Future research could expand the scope to include comparative studies with other potential smart tourism destinations.

This research contributes to the growing body of literature on smart tourism by offering strategic recommendations tailored to the unique context of Şile, a district with significant but underutilized potential.

The purpose of this study is to evaluate an intelligent office building based on the post occupation evaluation (POE) criteria in relation to physical and psychosocial user needs.

Data were collected using a questionnaire. The first group of questions was to determine demographic information; the second group of questions includes questions, which aim to measure the comfort level of the building users. The questionnaire also includes open-ended questions on other comments and suggestions. The data of the research were evaluated with the SPSS program and frequency and percentage distributions were determined. A total of 110 employees participated in the survey.

The results showed that the participants were generally satisfied with the design quality, indoor air quality and building support services quality of the building, while there were also participants who report dissatisfaction about the floor covering materials and ventilation of the spaces. At the end of the study, attention was drawn to the importance of POE in connection with user requirements.

It is considered that this study will shed light on future works by developing a perspective on intelligent office buildings in terms of user requirements and research process with the results obtained.

This study aims to fill the lack of quantitative studies of user preferences in quasi-public spaces to observe the use of quasi-public spaces by questioning the contemporary needs of urban communities and to develop design strategies accordingly.

Within the scope of this study, public space design elements affecting users' preferences in the quasi-public spaces of the Podium Park shopping center in Bursa, Turkey were evaluated. By considering the spatial characteristics of the study area, 4 main and 15 subcriteria were determined and utilized by analytic hierarchy process (AHP). These criteria were evaluated by experts and locals with a participatory approach.

According to the obtained results, “events” (S2), “sun/shade” (C2), “safety” (P3) and “planting” (U4) subcriteria were determined as the vital elements for quasi-public spaces.

Although the concept of quasi-public space has been discussed for nearly 30 years, it has been observed that there are no quantitative studies to determine the criteria of user preferences in these open spaces in the literature. This study is the first quantitative research for user preferences in quasi-public spaces and there is no previous study on this subject and study area in Turkey.

The main purpose of examining innovation development (ID), technology management (TM) and big data analytics capability (BDAC) from the perspective of information technology companies is to help these companies optimize their business strategies and increase their competitiveness. When these concepts are considered together, it is aimed to present suggestions that information technology companies can increase their innovation capacities, optimize their technology portfolios and develop their big data analytics capacities.

Data were collected from information technology companies working on big data analytics in technoparks in Istanbul. In the research, the Marmara region of Turkey was preferred because it is the region where the information technology sector is most common. In total, 503 questionnaires were collected. SmartPLS (4.0.8.4) licensed software was used in the research, and the results are presented with tables and figures.

As a result of the analysis of the data, it is supported by hypotheses that ID and TM have positive effects as independent variables and BDAC has positive effects as both independent and mediation variables.

In terms of the limitations of the research, since the data were collected only from the information technology companies in the technoparks in Istanbul, it would not be correct to generalize the analysis results. For this reason, it is recommended to develop a research model and contribute to the literature by considering this limited situation for similar studies to be conducted in the future.

By focusing on ID, it is important for companies to analyze their innovation processes and increase their ID capacity. On the subject of TM, analyses help companies identify their current technological infrastructure and development needs and optimize their technology portfolios. Big data analytics is an important tool that companies can use in their decision-making processes. Therefore, analyses of big data analytics can evaluate companies' current data analytics capacities and offer improvement suggestions.

So why are ID, TM and BDAC important? Why should a research model be developed to examine the effects of these variables? This situation can be understood by looking at the investments made by two world-class companies with headquarters in Istanbul/Turkey. L'Oréal Turkey integrates big data, cloud computing, artificial intelligence and digital platforms into its business processes by investing in new technologies and also makes a difference with innovation in environmental sustainability and social responsibility. PepsiCo, on the other hand, placed a great emphasis on innovation by opening its third Design and Innovation Center in Turkey and Europe in Istanbul. For this reason, examining the effects of ID, TM and big data analytics together in the research is important for the originality of the research. Examining these variables by focusing on their interactions and effects increases the originality of the subject.