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The young population in Turkey is gradually increasing. Generation Y, which comprises the people born between 1980 and 1999 (Broadbridge et al., 2007) and free-spirited and tech-savvy, forms a large part of the population of the world, especially Turkey, and is of great importance to the housing sector for their home-buying preferences. In this study, housing preferences of students in Turkey’s two socio-economically different universities were comparatively analysed through quantitative methods.

A survey was simultaneously distributed among students of two universities. The survey consists of six main factors: “reliability”, “economic opportunities”, “transportation opportunities”, “quality of life and social opportunities”, “quality standards”, and “technological opportunities”, with 25 statements. The questionnaire was developed through a comprehensive literature review and the opinions of university stakeholders.

Results showed that the structure of the family and socio-economic differences affect home-buying preferences. The Mann–Whitney U test indicated that there was a meaningful difference of opinion between students of two universities. Munzur University students paid attention to economic opportunities when buying a home. Additionally, there was a meaningful relationship among the age groups in factors of “having a parking place” (p = 0.026) and “having a playground” (p = 0.026). As the age increases, students desire a playground around their future home.

The most important limitation of this study is the non-parametric data. Non-parametric data structure and the tests performed accordingly are less preferred than parametric data structure. For that reason, to what extent the results accurately represent Generation Y needs to be assessed through future study. Also, a certain number of sampling could be reached as purposive sampling was used.

This study contributes to the literature in terms of comparatively analysing buying preferences of Generation Y through statistical methods and showing the relationship between these preferences and socio-economic features statistically. Due to the insufficient quantitative research on the literature, this quantitative study was carried future home-buying preferences of Generation Y university students, who will also be actively involved in the housing market. The purpose of this study investigates marketing factors that affect housing preferences of students in Turkey.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.