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Günümüzde küreselleşmeyle beraber yaşanan toplumsal, kültürel ve aile hayatında yaşanan deǧişimler sonucu insanların talep ve ihtiyaçları da deǧişmiştir. Yaşam tarzındaki deǧişiklikler, ailedeki küçülme, bireyselleşme ve tek kişilik yaşama biçimi gibi nedenlerden dolayı konutlarda mekânsal olarak küçülmeye gidilmiş ve günümüzde 1+0, 1+1, 2+1 olarak üretilen küçük konut tiplerinden en yaygın kullanım gösteren 'stüdyo daire' konutların üretimi artmıştır. Bu çalışmanın odaklandıǧı problem Konya'da üretilen küçük konut kullanıcılarının yaşam biçimlerine uygunluǧu ve bu konutları neden tercih ettikleri sorgulanmıştır. Çalışmada varsayılan küçük konut kullanıcılarının demografik özelliklerine göre, küçük konut tip seçimlerinde ve küçük konut özelliklerinde farklılıkların olacaǧı yönündedir. Bu varsayımdan hareketle, Konya'nın Selçuklu ilçesindeki stüdyo tipi konutta 259 küçük konut kullanıcısına bir anket uygulanmıştır. Araştırma sonucunda, bekâr kullanıcılarının en çok 1+1 daire tiplerini, evli bireylerin ise 2+1 daire tiplerini tercih ettikleri tespit edilmiştir. Konuttaki hane halkı sayısı arttıkça 2+1 dubleks daire tipine talebin arttıǧı görülmüştür. Ayrıca kullanıcıların medeni durumlarına göre konutun konumuna ve cinsiyetlerine göre de konuttaki güvenliǧe önem verdikleri tespit edilmiştir. Bu çalışmadan elde edilen sonuçların, küçük konut kullanıcılarının beklenti ve tercihlerini, tutum ve davranışlarını ortaya koyarak küçük konut tasarımının başlangıcında, tasarımcılara ve konut üreticilerine yol gösterici olduǧu düşünülmektedir.

[Purpose/Significance] The development of the Internet and digital economy has brought new opportunities for the development of digital marketing. At the same time, digital marketing is the implementer and forerunner of digital economy. Digital marketing is an important way to promote the digital transformation of enterprises. Therefore, the research of this paper has certain significance for the development of digital economy and digital marketing as well as the digital transformation of enterprises. [Method/Process] In line with the development direction and policy of digital marketing, this paper analyzes the policy direction of digital marketing and reviews and summarizes the enlightenment of the policy. Combined with the development process of the Internet technology, it visually reviews the evolution of digital marketing practice, providing reference for digital marketing research and guidance for the practice of digital marketing. Following the path of user-preference-evaluation-promotion, the research emphases of digital marketing were analyzed and reviewed. [Results/Conclusions] In terms of policy direction, the policy enlightenment of digital marketing can be summarized as follows: (1) Digital marketing should serve the national strategy and accelerate the construction and development of digital economy. (2) Data security belongs to the national strategy, and privacy security is the basis of digital marketing. (3) Connectivity builds a new ecology and new way of digital marketing, and the whole-area marketing reshapes the brand growth. (4) It is suggested to vigorously develop digital marketing to promote the rise of domestic products. In terms of practical development, the practical development of digital marketing is divided into four stages: one-way marketing, interactive marketing, precision marketing and smart marketing. The practical development of digital marketing can be summarized as follows: (1) The application scope of digital marketing is more extensive, involving all aspects of market behavior, such as medical industry, supply chain, and agricultural products. (2) The current focus of digital marketing has changed from the original dissemination of customer acquisition to user operation, the whole process of interaction, user experience, etc. (3) Data can drive marketing. The core of digital marketing is the analysis and mining of data. The current digital marketing practice focuses more on the collection and analysis of user data and the generation of marketing strategies and behaviors driven by data. In terms of research focus, it can be divided into the research of digital community user group, digital community user preference, consumption potential evaluation and development, and digital marketing effectiveness evaluation.

Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated preference method—a quantitative methodology that argues that individuals make trade-offs when engaging in a decision by choosing an alternative of a product or a service that offers the greatest utility, or benefit. This methodology is widely used in health economics in situations in which revealed preferences are difficult to collect but is much less used in the field of digital health. This paper outlines the stages involved in developing a DCE. As a case study, it uses the application of a DCE to reveal preferences in targeting the uptake of smoking cessation apps. It describes the establishment of attributes, the construction of choice tasks of 2 or more alternatives, and the development of the experimental design. This tutorial offers a guide for researchers with no prior knowledge of this research technique.

The wide spread occurrence of geogenic arsenic (As) in Bangladesh groundwater drastically reduced the safe water access across the country. Since its discovery in 1993, different mitigation options tested at household and community scale have resulted in limited success. In an arsenic hotspot of southeastern Bangladesh, 841 arsenic removal filter (ARF), 190 surface water filter membrane, 23 pond sand filter (PSF), 147 rain water harvester (RWH) and 59 As-safe tubewell were distributed among the severely exposed population by AsMat, a Sida supported project. After three-four years of providing these safe water options, this study was carried out during 2010-2011 for performance analysis of these options, in terms of technical viability and effectiveness and thus to evaluate the preference of different options to the end users. Household and community based surveys were done to make an assessment of the current water use pattern as impact of the distributed options, overall condition of the options provided and to identify the reasons why these options are in use and/or abandoned. In total, 284 households were surveyed and information was collected for 23 PSF, 147 RWH and 59 tubewells. None of the filters was found in use. Among other options distributed, 13% of PSF, 40% RWH and 93% of tubewell were found functioning. In all cases, tubewells were found As-safe. About 89% of households are currently using tubewell water which was 58% before. Filter was abandoned for high cost and complicated maintenance. The use of RWH and PSF was not found user friendly and ensuring year round water quality is a big challenge. Arsenic-safe tubewell was found as a widely accepted option mainly because of its easy operation and availability of water, good water quality and negligible maintenance. This study validated tubewell as the most feasible option and holds significance for planning water supply projects, improving mitigation policy as well as developing awareness among users.

Urban green areas, such as parks, are becoming increasingly important in densifying cities. Urban parks encourage physical and social activity, recreation and relaxation, and thus eventually promote people’s well-being. The aim of the current study is to examine which urban park attributes influence the preferences of park users, in order to offer recommendations regarding how urban parks of quality can be designed. To elicit the preferences of park visitors we designed an online stated-choice experiment. Seven park attributes, in particular the number and composition of trees and the presence of benches, side paths, a playground, litter, and flowers, were manipulated in a virtual park. In an online stated-choice task, videos of these park alternatives were presented and the preferences of 697 participants were measured. It is found that especially the number of trees and the presence of flowerbeds, particularly with a diversity of flowers, influenced participants’ preferences. The presence of many benches and a playground were valued as well, but to a lesser extent. The presence of litter was found to be less troublesome than expected. Alternatives with all trees placed in one cluster were disliked. Moreover, significant standard deviations were found for the presence of side paths, a playground, and the absence of litter, which indicates that preference heterogeneity for these attributes exist. In a latent class analysis, two groups were identified, namely a Nature-loving group, who mainly valued the trees and the flowers, and an Amenity-appreciating group, who valued almost all attributes. It can be concluded that natural elements and a variety of flower species are important in an urban park, while facilities are evaluated differently by different groups of people. These findings may support park designers and policymakers in decision-making. Moreover, it illustrates the usefulness of creating a virtual park in environmental preference research.

Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

Recent years have seen a growing focus on automated personalized services, with music recommendations a particularly prominent domain for such contributions. However, while most prior work on music recommender systems has focused on preferences for songs and artists, a fundamental aspect of human music perception is that music is experienced in a temporal context and in sequence. Hence, listeners' preferences also may be affected by the sequence in which songs are being played and the corresponding song transitions. Moreover, a listener's sequential preferences may vary across circumstances, such as in response to different emotional or functional needs, so that different song sequences may be more satisfying at different times. It is therefore useful to develop methods that can learn and adapt to individuals' sequential preferences in real time, so as to adapt to a listener's contextual preferences during a listening session. Prior work on personalized playlists either considered batch learning from large historical data sets, attempted to learn preferences for songs or artists irrespective of the sequence in which they are played, or assumed that adaptation occurs over extended periods of time. Hence, this prior work did not aim to adapt to a listener's current song and sequential preferences in real time, during a listening session. This paper develops and evaluates a novel framework for online learning of and adaptation to a listener's current song and sequence preferences exclusively by interacting with the listener, during a listening session. We evaluate the framework using both real playlist datasets and an experiment with human listeners. The results establish that the framework effectively learns and adapts to a listeners' transition preferences during a listening session, and that it yields a significantly better listener experience. Our research also establishes that future advances of online adaptation to listener's temporal preferences is a valuable avenue for research, and suggests that similar benefits may be possible from exploring online learning of temporal preferences for other personalized services.

Chinese wooden furniture occupies a central role in the nation’s cultural and historical heritage, serving not only as practical household items for period classification, but also as symbols of social status and artistic achievement. Recently, a new wave of Chinese-style furniture that blends traditional design elements with modern aesthetics has gained considerable market attention and recognition. This paper utilizes Conjoint Analysis to thoroughly investigate and assess consumer preferences and the visual appeal of contemporary Chinese-style furniture, leveraging a combination of user experience surveys and eye-tracking technology. This study suggests a sustained social interest in the materiality of Chinese heritage, emphasizing its relevance in today’s culture. The findings show that in subjective evaluations, consumers prioritize material selection. Eye-tracking data reveals that “material,” particularly “redwood,” demands more intensive cognitive processing during the fixation stage. However, “decoration type” plays a dominant role in visual searches across multiple stages, indicating that consumers employ varied cognitive strategies when interacting with different product attributes. Additionally, consumers’ focus on backrests and pattern craftsmanship offers valuable insights into future market trends.

Developing personalized travel recommendation systems is significant for better user experiences. This work introduces an advanced model using Neural Collaborative Filtering (NCF) to address diverse user preferences in travel planning. Traditional collaborative filtering struggles with data sparsity, cold start, and less common items, leading to suboptimal predictions. Our novel NCF model, learned by a neural network, overcomes these limitations and captures complex user-travel relationships. Employing a multi-layer perceptron, it refines predictions based on past interactions and real-time behavior updates. Experiments on real travel data show NCF significantly outperforms traditional methods in accuracy and user satisfaction, advancing personalized travel recommendation by effectively handling data sparsity and preference diversity.

Innovations in the mobility industry such as automated and connected cars could significantly reduce congestion and emissions by allowing the traffic to flow more freely and reducing the number of vehicles according to some researchers. However, the effectiveness of these sustainable product and service innovations is often limited by unexpected changes in consumption: some researchers thus hypothesize that the higher comfort and improved quality of time in driverless cars could lead to an increase in demand for driving with autonomous vehicles. So far, there is a lack of empirical evidence supporting either one or other of these hypotheses. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as indicators for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 participants in Germany. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether conventional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, the findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more emphasis should be placed in making public transport more attractive if sustainable mobility is to be developed.