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The purpose of this study is to propose and test a theoretical model investigating factors that influence travelers’ intention to use travel-related information shared by other travelers through a novel category of travel-focused mobile applications, referred to as travel-tracking mobile applications (TTMAs).

Data collected from mobile app users who traveled for leisure purposes were used to test an extended version of the Unified Theory of Acceptance and Use of Technology 2, adding trust and privacy concerns as new constructs to the existing framework.

The current study found empirical and significant evidence that performance expectancy, effort expectancy, hedonic motivation and trust positively; and system privacy negatively influenced travelers’ intention to adopt TTMA to follow other travelers’ experiences.

Limitations and directions for future research are widely explored in the final section of this study.

Practical implications for tourism, travel and hospitality technology companies, TTMA developers and entrepreneurs of travel-related and/or location-based mobile applications are discussed following the empirical findings of this study.

The current study findings add to the body of knowledge in the field of travel technology acceptance, and provide practical implications, especially for entrepreneurs and tourism technology vendors.

本研究提出并测试了一个理论模型, 以探讨影响旅行者使用由其他旅行者提供的旅行相关信息的意图的因素。该信息通过一种名为“旅行追踪移动应用程序（TTMA）” 的新型旅游应用程序提供。

本研究从休闲目的旅行的移动应用用户处收集数据, 测试了扩展版的“统一技术接受与使用模型 2（UTAUT2）”, 并在现有框架中新增了信任和隐私担忧作为构建要素。

本研究发现的显著证据表明, 绩效预期、努力预期、享乐动机和信任对旅行者采用TTMA跟随其他旅行者体验的意图有正向影响; 而系统隐私对其意图有负面影响。

根据本研究的实证发现, 旅游、旅行和酒店技术公司, TTMA开发者, 以及与旅行或基于位置的移动应用相关的企业家可以从中获得实际应用启示。本研究的结果丰富了旅游技术接受领域的知识体系, 并为企业家和旅游技术供应商, 尤其是旅行科技企业家, 提供了实际应用指导。

This study aims to investigate preschool teachers' perspectives on diversity in the unique educational context of Türkiye, a nation-state often underrepresented in the predominantly Western-centric diversity literature.

Recognizing teachers as critical content integrators, the study involved semi-structured interviews to explore their views on diversity within early childhood education.

Analysis of the interviews revealed two key themes: first, the challenge of maintaining nation-state ideology, where teachers balance fostering a national identity with acknowledging diverse cultural backgrounds; and second, a willingness among educators to embrace diversity, highlighting their efforts to create inclusive, multicultural classrooms.

The authors call for developing context-specific teacher education programs addressing diversity in depth, equipping teachers to effectively promote context-specific multicultural education that would be different from Western countries.

To the best of the authors’ knowledge, this study is original in researching preschool teachers’ understandings of multicultural education in a nation-state country.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

This study aims to understand the impacts of Covid-19 on the progression of digitalization of banks in an emerging market. For this purpose, business model canvas (BMC) is used as a theoretical framework to explore these effects on each business elements of Turkish Banks’ business strategies.

Data are collected through structured interviews with the top managers of seven diversified banks. Interview questions are designed based on BMC.

The results show that the onset of the Covid-19 is a shock that has made digitalization a strategic issue that necessitates an urgent change in many business elements of banks such as customer relationships, communication channels, resource allocation, partnerships and financing. Further, it has stimulated redefining value proposition and collaboration/interaction among all financial institutions through digital platforms.

BMC can be used to explain decision-making and business processes of banks for exploring the effect of recent and/or unexpected developments in the business environment of an emerging economy. The results provide insights and recommendations to managers of financial institutions into the impacts of Covid-19 on banks’ operational and strategic processes. That allows financial institutions, including Fintechs, to use this information for taking precautions and proactive actions against shocks.

This study is an initial attempt to explore the impacts of the Covid-19 on banks in an emerging economy by using BMC. With that, this study contributes to the literature by explaining the effect of progression of digitalization in banking from a strategic business model perspective using a qualitative research method.

This study aims to propose a novel method for the conflict detection and eradication of autonomous vehicles which has predetermined routes to establish multi pickup and delivery tasks according to task priorities and vehicle capacity status on each pickup and delivery nodes in assembly cells in the automotive production.

In the designed system, the routing of autonomous vehicles (AVs) and scheduling of pickup and delivery tasks are established in production logistics. Gantt chart is created according to vehicle routes, and conflicts are detected using the proposed conflict-sweep algorithm. The proposed conflict-solving algorithm eliminates conflicts on intersections and roads by considering vehicle routes and task priorities.

In many production systems, there is a need to obtain flexible routes in each pickup delivery task group that changes during day, week, etc. Proposed system provides remarkable advantages in obtaining conflict-free routes for pre-scheduled multi transport tasks of vehicles by considering efficiency in production systems.

A novel method is proposed for the conflict detection and eradication of AVs. Proposed system eliminates conflicts on intersections and roads by considering pre-planned vehicle routes for a fleet of heterogeneous AVs. Unlike most of the other conflict-free algorithms, in which conflicts are solved between two points, proposed system also considers multi pickup and delivery points for AVs. This is pioneering paper that addresses conflict-free route planning with backhauls and scheduling of multi pickup and delivery tasks for AVs.

This study aims to reveal new information about the relationship between energy consumption and economic growth for the time-varying causality.

Economic growth and renewable and nonrenewable energy consumption data of the G7 countries (Canada, France, Germany, Italy, Japan, the UK and the USA) for the 1980–2016 period were used in the study. The nonasymmetric causality test developed by Hacker and Hatemi-J (2006) and both traditional and time-varying forms of the asymmetric causality test by Hatemi-J (2012) were used as the study method.

While the study favors feedback hypothesis for renewable energy consumption in the nonasymmetric causality tests in the UK economy, it favors the same hypothesis for nonrenewable energy consumption in the US economy. However, according to the results reported by Hatemi-J (2012), the feedback hypothesis, which is supported for the UK, is supported only in positive shocks, yet not for each period of analysis. Similarly, feedback hypothesis, which is supported in the USA, is supported only in the negative shocks, yet not for each period of analysis.

This study examined that the asymmetric causality relationship between variables can be analyzed in time-varying form. Therefore, whether positive and negative shocks in renewable and nonrenewable energy consumption always provide useful information in estimations about economic growth is analyzed.

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.