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This study aims to explore the intricate relationship between housing prices and transaction volumes in Tehran, a city with diverse socioeconomic and regional characteristics. This research addresses a critical gap in understanding the role of local spatial factors, which previous studies have often overlooked, focusing instead on macroeconomic variables.

Using a data set of housing transactions of Metropolitan Tehran from 2010 to 2020 sourced from secondary data, this study uses generalized linear mixed models and spatial clustering techniques. These methods enable an examination of geographical clustering and the effects of local contextual variables on the dynamics between housing prices and transaction volumes.

Results indicate significant spatial heterogeneity within Tehran’s housing market. Higher prices and transaction volumes are concentrated in the northern and western regions, influenced by factors such as employment rates, rental housing supply and the physical attributes of the housing stock. The findings suggest that macroeconomic policies alone are insufficient to address housing challenges in Tehran; targeted, localized interventions are necessary.

This study’s reliance on secondary data and its focus on a single urban environment may limit the generalizability of the findings. Further research incorporating a wider range of local and macro variables could strengthen the applicability of the results across different contexts.

This study underscores the need for region-specific housing policies that consider local economic, social and spatial conditions. Policymakers could improve housing affordability and accessibility in Tehran by implementing tailored strategies to address the distinct needs of different districts.

This study offers a novel perspective by integrating spatial and contextual factors in housing market analysis, providing insights that challenge the traditional macroeconomic focus. The use of advanced statistical and spatial analysis techniques contributes to a deeper understanding of urban housing market dynamics.

The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers.

The purpose of this paper is to investigate the potential influence of the big five personality traits − extraversion, neuroticism, agreeableness, openness and conscientiousness − on the time taken by travelers to submit online reviews after their hotel stay.

The study analyzed 83,235 TripAdvisor reviews from 415 hotels in six major US tourism cities using random forest algorithms and Poisson regression. The research investigated the influence of the big five personality traits on the time taken by travelers to submit online reviews post-hotel stay, merging personality psychology with consumer behavior research through a combination of machine learning and statistical analysis.

The findings reveal significant correlations between certain personality traits and the time taken to post online hotel reviews. Extraversion, neuroticism and agreeableness were found to be negatively correlated with response time, suggesting that individuals scoring higher in these traits tend to submit their reviews more quickly. Conversely, openness exhibited a positive correlation, indicating that those with higher levels of openness tend to delay their feedback. Conscientiousness showed no significant correlation with response time.

This study represents a novel approach to understanding the relationship between personality traits and online review behavior in the hospitality industry. By leveraging advanced machine learning techniques, such as random forest algorithms, alongside traditional statistical methods like Poisson regression, this research offers a unique perspective on the influence of personality on consumer behavior. The innovative application of these technologies to a large data set of TripAdvisor reviews provides fresh insights that can inform the development of personalized customer engagement strategies. The findings contribute to the growing body of literature on the intersection of personality psychology, consumer behavior and hospitality management in the digital age.

本研究旨在探讨五大人格特质（外向性、神经质、宜人性、开放性和尽责性）对旅行者在入住酒店后提交在线评论时间的潜在影响.

本研究分析了来自美国六大旅游城市 415 家酒店的 83,235 条TripAdvisor评论, 采用随机森林算法和泊松回归方法, 探讨了五大人格特质对旅行者在入住酒店后提交在线评论时间的影响。研究将人格心理学与消费者行为研究相结合, 运用了机器学习和统计分析的组合方法。

研究结果表明, 某些人格特质与发布在线酒店评论的时间存在显著相关性。外向性、神经质和宜人性与响应时间呈负相关, 表明具有较高这些特质的人倾向于更快速地提交评论。相反, 开放性与响应时间呈正相关, 暗示开放性较高的人往往延迟反馈。尽责性与响应时间无显著相关性。

本研究通过利用随机森林等先进的机器学习技术和泊松回归等传统统计方法, 为理解人格特质对消费者行为的影响提供了新颖视角。这种创新性技术应用于包含大量TripAdvisor评论的数据集, 提供了新的见解, 可用于开发个性化客户互动策略。本研究成果为人格心理学、消费者行为和酒店管理领域在数字时代的交叉研究增添了新的学术价值。

This study aims to investigate whether eXtensible Business Reporting Language (XBRL) disclosure management solution improves public companies’ earnings release efficiency and mitigates earnings management.

This study adopts a unique survey data set from the Financial Executives Research Foundation 2013 to identify companies’ XBRL implementation strategies. Earnings release efficiency is measured by earnings announcement time lag. Multiple indicators of both accruals- and real activities-based earnings management are adopted to examine the research hypotheses.

The authors find that the disclosure management solution (DMS) XBRL implementation is positively associated with earnings release efficiency for companies with good news. The authors also find that DMS implementation strategy is negatively related to accruals-based earnings management, but positively related to real activities-based earnings management measured by abnormal cash flows.

The results of this study can inform regulators, investors and corporate management on how XBRL adoption is associated with corporate financial reporting.

The study contributes to the XBRL literature by providing empirical evidence on how the strategies adopted by companies to implement XBRL may affect the results of XBRL mandatory adoption.

This study presents a combined artificial neural network (ANN) and multivariate approach for performance evaluation and optimization of gas refineries. This study introduces standard financial and non-financial indicators for performance evaluation of the gas refineries. Data are collected from gas balance sheets and the detailed statistics of gas refineries. Two cases have been considered for performance evaluation. In the first case the financial indicators and in the second case the financial and non-financial indicators are used and tested over five years period. The refineries are evaluated by data envelopment analysis (DEA), principal component analysis (PCA), numerical taxonomy and artificial neural network (ANN). Finally, a complete sensitivity analysis is performed for each stated method. The results show that DEA is more resistant to noise than other methods. Also, there is slight difference between results of financial and combined financial and operational indicators. This suggests the use of combined financial and operational indicators for future practical studies in gas refineries. This is the first study that presents an integrated approach for combined performance of financial and operational indicators in gas refineries.

The purpose of this paper is to examine the need for consensus on a model that encapsulates the relationship between the manager and the effective operational performance of leisure facilities. This is particularly relevant for Australian local government sports and leisure centres.

On‐site and postal questionnaires from 140 managers, 71 peers, and 277 staff were collected and analysed to develop an empirical model of the relationship between Australian sports and leisure centre managers and the performance of their operations.

This study proposes an initial path model of eight predictive variables, including the managerial qualities of the manager and their relationship with the operational performance of their centre.

The proposed model provides a new basis for reviews and changes to current practice in the development of managers, and of professional practice in this context.

Unlike copious studies of managers, this study includes empirical testing of a model that links the relationship between the manager and an organisation's performance; this is relatively rare in the literature.

Multimodal writing portfolios were introduced and integrated into an undergraduate course and a graduate course in a research-oriented university in northwest Taiwan. This study aims to examine the influence of multimodal writing portfolios of novice researchers' academic writing.

Comparative case studies involve collecting data from several cases and analyzing the similarities, differences and patterns across cases (Merriam, 2009). To address this underdeveloped area of research, a comparative case study method was employed to understand undergraduate and graduate students' multimodal writing portfolios in academic writing in two courses in Taiwan.

First, multimodal writing portfolios enabled novice researchers to be more familiar with the structure of academic paper and they had better performance in intrapersonal and linguistic aspects. Second, novice researchers held positive attitude toward multimodal writing portfolios because they regarded process of making multimodal writing portfolios as preparation for their future academic writing. Finally, participants highly valued the class PowerPoint slides, weekly writing tasks and the instructor's modeling as effective facilitation for making multimodal writing portfolios.

Limited studies focus on multimodal writing portfolios (e.g. Silver, 2019). The present case study explores the integration of a multimodal writing portfolio into one undergraduate and one graduate course to explore learners' attitude and performance in academic writing.

Novice researchers can learn to compose multimodal academic texts for the academic writing community.

Suggestions on effective integration of multimodal writing portfolios into academic writing instruction were provided based on the research findings.

The findings of the study provide the field of L2 writing with insights into the pedagogical development of multilingual writing portfolios and help educators to be better prepared for teaching novice researchers to comprehend and compose multimodal texts and enter the academic writing community. The framework in Figure 1 and suggestions on course designs for academic writing can inform educators on the integration of multimodality in academic discourse. Moreover, this study moves beyond general writing courses at the tertiary level and could contribute to L2 writers' deeper understanding of how multimodal writing portfolios can be constructed.

The financial world of today is evolving at a rate that can be challenging to keep up with and comprehend due to developments in information and communication technology. When compared to a conventional disclosure, the eXtensible Business Reporting Language (XBRL), which was named one of the top ten accounting technologies, has a clear advantage in reducing information asymmetry by providing interactive data disclosure. This study aims to examine whether forcing companies to adopt XBRL would cause them to prefer misclassifying income statement items as an alternative to more risky earnings management methods.

The study sample includes nonfinancial UAE companies listed on Dubai Financial Market and Abu Dhabi Securities Exchange from 2012 to 2019. Fixed effect and system General Method of Moments regressions were used to analyze the study data.

The study found that XBRL reporting resulted in lowering the quality of financial reporting as companies have a higher tendency to misclassify income statement items as earnings management mechanism.

The findings of this research can be used by stakeholders and practitioners in the UAE to better understand whether the use of XBRL is linked to the engagement of financial reporting manipulative practices. The findings of this study also inform policymakers and regulators about the consequences of companies formally adopting digital disclosure language in an effort to improve the quality of their reporting. Besides, the results offer guidance to regulators considering imposing XBRL usage regulations.

Limited number of studies have tested the association between XBRL mandatory adoption and misclassification of income statement items as an earnings management tool in the Gulf Cooperation Council region.

eXtensible Business Reporting Language (XBRL) is an internet-based interactive form of reporting language that is expected to enhance the usefulness of financial reporting (Yuan and Wang, 2009). In the UK and the USA, XBRL is mandatory, and in Australia, it is voluntarily adopted. It has been reported that in the not too distant future, XBRL will be the standard format for the preparation and exchange of business reports (Gettler, 2015). Using an experimental approach, this study assesses the usefulness of financial reports with XBRL tagged information compared to PDF format information for non-professional investors. The authors investigate participants’ perceptions of usefulness in relation to the qualitative characteristics of relevance, understandability and comparability.

This paper uses an experimental approach featuring a profit-forecasting task to determine if participants perceive XBRL-tagged information to be more useful compared to PDF-formatted information.

Results reveal that financial information presented with XBRL tagging is significantly more relevant, understandable and comparable to non-professional investors.

The authors address a gap in the literature by examining XBRL usefulness in Australia where XBRL adoption will be mandated within the not too distant future. Currently, the voluntary adoption of XBRL by preparers and users is low, possibly, because of a lack of awareness about XBRL and its potential benefits. This study yields significant implications for the accounting regulators in creating more awareness on the benefits of using XBRL and to create an impetus for XBRL adoption.

While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.

To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.

The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.

In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.