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This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

The purpose of this study is to examine how five different multi-level governance (MLG) models affect place branding (PB) performance in Saudi Arabia.

In hierarchical administrative systems, central governments exert control on PB, influencing its effectiveness. While PB as such is widely studied, the effect of MLG on PB performance in centralized administrative systems remains understudied. The study is approached as a multiple case study of nine cities.

The study reveals that different MLG models indeed affect PB performance differently. Direct access to central leadership and resources boosts branding performance, while privatization promotes flexibility with similarly positive effects. Study findings, furthermore, show that some cities are considered too big to fail. Cities such as Riyadh and Neom are of prime importance and receive plenty of resources and leadership attention, while others are considered peripheral, are under-resourced and branding performance suffers accordingly. Emerging differences in PB performance associated with different MLG models are thus likely to deepen the gap between urban economic winners and losers.

This paper introduces five MLG models based on the actors involved in PB, their interactions and their access to resources. For each model, this paper assesses other factors which may influence the effectiveness of PB as well, such as access to the national leadership and staff capacity. This research thereby adds to the literature by identifying specific factors within MLG models influencing PB performance in hierarchical administrative systems.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

It has been a tremendous challenge for organisations to navigate and survive the COVID-19 pandemic. This paper proposes a novel multi-level conceptual model, based on the resource-based view (RBV), to address the pandemic crisis and highlight the importance of strategic human resource management (SHRM) in promoting employee motivation and organisational resilience. The paper aims to provide insights and practical guidance for organisations to leverage their internal resources and capabilities to thrive in the post-pandemic landscape.

This paper conducted a comprehensive literature review of the existing research on the impact of the COVID-19 pandemic, SHRM, RBV, job satisfaction, turnover intentions and organisational resilience, covering the main contributors to this research stream and their findings.

The conceptual model presented in this paper offers five testable propositions to examine the impact of the COVID-19 pandemic on employee motivation and organisational resilience and how organisations can navigate challenging times through SHRM, which enables RBV. It highlights the foundational role of RBV and identifies the resources employed by SHRM as intervening constructs that significantly enhance employee outcomes, including motivation, job satisfaction, turnover intention and organisational resilience.

The proposed model counsels that organisations need to look beyond the straightforward application of SHRM and should enable the RBV climate to protect and uphold employee motivation and organisational resilience. Finally, measurement and analysis implications are presented for future research opportunities and managerial interventions to mitigate the impact of difficult times such as the COVID-19 pandemic.

Currently, there is a lack of research regarding the conceptualisation of the COVID-19 pandemic and its impact on employee and organisational resilience. Therefore, the proposed model discusses the significance of the alignment of SHRM and RBV as a moderator of the relationships between the impact of COVID-19 pandemic, employee motivation and organisational resilience.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The present study aims to examine the moderating impact of governance quality on the tourism poverty nexus using a panel of six South Asian Association for Regional Cooperation (SAARC) countries during the period 2002 to 2019.

For the soundness of the results, fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) econometric models were applied to determine the long-run relationship.

The findings confirmed the positive and significant impact of tourism development (international tourism arrival) and governance quality (effectiveness of governmental services) on poverty (per capita household consumption) reduction. Interestingly results confirm that governance quality and tourism development have complementary impacts on poverty reduction.

The present study has twofold contributions; First, despite the high potential of SAARC tourism, research remains limited in studies examining the role of tourism and governance quality on poverty reduction within the SAARC region. As a result, the present paper presents critical insights into the impact of tourism inflow and governance quality on poverty reduction in South Asian countries. Second, to the best of the author's knowledge, this is the first attempt to conduct an econometric analysis to examine the role of governance quality on the relationship between tourism inflow and poverty reduction in SAARC countries.