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The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

This article seeks to offer an answer. It explores the criteria on which commercial property market participants can develop strategies in hugely challenging circumstances. For this purpose, a survey-based approach was developed with work conducted with property-market professional in the United Kingdom (UK), France, Germany and Sweden to produce a criteria-based tool supporting adaption to changing market circumstances.

The data have been analyzed using statistical analysis. The data's statistical analysis included Cronbach's alpha's application to evaluate the respondents' replies' reliability. A entral tendency test was used to identify the means of relevance of the criteria. The Mann–Whitney U test was used to determine potential material differences between the UK and other countries with Bonferroni corrections applied to minimize type-I errors.

Thirty characteristics have been identified that impact the dynamics of the commercial property market. Their relevance to the commercial property market was determined using a survey. The literature analysis showed that the researchers paid more attention to quantitative criteria and their comparison. The survey showed that the relevance of criteria to the commercial property market dynamics is unequal. However, the survey results showed that it is most important to pay attention to emotional criteria to adapt to uncertainty changing conditions. The problem of the environment has been on the agenda for the last four decades. Therefore, the fact that the results of the study showed that the environmental criteria are the least significant is unexpected.

The study involved economically developed countries of Europe. Extending the study's geographical scope would be valuable in revealing whether the same differences exist in other geographical areas (such as Australia or the USA).

The practical implication of the analysis may be to facilitate the decision-making process of either selecting a country for commercial property investment or selecting the most sensitive and relevant criteria for the decision-making.

Criteria for commercial property market performance which promote successful property investment have been developed. Moreover, the criteria affecting the commercial property market have been weighted by their relevance to the market and their sequence of relevance has been established. And finally, the developed criteria have been placed into five groups that could serve as a foundation for a macro-level assessment of commercial property market dynamics.

This study aims to investigate the comparative importance of factors influencing the customer shift behavior from conventional to Islamic banking for consumer finance in Pakistan.

First, a comprehensive analysis of the existing literature was conducted to identify a broad range of factors related to customer shift behavior. Through an expert sampling, 14 essential factors were chosen for further investigation. Second, a questionnaire was developed using the analytical hierarchy process (AHP). This questionnaire was then distributed among customers who had previously been using conventional banking services but had made a shift toward Islamic banking. The purpose of this questionnaire was to gather data and insights regarding their motivations and decision-making process behind the shift, and a sample 215 customers are taken in the study.

The results of AHP depicts that the religiosity is a most important factor influencing customers to shift from conventional to Islamic banking, and the second most important factor is pricing. The other subsequent important factors are reputation of the bank, marketing and promotion, service quality, behavior of banks staff, Shariah compliance, management, convenience, fastness and charges/fees. Whereas documentation, ambiance and recommendation are found least important factors to patronize Islamic banking.

The study recommends Islamic banks to create awareness, concentrating on religious factor to have a greater impact on growth of Islamic banking and shrinking of conventional banking. Further, it suggests Islamic banks to apply Shariah-recommended approach of doing business, to help community in best possible way and to launch differentiated marketing techniques to attract customers. It also proposes regulatory authorities to provide facilitation to Islamic banking business by providing level playing field similar to conventional banking, tax equality and conversion of public financing from conventional banking to Islamic banking.

The originality of this study lies in its comprehensive analysis of factors influencing consumer shift behavior from conventional to Islamic banking in the context of consumer finance in Pakistan. By using the AHP, the study provides a structured approach to understanding the relative importance of these factors. This is the uniqueness of the paper that it applies the AHP for the analysis. Furthermore, the study offers practical implications for Islamic banks and regulatory authorities to effectively address and capitalize on this consumer shift trend.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

Showrooming presents a complex and evolving challenge to retail managers, as it signifies the emergence of new forms of exchange rules. The purpose of this research is to investigate how factors responsible for information search and evaluation affect showrooming and also consider the consumer mindset as a moderator.

This research undertakes three experimental designs to investigate how the push (i.e. assortment size), pull (i.e. price discount), and mooring (i.e. sunk cost) factors influence consumers' showrooming intention. Specifically, consumers' maximizing tendency plays the role of moderator.

The results reveal that push, pull, and mooring factors are significantly related to consumers' showrooming intention. Furthermore, the findings show that maximizers have higher showrooming intention than satisficers in the context of the push, pull, and mooring factors.

By integrating the push-pull-mooring framework and the maximizing mindset theory, this research proposes a novel research model and the empirical testing results support six hypotheses. The findings add to the body of knowledge in showrooming behavior by taking consumer mindset into account. The results also provide implications for practitioners to develop their retail strategies.

The promulgation of group purchasing organizations (GPOs) into the healthcare (HC) sector is an invaluable procurement strategy to manage the suppliers effectively. This study aims to identify and prioritize the factors of integrating GPOs into the HC sector on the perspectives of the developing countries such as India.

The factors are identified from current literature exploration, experts’ support and experience surveys. The factors are scrutinized and shortlisted using the Delphi technique and analysed further using the best-worst model method.

The findings of the study highlight the cost reduction, fair distribution of savings and healthcare supply chain (HCSC) data standardization among others to be the most prioritized drivers. The consulting services provided by GPOs including training and development as a result of high competitiveness in the HC market has been prioritized the least.

The study bears some important implications for decision and policymakers. The managers should consider factors, namely, cost reduction, fair distribution of savings and HCSC data standardization on a priority basis that acts as motivation for the HC providers to join the GPOs.

The study provides valuable insights for HC providers to participate in the GPOs for cost savings and enhance the performances.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.