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Global brands have become increasingly vulnerable to external disruptions that have negative spillover effects on consumers, business and brands. This research area has recently garnered interest post-pandemic yet remains fragmented. The purpose of this paper is to recognize the most impactful exogenous brand crisis (EBC) and its affective and behavioural impact on consumers.

In Study 1, we applied repertory grid technique (RGT), photo elicitation method and ANOVA comparisons, to identify the most significant EBC, in terms of repercussions on consumer purchases. In Study 2, we performed collage construction and content analysis to ascertain the impact of the identified significant crisis (from Study 1) on consumer behaviour in terms of affective and behavioural changes.

Study 1 results reveal Spread-of-diseases and Natural disaster to be the most impactful EBC based on consumer’s purchase decisions. Study 2 findings uncover three distinct themes, namely, deviant demand, emotional upheaval and community bonding that throws light on the affective and behavioural changes in consumer behaviour during the two significant EBC events.

The collated results of the two studies draw insights towards understanding the largely unexplored conceptualisation of EBC from a multi-level (micro-meso-macro) perspective. The integrated framework drawn, highlight the roles and influences of different players in exogenous brand crisis management and suggests future research agendas based on theoretical underpinnings.

To the best of our knowledge, this is the first study which identifies the most important EBC and explicates its profound impact on consumer purchase behaviour, providing critical insights to brand managers and practitioners to take an inclusive approach towards exogenous crises.

This study aims to examine the influence of two significant product labelling strategies (geographical indication [GI] vs country-of-origin [COO]) on shaping customer product attitude and purchase likelihood, considering consumers’ ethnocentric and cosmopolitan tendencies. The authors also investigate the boundary conditions and intervening mechanisms to manage the adverse consumer product evaluations and present mitigating procedures which reinstate favourable product evaluations and purchase likelihood.

The collected data from these all these studies were analysed using ANOVA and mediation anlaysis. The study tests the proposed hypotheses using three follow-up experimental investigations.

The study found that GI (vs COO) labels have a more significant impact on customers’ product evaluation and likelihood of purchase and supported the dispositional effect of ethnocentric and cosmopolitan inclinations. Further, the results indicated that self-product congruence can efficiently regulate consumer dispositions. Also, the results confirmed the significant impact of product identification on influencing consumer attitudes.

The above-said insights add practical insights, particularly concerning product labelling. Also, the insights on product attitudes and purchase likelihood intricacies in the context of product labelling enable companies to comprehend better the significance of GI labels, COO labels and self-product congruence.

To the best of the authors’ knowledge, this is the first time a study has compared the role of two significant product labelling strategies (GI vs COO) in shaping customer product evaluations, confirmed its boundary conditions and shown how to transform them into helpful customer product outcomes.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

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.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

For close to two decades, the West African Monetary Zone (WAMZ) has been preparing to launch a second monetary union within the ECOWAS region. This study aims to determine the impact such a unionised monetary regime will have on financial stability as represented by the nonperforming loan ratios of Ghana in a counterfactual framework.

This study models nonperforming loan ratios as dependent on the monetary policy rate and the business cycle. The study then used historical data to estimate the parameters of the nonperforming loan ratio response function using an Autoregressive Distributed Lag (ARDL) approach. The estimated parameters are further used to estimate the impact of several counterfactual unionised monetary policy rates on the nonperforming loan ratios and its volatility of Ghana. As robustness check, the Least Absolute Shrinkage Selection Operator (LASSO) regression is also used to estimate the nonperforming loan ratios response function and to predict nonperforming loans under the counterfactual unionised monetary policy rates.

The results of the counterfactual study reveals that the apparent cost of monetary unification is much less than supposed with a monetary union likely to dampen volatility in non-performing loans in Ghana. As such, the WAMZ members should increase the pace towards monetary unification.

The paper contributes to the existing literature by explicitly modelling nonperforming loan ratios as dependent on monetary policy and the business cycle. The study also settles the debate on the financial stability cost of a monetary union due to the nonalignment of business cycles and economic structures.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

The dissemination of misleading and false information through media can jeopardize a company’s reputation, thus posing a threat to its stock and performance. Institutional investors are known to influence capital markets. Therefore, this paper investigates whether institutional investors engage in shaping the media sentiment stock nexus, stabilize company stocks and enhance performance.

We first investigate the effect of media sentiment on market reactions by using panel regression models. To examine the role of institutional investors, we design a quasi-experiment by exploiting the Financial Crisis of 2008 and go further by examining the heterogeneity across levels of institutional ownership. Due to risk-averse, investors may respond asymmetrically to pessimistic and positive sentiment. Accordingly, we split the sample into two sub-types, good news and bad news, based on keywords representing positive or negative content.

We find supportive evidence that institutional investors have impacts on how the markets react to media news, and the impacts are heterogeneous in the face of bad and good news. We conjecture that institutional investors act as a stabilizer of stock prices through media sentiment management.

This paper confirms the distinctive effects of institutional investors on capital markets, and uncovers the behind-the-scenes intervention and possible causal link running from institutional investors to media sentiment management. It contributes to the broad field of institutional investors' behavior, media news involvement in capital markets and market efficiency.

Despite the widespread prevalence of share pledging by Indian promoters, this area remains out of the researchers’ purview. This study aims to bridge this research gap by delineating the impact of promoter share pledging on future stock price crash risk and financial performance in India.

A sample of 257 companies listed on the Standard and Poor’s Bombay Stock Exchange 500 (S&P BSE 500) Index has been analysed using panel (fixed-effects) data regression methodology over 2011–2020. Further, alternative proxies for crash risk and financial performance are adopted to ensure that the study’s initial findings are robust. Finally, the instrumental variable with the two-stage least squares (IV-2SLS) method has also been employed to alleviate endogeneity concerns.

The results suggest a significantly positive relationship between promoter share pledging and future stock price crash risk in India. Conversely, this association is significantly negative for future financial performance. Moreover, the results hold, even after including alternative proxies of stock price crash risk and financial performance and addressing endogeneity concerns.

Owing to the sizeable equity shareholdings of the promoters, share pledging has remained a lucrative source of finance in India. Despite the popularity, the findings of this study question the relevance of share pledging by Indian promoters considering its impact on aggravating future stock price crash risk and deteriorating future financial performance.