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The purpose of this study is to investigate the Ripple Effect of house prices at an inter-suburban level of analysis in the Sydney metropolitan area. By doing this, more practical information of price transmission can be provided to improve residential real estate purchasing decisions of market participants. Equity from residential real estate is a major component of household wealth and is frequently used to improve and upgrade homes. With the ever-increasing prices of real estate in Sydney making more efficient purchasing decisions can grow this wealth quicker allowing a household to obtain financial related goals at a quicker pace.

Using a two-stage sampling technique strings of adjoining suburbs from different Sydney regions were analysed using a combination of price graphs, Engle–Granger and Johansen co-integration techniques and Granger causality tests.

Pairwise co-integration was lacking throughout the various suburb strings, whereas multivariate co-integration was found in the lower priced areas further from the central business district, as these areas also experience less price volatility. The geographical location of suburbs therefore plays an important role in the ability to predict an individual suburb’s price movements. For a prominent Ripple Effect to exist at this level the best conditions would consist of a singular demand centre with restricted geographical space to which this demand can spread. Causal pathways were subsequently mapped for each suburb string identifying price transmission pathways and confirming support that while the standard Ripple Effect does not exist at an inter-suburban level, it is still possible to predict price movements by considering the price behaviour of surrounding suburbs.

This paper adds to the literature by examining the Ripple Effect across different suburbs in Sydney. This is done via an extensive search through the literature and analysing recent real estate data.

The influence of loyal customers can reach far beyond their proximate impact on the company. This impact is analogous to the ripple caused by a pebble tossed into a still pond. In this article we introduce the loyalty ripple effect construct and define it as the influence, both direct and indirect, customers have on a firm through (1) generating interest in the firm by encouraging new customer patronage or (2) other actions or behaviours that create value for the organization. That is, in addition to their revenue stream, we suggest loyal customers may engage in several behaviours, including word‐of‐mouth communication, that add value to or reduce costs for the firm. In our discussion, we provide some examples to illustrate our point and conduct an exploratory study related to arguably the most salient ripple generator, word‐of‐mouth communication. The paper concludes with managerial implications and provides some suggestions for future research.

Recent studies have argued that companies may actively implement practices to mitigate disruptions in their supply chain and reduce the extent of damage on performance. Other studies have shown that disruptions may propagate in supply chains, leading to consequences that are more negative and raising doubts on the effectiveness of mitigation strategies implemented downstream. This study investigates the influence of supply chain complexity on the two phenomena and their interplay, taking a focal company's perspective.

A systematic procedure for data collection, encoding and aggregation based on incident data mainly from secondary sources was used. Multiple regression models were run to analyse direct and moderation effects involving resilience, distance of impact location from trigger point, and supply chain complexity on weighted performance change.

Supply chain complexity is found to have positive moderation on the ripple effect of disruption. Resilience capability remains to have dominating direct positive effect in mitigating disruptions when supply chain complexity is taken into account.

This study extends the research discourse on supply chain resilience and disruption management with focus on the supply side. It demonstrates that, along with the severity of the disruption scenario, the ripple effect must also be considered when analyzing the benefits of resilience practices implemented by the focal company.

Complexity in the supply chain can only help to smooth-out the rippling effects of a disruption, which go largely beyond supply-demand unbalances and lead time fluctuations. To mitigate it better, the focal company has to act proactively with adequate resilience practices, which also connects to the importance of better visibility across multiple supply chain tiers.

To the best of the authors' knowledge, this is the first study that empirically tests the benefits of resilience practices and the ripple effect of disruptions under the moderation role of supply chain complexity.

This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.

The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.

The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland.

The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.

This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

The European Union (EU) has recently adopted gender quotas for corporate boards (CBQ), anticipating ripple effects on women’s careers in the companies concerned, as well as throughout the economy. The purpose of this paper is to investigate whether CBQ has spurred ripple effects and discuss mechanisms hindering or facilitating women’s occupancy of top executive positions.

Norway was the first country in the world to introduce CBQ in 2003, with full effect from 2008. The policy requires company boards to be composed of 40% of each gender. Drawing on original data mapping boards and executive committees in Norway’s 200 largest companies, the authors analyze the association between CBQ and the gender composition of executive management almost 15 years after the full implementation. The data include both companies covered by the CBQ and large companies not covered.

The investigation does not find a positive association between CBQ and more women in executive positions. Thus, the ripple effect hypothesis of CBQ is not supported. CBQ may have contributed to an increased awareness of gender imbalances, yet these findings indicate that to achieve more gender balance in executive positions, scholars and practitioners may need to focus more on gendered conditions and processes in organizations and society throughout executive careers than on the gender composition of boards.

This paper provides empirical analyses of original data 15 years after the implementation of CBQ. The authors further contribute to scholarly debate by identifying and discussing possible mechanisms that explain how requiring more women on corporate boards may – or may not – have ripple effects on executive management.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

This paper seeks to explore factors that can help generate a bigger ripple for diffusive Word‐of‐Mouth (WOM) in an online environment. Specifically, the paper aims to demonstrate how the characteristics of WOM information influence the ripple effect.

The paper used a survey based on real posts in online discussion forums to collect the data. Structural Equation Modelling (SEM) was utilized for analysis.

WOM as diffusive communication could generate a ripple effect. All of the WOM characteristics, which were investigated (quality, authority, authenticity and interestingness), have a positive effect on resenders' acceptance toward WOM, which, in turn, have a significant positive impact on resenders' resending intention. Furthermore, the positive impacts of WOM characteristics on resenders' acceptance exist, regardless of whether the valence of WOM information is positive or negative.

This study provides insights into the role of WOM information in facilitating the WOM ripple effect. However, the specific characteristics of WOM information that influence consumers' perceptions of WOM are not explored in the present research.

The results of the study may help practitioners manipulate and use online WOM information in order to make marketing communications more efficient and influential.

This paper aims to introduce a web-based pilot system for minimizing the ripple effect (i.e. spreading of the negative impact of an exceptional event along the supply chain from its original position) caused by operational risks in the context of make-to-order supply chains.

The study employs a design science research methodology, which covers three major stages: system requirements identification, business process design and the implementation and evaluation of a web-based pilot system.

The developed system has the potential to significantly reduce the negative impact of the ripple effect caused by operational risks in a make-to-order supply chain in terms of material shortages, late deliveries and subsequent additional costs associated with expediting measures when a customer order is running late.

The system presented in this paper is a prototype that needs to be further developed and tested in the future.

This paper provides integrated business processes and useful guidelines for supply chain managers and information system developers in dealing with the ripple effect in the supply chain.

In contrast to prior studies that have focused on investigating the impact of the ripple effect on the supply chain caused by natural or man-made disasters, this research attempts to offer a novel approach to address the research problem (i.e. the ripple effect caused by operational risks such as delays) directly from the design science perspective.

Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.

Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.

The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.

This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

The purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.

The environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.

The adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.

The extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.

The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.

Previous research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.