Peng Yew Wong, Woon-Weng Wong and Kwabena Mintah
The purpose of this paper is to validate and uncover the key determinants revolving around the Australian residential market downturn towards the 2020s.
Abstract
Purpose
The purpose of this paper is to validate and uncover the key determinants revolving around the Australian residential market downturn towards the 2020s.
Design/methodology/approach
Applying well-established time series econometric methods over a decade of data set provided by Australian Bureau of Statistics, Reserve Bank of Australia and Real Capital Analytics, the significant and emerging drivers impacting the Australian residential property market performance are explored.
Findings
Besides changes in the significant levels of some key traditional market drivers, housing market capital liquidity and cross-border investment fund were found to significantly impact the Australian residential property market between 2017 and 2019. The presence of some major positive economic conditions such as low interest rate, sustainable employment and population growth was perceived inadequate to uplift the Australian residential property market. The Australian housing market has performed negatively during this period mainly due to diminishing capital liquidity, excess housing supplies and retreating foreign investors.
Practical implications
A better understanding of the leading and emerging determinants of the residential property market will assist the policy makers to make sound decisions and effective policy changes based on the latest development in the Australian housing market. The results also provide a meaningful path for future property investments and investigations that explore country-specific effects through a comparative analysis.
Originality/value
The housing market determinants examined in this study revolve around the wider economic conditions in Australia that are not new. However, the coalesce analysis on the statistical results and the current housing market trends revealed some distinguishing characteristics and developments towards the 2020s Australian residential property market downturn.
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Woon Weng Wong, Kwabena Mintah, Peng Yew Wong and Kingsley Baako
This study aims to examine the impact of lending liquidity on house prices especially during black swan events such as the Global Financial Crisis of 2007–08 and COVID-19…
Abstract
Purpose
This study aims to examine the impact of lending liquidity on house prices especially during black swan events such as the Global Financial Crisis of 2007–08 and COVID-19. Homeownership is an important goal for many, and house prices are a significant driver of household wealth and the wider economy. This study argues that excessive liquidity from central banks may be driving house price increases, despite negative changes to fundamental drivers. This study contributes to the literature by examining lending liquidity as a driver of house prices and evaluating the efficacy of fiscal policies aimed at boosting liquidity during black swan events.
Design/methodology/approach
This study aims to examine the impact of quantitative easing on Australian house prices during back swan events using data from 2004 to 2021. All macroeconomic and financial data are freely available from official sources such as the Australian Bureau of Statistics and the nation's Central Bank. Methodology wise, given the problematic nature of the data such as a mixed order of integration and the possibility of cointegration among some of the I(1) variables, the auto-regressive distributed lag model was selected given its flexibility and relative lack of assumptions.
Findings
The Australian housing market continued to perform well during the COVID-19 pandemic, with the house price index reaching an unprecedented high towards the end of 2021. Research using data from 2004 to 2021 found a consistent positive relationship between house prices and housing finance, as well as population growth and the value of work commenced on residential properties. Other traditional drivers such as the unemployment rate, economic activity, stock prices and income levels were found to be less significant. This study suggests that quantitative easing implemented during the pandemic played a significant role in the housing market's performance.
Originality/value
Given the severity of COVID-19, policymakers have responded with fiscal and monetary measures that are unprecedented in scale and scope. The full implications of these responses are yet to be completely understood. In Australia, the policy interest rate was reduced to a historic low of 0.1%. In the following periods house prices appreciated by over 20%. The efficacy of quantitative easing and associated fiscal policies aimed at boosting liquidity to mitigate the impact of black swan events such as the pandemic has yet to be tested empirically. This study aims to address that paucity in literature by providing such evidence.
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Woon Weng Wong, Kwabena Mintah, Kingsley Baako and Peng Yew Wong
The paper is motivated by the paucity of empirical research on the determinants of capitalisation rates/yield in the commercial property market. Compared to property price…
Abstract
Purpose
The paper is motivated by the paucity of empirical research on the determinants of capitalisation rates/yield in the commercial property market. Compared to property price determinants, the capitalisation rate has received significantly less attention. This is somewhat surprising given that the capitalisation rate is a more insightful indicator for investors on commercial property market performance than merely price changes or trends. The capitalisation rate, measured as the ratio of net operating income to the property’s capital value, captures the asset’s overall ability to generate income which is crucial for investors who typically invest in property for their income-generating capacity. The purpose of this paper is to address these issues.
Design/methodology/approach
To evaluate the determinants of capitalisation rates, time series analysis was used. The data capture performance in the Australian commercial property market between 2005 and 2018. All macroeconomic and financial data are freely available from official sources such as the Australian Bureau of Statistics and the nation’s central bank. Methodology wise, given the problematic nature of the data such as a mixed order of integration and the possibility of cointegration amongst some of the I (1) variables, the autoregressive distributed lag model was selected given its flexibility and relative lack of assumptions.
Findings
Bond rates, market risk premiums, stock market excess returns and other macroeconomic variables were found to drive capitalisation rates of Australian commercial properties. A 1% increase in the bond rate results in approximately 0.3–2.4% increase in capitalisation rates depending on the sub-market. Further, a 1% increase in excess market returns results in a 0.01–0.02% increase in capitalisation rates. Regarding risk premiums, a 100 basis point increase in the BBB spread results in approximately 0.92–1.27% reduction in cap rates in certain markets.
Practical implications
Asset managers will find these results useful in asset allocation strategies. Commercial properties offer attractive investment qualities such as yield stability in periods of economic uncertainty while allowing for the possibility of capital growth through appreciation of the underlying asset. By understanding the factors that affect the capitalisation rate, practitioners may predict emerging trends and identify threats to portfolio return and stability. This allows better integration of commercial property in the construction of portfolios that remain robust in a variety of market conditions.
Originality/value
The contribution to literature is significant given the lack of similar studies in the Australian market. The performance of real estate assets using cap rates as a comparative measure to equities and bonds influences decisions in asset allocation strategies. It provides crucial information for investors to estimate the performance of commercial property. This research supports the notion that both space and capital market indicators jointly affect capitalisation rates. The findings expand the knowledge base relating to commercial properties and validate the assessments of investors, developers and valuers who utilise yield as a performance benchmark for asset allocation strategies.
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Peng Yew Wong, David Higgins and Ron Wakefield
This research aims to focus on the emerging determinants for the Australian residential property market subsequent to the Global Financial Crisis 2008.
Abstract
Purpose
This research aims to focus on the emerging determinants for the Australian residential property market subsequent to the Global Financial Crisis 2008.
Design/methodology/approach
Quantitative models built on secondary data were tested on three residential property markets comprising metropolitan Melbourne and two key suburbs in the state of Victoria. The relationship between the house price performances and various leading Australian economic indicators was assessed.
Findings
As a result of the increasing relevance of Asia Pacific private wealth in the Australian residential property market, non-traditional determinants such as residential tourism have emerged as significant in the Melbourne residential property market.
Research limitations/implications
The result of this study can provide a better understanding on the relationship between the Australian residential property market and both the existing and emerging leading economic indicators.
Originality/value
A better understanding of foreign investment activities will assist policymakers to effectively manage inflated Australian residential property market without compromising the steady flow of foreign real estate investment.
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Nurhidayah Bahar, Siti Norida Wahab and Mahfuzur Rahman
This paper aims to examine the impact of knowledge management capability (KMC) on supply chain management practices (SCMPs), organizational learning (OL) and organizational…
Abstract
Purpose
This paper aims to examine the impact of knowledge management capability (KMC) on supply chain management practices (SCMPs), organizational learning (OL) and organizational performance (OP) in the Malaysian logistics industry.
Design/methodology/approach
The data were gathered using a self-administered questionnaire from the management team in the logistics companies. A total of 412 questionnaires were collected out of which 183 responses were included in the data analysis. This represents a response rate of 44.4%. The respondents were those with managerial and/or supervisory experience where their job title or functions included Managers, Head of the Department, Owners, Chief Executive Officer, Senior Executive Officer and at the very least, Assistant Manager or Supervisors. To investigate the correlations between all the elements (e.g. KMC, OL, SCMPs and OP), this study used different analysis techniques including correlation analysis, reliability and validity test, as well as a structural model.
Findings
The results indicated that KMC is strongly correlated and has a positive impact on SCMPs in addition to being positively correlated to OL and OP. Also, OL is positively related to OP and SCMPs.
Research limitations/implications
The findings of this research contribute to the growing body of literature linking KMC with SCMPs, OL and OP.
Practical implications
The findings provide insight on the importance of knowledge management and OL toward improving SCMPs within organizations. Therefore, the findings are useful for shedding light upon formulating strategies for SCMPs among the decision-makers that will ultimately enhance the overall OP.
Originality/value
This study meaningfully contributes to enhancing the understanding of the state of affairs of the impact of management capability on SCMPs, OL and OP in the logistics industry. Practitioners may formulate strategies to further improve the study presented here for a better implementation of knowledge management and SCMPs within their organizations.
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Mahdi Rezaei, Mohsen Akbarpour Shirazi and Behrooz Karimi
The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the…
Abstract
Purpose
The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics.
Design/methodology/approach
The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea.
Findings
This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels.
Originality/value
The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.
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Hung Nguyen and Norma Harrison
Nowadays, companies compete and win based on the capabilities they can leverage across their supply chains. With unpredictable and turbulent business environment, supply chains…
Abstract
Purpose
Nowadays, companies compete and win based on the capabilities they can leverage across their supply chains. With unpredictable and turbulent business environment, supply chains are seeking to customer knowledge as sources of competitive advantage. The purpose of this paper is to empirically test a conceptual framework to investigate the roles of customer leverage (CL) on process innovation and the relationships to performance.
Design/methodology/approach
Drawing upon the knowledge-based view, this study argues that CL is the sources of firms’ process innovation. This study also posits that process innovation mediates the relationship between CL and performance based on transaction cost economics. This empirical study employed 650 manufacturers across different regions.
Findings
This study showed that strong association exists between a manufacturing firm’s CL capability and its process innovation and performances. Process innovation play critical mediating roles in absorbing and transforming customer knowledge in supply chains. In a more dynamic market, CL strengthens the positive impacts on process innovation.
Research limitations/implications
This study further highlights the need to emphasize both strategic and CL capability in dynamic environments as these may be needed to enable the firm to seize market niches that may open up in such environments. Similarly, managers should emphasize CL capability and process changes in competitive environments as they are more difficult to imitate from competitors in regards of new product or services.
Practical implications
These results extend the limited existing research on global manufacturing context that the customer knowledge are effective sources for increasing innovative processes. The higher the market turbulence, the stronger the pressures for CL demanded by process innovation. The findings also confirm that process innovation plays a mediating role in absorbing and transforming customer knowledge in improving costs and financial measures. This is an important result that highlights the mechanism by which customer knowledge can influence a firm’s bottom line.
Originality/value
This study examined the linkages between a marketing concept and operations and supply chain management.
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Jiasen Sun, Shuqi Xu and Guo Li
The power industry is the pillar industry of the Chinese economy, and also a major carbon emitter. The performances of both the production and operation of the power industry are…
Abstract
Purpose
The power industry is the pillar industry of the Chinese economy, and also a major carbon emitter. The performances of both the production and operation of the power industry are crucial for a harmonious development of society. This study proposes an improved data envelopment analysis (DEA) model to analyze the sustainable performance of China's power supply chain (PSC).
Design/methodology/approach
To analyze the sustainable performance of PSC systems in China's provincial regions, this study proposes a two-stage directional distance function (DDF) model. The proposed model not only considers the leader–follower game relationship between the power-generation system and the retail system, but also considers the factors that measure the sustainability level of the PSC.
Findings
The proposed model is applied to assess the sustainable performance of the PSCs of China's provincial regions. The findings are valuable and mainly include the following aspects: First, compared with other models, this study regards the intermediate variable of the power system as a freely disposable variable; therefore, the efficiency of the proposed model is more realistic. Second, the average efficiency of China's power retailing system is generally lower than the average efficiency of its power-generation system. Third, significant regional differences affect the power-generation efficiency, while the regional differences in power retail efficiency are not significant. The power-generation performances of PSCs in East China and Northeast China are generally higher than in other regions.
Originality/value
This study introduces the convex technique into a DEA model and thus proposes an improved two-stage DDF DEA model. In response to the game-theoretic inherent in power systems, this study also introduces the leader–follower game into the two-stage model. In addition to the theoretic novelty, all PSCs can be classified with this model. Moreover, specific recommendations for each type of PSCs are proposed based on the efficiency results, thus providing vital guidance for the practice.
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Romane Guillot, Rameshwar Dubey and Sushma Kumari
Globalisation, trade barriers, unprecedented health crises and geo-political crises have forced organisations to revisit their performance measurement systems (PMS) to better…
Abstract
Purpose
Globalisation, trade barriers, unprecedented health crises and geo-political crises have forced organisations to revisit their performance measurement systems (PMS) to better prepare their supply chain against the risk and improve performance in times of crisis. This study aims to review the supply chain operation reference (SCOR)-based PMS and propose a dynamic SCOR-based PMS for supply chain risk management (SCRM).
Design/methodology/approach
Due to the need for multi-stakeholder perspectives on SCOR-based PMS for the SCRM, the authors aimed to develop a theory rather than to elaborate upon or test the theory. Hence, the authors adopted an inductive theory-building approach to build research propositions. The authors also gathered 12 semi-structured interviews with knowledgeable managers from B2B international companies.
Findings
The findings of the study highlight the challenges faced by the organisations during the implementation of the SCOR-based performance indicators and the positive impacts they have on decision-making and on the continuous improvement strategy of organisations to tackle supply chain risks and improve performance. The findings suggest that the effects of these indicators are more felt during risk management and risk monitoring stages.
Research limitations/implications
Like any other study, this study has some rules, and, thus, the authors caution the readers that they must interpret the findings of the research considering these limitations. The study is based on semi-structured qualitative interviews. The interviews were conducted with 12 knowledgeable managers from France; thus, the insights drawn from the study cannot be generalised to other settings. Furthermore, the samples represent something other than small and medium enterprises. In the future, the samples from small and medium firms can offer a nuanced understanding of the performance indicators for SCRM.
Originality/value
To the best of the authors’ knowledge, this is one of the few studies which has attempted to revisit the SCOR-based PMS in the B2B supply chain for risk management. The study’s findings help expand the SCOR-based PMS literature and offer numerous insights to the management and consultants facing challenges in SCOR implementation.
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Pravin Suryawanshi and Pankaj Dutta
The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors…
Abstract
Purpose
The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).
Design/methodology/approach
The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.
Findings
The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.
Originality/value
The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.