Purpose Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model. Design/methodology/approach A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives. Findings The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network. Research limitations/implications This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers. Practical implications These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high. Originality/value This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.

Consumer behavior is becoming increasingly heterogeneous due to the changing culture patterns and effects of globalization. This phenomenon increases the importance of focusing on the social dimension of sustainability in a consumer market. This research contributes to the body of knowledge by emphasizing the consequences of individual cultural values and individual materialistic values in the Chinese consumer market. In this endeavor, Hofstede’s framework of individual culture with materialistic effect is applied to understand consumer behavior in a processed food market. Rigorous research activity was conducted at the point of sale in different supermarkets to record the responses of random consumers. The results of multi-variate covariance-based structure equation modeling show that individual materialistic values have emerged as a key determinant, which reflects the individual culture for consumer buying behavior in a state of globalization. Power distance, long-term orientation, and uncertainty avoidance were found to be important measures of individual culture. The findings of the study are useful in assisting the industry for product launching and marketing strategies to achieve future sustainability in the processed food market. In the pursuit of a sustainable processed food market, the focus should shift toward individual cultural values away from national and group cultures.

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

Persistent with the problem of quantifying the risk associated with securities, this study examines the applicability and validity of Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) while evaluating the stock prices and returns of listed companies in the Pakistan stock exchange. While examining the applicability of CAPM and APT, this study considers the stock return of top ten sectors listed in stock exchange from the period of 2014 to 2019. The result shows that the application of APT for risk estimations may not be showing satisfactory results from the observed data. On average, the p-value is more than 30% for all factors which should be less than 5%. Therefore, in order to compare the application of methods and find out the stock risk, it can be concluded that CAPM approach is more reliable than APT. Thus, it is suggested to adopt the CAPM approach to estimate the realistic stock returns. Additionally, the investor can also consider different indigenous and exogenous economic factors according for calculating market risk and maximizing the return. Contribution/Originality:This study contributes in the existing literature in a way to show that CAPM is still a valid tool to estimate the return in Pakistani capital market, which implies that the market risk can better be estimated by the companies. Investors must consider the market index performance for realistic stock return rather to follow other economic indicators.

Purpose Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost of capital, this study aims to sermon a significant role of investor’s risk perception as unsystematic risk in PPP-based energy projects. Design/methodology/approach To investigate the effective mechanism of determining cost of capital and valuing the capital budgeting, a pure-play method has been acquired to measure systematic risk. In addition, dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models have been applied to calculate weighted average cost of capital. Findings Initially, a joint cost of capital of energy-related projects has been calculated using DCC-GARCH and pure-play method. Investors risk perception has been discussed through market point of view using country risk premium modeling. Latter yearly betas have been calculated using DCC signifying the final outcomes that applying a dynamic model can provide a better cost estimation in emerging economies. Practical implications The findings are implicating that due to the involvement of international investors, domestic risk is linked with country risk. In such situations, market-related information is a key factor to find out the market performance, helping in the estimation of cost of capital through capital asset pricing model (CAPM). High dynamic nature of emerging economies causes an impediment in the estimation of cost of capital. Consequently, to calculate risk in dynamic markets, this study has acquired DCC model that can predict the value of beta factor. Originality/value Study contributes to the body of knowledge by addressing an important issue of investor’s risk perception and effective implication of CAPM using pure-play and DCC-GARCH when data is not promptly available in dynamic situations.