Megaprojects are usually complex and in many cases encounter failure in terms of finish late or overspent. This study aims to investigate the critical risk factors behind these projects as well as their priority. Project risk management is a mature research stream. But when focus on megaprojects the amount of research decreases significantly. This research provides a hierarchy of risk structure in Tehran-Rasht railway megaproject and prioritizes the risk factors through a two-phase methodology. This method is a new hybrid MCDM technique consist of group fuzzy TOPSIS and fuzzy Best-Worst Method. BWM is the latest MCDM technique which in this paper, its fuzzy version combined with fuzzy TOPSIS is employed.  This research also considers all the project success criteria including time, cost and quality simultaneously and calculates the risk priority Index (RPI) accordingly. The results imply that quality is the most important project success factor and the risk elements with greater impact on project quality, get higher PRI. The identified and ranked risk factors help practitioners and academics to follow the subsequent steps of the risk management process of Iranian transportation megaprojects.

Identifying and selecting the most profitable customers from a shareholder’s perspective is of great interest to marketing managers. One promising line in this regard is to explore the customer lifetime value and its profitable management over time. There is a significant body of marketing literature about CLV evaluation in terms of various perspectives. However, much less attention has been paid to the risk associated with customer relationships. Previous researches in this area considered risk as “variability of cash flows generated by customers”, regardless of the trend of variability. Whereas the upside and downside variability from the customer’s expected profitability are extremely different in CRM context. This paper provides a quantitative model based on downside Capital Asset Pricing Model (D-CAPM) to evaluate risk-adjusted CLV and compares the results by employment of traditional CAPM. This paper contributes to this field by extending the discussion on customer risk measurement and provides an approach that enables marketing managers to evaluate the risk of decline from average profitability for different customer segments

Purpose Marketing/finance interface and application of its new insights in marketing decisions have recently found great interest among marketing researchers and practitioners. There is a relatively large body of marketing literature about incorporating modern portfolio theory (MPT) into customer portfolio context and taking advantage of it in marketing resource allocation decisions. Previous studies have modelled customer portfolio risk in the form of historical return/profitability volatility of customer base. However, the risk is a future-oriented measure, and deals with future volatility associated with return stream. This study aims to address this research problem. Design/methodology/approach The well-known Pareto/non-binomial distribution (NBD) approach is used to model customer purchases in a non-contractual setting of research practice. Then, the results were used to simulate the customers’ future buying behaviour and associated returns via the Monte Carlo simulation approach. Subsequently, the mean-variance portfolio optimization model was applied to find the optimal customer portfolio mix. Findings The results illustrated the better performance of the proposed efficient portfolio versus the current customer portfolio. These results are applicable in analyzing customer portfolio composition, and can be used as a guidance to make decisions about marketing resource allocation in different segments. Originality/value This study proposes a new approach to analyze customer portfolio by using the customers’ future buying behaviour. Taking advantage of rich marketing literature about statistical assumptions describing the customers’ buying behaviour, this study tries to take some steps forward in the application of the MPT theory in customer portfolio management context.

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Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.