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This paper aims to show how proper risk management capabilities can lead to competitive advantage for a company. There is much evidence that suggests that the current very high level of volatilities in the business world is going to get worse in the years and decades to come. This trend of increasing uncertainties and the resulting risks for businesses, demands a strategic‐level attention to risk management. This strategic‐level attention is warranted by the fact that proper risk management capabilities can lead to competitive advantage.

The work is conceptual in its approach. The paper also provides many examples from a wide range of industries, as well as the results from other research works to support the finding of the paper.

The paper first shows how firms' perspective of risk management is evolving. It then characterizes the main drivers behind the trend of increasing uncertainties in the business world which results in higher levels of risk exposure for companies. Finally, the paper characterizes four different ways through which proper risk management capabilities can lead to competitive advantage (depending on different risk categories).

Although the importance of risk management and its potential strategic role has been widely studied in the literature, the question of how risk management capabilities can turn into a competitive advantage has received less attention. The answer to this question might help firms to better understand the strategic role of risk management and the importance of developing a proper set of risk management capabilities. This paper tries to identify the relationship between risk management capabilities and competitive advantage under different types of risks.

During the last decade, there has been an increasing focus on commercialization of knowledge and technology from universities. However, universities report financing as being the main impediment to successful university spin‐off companies (USOs) creation, leaving valuable inventions un‐commercialized. The purpose of this paper is to develop a conceptual model in order to explain financing challenges experienced by USOs.

This paper presents a conceptual model illustrating financing challenges met by USOs, and provides an explanation why TTOs report that obtaining financing is their biggest impediment to spin‐off creation. Two different theoretical perspectives back this conceptual development: Knightian uncertainty and agency theory.

This theoretical examination suggests that increasing levels of uncertainty affect the investor's willingness to fund new companies in a negative way. Through the literature review, clear indications were also found for the increased uncertainty with which USOs are faced. It is therefore natural to conclude that investors are more reluctant to invest in USOs because of the level of uncertainty compared with other entrepreneurial companies.

This study made a generalization of USOs as a homogeneous group of companies, which is an oversimplification. Further research should address how different business models, types of resources and their institutional link affect USOs' capital requirements and their problems in raising the required capital.

The main contribution from this paper is the combination of theoretical insights from the concepts of Knightian uncertainty and agency theory. These combined with insights from previous empirical studies explain why USOs face specific challenges in order to raise risk capital.

The aim of this paper is that of individualizing a framework for the management of uncertainty in supply chain finalized to reduce the firm risks.

Since a way for reducing the damages deriving from uncertainty sources is increasing the level of flexibility inside the supply chain, and the real option theory allows the increase of the flexibility level, in order to achieve the aim of this work, we utilize the real options theory to coverage of one or more risks inside the supply chain.

A useful theoretical framework has been individualized enabling the selection of possible options to protect the firm against the risk originating from every source of uncertainty. In particular, on two types of risks, using Matlab software, a test has been conducted that proves the ability of the outsource option to cover risks under examination.

In the paper a framework providing useful information for the supply chain management is presented.

The paper attempts to provide an original tool for the risks management deriving from production activities inside a supply chain.

This paper aims to discuss how firms make investment decisions and the impact of these decisions on firm value, considering the strategic impacts of such investments.

Built on real options and game‐theoretic models, simulations are used to find out how investment decisions and firm values change in face of network effects and potential competition.

It is found that high intensity of network effects lowers the investment threshold as well as the expected value of the firm at the investment threshold. It is also found that potential competition makes an innovating firm less likely to invest. Moreover, in a more competitive environment, the value of the firm when it is indifferent between investing and waiting tends to be high.

It is shown that overestimating and failure to capture the network values may lead to poor investment decisions, resulting in firms or projects with little value. The research also has important implications for the management of R&D. When an innovation is likely to face fierce competition, the owner may invest more aggressively under high uncertainty. However, if competitors are likely to provide substitutes, firms should be cautious making upfront investments under high uncertainty.

This paper discusses the implications of new developments in the field of real options to firms’ strategic investment decisions and the valuation of firms.

The purpose of this paper is to investigate entrepreneurs’ investment decisions under uncertainty in continued investments where the authors test the role of emotions to continue or discontinue the investment.

A conjoint analysis is carried out on 101 entrepreneurs’ 3,232 investment decisions. The entrepreneurs were provided with a scenario of an investment where the dependent variable was the entrepreneur's propensity to allocate further resources to the described investment. They assessed their willingness to allocate further resources to the investment on a seven-point Likert-type scale. The independent variables in the experiment were the experienced emotions of the entrepreneur each of which was described by the two levels of high and low.

It was found that self-confidence, challenge, and hope increase the propensity to continue investments as do increased level of uncertainty. Embarrassment and strain do not increase this propensity, however, high uncertainty decreases the propensity to continue investments. In contrast to the escalation of commitment theory, embarrassment does not make entrepreneurs more prone to invest under uncertainty. Frustration does not yield significant results, which runs contrary to the theory and the hypothesis finds no support.

The paper focussed on a limited number of emotions, and also on one specific moderating factor that impacts the effect of these emotions on the investment decision.

To understand the role of their emotions in investment decisions under different levels of uncertainty may help entrepreneurs to improve the quality of their decision making.

This study is an experiment where practitioner entrepreneurs participate which increases the ecological validity of the study. Emotions can explain, partly, why entrepreneurs persist with some underperforming projects, but not others. Uncertainty is a powerful moderating variable in the decision-making process. The results enhance existing knowledge about the emotive side of entrepreneurs’ propensity to make investment decisions under uncertainty. The results also supplement and refine existing theories on self-justification.

While it is generally accepted that scanning supports organizational adaptation to the environment, scanning behavior may vary with the amount of environmental change. Information processing perspectives suggest that scanning activity will increase in response to increasing environmental uncertainty. Yet, social cognition perspectives suggest that scanning decreases at high and low levels of uncertainty since useful information is either unattainable or is already known. Using a combined perspective in a strategic decision‐making context, this study hypothesized that scanning would be greatest at high and low levels of uncertainty to support identification of previously unexploited niches. Results indicate that the level of environmental dynamism combined with the manager's functional position explains scanning behavior.

The purpose of this paper is threefold: the primary purpose is to suggest a real estate paradigm spectrum to act as reference for the contextualisation of observed market phenomenon in system terms; the secondary purpose is for the spectrum to contextualise the efficacy of real estate and valuation theory, methods and techniques; and the tertiary purpose is to propose a confidence score for reporting uncertainty to the end user of a valuation report.

Literature was reviewed on the concepts of risk and uncertainty, rationality and several systems thinking domains.

The framework can provide context to observed market phenomenon and distinguishes between agency and mechanism in contributing to conditions of certainty and uncertainty. The argument followed in this paper is that it is necessary to contextualise the efficacy of real estate and valuation theory, methods and models under conditions of certainty, normal uncertainty and abnormal uncertainty. The characteristics of conditions can be used as basis to develop new theory and practical application or modify existing.

Real estate economic theory can be organised in terms of the spectrum and the framework can potentially identify where further research is required and the requirements it must meet as measured against the characteristics of the framework. Current valuation methods and models can continue to be used when valuing under conditions of certainty, however, modifications to methods and models are required to account for complexity when valuing under conditions of normal uncertainty and abnormal uncertainty. The confidence score included in this paper can also be used to report the conditions of certainty/uncertainty under which the valuation was performed.

This paper aims to set the basis for new theoretical and practical developments of insights into real estate economic and valuation theory, methods and models while also contributing to the reporting of uncertainty through the proposed confidence score.

This study examines the relationship between oil price uncertainty (OPU) and corporate inventory investments using a sample of 6,072 USA manufacturing firms from 1992 to 2019.

The author's study employs a panel dataset to examine the relationship between OPU and corporate inventory investments. The author uses several alternative specifications such as fixed effects models, an instrumental variable analysis, an impact threshold for confounding variable (ITCV) analysis, alternative measures, additional control variables and the percent bias analysis to account for endogeneity issues.

Corporate inventory investments decrease in response to high OPU. This decrease in inventory investments happens regardless of firms' expected stockout costs, information environment and reliance on external financing. As a potential mechanism, an uncertainty-induced increase in cash holdings contributes to this reduction in inventory investments. Also, the effect of OPU is non-linear and asymmetric. In response to the volatility of positive (negative) oil price changes, inventory investments decrease (increase) up to a certain point and increase (decrease) after that. Further, uncertainty-induced adjustments in inventory investments positively influence the operating performance of firms.

The author's study adds to the growing literature that examines the impact of OPU on corporate outcomes. Inventory investments directly affect business operations and could better reflect firms' responses to an uncertain environment.

Supply chain risk management (SCRM) has recently gained increasing attention in the supply chain context, both from the practitioners' perspective and as a research area. Given the relevance of the topic, the aim of the present paper is to present a focused literature review, investigating the process of knowledge creation, transfer and development from a dynamic perspective within the context of SCRM.

A review of the literature on SCRM was undertaken. The new proposed methodology combines the systematic literature review approach to identify the most relevant articles to be included in the study with the citation network analysis in order to unfold the dynamics of the field under study. The authors define this new methodology as systematic literature network analysis.

The paper demonstrates that there are a number of key themes in the field of SCRM. The contributions that influenced the field were identified and, by analysing the evolution over time of key concepts, a number of research directions were identified and discussed.

The dynamic nature of current literature review allows the identification of the directions in which research is moving and thus the recognition of streams of research that appear most promising. However, the application of the research methodology, and in particular of the citation network analysis, requires the support of specific computer programs. Moreover, the underlying assumption of the citation network analysis is that, by analysing the network of citations made to and from articles, it is possible to explain the flows of knowledge used to generate new results. This is only partially true since the spread of measures based on impact assessment led many researchers to an excessive use of citations, even if their content is not always decisive for the outcome of their work.

The present paper outlines a research agenda that may facilitate the development of models for managing supply chain risk. Furthermore from the evidence of the performed literature review some managerial insights can be derived on how to manage supply chain risk: by considering uncertainty in the design of supply chains, by understanding the impact of risks arising from network collaboration and interactions between supply chain partners, by developing proactive mitigation capabilities to hedge the increasing level of risk.

The novelty of this research lies in the combination of two existing methodologies for reviewing the literature and in the adoption of a dynamic perspective in order to analyse theory development.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).