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Strategic alliances are often described as risky, dangerous, and instable. When firms adopt these strategies, they are confronted with a relational risk. Nevertheless, little empirical work has been down on relational risk in alliances. For this reason, this research is founded and constructed on two principal questions: what is relational risk? And how is this risk to be managed?

From a methodological point of view, neither one paradigm nor the other concerning previous research was favoured. The process of the empirical research is based on an inductive non‐demonstrative step. It was carried out in two phases. Firstly, exploratory research was aimed at complementing previous research and formulating hypotheses. These hypotheses were tested with survey data on 87 partnerships of French biotechnology firms.

The results demonstrate the multidimensional character of relational risk and the duality of relational control. Relational control includes autonomous control and informal control.

Nevertheless, this research adopts a static perspective. It is known that alliances evolve, and develop. Consequently, future research should include the interactive process to understand how these two forms of management – autonomous and informal – evolve and in what cases they complement themselves.

From a managerial point of view, the results emphasize the need to be aware of existing dynamics between systems of control and relational risk perceptions.

This research proposes an empirical study of risk management model in alliance relationships and demonstrates the importance to adopt a multidimensional view of relational risk.

This paper aims to provide a model reflecting the mediating role of risk in the transaction as well as the social risk between the channel and repeat purchase intention and also to test the moderating role of the country on that model.

Using a sample of 228 Spanish online buyers and another of 158 Japanese online buyers, a test of mediating effects for the influence of risk on purchase intention and a multigroup analysis for the moderating effect of buyer country are performed.

Online risk has a multidimensional structure: risk in the channel, social risk and risk in the transaction. Risk in the channel has a positive effect on the other two. In Spain the influence of risk on purchase intention is clearly lower than in the case of Japan.

Organisations should design different websites to adapt to the values of each target group in each country and to get the maximum benefit from their differences so as to ultimately encourage individuals to repeat purchase.

It is one of the few studies that analyse the relations between dimensions of online risk and their differential impact on repeat purchase intention. A dual impact of risk is proposed: negative and positive effects of the risk in the channel on repeat purchase intention. Moreover, this is a cross‐national study, following the postulates of ELM.

This chapter discusses the current risk perception literature in the tourism field. The chapter critiques the literature and offers a solution through a more conceptual and operational definition of risk perceptions. Specifically, the inclusion of affective risk perceptions will be added to the literature via the risk-as-feelings hypothesis. Extension of the current literature will enhance research moving forward.

The chapter will provide a literature review, propose a conceptual model, and operationalize the risk perception variables.

The outcome of this chapter is to provide a conceptual model as a framework to address risk perception studies in tourism and hospitality in the future. The model will provide clear measurement scales to be tested.

This chapter gives a much needed theoretical and conceptual foundation to the study of risk perceptions in the travel and tourism literature.

The purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market βs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.

Following Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.

Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market ßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.

Given data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.

Information about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.

To the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

This research aims to examine the decision-making process involved in saving for retirement and compare it with decision-making processes regarding other financial products (such as loans and savings plans) as well as real products (such as a car or a home).

This research is based on the distribution of 107 questionnaires. The questionnaire is composed of two parts: questions examining and focusing on the individual’s decision-making process and questions regarding socioeconomic factors. The average level of risk tolerance is calculated for each respondent with respect to the first four chapters. (These chapters include buying a car or a home, opening a savings plan and taking a loan). Afterward, the consistency (rationality) of the respondents is examined with regard to their decision-making concerning retirement savings plans. Then, an econometric model is used to further test the consistency of the respondents.

The results suggest that the level of risk tolerance associated with a retirement savings plan is consistent with that associated with the other financial products, but not with the real products. Majority of the respondents demonstrate high risk tolerance with respect to retirement savings, and their decision-making process is similar to a random thinking process. The level of deliberation and information-gathering regarding retirement savings is the lowest when compared with the other financial and real products examined in this paper. Majority of the respondents are less risk-tolerant toward the other financial and real products.

In this research, the authors examine how different individuals with different characteristics get different decisions about their personal retirement savings. The authors also examine these decisions’ deviation from the rational model, and compare it with decision-making processes regarding other financial products as well as real products.

This study aims to investigate the influence of various risk factors, specifically investment risk, legal risk and technology risk, on the decision of investors to invest in equity crowdfunding ventures in Malaysia.

A total of 169 valid responses out of a total of 195 questionnaires were distributed to individuals with prior knowledge of equity crowdfunding. The data from the responses are used to test the relationships using structural equation modeling partial least squares (SEM-PLS).

Investigation into the influence of risk factors on the willingness to support equity crowdfunding shows that investment risk and legal risk significantly influence the decision to support equity crowdfunding ventures, but technology risk does not. However, while the influence of investment risk is negative, legal risk is found to have a positive influence.

This study is important as, to the authors’ knowledge, this is the first study to empirically test the relationship between the various risks inherent in equity crowdfunding investments and the decision to invest. The study is also important to entrepreneurs and start-ups as it provides evidence that while the equity crowdfunding investment community follows the norms of investment, i.e. lower risk is preferred, stricter laws and regulations governing equity crowdfunding may not be needed or may only be relevant in countries where there are more retail, unsophisticated investors.

The purpose of this paper is to investigate the relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

A mail survey was used to collect information about utilization of risk management tools, perceived effectiveness of risk management tools, and factors that could influence choice of risk management tools by Illinois farmers. Cluster analysis, one-way ANOVA, χ2 tests of independence, and multinomial logistic regression were utilized to detect possible relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

Multinomial logistic regression analysis revealed that age and gross farm income (GFI) were the strongest predictors of the risk management tool utilization group to which an individual would be assigned. The number of risk management tools utilized decreased with age but increased with GFI. Neither self-reported risk attitude nor education was a significant independent variable in the multinomial logistic regression model, but both were strongly impacted by age. Younger farmers with higher GFI were the most likely users of hedging.

The results of this study provide support for the idea that farmers who are better able to generate revenue are better able to manage risk, but the direction of causality was not investigated.

Risk management service providers could benefit from this study as a benchmark for understanding their current and potential farmer clients’ risk management strategies.

This study used cluster analysis and multinomial logistic regression to address the complexity of decisions regarding multiple risk management tools. The number of tools utilized by individuals was investigated.

Reports on a study that examines a model of risk behaviour in which the effects of risk propensity and problem framing are jointly evaluated. Managers from 58 oil industry organizations were presented with hypothetical business decisions involving significant gains and losses, and asked to choose between action alternatives resulting in certain outcomes or probabilistic outcomes. Also evaluates the notion that tendencies towards risk taking are complex, reflecting personality traits, habits and experience. There was support for a historical basis for risk propensity, but risk preferences were not found to be influential. Further, although both risk propensity and problem framing were found to be significant predictors of risk behaviour, there was no support for prospect theory predictions. For researchers, suggests the need to incorporate individual differences into models of risk behaviour. For organizations, suggests the need for management attention to members’ risk experiences.

The COVID-19 pandemic, which appeared in China in late 2019, has affected the world psychologically, socially and economically in 2020. Tourism is one of the areas where the effects of COVID-19 have been felt most clearly. The study aims to determine the effect of negative problem orientation (NPO) and perceived risk related to the COVID-19 pandemic on travel and destination visit intention.

This study employed a convenience and probabilistic sampling method for collecting data from 531 respondents using an online questionnaire. Partial least square structural equation modeling (PLS-SEM) was used for testing research model.

According to the findings, NPO and perceived risk related to the pandemic were found to have direct and indirect effects on the travel behavior of tourists. The results of this research provide theoretical and practical implications for hospitality and travel businesses on topics such as the psychological effects of the pandemic and the travel behaviors of tourists.

It is estimated that the pandemic will also affect tourist behavior due to its effects on human psychology. For this reason, a study conducted in the context of tourist behavior theories is expected to contribute to the literature, managers and future of the tourism.

In order to decrease patient waiting time and improve efficiency, healthcare systems in some countries have recently begun to shift away from decentralized systems of patient referral from general practitioners (GPs) to specialists toward centralized ones. From a queueing theory perspective, centralized referral systems can decrease waiting time by reducing the variation in the referral process. However, from a social psychological perspective, a close relationship between referring physician and specialist, which is characteristic of decentralized referral systems, may safeguard against high referral rates; since GPs refer patients directly to the specialists whom they know, they may be reluctant to damage that relationship with an inappropriate referral. The purpose of this paper is to examine the effect upon referral behavior of a relationship between physicians, as is found in a decentralized referral system, vs a centralized referral system, which is characterized by an anonymous GP-specialist relationship. In a controlled experiment where family practice residents made decisions concerning referral to specialists, physicians displaying high confidence referred significantly fewer patients in a close relationship condition than in a centralized referral system, suggesting that for some physicians, referral behavior can be affected by the design of the service system and will, in turn, affect system performance.

The authors used a controlled experiment to test the research hypotheses.

Physicians displaying high confidence referred significantly fewer patients in a close relationship condition than in a centralized referral system, suggesting that for some physicians, referral behavior can be affected by system attributes and will, in turn, affect system performance.

The current study has some limitations, however. First, the sample consisted only of family practice residents and did not have the knowledge and experience of GPs regarding the referral process. Second, the authors used hypothetical patient case descriptions instead of real-world patients. Repeating this experiment with primary care physicians in real setting would be beneficial.

The study indicates that decentralized referral systems may act (rightly or wrongly) as a restraint on the rate of referrals to specialists. Thus, an implementation of a centralized referral system should be expected to produce an increase in referrals simply due to the change in the operational system setup. Even if centralized referral systems are more efficient and can facilitate the referral process by creating a central queue rather than multiple single queues for patients, the removal of social ties such as long-term social relationships that are developed between GPs and specialists in decentralized referral systems may act to counterbalance these theoretical gains.

This study provide support for the idea that non-clinical factors play an important role in referrals to specialists and hence in the quality of provided care, as was suggested by previous studies in this area (Hajjaj et al., 2010; Reid et al., 1999). The design of the service system may inadvertently influence some doctors to refer too many patients to specialists when there is no need for a specialist visit. In high-utilization health systems, this may cause some patients to be delayed (or even denied) in obtaining specialist access. Healthcare systems may be able to implement behavioral-based techniques in order to mitigate the negative consequences of a shift to centralized referral systems. One approach would be to try to create a feeling of close relationship among doctors in centralized referral systems. High communication and frequent interaction among GPs and specialists can boost the feelings of teamwork and personal efficacy through social comparison (Schunk, 1989, 1991) and vicarious learning (Zimmerman, 2000), which can in turn motivate GPs to take control of the patient care process when appropriate, instead of referring patients to specialists.

The authors’ study is the first examining the effect of social relationships between GPs and specialists on the referral patterns. Considering the significant implications of referral decisions on patients, doctors, and the healthcare systems, the study can shed light into a better understanding of the social and behavioral aspects of the referral process.