Regret Avoidance Research Articles

On the advantages and disadvantages of choice: future research directions in choice overload and its moderators.

Researchers investigating the psychological effects of choice have provided extensive empirical evidence that having choice comes with many advantages, including better performance, more motivation, and greater life satisfaction and disadvantages, such as avoidance of decisions and regret. When the decision task difficulty exceeds the natural cognitive resources of human mind, the possibility to choose becomes more a source of unhappiness and dissatisfaction than an opportunity for a greater well-being, a phenomenon referred to as choice overload. More recently, internal and external moderators that impact when choice overload occurs have been identified. This paper reviews seminal research on the advantages and disadvantages of choice and provides a systematic qualitative review of the research examining moderators of choice overload, laying out multiple critical paths forward for needed research in this area. We organize this literature review using two categories of moderators: the choice environment or context of the decision as well as the decision-maker characteristics.

Investment risk assessment of anaerobic fermentation based hydrogen production project based on regret linguistic Z-number averaging Choquet integral operator

Anaerobic fermentation based hydrogen production technology has a good market investment prospect for its advantages of abundant raw material sources, low cost and low energy consumption. The industry is in the early stage of development and will face many risks. The corresponding risk assessment has not yet attracted attention. Therefore, this paper constructs a risk assessment framework. Firstly, 17 critical risk factors affecting the anaerobic fermentation based hydrogen production project is identified and grouped into five categories. The linguistic Z-number is then used to describe the uncertainty. Later, a regret linguistic Z-number averaging Choquet integral operator is put forward to deal with interrelationships between criteria and simulate the decision-makers’ regret avoidance attitudes. In addition, a novel relative entropy measure is determined, wherein the expert weight model is established by combining the reliability measure. Subsequently, a case study is conducted and the result demonstrates the risk level of the project located in Hubei, China is between “Medium” and “Slightly high”. Finally, some measures are proposed to deal with these risks. This work can provide a theoretical basis for the risk prevention and investment decision of anaerobic fermentation based hydrogen production projects.

Fuzzy cross-efficiency evaluation based on prospect theory and regret theory

Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process.

Should I invest or not? Investigating the role of biases and status quo

PurposeThe purpose of this study is to examine the behaviour of retail investors while making an investment decision and how it gets affected by the behavioural biases of the investors using a moderated-mediation framework.Design/methodology/approachA mixed method approach has been used to fulfil the objectives of the study. In the first study, a qualitative analysis of the interviews with 15 retail investors was conducted. As part of the quantitative study, a total of 201 responses from Indian retail investors were collected using systematic sampling and analysed using structural equation modelling and Process Macro.FindingsThe results indicate that anchoring bias, availability bias, herding bias, switching cost, sunk cost, regret avoidance and perceived threat have a significant effect on retail investors’ investing intention. The attitude of the investors towards investing decisions mediates the effects of behavioural bias and the status quo on investment intention. The results of the moderated-mediation analysis indicate that mediating effect of attitude varied at the low and high-risk aversion of investors.Practical implicationsThe findings of this study will help regulators and retail investors to understand the critical behavioural biases which affect the investors’ investing intention.Originality/valueThe paper contributes to the literature on investors’ behaviour, status quo bias theory (SQB) and behavioural bias. This study uniquely proposes a moderated-mediation framework to understand the effects of biases on retail investors’ investment intention.

Should I adopt an integrated virtual clinic? A dual‐factor theory perspective

AbstractThis study investigates consumers' adoption intention toward the virtual clinic. The theory of planned behavior and status‐quo‐bias theory are used as underpinning theories to investigate the effects of enablers and inhibitors, respectively. Further, the mediating role of perceived values on the relationship between enablers, inhibitors, and adoption intention is also investigated. The study also investigates how mediating effect of perceived values varies at different levels of perceived information privacy concerns. The results of this study reveal that attitude, subjective norm, regret avoidance, inertia, and switching costs significantly influence the adoption intention toward virtual clinics. The study's findings will enrich technology adoption and online health literature, especially emerging literature related to the virtual clinic. The study helps healthcare service providers understand consumers' adoption intention of the virtual clinic.

Service quality evaluation of crowdsourcing logistics platform based on Fermatean fuzzy TODIM and regret theory

The service quality valuation of crowdsourcing logistics platforms can be considered as a typical multi-index evaluation issue. Fermatean fuzzy numbers can fully express uncertain and blurry information for service quality evaluation. Hence, considering that evaluator has limited rationality, with the psychology of loss aversion and regret avoidance in the evaluation process, this article constructs a Fermatean fuzzy TODIM model combined with regret theory to assess the service quality of crowdsourcing logistics platforms. Of note, the range maximum method is utilized to calculate the weight of index, which avoids the influence of the information fluctuation of the weight obtained by the simple weighted summation of subjective and objective weights on the final consequence. Finally, the effectiveness and superiority of the proposed method are verified using a realistic example and comparison analysis.

Bilateral Matching Decision-Making Method Considering Regret Avoidance in Second-Hand Transactions

Effective methods of bilateral matching decision-making could make the second-hand transaction market and platforms more dynamic and healthier for development. However, the loss of description about the characteristics of sellers and buyers and the weight information of their inner preference can affect the matching result. By developing a method for a second-hand transaction, this paper proposes a novel approach that considers the matching parties' psychologies, which can improve the matching efficiency. A mathematical model of seller and buyer is performed, and then the price and other evaluation indicators of sellers and buyers are described by five different types. A bilateral matching multi-objective decision-making model is constructed and solved. A real case study of property trade platform is presented to illustrate how the proposed approach could be applied in practice. The results of this research indicate that the proposed approach is a useful tool for matching the sellers and buyers and has substantial practical application.

Insurance and portfolio decisions: Two sides of the same coin?

We study insurance and portfolio decisions, two opposite risk retention tradeoffs. Using household level data, we identify the first joint determinants (e.g. subjective expectations, risk attitude) and frictions (e.g. liquidity constraints, financial literacy) in the literature. We also find key differences between the two decisions. Notably, contrary to economic intuition, risky asset holding and insurance coverage both increase with wealth. We show that this apparent puzzle is driven in part by a specific behavioral pattern (the poor invest too conservatively, while the rich over-insure), and can be explained by two factors: regret avoidance and nonperformance risk.

Analysis Behavioral Financial Bias in Invesment Decision Making

Traditional finance suggests that any investment made by a rational investor considers risk and return before making a decision to maximize return. Behavioral finance then challenges conventional finance and introduces psychological factors that influence decision-making on invesment. The purpose of this study is to investigate how behavioral biases affect investment decisions under. Relying on variables to make investment decisions is a complex activity that relies on individual resources. Based on this research, In this study examine the impact of alternative investment decisions by human rational and irrational behavior and then examine the impact of behavioral finance on the decision-making process. behavioral financial phenomenon variables; heuristics, prospect theory, Role of personality, and environmental factors are explored as part of this study. Overconfidence, representativeness, anchoring, regret avoidance, hindsight, the harding effect, and home bias are inherent in investor psychological behavior. A research questionnaire tool for collecting samples and conducting quantitative research. To test the hypothesis regression analysis performed by the SPSS as a result. We found that investment decisions are influenced by behavioral bias. Empirical results concluded that investment decisions are influenced more by heuristic behavior than by prospect theori or role of personality. The results showed that heuristic behavior, prospect theories and the role of personality. Significantly influences investment decisions. With this research, it is hoped that it can help novice investors who will make an investment and to other researchers in the financial institutions.

Indulgence, Self-Control, and Annuity Preferences: Annuity Choices by Members of the Slovak-Funded Private Pension Pillar

This paper employs data from an original survey to analyse the annuity preferences of members of the Slovak-funded private pension pillar. The focus is on the impact of socioeconomic variables as well as various behavioural attitudes upon annuitisation decisions. The research first considers some well-known behavioural factors (framing, mental accounting) and then turns to some underresearched traits (indulgence, self-control) and behaviours (compulsive shopping, saving habits) and links them to annuity choices. Moreover, the research studies alternative allocations of a lump sum and relates them to annuitisation decisions. Indulgence emerged as a significant negative predictor of annuitisation. This result likely refers to preferences towards hedonic lifestyles in early retirement. Interestingly, low self-discipline was also significant but positively related to an intention towards annuity purchases. People who are aware of their low financial discipline may introduce internal self-protection mechanisms and resort to tried and tested financial products to avoid anticipated feelings of regret and desperation. Annuitisation, in this case, is a regret avoidance behaviour.

A multi-criteria decision-making with regret theory-based MULTIMOORA method under interval neutrosophic environment

The MULTIMOORA (multiple multi-objective optimization by ratio analysis) method is useful for multiple criteria decision-making method. It is based on expected utility theory and assumes that decision makers are completely rational. However, some studies show that human beings are usually bounded rational, and their regret aversion behaviors play an important role in the decision-making process. Interval neutrosophic sets can more flexibly depict uncertain, incomplete and inconsistent information than single-valued neutrosophic sets. Therefore, this paper improves the traditional MULTIMOORA method by combining the regret theory under interval neutrosophic sets. Firstly, the regret theory is used to calculate the utility value and regret-rejoice value of each alternatives. Secondly, the criteria weights optimization model based on the maximizing deviation is constructed to obtain the weight vector. Then, the MULTIMOORA method is used to determine the order of the alternatives. Finally, an illustrative example about school selection is provided to demonstrate the feasibility of the proposed method. Sensitivity analysis shows the validity of the regret theory in the proposed method, and the ranking order change with different regret avoidance parameter. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological preference.

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.

Three-way decision for probabilistic linguistic conflict analysis via compounded risk preference

Three-way decision, an essential granular computing research tool, provides an efficient solution to complex and uncertain problems. Behavioral decision theory can analyze the risk preferences of decision-makers effectively. Scholars have conducted preliminary exploration on the fusion of these two theories, but it is still challenging to describe the different types of risk preferences of decision-makers. This paper combines prospect theory with regret theory and studies the compound risk preference modeling of three-way decision to address this issue. Because three attitudes of conflicts coincide with three-way decision, many scholars have conducted multi-dimensional research on three-way conflict analysis and accomplished remarkable results. However, few relevant studies consider psychological factors and risk attitudes of decision-makers, and it is more appropriate to describe agents' attitudes on issues using linguistic terms. This paper applies the proposed three-way decision model based on compounded risk preference and probabilistic linguistic term sets to the conflict analysis problem. We utilize examples to explain the decision-making process of the proposed model and three-way conflict analysis method with the influence of the compounded risk preference under the action of reference point and regret avoidance coefficient. The illustrative example illustrates that the proposed three-way decision model can effectively solve the software development conflict analysis problem for different decision-makers and the comparative analysis shows the advantages of the proposed model and method compared with the two existing methods. Finally, we verify the performance of the three-way decision model based on compounded risk preference by UCI data sets in parameter experiments. The changes of the reference point from 10 to 0 and regret avoidance coefficient in 0, 0.15 and 0.3 respectively demonstrate the trend rule of the model's thresholds and delay-decision rate index.

A complex grid investment decision method considering source-grid-load-storage integration

With the widespread use of renewable energy worldwide, the impact of its randomness and volatility on the grid is increasing. To promote the consumption of renewable energy, the traditional grid is being transformed into a complex grid with integrated source–grid–load–storage. Since the complex grid has the characteristics of source–grid–load–storage interaction, the traditional grid investment decision method will no longer be applicable. First, this study proposes the unilateral indexes of source, grid, load, and storage in complex grids and the interactive indexes considering grid–source interaction, load–grid interaction, source–load interaction, source–storage interaction, load–storage interaction, and grid–storage interaction are proposed to establish the investment decision system. Then, a hesitance fuzzy linguistic term set combined with regret theory is used to calculate the specific values of the subjectivity index, taking into full consideration the regret avoidance and loss avoidance psychology of investors. In order to comprehensively consider the index preference of investors and the objectivity of weight assignment, a combined weighting method based on the analytic network process (ANP) and entropy weight method (EWM) is obtained according to the game theory method. Finally, using a grid in a region of southwest China as an example, the results demonstrate that the construction order obtained in this study can prioritize the projects with the largest comprehensive benefits while considering the subjective preferences of decision-makers and the objectivity of the indexes.

A method based on cloud model and FCM clustering for risky large group decision making

A risky large group decision-making method based on FCM clustering and cloud models is proposed for risky large group decision-making problems with linguistic evaluation scales, unknown attribute weights, and many decision members with unknown weights, considering the psychological behavioral characteristics of decision makers’ regret avoidance. The method first uses the golden partition method to improve the cloud model to transform the uncertain linguistic evaluation matrix into a comprehensive cloud model, which quantifies the fuzziness and randomness of linguistic values. The cloud model expectation values are then extracted to determine the attribute weights using the entropy weighting method. Secondly, the three numerical features of the cloud model are extracted as sample features for FCM clustering to obtain the decision maker’s preference clustering information, and the initial weights of decision-makers are determined according to the majority principle, which improves the existing studies that simply use the expected value of the cloud model for clustering analysis, ignoring the entropy and super entropy for portraying the ambiguity and randomness. On this basis, the Hamming distance is introduced to calculate the closeness to adjust the initial weights of decision-makers, improving the way that the weights of aggregation members are equally distributed in previous studies. Finally, considering the influence of the decision maker’s psychological behavior on decision information in the risky decision-making process, regret theory is introduced to construct a decision maker’s perceived utility matrix, which is combined with the decision maker’s weights to determine and rank the combined perceived utility. Through comparison with existing methods, it is found that the proposed method of recalibration of decision-maker preference clustering, while considering the psychological behavior of decision-maker regret avoidance, not only solves the situation of large group decision making in which expert information is easily distorted but also satisfies the convenience of the calculation process and is more suitable for the situation where there are many decision-makers and their preferences are complicated.

Dynamic Regret Avoidance

In a stock market experiment, we examine how regret avoidance influences the decision to sell an asset while its price changes over time. Participants know beforehand whether they will observe the future prices after they sell the asset or not. Without future prices, participants are affected only by regret about previously observed high prices (past regret), but when future prices are available, they also avoid regret about expected after-sale high prices (future regret). Moreover, as the relative sizes of past and future regret change, participants dynamically switch between them. This demonstrates how multiple reference points dynamically influence sales. (JEL C91, G12, G41)

Application of catastrophe progression method on hybrid multiple attribute decision-making problems based on regret theory

In order to effectively solve the decision-making problems with the diversity of evaluation information, the dynamics of research objects, the limitations of subjective authorization, and the irrational behavior of decision-makers, this paper extends catastrophe progression method to solve hybrid multiple attribute decision-making problems based on regret theory. Firstly, some basic theories are introduced. Secondly, the original catastrophe progression method is extended by using the regret theory, which is employed to solve the multiple attribute decision-making problems with hybrid evaluation information. Finally, a real-life case study of selecting a fresh cold chain logistics service provider is used to verify the practicality and effectiveness of the proposed method, and a comparative analysis with the TOPSIS method and the sensitivity of the regret avoidance coefficient is analyzed in this article.

Explaining resistance intention towards mobile HRM application: the dark side of technology adoption

PurposeThe mobile human resource management application (mHRM app) has recently been seen as an innovative cloud-based solution to manage human resource management (HRM) within organisation. Despite its great potential, organisations have shown resistance towards the usage of the mHRM app. This study investigates the dark side of electronic HRM (eHRM) by examining factors affecting HR professionals' resistance to the mHRM app using status quo bias (SQB) theory. The study also examines the moderating effect of personal innovativeness.Design/methodology/approachResponses were collected from 239 HR professionals using an online survey. Structural equation modelling (SEM) and PROCESS macro were used to examine the hypotheses.FindingsThe results indicated that regret avoidance, inertia, switching costs and perceived threat significantly affect HR professionals' resistance towards mHRM app adoption. Results also indicated that high personal innovativeness negatively moderates the association between inhibitors and resistance to adopt the mHRM app.Practical implicationsThe study's findings will help HR professionals reduce their resistance towards mHRM app adoption.Originality/valueThis study enriches eHRM, mobile applications and the SQB literature.

Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

A probabilistic interval-valued hesitant fuzzy gained and lost dominance score method based on regret theory

With the increasing complexity of the decision environment, the fuzzy theory has attracted the extensive attention of scholars. Probabilistic interval-valued hesitant fuzzy set, as a complex ambiguous information representation tool, dedicates to depict uncertain information as complete as possible. Thus, this paper proposes a probabilistic interval-valued hesitant fuzzy gained and lost dominance score method based on regret theory and interval evidential reasoning approach. In view of the uncertainties in evaluations, the interval evidential reasoning approach is an effective tool to deal with the fuzziness and ignorance, which is first applied in probabilistic interval-valued hesitant fuzzy information in this paper. Besides, an interval projection measure is proposed to consider both distance and direction simultaneously, so as to avoid the problem of information losses in the decision process. On this basis, a novel regret-rejoice function is developed. Then, an extended gained and lost dominance score method based on regret theory and interval evidential reasoning approach is developed, which considers the regret avoidance behavior of experts. Finally, a case study on supplier selection of epidemic prevention products is applied to elucidate and illustrate the application of the proposed method in this paper.