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This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

This study examines the effect of social media marketing on voting intention applying a combination of fuzzy logic methodology and a multidimensional panel data model.

The study adopts a multidimensional panel data method that includes several fixed effects. The dependent variable is a multifaceted construct that measures the participants’ intention to vote. The independent variables are electronic word of mouth (eWOM), customisation (CUS), entertainment (ENT), interaction (INT), trendiness (TRD), candidate’s perceived image (CPI), religious beliefs (RB), gender and age. The grouping variables that signify fixed effects are employment status, level of education, mostly used social media and religion. First, the significance of said fixed effects was tested through an ANOVA process. Then, the main model was estimated, including the significant grouping variables as fixed effects.

Employment status and level of education were significant fixed effects. Also, eWOM, ENT, INT, CPI, RB and gender significantly affected participants’ voting intention.

Being based on a questionnaire that asked participants about how they perceive different aspects of social media, the present study is limited to their perceptions. Therefore, further studies covering the voters’ behaviour in action could be efficient complements to the present study.

The findings could guide the political parties into prioritizing the aspects of social media in forming an effective campaign resulting in being elected.

The findings have the potential to help the public in making better informed decisions when voting. Furthermore, the results of this study indicate applications for social media which are beyond leisure time fillers.

Fuzzy logic and multidimensional panel data estimates are this study’s novelty and originality. Structural equation modelling and crisp linguistic values have been used in previous studies on social media’s effect on voting intent. The former refines the data gathered from a questionnaire, and the latter considers the possibility of including different grouping factors to achieve a more efficient and less biased estimation.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

This paper aims to analyze decisions about payment rates on credit card statements by using background factors and perceptions that indirectly influence beliefs, according to the theory of planned behavior.

Since legal and institutional frameworks and household financial surveys are heterogeneous among countries, household data on the Chilean economy is used as the starting point in this matter.

The probability that an individual chooses to pay amounts less than the total billing of their credit cards rises with essential variables related to perceived behavioral control. Being the head of the household, being younger, perceiving a high or excessive financial burden of debt and facing unfavorable and unexpected situations that divert the budget, among others, are relevant to repayment decisions.

The novelty of this article is that its psychological approach differs from the traditional focus of economic rationality regarding credit cards. The results are relevant for policymakers and financial regulators due to implications for household behavioral finance and means of payment.

Analizamos la decisión de la tasa de pago de los estados de cuenta de tarjetas de crédito a través del uso de factores de fondo y percepciones que indirectamente inciden en las creencias de acuerdo a la teoría del comportamiento planeado.

Debido a que los marcos legales e institucionales, así como también las encuestas financieras de hogares son heterogéneas entre países, se utilizan datos de los hogares de la economía chilena como un punto de partida en esta materia.

La probabilidad de que un individuo elija pagar un monto menor que el total de facturación de sus tarjetas de crédito es afectada por variables proxy asociadas al control conductual percibido. La condición de ser jefe de hogar, ser más joven, la percepción de una alta o excesiva carga financiera de la deuda, y enfrentar situaciones desfavorables e inesperadas que desvían del presupuesto, entre otras, son relevantes para las decisiones de pago.

La novedad de este artículo es que su enfoque difiere del enfoque tradicional de la racionalidad económica en relación a las tarjetas de crédito. Los resultados son relevantes para los hacedores de política y reguladores financieros debido a sus implicancias para las finanzas conductuales de los hogares y sus medios de pago.

Housing market research involves observing the relationships between housing value and its indicators. However, recent literature indicates that the disruption of the COVID-19 pandemic could have an impact on the forecasting properties of some of the housing indicators. This paper aims to observe the relationships between the home value index and three potential indicators to verify their forecasting properties pre- and post-COVID-19 and provide general recommendations for time series research post-pandemic.

This study features three vector autoregression (VAR) models constructed using the home value index of the USA, together with three indicators that are of interest according to recent literature: the national unemployment rate, private residential construction spending (PRCS) and the housing consumer price index (HCPI).

Unemployment, one of the prevalent indicators for housing values, was compromised as a result of the COVID-19 pandemic, and a new indicator for housing value in the USA, PRCS, whose relationship with housing value is robust even during the COVID-19 pandemic and HCPI is a more significant indicator for housing value than the prevalently cited All-Item consumer price index (CPI).

The study adds residential construction spending into the pool of housing indicators, proves that the finding of region-specific study indicating the unbounding of housing prices from unemployment is applicable to the aggregate housing market in the USA, and improves upon such widely accepted belief that overall inflation is a key indicator for housing prices and proves that the CPI for housing is a vastly more significant indicator.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.

The authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.

The results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.

This paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.

This study investigates the observed resurgence in religious beliefs seen across many societies during the COVID-19 pandemic. Using the economic theory of religious clubs, the author models religious participation during the pandemic as a mechanism for alleviating the financial distress associated with the health distress from the pandemic.

Using data from the COVID-19 National Longitudinal Phone Survey (NLPS) in Nigeria, the author investigates the economic motivation for religious intensity during the COVID-19 pandemic. To address endogeneity concerns, the author exploits geographic variables of temperature and longitudes as sources of COVID-19 risk.

Overall, health distress stimulates religious intensity. Consistent with the economic theory of religious clubs, adverse health shocks stimulate financial distress, and the effect is stronger among religious participants. Similarly, people see God and not the government as a source of protection against COVID-19.

The study’s model sees religious organizations as public goods providers, especially when governments and markets are inefficient.

The study’s recommendations support an expanded role for religious networks in healthcare delivery and more public funding to attenuate the post-pandemic resurgence of social violence in economically distressed regions.

Despite the research interest in the COVID-19 pandemic, the long-term implications, many of which relate to social behavior adjustments that cause individuals to identify more closely with their social group, need greater understanding. Suppose religious intensity is linked to economic distress. In that case, this is a major source of worry for countries whose economies are subject to higher fluctuations and where the governments and markets are inefficiently organized. These regions may be more susceptible to a resurgence in religious fundamentalism associated with the economic shocks from the pandemic. Consequently, these regions would require more public funding to attenuate the potential for costly activities like organized violence, suicide attacks and terrorist activities in the aftermath of the pandemic.

Prompted by the observation of the increase in religious identity through religious intensity during the pandemic, the author contributes by developing theoretically-based hypotheses that are incentive-compatible to provide a rational justification for the observation. The author empirically validates the hypothesis by taking advantage of the COVID-19 National Survey in Nigeria by specifically using survey rounds 4 and 7 which have more comprehensive religious items included.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0719

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).