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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.

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 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).

The purpose of this study is to investigate the role of sales, as a proxy for size, in moderating the impact of institutional incongruence between formal and informal institutions on the formalization of microenterprises in middle-income countries in Latin America.

The paper uses a probit regression model to examine business formalization as a binary outcome of formal and informal institutions. Data was collected through interviews and surveys across 52 municipalities in the Metropolitan Region of Santiago, Chile. The study used a stratified sampling approach and was conducted between November 2022 and January 2023.

The results offer three key insights into the formalization of microenterprises in middle-income countries. First, we show that formal institutions do not significantly influence formalization decisions among microentrepreneurs in middle-income countries, challenging the traditional belief that formal institutions alone significantly influence formalization in these contexts. Second, we show that informal institutions are significant predictors of informality, especially among smaller microenterprises. Third, we highlight that the smaller the business, the stronger the negative effect of informal institutions on formalization, and thus, the institutional incongruence between formal and informal institutions decreases for larger businesses.

This paper contributes to management literature by shedding light on the drivers of formalization in middle-income countries, a departure from most formalization studies wherein the focus is primarily on low-income economies. The findings suggest that policymakers in middle-income countries should focus on enabling microenterprise growth through sales, rather than targeting specific demographic groups or relying solely on formal institutional enforcement to promote formalization.

El objetivo de este estudio es investigar el papel de las ventas, utilizadas como un indicador de tamaño, en la mediación del impacto de la incongruencia institucional entre instituciones formales e informales en la formalización de microempresas en países de ingresos medios en América Latina.

Utilizamos un modelo de regresión Probit para examinar la formalización empresarial como un resultado binario de instituciones formales e informales. Los datos se recopilaron a través de 110 entrevistas y encuestas en 52 municipios de la Región Metropolitana de Santiago, Chile. El estudio empleó un enfoque de muestreo estratificado y se llevó a cabo entre noviembre de 2022 y enero de 2023.

Nuestros resultados ofrecen tres ideas clave sobre la formalización de microempresas en países de ingresos medios. Primero, demostramos que las instituciones formales no influyen significativamente en las decisiones de formalización entre las microempresas en países de ingresos medios; esto desafía la creencia tradicional de que las instituciones formales por sí solas influyen significativamente en la formalización en estos contextos. Segundo, nuestro estudio muestra que las instituciones informales son predictores significativos de la informalidad, especialmente entre las microempresas más pequeñas. Tercero, nuestro estudio destaca que el efecto negativo de las instituciones informales sobre la formalización es más fuerte para negocios de menor tamaño; por lo tanto, la incongruencia institucional entre instituciones formales e informales disminuye para negocios de mayor tamaño.

Este artículo contribuye a la literatura iluminando sobre los impulsores de la formalización en países de ingresos medios, a diferencia de la mayoría de los estudios de formalización en la región latinoamericana que se centran principalmente en países de bajos ingresos. Nuestros hallazgos sugieren que los responsables de políticas en países de ingresos medios deberían centrarse en impulsar el crecimiento de las microempresas a través de las ventas, en lugar de enfocarse en grupos demográficos específicos o depender únicamente del cumplimiento institucional formal para promover la formalización.

O objetivo deste estudo é investigar o papel das vendas, usadas como um indicador de tamanho, na mediação do impacto da incongruência institucional entre instituições formais e informais na formalização de microempresas em países de renda média na América Latina.

Utilizamos um modelo de regressão Probit para examinar a formalização empresarial como um resultado binário de instituições formais e informais. Os dados foram coletados por meio de 110 entrevistas e pesquisas em 52 municípios da Região Metropolitana de Santiago, Chile. O estudo empregou uma abordagem de amostragem estratificada e foi realizado entre novembro de 2022 e janeiro de 2023.

Nossos resultados oferecem três ideias-chave sobre a formalização de microempresas em países de renda média. Primeiro, demonstramos que as instituições formais não influenciam significativamente as decisões de formalização entre as microempresas em países de renda média; isso desafia a crença tradicional de que as instituições formais, por si só, influenciam significativamente a formalização nesses contextos. Segundo, nosso estudo mostra que as instituições informais são preditores significativos da informalidade, especialmente entre as microempresas menores. Terceiro, nosso estudo destaca que o efeito negativo das instituições informais sobre a formalização é mais forte para negócios de menor porte; portanto, a incongruência institucional entre instituições formais e informais diminui para negócios de maior porte.

Este artigo contribui para a literatura iluminando os impulsionadores da formalização em países de renda média, ao contrário da maioria dos estudos de formalização na região latino-americana, que se concentram principalmente em países de baixa renda. Nossos achados sugerem que os responsáveis pelas políticas em países de renda média deveriam focar em impulsionar o crescimento das microempresas por meio das vendas, em vez de se concentrar em grupos demográficos específicos ou depender exclusivamente do cumprimento institucional formal para promover a formalização.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.