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To provide high torques needed to move a robot’s links, electric actuators are followed by a transmission system with a high transmission rate. For instance, gear ratios of 100:1 are often used in the joints of a robotic manipulator. This results into an actuator with large mechanical impedance (also known as nonback-drivable actuator). This in turn generates high contact forces when collision of the robotic mechanism occur and can cause humans’ injury. Another disadvantage of electric actuators is that they can exhibit overheating when constant torques have to be provided. Comparing to electric actuators, pneumatic actuators have promising properties for robotic applications, due to their low weight, simple mechanical design, low cost and good power-to-weight ratio. Electropneumatically actuated robots usually have better friction properties. Moreover, because of low mechanical impedance, pneumatic robots can provide moderate interaction forces which is important for robotic surgery and rehabilitation tasks. Pneumatic actuators are also well suited for exoskeleton robots. Actuation in exoskeletons should have a fast and accurate response. While electric motors come against high mechanical impedance and the risk of causing injuries, pneumatic actuators exhibit forces and torques which stay within moderate variation ranges. Besides, unlike direct current electric motors, pneumatic actuators have an improved weight-to-power ratio and avoid overheating problems.

The aim of this paper is to analyze a nonlinear optimal control method for electropneumatically actuated robots. A two-link robotic exoskeleton with electropneumatic actuators is considered as a case study. The associated nonlinear and multivariable state-space model is formulated and its differential flatness properties are proven. The dynamic model of the electropneumatic robot is linearized at each sampling instance with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Within each sampling period, the time-varying linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. An H-infinity controller is designed for the linearized model of the robot aiming at solving the related optimal control problem under model uncertainties and external perturbations. An algebraic Riccati equation is solved at each time-step of the control method to obtain the stabilizing feedback gains of the H-infinity controller. Through Lyapunov stability analysis, it is proven that the robot’s control scheme satisfies the H-infinity tracking performance conditions which indicate the robustness properties of the control method. Moreover, global asymptotic stability is proven for the control loop. The method achieves fast convergence of the robot’s state variables to the associated reference trajectories, and despite strong nonlinearities in the robot’s dynamics, it keeps moderate the variations of the control inputs.

In this paper, a novel solution has been proposed for the nonlinear optimal control problem of robotic exoskeletons with electropneumatic actuators. As a case study, the dynamic model of a two-link lower-limb robotic exoskeleton with electropneumatic actuators has been considered. The dynamic model of this robotic system undergoes first approximate linearization at each iteration of the control algorithm around a temporary operating point. Within each sampling period, this linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. The linearization process relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modeling error which is due to the truncation of higher-order terms from the Taylor series is considered to be a perturbation which is asymptotically compensated by the robustness of the control algorithm. To stabilize the dynamics of the electropneumatically actuated robot and to achieve precise tracking of reference setpoints, an H-infinity (optimal) feedback controller is designed. Actually, the proposed H-infinity controller for the model of the two-link electropneumatically actuated exoskeleton achieves the solution of the associated optimal control problem under model uncertainty and external disturbances. This controller implements a min-max differential game taking place between: (i) the control inputs which try to minimize a cost function which comprises a quadratic term of the state vector’s tracking error and (ii) the model uncertainty and perturbation inputs which try to maximize this cost function. To select the stabilizing feedback gains of this H-infinity controller, an algebraic Riccati equation is being repetitively solved at each time-step of the control method. The global stability properties of the H-infinity control scheme are proven through Lyapunov analysis.

Pneumatic actuators are characterized by high nonlinearities which are due to air compressibility, thermodynamics and valves behavior and thus pneumatic robots require elaborated nonlinear control schemes to ensure their fast and precise positioning. Among the control methods which have been applied to pneumatic robots, one can distinguish differential geometric approaches (Lie algebra-based control, differential flatness theory-based control, nonlinear model predictive control [NMPC], sliding-mode control, backstepping control and multiple models-based fuzzy control). Treating nonlinearities and fault tolerance issues in the control problem of robotic manipulators with electropneumatic actuators has been a nontrivial task.

The novelty of the proposed control method is outlined as follows: preceding results on the use of H-infinity control to nonlinear dynamical systems were limited to the case of affine-in-the-input systems with drift-only dynamics. These results considered that the control inputs gain matrix is not dependent on the values of the system’s state vector. Moreover, in these approaches the linearization was performed around points of the desirable trajectory, whereas in the present paper’s control method the linearization points are related with the value of the state vector at each sampling instance as well as with the last sampled value of the control inputs vector. The Riccati equation which has been proposed for computing the feedback gains of the controller is novel, so is the presented global stability proof through Lyapunov analysis. This paper’s scientific contribution is summarized as follows: (i) the presented nonlinear optimal control method has improved or equally satisfactory performance when compared against other nonlinear control schemes that one can consider for the dynamic model of robots with electropneumatic actuators (such as Lie algebra-based control, differential flatness theory-based control, nonlinear model-based predictive control, sliding-mode control and backstepping control), (ii) it achieves fast and accurate tracking of all reference setpoints, (iii) despite strong nonlinearities in the dynamic model of the robot, it keeps moderate the variations of the control inputs and (iv) unlike the aforementioned alternative control approaches, this paper’s method is the only one that achieves solution of the optimal control problem for electropneumatic robots.

The use of electropneumatic actuation in robots exhibits certain advantages. These can be the improved weight-to-power ratio, the lower mechanical impedance and the avoidance of overheating. At the same time, precise positioning and accurate execution of tasks by electropneumatic robots requires the application of elaborated nonlinear control methods. In this paper, a new nonlinear optimal control method has been developed for electropneumatically actuated robots and has been specifically applied to the dynamic model of a two-link robotic exoskeleton. The benefit from using this paper’s results in industrial and biomedical applications is apparent.

A comparison of the proposed nonlinear optimal (H-infinity) control method against other linear and nonlinear control schemes for electropneumatically actuated robots shows the following: (1) Unlike global linearization-based control approaches, such as Lie algebra-based control and differential flatness theory-based control, the optimal control approach does not rely on complicated transformations (diffeomorphisms) of the system’s state variables. Besides, the computed control inputs are applied directly on the initial nonlinear model of the electropneumatic robot and not on its linearized equivalent. The inverse transformations which are met in global linearization-based control are avoided and consequently one does not come against the related singularity problems. (2) Unlike model predictive control (MPC) and NMPC, the proposed control method is of proven global stability. It is known that MPC is a linear control approach that if applied to the nonlinear dynamics of the electropneumatic robot, the stability of the control loop will be lost. Besides, in NMPC the convergence of its iterative search for an optimum depends on initialization and parameter values selection and consequently the global stability of this control method cannot be always assured. (3) Unlike sliding-mode control and backstepping control, the proposed optimal control method does not require the state-space description of the system to be found in a specific form. About sliding-mode control, it is known that when the controlled system is not found in the input-output linearized form the definition of the sliding surface can be an intuitive procedure. About backstepping control, it is known that it cannot be directly applied to a dynamical system if the related state-space model is not found in the triangular (backstepping integral) form. (4) Unlike PID control, the proposed nonlinear optimal control method is of proven global stability, the selection of the controller’s parameters does not rely on a heuristic tuning procedure, and the stability of the control loop is assured in the case of changes of operating points. (5) Unlike multiple local models-based control, the nonlinear optimal control method uses only one linearization point and needs the solution of only one Riccati equation so as to compute the stabilizing feedback gains of the controller. Consequently, in terms of computation load the proposed control method for the electropneumatic actuator’s dynamics is much more efficient.

Amidst unpredictable and turbulent periods, such as the COVID-19 pandemic, service organization’s responses are required to be innovative, adaptable and resilient. The purpose of this study is to explore the utilization of both reconfiguration and transformational strategies as instruments for cultivating resilience and advancing sustainability in service organizations.

The study examines a proposed resilience model using fuzzy logic. The research also used a semantic differential scale to capture nuanced and intricate attitudes. Finally, to augment the validity of the resilience model, a measurement scale was formulated using business mathematics and expert opinions.

Although investing in resilience training can help organizations gain control and maintain their operations in times of crisis, it may not directly help service organizations understand the external turmoil, seek available resources or create adaptive remedies. Conversely, high levels of reconfiguration and transformation management vigour empower a service organization’s revolutionary, malleable vision, organizational structure and decision-making processes, welcoming talented and innovative employees to enhance capabilities during crises.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations identifying the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research guides service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. The study elaborates on the enhancement of resilience, increasing innovation, improving efficiency and enhancing customer satisfaction for service organizations to remain competitive and contribute to positive social and economic outcomes through the adoption of both reconfiguration and transformational strategies.

The study also guides the service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. Rapid innovation and business model innovation are essential components, enabling service organizations to foster a culture of innovation and remain competitive. In addition, the adoption can lead to improved financial performance, job creation and economic growth, contributing to positive social and economic impacts.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations. It identifies the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research also provides a foundation for further investigation into the effectiveness of these strategies and their impact on organizational performance and sustainability. By better preparing service organizations for disruptions and uncertainties, this research triggers ameliorated organizational performance and sustainability.

Within the realm of the service industry, the present investigation has undertaken the development, quantification and scrutiny of both resilience and tenacity. In addition, it has delved into the intricate dynamics surrounding the influencing factors and antecedents that bear upon resilience, elucidating their consequential impact on the operational performance and outlook of service-oriented organizations. The findings derived from this research furnish valuable insights germane to enhancing operational efficacy and surmounting impediments within the sector.

The purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.

It becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input X_j (j = 1, 2, ………. s), and output y_i. The inputs and output variables are formed by the membership functions µ_Aij(x_j) and µ_Ci(y_i) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.

The fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.

The novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.

Today, with the increasing involvement of the environment and human beings business units, paying attention to fulfilling social responsibility obligations while making a profit has become increasingly necessary for achieving sustainable development goals. Attention to profit by organizations should not be without regard to their social and environmental performance. Social responsibility accounting (SRA) is an approach that can pay more attention to the social and environmental performance of companies, but it has many barriers. Therefore, the purpose of this study is to identify barriers to SRA implementation and provide strategies to overcome these barriers.

In this study, the authors identify barriers to social responsibility accounting implementation and provide strategies to overcome these barriers. By literature review, 12 barriers and seven strategies were identified and approved using the opinions of six academic experts. Interpretive structural modeling (ISM) has been used to identify significant barriers and find textual relationships between them. The fuzzy technique for order performance by similarity to ideal solution (TOPSIS) method has been used to identify and rank strategies for overcoming these barriers. This study was undertaken in Iran (an emerging market). The data has been gathered from 18 experts selected using purposive sampling and included CEOs of the organization, senior accountants and active researchers well familiar with the field of social responsibility accounting.

Based on the results of this study, the cultural differences barrier was introduced as the primary and underlying barrier of the social responsibility accounting barriers model. At the next level, barriers such as “lack of public awareness of the importance of social responsibility accounting, lack of social responsibility accounting implementation regulations and organization size” are significant barriers to social responsibility accounting implementation. Removing these barriers will help remove other barriers in this direction. In addition, the results of the TOPSIS method showed that “mandatory regulations, the introduction of guidelines and social responsibility accounting standards,” “regulatory developments and government incentive schemes to implement social responsibility accounting,” as well as “increasing public awareness of the benefits of social responsibility accounting” are some of the essential social responsibility accounting implementation strategies.

The findings of the study have implications for both professional accounting bodies for developing the necessary standards and for policymakers for adopting policies that facilitate the implementation of social responsibility accounting to achieve sustainability.

This paper creates a new perspective on the practical implementation of social responsibility accounting, closely related to improving environmental performance and increasing social welfare through improving sustainability.

Experts believe that the strategies mentioned above will be very effective and helpful in removing the barriers of the lower level of the model. To the best of the authors’ knowledge, for the first time, this study develops a model of social responsibility accounting barriers and ranks the most critical implementation strategies.

Small and medium-scale enterprises (SMEs) that operate with modest financial investments and commodities face numerous challenges to remain in business. One major philosophy used by SMEs these days is the implementation of lean manufacturing to get solutions for various issues they encounter. But is lean getting sustained over time? The purpose of this research is to design a Sustainable Lean Performance Index (SLPI) to assess the sustainability of lean systems and to pinpoint the variables that might be present as potential lean system inhibitors which hinder the sustainability of leanness.

A multi-level sustainable lean performance model is constructed and presented based on the literature research, field investigation and survey conducted by administering a questionnaire. Fuzzy logic approach is used to analyse the multi-level model.

SLPI for the SMEs is found using fuzzy logic approach. Additionally, the ranking score system is applied to categorise attributes into weak and strong categories. The performance of the current lean system is determined to be “fair” based on the Euclidean distance approach and the SLPI for SMEs.

This work is concentrated only in South India because of the country’s vast geographical area and rich and wide diversity in industrial culture of the nation. Hence, more work can be done incorporating the other parts of the country and can analyse the lean behaviour in a comparative manner.

The generalised sustainable lean model analysed using fuzzy logic identifies the inhibitors and level of performance of SMEs in South India. This can be implemented to find out the level of performance in the SMEs after a deeper study and analysis around the SMEs of the country.

The sustainable assessment of lean parameters in the SMEs of India is found to be very less in literature, and it lacks profundity. The model established in this study assesses the sustainability of the lean methodology adopted in SMEs by considering the lean and sustainability attributes along with enablers like technology, ethics, customer satisfaction and innovation with the aid of fuzzy logic.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

To cater to the senior tourist market, it is essential to comprehend the factors motivating and deterring them from international travel post-COVID-19. This study aims to focus on senior citizens’ destination choice intentions and aims to develop a model that prioritizes positive and negative factors leading to international travel destination choices. It uses push–pull factors, perceived travel risks (PTRs) and perceived travel constraints (PTCs).

Decision-making trial and evaluation laboratory (DEMATEL) and fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS) are two multi-criteria decision-making (MCDM) techniques used to identify connections between variables and determine their relative importance in the decision-making model.

DEMATEL found push and pull factors are “effects” while PTCs and PTRs are “causes” affecting senior citizens’ destination choices. Push factors and PTCs have a greater impact than pull factors and PTRs. Fuzzy TOPSIS highlighted “improving health and wellness” and “self-fulfillment and spirituality” as key push factors, “health safety and security quotient” as the most important pull factor, and “interpersonal constraints” as the most critical PTC. Finally, “health risks” is the top PTR.

This paper adds to the tourism literature by looking at the relationship between senior tourists’ motivation, PTRs and PTCs and showing how the subfactors affect their choice of destination rank. The data analysis techniques used in this study are also novel, having never been used before in senior tourism research. Finally, even though there is a lot of research on senior tourism, not much is known about how Indian senior tourists act. In light of this study’s findings, practical recommendations were offered to tourism stakeholders worldwide, interested in tapping into the market of Indian outbound senior tourists or repositioning product or destination offerings to take this promising market or similar markets into account.

为了成功迎合蓬勃发展的老年旅游市场, 了解激励和阻止老年人国际旅行的因素尤为重要, 尤其是在 COVID-19 之后。本研究侧重于老年人的目的地选择意向, 并基于推拉因素、感知旅行风险 (PTR) 和感知旅行限制 (PTC), 旨在开发影响老年人国际旅游目的地选择的积极和消极因素的模型。

决策试验和评估实验室 (DEMATEL), 和与理想解决方案相似度的模糊偏好顺序 (Fuzzy TOPSIS) 是两种多标准决策 (MCDM) 技术, 用于识别变量之间的联系并找出它们在决策模型中的相对重要性。

DEMATEL的结果表明, 推力和拉力因素是“影响”, 而感知旅行约束（PTC）和感知旅行风险（PTR）是影响老年人目的地选择意愿的因素中的“原因”。推动因素和 PTC 比拉动因素和 PTR 发挥更重要的作用。 Fuzzy TOPSIS分析结果表明, “改善健康”和“自我实现和精神”是推动因素下最重要的因素。此外, 目的地的“健康安全商数”是拉动因素中最重要的, “人际约束”是PTC中最重要的。最后, 研究结果表明, “健康风险”是 PTR 中最重要的。

本文通过评估旅游动机、PTR 和老年游客 PTC 之间的相互关系, 为现有的旅游文献做出了贡献。此外, 该研究展示了影响老年游客目的地选择意愿的因素中各个子因素的比较优先级。本研究中使用的数据分析技术也很新颖, 以前从未在老年人旅游研究中使用过。最后, 虽然对老年旅游有丰富的研究, 但印度老年旅游者的行为相对不为人知。研究结果向有兴趣进入印度出境老年游客市场或重新定位产品或目的地的全球旅游利益相关者提供了切实可行的建议, 以考虑这个有前景的市场或类似市场。

Para atender a un mercado turístico de la tercera edad, es esencial comprender los factores que les motivan y les disuaden de realizar viajes internacionales tras el COVID-19. Este estudio se centra en las intenciones de elección de destino de las personas mayores y pretende desarrollar un modelo que priorice los factores positivos y negativos que conducen a la elección de un destino de viaje internacional. Utiliza los factores push-pull, los riesgos de viaje percibidos (PTR) y las limitaciones de viaje percibidas (PTC).

Decision Making Trial and Evaluation Laboratory (DEMATEL) y Fuzzy Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) son dos técnicas de toma de decisiones multicriterio (MCDM) utilizadas para identificar las conexiones entre variables y determinar su importancia relativa en el modelo de toma de decisiones.

DEMATEL descubrió que los factores de empuje y atracción son “efectos,” mientras que las PTC y las PTR son “causas” que afectan a las elecciones de destino de las personas mayores. Los factores de empuje y los PTC tienen un mayor impacto que los factores de atracción y los PTR. El Fuzzy TOPSIS destacó la “mejora de la salud y el bienestar” y la “autorrealización y espiritualidad” como factores de empuje clave, el “cociente de seguridad y protección de la salud” como el factor de atracción más importante y las “limitaciones interpersonales” como el PTC más crítico. Por último, los “riesgos para la salud” son el principal PTR.

Este artículo se suma a la literatura turística al estudiar la relación entre la motivación de los turistas sénior, los PTR y los PTC y mostrar cómo afectan los subfactores a su elección del destino. Las técnicas de análisis de datos empleadas en este estudio también son novedosas, ya que nunca se habían utilizado en la investigación sobre el turismo senior. Por último, aunque existen muchas investigaciones sobre el turismo sénior, el comportamiento de los turistas de la tercera edad en la India es relativamente desconocido. A la luz de los resultados del estudio, se ofrecen recomendaciones prácticas a las partes interesadas en el turismo de todo el mundo, interesadas en aprovechar el mercado de los turistas senior indios emisores o en reposicionar la oferta de productos o destinos para tener en cuenta este prometedor mercado o mercados similares.