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This paper aims to “identify,” “analyse” and “categorise” the readiness factors of lean sustainability in health-care organisation using total interpretive structural modelling (TISM).

To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview. To identify how the factors interact, the TISM approach was used, and the matriced’ impacts croise’s multiplication applique’e a UN classement (MICMAC) analysis was used to rank and categorise the lean sustainability readiness factors.

This study identified ten lean sustainability readiness factors for health-care organisation. The identified factors are resources utilization practice (F1), management commitment and leadership (F2), operational flexibility (F3), workforce engagement and time commitment (F4), sustainability motivational factors (F5), awareness of lean and sustainable practice (F6), hospital design (F7), energy efficiency practices in hospitals (F8), responsible autonomy (F9) and new system adoptability training (F10). The key/driving factors are identified in this study are operational flexibility, sustainability motivational factors, management commitment and leadership, new system adoptability training.

The study focussed primarily on lean sustainability factors for the health-care sector.

This research will aid key stakeholders and academics in the better understanding the readiness factors that influence lean sustainability in health-care organisation. This study emphasises the factors that must be considered when applying lean sustainable practices in health care as a real-world application in a health-care organisation. These readiness factors for lean sustainability can be used by an organization to comprehend more about the concept and the components that contribute to health-care lean sustainability.

This study proposes the TISM technique for health care, which is a novel attempt in the subject of lean sustainability in this sector.

Green transformation is more than simply a trend; it is a way of life, a set of habits, a field of knowledge and a dedication to resource conservation. Going green is surely a creative and transformative process for both individuals and organizations. This paper aims to “identify,” “analyse” and “categorise” the readiness factors for green transformation process in health care using total interpretive structural modelling (TISM) and neutrosophic-MICMAC.

To address the study objectives, the study used TISM and neutrosophic-MICMAC analysis. To identify the readiness factors, a literature study was conducted, and the factors were face-validated by the healthcare experts. The factors influence on one another were captured by using a scheduled interview with a closed ended questionnaire. The TISM addressed the identification and analysing of factors and the categorization and ranking the readiness factors is addressed by using neutrosophic-MICMAC analysis.

This study identified 11 green transformation process readiness factors for healthcare organizations. The study states that the key factors or driving factors are awareness of green governance principle, environment leadership and management, green gap analysis, information and communication technology and innovation dynamics.

The factor ranking is sensitive to the respondents’ ratings. The study relied on the past literature and experts’ opinion may result in the subjective biases. The complex nature of healthcare ecosystem challenges to capture all the factors. The study focussed on Indian hospitals.

Study significantly impacts the healthcare practitioners, academicians and policymakers by providing critical insights into the readiness factors required for the healthcare green transformation process. The study offers a better understanding of the crucial or key or driving factors that aid in embracing green and sustainable practices.

Identifying a gap in conceptual and theoretical frameworks for green transformation readiness factors in healthcare organizations and in Indian context. The study addresses this gap by aiming to create a thorough theoretical framework and highlighted by its focus on Indian hospitals.

The purpose of this study is to find out what is the relationship between sustainability toward consumer behavior. Consumer behavior is the method of choosing, buying and using goods and services with an attachment to needs and wants. Now consumers are aware about sustainability, they make purchase decisions according to environmental safety, benefit to the society and increase economic growth.

This study validates the result through experts in textile industry by using the Decision-Making Trial and Evaluation Laboratory approach. This method has many benefits which provide decision makers and experts to understand the interdependence and influential relation between the criteria by hierarchical approach.

According to the results, green culture (F8) and green brand (F3) are the most influential (causal) factors and exert a substantial amount of influence over other factors for achieving organizational performance and sustainability. On the other hand, past experience (F14) and time pressure (F12) are the most influenced (effect) factors that are highly influenced by other factors.

The study conducted in Pakistan underscores the significance of maintaining a healthy and pristine environment for future generations. Both consumers and organizations play a vital role in this endeavor. It is imperative that they actively promote and support goods and services that advocate for sustainability.

Mangers should use long-term strategies that meet the high product value to enhance the organization’s reputation, so it will have positive consumer perception. If managers make policies to implement natural resources in their raw material, so this policy avoids conflicts and maintains a balance in our society.

This research delves into the complexities and subtleties associated with the identification and examination of the interconnections between the success factors of sustainability and consumer behavior.

This study proposed and tested a new conceptual model of memorable volunteer tourism experiences (MVTEs) by examining the effects of novelty, meaningfulness, experience co-creation and experience intensification on MVTEs. It also examined the relationships among MVTEs, psychological resilience (PR) and behavioural intention (BI), including that between novelty and BI.

The study modelled the proposed relationships by analysing data from an online survey using Amazon Mechanical Turk. In total, 241 responses were used in the data analysis.

This study extended the MVTE construct and included four key antecedents that influence tourists' MVTEs. The study also documented the predictive capability of MVTEs for PR and BI.

Volunteer tourism organisations should offer new and diverse activities for volunteer tourists, such as nature conservation, wildlife protection and construction.

This is the first study to examine the antecedents and outcomes of MVTEs using the stimuli–organism–response theory.

The purpose of this study is to find the suitable trajectory path of the Numerical model of the Quadcopter. Quadcopters are widely used in various applications due to their compact size and ease of assembly. Because they are quite unstable, autonomous control systems would be used to overcome this problem. Modelling autonomous control is predominant as the research scope faces challenges because of its highly non-linear, multivariable system with 6 degree of freedom.

Quadcopters with antonym systems can operate in an unknown environment by overcoming unexpected disturbances. The first objective when designing such a system is to design an accurate mathematical model to describe the dynamics of the system. Newton’s law of motion was used to build the mathematical model of the system.

Establishment of the mathematical model and the physics behind a four propeller drone for the frame TAROT 650 carbon was done. Simulink model was developed based on the mathematical model for simulating the complete dynamics of the drone as well as location and gusts were included to check the stability.

The control response of the system was simulated numerically results are discussed. The trajectory path was found. The phases with their own parameters can be used to implement the mathematical model for another type of quadcopter model and achieve quick development.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

Although women have advanced in the economic sphere, the gender pay gap (GPG) remains a persisting problem for gender equality. Using Acker's theory of gendered organisations, this study strives to gain a better understanding from a macro and micro approach, how family and work-related policies, especially family-friendly measures (FFMs), and their uptake, contribute and maintain the GPG in Malta and specifically within the Financial and Insurance sector.

Two research instruments were used. National policy documents were analysed through the gender lens, followed by structured interviews with HR managerial participants within this sector.

Findings suggest that at a macro level, family and work-related policies could be divided into two broad categories: A set of family-friendly policies that contribute to the GPG because of their gendered nature, or because the uptake is mostly taken by women. These include make-work pay policies, which initially appear to be gender neutral, but which attracted lower educated inactive women to the Maltese labour market at low pay, contributing to an increase in the GPG. Second, a set of policies that take on a gender-neutral approach and help reduce the GPG. These include policies like the free childcare and after school care scheme that allow mothers to have a better adherence to the labour market. At the micro level within organisations, pay discrepancies between women and men were largely negated and awareness about the issue was low. Here, “ideal worker” values based on masculine norms seemed to lead to covert biases towards mothers who shoulder heavier care responsibilities in the families and make a bigger use of FFMs. Because men are better able to conform to these gendered values and norms, the GPG persists through vertical segregation and glass ceilings, among others.

Since not all the companies in the Eurostat NACE code list participated in this research, results could not be generalised but were indicative to future large-scale studies..

At the macro and policy level, some FFMs take on a clear gendered approach. For example, the disparity in length between maternity (18 weeks) and paternity leave (1 day) reinforces gender roles and stereotypes, which contribute to the GPG in the long run. While some FFMs like parental leave, career breaks, urgent family leave, telework, flexible and reduced hours seem to take on a more gender-neutral approach, the uptake of FFMs (except childcare) seems to generate discriminatory behaviour that may affect the GPG. When considering the make-work pay policies such as the “in-work benefit” and the “tapering of benefits”, this study showed that these policies attracted lower educated and low-skilled women into the labour market, which in turn may have further contributed to the increasing GPGs. On the other hand, the childcare and after school policies relieve working mothers from caregiving duties, minimising career interruptions, discriminatory behaviour and overall GPGs.

This study confirmed that organisations within the Financial and Insurance sector are gendered and give value to full-time commitment and long working hours, especially in managerial roles. Managerial positions remain associated with men because mothers tend to make more use of FFMs such as parental leave, reduced, flexible hours and teleworking. Mothers are indirectly penalised for doing so, because in gendered organisations, the uptake of FFMs conflict with the demands of work and ideal worker values (Acker, 1990). This maintains the vertical segregation and widens the GPG within the Financial and Insurance sector.

By using the gender lens and taking a wider and more holistic approach from the macro and micro level, this study highlights how interlinking factors lead to and sustain the GPG in the Financial and Insurance sector in Malta.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

The aim of this paper is to analyse the moderating effect of digital transformation (DT) on the relationship between innovation capacity and the performance of agrifood businesses.

Based on a structured questionnaire, data on 98 agrifood small and medium-sized enterprises operating in Spain were collected. The data were analysed using partial least squares structural equation modelling to test the proposed model and study the moderating effect of DT on the relationship between the innovation capacity and performance of agrifood businesses.

The study finds support for the hypotheses and shows the existence of a direct positive relationship between the DT and performance of agrifood businesses. Developing new practices focussed on customer service and performing actions such as improving sales, promoting a new product or service and including a post-sales service influence the capacity of a business to undertake DT.

Future studies should include other factors in the analysis. For example, the impact of knowledge transfer and research and development (R&D) on agrifood businesses' performance should be studied. Education and training, as well as having a diverse network, can help develop and boost businesses' capacity to use and apply the required innovation. Networks play an important role in learning how to apply DT.

This quantitative study is the first to examine the moderating effect of DT in the Spanish agrifood sector on the relationship between innovation capacity and performance. The study examines the role of the DT of companies and explores the competitiveness and efficiency tools that digitalisation offers. Innovation capacity is crucial for the application of these tools.

The purpose of this study is to examine the predictive value of psychopathy features on crime-related emotional states in forensic male patients with offence histories who were mandated to Dutch clinical care.

The study had a retrospective design in which psychopathy features were assessed using the Psychopathy Checklist-Revised. For each patient, information on the events leading up to the crime and a description of the crime itself were extracted from the hospital record to assess emotional states. These crime-related emotional states were assessed using the mode observation scale. The sample consisted of 175 patients with offence histories.

Multiple regression analyses indicated that affective features of psychopathy were a negative predictor for feelings of vulnerability in the events leading up to the crime but not predictive of loneliness. The interpersonal features were predictive of deceit during criminal behaviour.

This study leads to a better, more nuanced and substantiated understanding of which emotional states play a prominent role in criminal behaviour and how these states are affected by psychopathic traits. This knowledge can influence existing treatment programmes for patients with offence histories.

Several studies have examined the relationship between emotional states and criminal behaviour and between psychopathy and emotions, but less is known about the predictive relationship between psychopathy features and crime-scene-related emotional states.