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The study, grounded on the social cognitive career theory, seeks to analyze the upshot of psychological empowerment on career satisfaction via affective commitment. The study also aims to examine the impact of the interplay among affective commitment and resilience on the career satisfaction of Indian service industry professionals.

In this study, the data has been collected from 277 employees using standardized measures from the North Indian service industry. To test the proposed model, Analysis of Moment Structures (AMOS) (Hayes, 2013) was utilized.

The outcomes of the study offered substantial support for the theorized link between psychological empowerment, affective commitment, resilience and career satisfaction. The outcomes confirmed an affirmative association concerning psychological empowerment and career satisfaction through affective commitment as a mediator and resilience as a moderator. The study concludes that the workforce with extraordinary resilience will perceive a stronger influence of psychological empowerment on career satisfaction.

The study offers a few pertinent inputs for the organizations operating in high-power distance culture to comprehend the role of psychological empowerment and “resilient attributes” of personality in developing a sense of career satisfaction amongst Indian service sector employees.

The present research examines the association between psychological empowerment, affective commitment, resilience and career satisfaction for the first time as mediated moderation model and the same has neither been examined theoretically or empirically.

This preliminary study aims to examine the use of a co-designed immersive virtual reality intervention programme in improving access to health care for people with intellectual disability.

A co-production approach was used to design a virtual reality intervention in collaboration with people with intellectual disability, their families and carers. A mixed-method single sample pre-test-post-test design examined using a virtual reality intervention simulating health-care environments to improve access of attending health-care appointments. Qualitative feedback was used to understand participants’ experience and opinions of using the digital technology.

The study found that the intervention did help people access health-care appointment and reduced their fear. Improvements were also found in quality-of-life post intervention. Positive feedback was provided from participants on using digital technologies indicating the novelty of the approach and potential further applications.

To the best of the authors’ knowledge, this is the first study which has used virtual reality to support people with intellectual disability access health care.

This study aims to investigate whether consumers and small businesses can transition from disposable to reusable coffee cups, using a community social marketing intervention, led by a Social Purpose Organisation.

An emergent case study approach using multiple sources of data developed an in-depth, multifaceted, real-world context evaluation of the intervention. The methodology draws on citizen science “messy” data collection involving multiple, fragmented sources.

Moving from single-use cups to reusables requires collective commitment by retailers, consumers and policymakers, despite the many incentives and penalties applied to incentivise behaviour change. Difficult post-COVID economics, austerity and infrastructure gaps are undermining both reusable acceptance and interim solutions to our dependence upon disposables.

Although the non-traditional methodology rendered gaps and omissions in the data, the citizen science was democratising and inclusive for the community.

Our practical contribution evaluates a whole community intervention setting to encourage reusable cups, integrating multiple stakeholders, in a non-controllable, non-experimental environment in contrast to previous research. This paper demonstrates how small community grants can foster impactful collaborative partnerships between an SPO and researchers, facilitate knowledge-exchange beyond the initial remit and provide a catalyst for possible future impact and outcomes.

To assess the impact at both the outcome and the process level of the intervention, we use Pawson and Tilley’s realist evaluation theory – the Context Mechanism Outcome framework. The methodological contribution demonstrates the process of citizen science “messy” data collection, likely to feature more frequently in future social science research addressing climate change and sustainability challenges.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

I aimed to obtain a deeper insight into the link between supplier involvement in product development (SIPD), supplier relationship resilience and company performance.

To collect data, a survey among 500 Polish manufacturing companies was conducted. I used quantitative methods (structural equation modeling) to test several research hypotheses referring to a single supplier–customer relationship. Thanks to the use of multi-construct measurement of SIPD and supplier relationship resilience, the study provides detailed research results on the topic.

Collaborative practices implemented during SIPD increase procurement flexibility and decrease redundancy in the relationship with the involved supplier. Communication during SIPD increases supplier flexibility and procurement flexibility. Increased supplier flexibility and increased procurement flexibility in the relationship with the involved supplier as well as collaborative practices during SIPD positively impact company performance. I confirmed the indirect effect between communication during SIPD and company performance when the mediators are supplier flexibility and procurement flexibility. Decreased redundancy in relationship with involved supplier does not impact company performance.

Supply chain managers need to rethink SIPD practice to effectively ensure supply chain resilience (SCRES), especially in the face of the contemporary global crisis and black swans affecting the supplier base. My article provides important managerial insights into drivers of SCRES and company performance.

To the best of my knowledge, this research is among the first to conclude that SIPD does not have an unequivocally positive or direct impact on supplier relationship resilience. The research fills the gap by analyzing the impact of SIPD on two main SCRES elements. The study examines supplier relationship resilience, understood as flexibility and redundancy elements, in a single supplier–buyer relationship perspective. Thus, the presented considerations go beyond the traditional understanding of flexibility and redundancy in supplier relationship management, that is through the prism of double or multi sourcing and having back up-suppliers.

This paper aims to explore how sustainability practices were implemented in a higher education institution within a local setting in the Gulf and Arab Emirates Region. This study examined the impact of social and cultural requirements on the development of the master plan for the New Kuwait University campus with regards to sustainability to illustrate how current social and cultural requirements impact the design of a future learning environment whilst highlighting the essential role of organisational actors in this implementation process.

Using an in-depth case study approach, the authors conducted 21 semi-structured interviews with educators and administrative staff who had been involved in the sustainability implementation process at Kuwait University. These participants were involved at different stages in the implementation of a major sustainability project at Kuwait University. The interviews were further supplemented by analysing supporting documents and communications.

The analysis reveals that sustainability was embedded in a narrative that was repeated at the practice level; this directed the setting of objectives for the project and its various sub-tasks. It also helped actors to develop their understandings of practice and the importance of social emotions, self-intentions and patterns of culture in the process. This study further reveals that participants mainly focused on environmental issues regarding saving paper/electricity and overlooked aspects of a wider concepts and core values of sustainability, and there is a significant amount of lack of knowledge and awareness on matters about sustainability, especially with the understanding of its definition.

This study draws on practice-organisation framework used by Schatzki (2002, 2010), suggesting that sustainability implementation is a process led by rules, practical understanding, general understanding and teleoaffective structures, to highlight the role of agency and change among various actors in implementing sustainability. A practice-theory framing is used to signpost the roles played by various actors in establishing goals and tasks for the project while taking account of local understanding and independence in the implementation of sustainability practices. Engaging with practice theory framework offers us theoretical basis that is fundamentally different from the theories of interaction-oriented approaches in sustainable design.

The criticality of late deliveries in transportation lies in the threat of considerable multi-level supply chain costs. This study aims to reveal the dynamic capabilities playing a facilitating role in preventing delay, thus providing timely delivery, as well as developing an understanding of how and when those capabilities are activated within the supply chain network.

An exploratory study was conducted involving 16 semi-structured expert interviews with the representatives of logistics service providers and shippers. Following an interpretive phenomenology framework, the prevention phenomenon was explained.

Findings revealed two preventive capability categories in delay prevention: (1) proactive capabilities, referring to the enabling actions planned before departure, and (2) reactive capabilities, referring to actions planned after departure. Findings pinpoint that, in addition to the proactive capabilities, reactive capabilities enabled by innovative problem-solving actions are crucial for adapting to a dynamically changing environment in prevention. Moreover, this study shows that prevention capabilities are characterized by tangible and intangible resources and integration of resources with external links which constitute a delay prevention network within a wider service ecosystem.

This study stands out with its specific focus on delay prevention capabilities and enabling actions from the perspectives of logistics service providers and shippers. The premises of the resource-based view are combined with dynamic capabilities theory, leading to a proposed time-based taxonomy of proactive and reactive capabilities in supply chains, aimed at creating value and strengthening resilience.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.