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Growing environmental issues worldwide need the engagement of all stakeholders to compliance with the decisions of global leaders made at COP21 and COP26. In this regard, the present study looks at the influence of retailer social responsibility and consumer environmental responsibility by reinforcing consumer’s green consumption behaviors. Similarly, the proposed study incorporates the mediating role of customer trust and environmental concern to understand whether retailer corporate social responsibility and consumer environmental responsibility strengthen green consumption behavior.

Multiple hypotheses have been developed in light of the theoretical analysis of the available literature. The information was gathered through a survey method. A web-based portal was used to administer the survey, and 340 useable responses were processed by SPSS 23.0 and AMOS 23.0 for experiential analysis. First, the validity and reliability were evaluated. The authors then tested potential relationships using structural equation modeling.

Survey data analyzed using the SEM approach reveal that consumer environmental responsibility and retailer CSR does not drive green consumption behavior directly. However, green concern and consumer trust mediates the relation of consumer environmental responsibility and retailer CSR towards green consumption behavior. Another mediating path was also tested through environmental responsibility and green concern among retailer's CSR and green consumption behavior. The outcomes of this path are also significant.

The study holds promising implications for green consumption behaviors. The following can be achieved by implementing more sustainable supply chain strategies, such as lowering carbon footprint, purchasing eco-friendly goods and supporting environmental causes through retailers and consumers as well.

This study investigated the joint contribution of retailer CSR and environmental responsibility to green consumption for the first time. The work strengthens the body of knowledge in the field of managerial decision-making and creates new directions for scholarly investigation.

Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users can be made more accountable.

Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals.

AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI.

This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research.

The purpose of this article is twofold: to identify the characteristics of research on organisation and management in Arab countries and to find out whether research results support the culture‐free hypothesis or not. A thorough search of sixteen journals, research monographs, books and theses produced only 35 empirical studies. Most of these studies were exploratory, descriptive, and used small convenient samples. Although some findings supported the culture‐bound hypothesis, major conceptual and methodological weaknesses in these studies throw doubt upon the validity of their results.

Big data analytics has emerged as one of the most used keywords in the digital world. The hype surrounding the buzz has led everyone to believe that big data analytics is the panacea for all evils. As the insights into this new field are growing and the world is discovering novel ways to apply big data, the need for caution has become increasingly important. The purpose of this paper is to conduct a literature review in the field of big data application for humanitarian relief and highlight the challenges of using big data for humanitarian relief missions.

This paper conducts a review of the literature of the application of big data in disaster relief operations. The methodology of literature review adopted in the paper was proposed by Mayring (2004) and is conducted in four steps, namely, material collection, descriptive analysis, category selection and material evaluation.

This paper summarizes the challenges that can affect the humanitarian logistical missions in case of over dependence on the big data tools. The paper emphasizes the need to exercise caution in applying digital humanitarianism for relief operations.

Most published research is focused on the benefits of big data describing the ways it will change the humanitarian relief horizon. This is an original paper that puts together the wisdom of the numerous published works about the negative effects of big data in humanitarian missions.

The purpose of this study is to investigate the various challenges of humanitarian supply chains (HSC) and how these challenges can be addressed using artificial intelligence (AI).

This study employs exploratory analysis to identify various issues in HSC and the use cases of AI to address these issues through published literature. Subsequently, we collected tweets from Twitter and posts from LinkedIn using relevant keywords over four months. The collected data were cleaned, analyzed and interpreted to gain insights into users' perspectives on the various issues and use cases of AI in HSC.

The analysis reveals that various issues of HSC such as logistical challenges, security concerns, health and safety, access constraints, information gaps, coordination and collaboration, cultural sensitivity, funding constraints, climate and environmental factors and ethical dilemmas are predominantly discussed in published literature. Meanwhile, user-generated content reveals different levels of prioritization of these issues and AI attributes and offers AI-based solutions.

This study is subject to certain limitations, including a restricted data collection period of only four months and the use of just two social media platforms. These limitations could be addressed by conducting a more comprehensive and extended data collection across additional platforms to produce more conclusive findings. Another limitation is the lack of contextual information, which may have provided more specific insights.

To the best of the authors’ knowledge, this is possibly the first paper to explore both published literature and the collective intelligence of social media users to examine AI attributes, the various challenges of HSC and how AI can address these challenges.

As Generation Y is considered to be a lucrative segment for emerging devices, this study investigates the effect of emotional brand attachment, from the brand romance perspective, on brand loyalty of Generation Y smartphone users. Furthermore, this study examines gender differences in the same relationship.

The study adopted a cross-sectional survey method and data was collected from 300 respondents. Data was analyzed using the structural equation modelling (SEM) approach and multi-group analysis was performed to examine gender differences in the model.

Results revealed that all three aspects of brand romance (pleasure, arousal and dominance) have a positive impact on smartphone brand loyalty. It further denotes that the relationship between brand romance and brand loyalty differs from males to females.

This study makes a significant contribution by examining emotional attachment and brand loyalty of Generation Y consumers, which has been less investigated. Furthermore, both attitudinal and behavioral brand loyalty has been considered in this study, which has largely been overlooked in similar studies. Examining the gender difference in the above relationship is an additional contribution.

This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential “game changers” in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no “silver bullet” for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.

Based on a combination of the authors’ research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research.

Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions.

This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

This paper aims to provide a systematic literature review of the state-of-the-art applications of climate information in humanitarian relief efforts, to further the knowledge of how climate science can be better integrated into the decision-making process of humanitarian supply chains.

A systematic literature review was conducted using a combination of key search terms developed from both climate science and humanitarian logistics literature. Articles from four major databases were retrieved, reduced and analyzed.

The study illustrates the status of application of climate information in humanitarian work, and identifies usability, collaboration and coordination as three key themes.

By delivering an overview of the current applications and challenges of climate information, this literature review proposes a three-phase conceptual framework.

The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group.

In support of Priority 2 (“Strengthening disaster risk governance to manage disaster risk”) of the Sendai Framework and SDG17 (“Partnerships for the goals”), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry.

For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery, pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data.

Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine.