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Climatic changes caused by greenhouse gases (GHGs) emissions are an urgent challenge for all regions around the globe while the livestock sector is an important source of GHGs emissions. The adoption of low-carbon manure treatment technology (LMTT) by farmers is emerging as an effective remedy to neutralize the carbon emissions of livestock. This paper aims to incorporate environmental literacy and social norms into the analysis framework, with the aim of exploring the impact of environmental literacy and social norms on farmers' adoption of LMTT and finally reduce GHGs emission and climate effects.

This research survey is conducted in Hebei, Henan and Hubei provinces of China. First, this research measures environmental literacy from environmental cognition, skill and responsibility and describes social norms from descriptive and imperative social norms. Second, this paper explores the influence of environmental literacy and social norms on the adoption of LMTT by farmers using the logit model. Third, Logit model's instrumental approach, i.e. IV-Logit, is applied to address the simultaneous biases between environmental skill and farmers’ LMTT adoption. Finally, the research used a moderating model to analyze feasible paths of environmental literacy and social norms that impact the adoption of LMTT by farmers.

The results showed that environmental literacy and social norms significantly and positively affect the adoption of LMTT by farmers. In particular, the effects of environmental literacy on the adoption of LMTT by farmers are mainly contributed by environmental skill and responsibility. The enhancement of social norms on the adoption of LMTT by farmers is mainly due to the leading role of imperative social norms. Meanwhile, if the endogeneity caused by the reverse effect between environmental skill and farmers’ LMTT adoption is dealt with, the role of environmental skill will be weakened. Additionally, LMTT technologies consist of energy and resource technologies. Compared to energy technology, social norms have a more substantial moderating effect on environmental literacy, affecting the adoption of farmer resource technology.

To the best of the authors’ knowledge, a novel attempt is made to examine the effects of environmental literacy and social norms on the adoption of LMTT by farmers, with the objective of identifying more effective factors to increase the intensity of LMTT adoption by farmers.

This study proposes a model that influences customer citizenship behavior during COVID-19, and empirically tests the effects of fast-food restaurant customers' perceptions of justice (price and procedural justice) on trust; trust on satisfaction and loyalty; and trust, satisfaction and loyalty on customer citizenship behavior. Furthermore, it was questioned whether there was a disparity between customer expectations based on the restaurant's image and consumption experience.

The data were gathered from customers of fast-food restaurants in the shopping centers in Turkey. The data set, which included 437 valid questionnaires, was subjected to CFA for validity and reliability, SEM analysis for hypothesis and paired sample t-Tests for the research questions.

The findings of the study indicate that perceived justice affects customer trust, which, consequently, affects customer loyalty and satisfaction during the COVID-19 period. Findings also demonstrate that, while customer loyalty and trust increase customer citizenship behavior, customer satisfaction alone is insufficient to increase customer citizenship behavior. The study also shows that during the COVID-19 period, fast-food restaurants should have raised awareness of employees’ fair behaviors toward the customers and provided additional services to differentiate themselves in the market. Also, it indicates that customer expectations related to price, cleanliness and professional appearance of staff are not met after taking service.

No research has been found in the literature focusing on the expectations, justice, trust, satisfaction, loyalty and citizenship behaviors of fast-food restaurant customers in the COVID-19 pandemic process. Therefore, the results can fill the gap in relevant literature by testing the relationships between justice, trust, satisfaction, loyalty and citizenship during the pandemic and provide inferences for fast-food business owners.

This study aims to deeply explore the factors influencing mobile game players' willingness to make in-game purchases, providing references for game developers and marketers to formulate effective strategies.

This research integrates the coolness factors and hedonic motivation system acceptance model to construct a comprehensive theoretical model analyzing mobile game players' willingness to make in-game purchases. The framework includes multidimensional variables such as joy, coolness, immersion, and game experience. Using data from 392 surveys collected from mobile game forums and social networks, the study employs structural equation modeling to analyze the factors and mechanisms influencing players' willingness to make in-game purchases and to verify the related research hypotheses.

The findings reveal that coolness factors have a significant positive impact on game experience and immersion, which in turn affect players' willingness to make in-game purchases. Game experience has a significant positive impact on both immersion and purchase willingness. A good game experience not only increases players' immersion but also directly enhances their willingness to make in-game purchases. Immersion plays a mediating role in the influence of coolness factors and joy on purchase willingness.

By integrating coolness theory with the hedonic motivation system acceptance model, this study constructs a comprehensive model to explore mobile game players' willingness to make in-game purchases. The combination of variables, including personal psychological and social psychological factors, provides a thorough analysis of the factors influencing mobile game purchase willingness, enriching existing research.

This research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience.

Text mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint.

The study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations.

The dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement.

In e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development.

Drawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The purpose of this study is to explore brand customer erosion at both the category and brand levels while considering consumer socio-demographic characteristics and weight of purchase factors.

Data from 3,563 buyers encompassing 20,601 purchases were collected from a prominent household data panel.

Brand customer erosion varies depending on socio-demographic factors (householder age, family size, life cycle and social class) and weight of purchase; variations are evident depending on the specific brand.

The paper makes a substantial contribution to the established fields of marketing and consumer behavior literature by opening a new line of research. It does so by demonstrating, the impact of socio-demographic factors on customer erosion. Simultaneously, it presents results that contradict the limited existing research on the influence of weight of purchase on brand customer erosion.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

This study aims to propose a comprehensive conceptual framework for the characterization of agricultural heritage in desert-prone areas, with a focus on Siwa Oasis in Egypt.

The research utilizes a multidimensional approach, integrating perspectives from sustainability principles, stakeholders and the agricultural heritage frameworks of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and Food and Agricultural Organization (FAO). It involves thematic analysis of qualitative data collected through focus group discussions and interviews with a diverse range of participants, including community members, government officials, nongovernmental organizations (NGOs) and researchers.

The study identifies five key elements of agricultural heritage in Siwa Oasis: cultural, ecological, economic, social and scientific. Cultural elements include traditional farming practices, irrigation systems and architectural features, while ecological elements encompass crop biodiversity, wildlife habitats and traditional landraces. Economically, date palm cultivation, olive production and agritourism activities play crucial roles in livelihood security and local economies. Socially, community cohesion, gender roles and cultural practices shape the social fabric of the oasis, with festivals and rituals fostering a sense of belonging. From a scientific perspective, the integration of traditional knowledge with modern agricultural practices enhances sustainability and resilience.

The study focused solely on Siwa Oasis as a case study, limiting generalizability to other desert-prone areas. Further research could explore additional regions to enhance the understanding of agricultural heritage in diverse contexts. Additionally, the qualitative nature of the study may limit statistical analysis. Future studies could employ mixed-methods approaches for a more comprehensive understanding of agricultural heritage dynamics.

Understanding the multifaceted aspects of agricultural heritage in Siwa Oasis can inform sustainable development initiatives, tourism planning and cultural preservation efforts. Local policymakers and stakeholders can utilize these insights to develop policies that support traditional farming practices, ecotourism and community development. Furthermore, the promotion of agricultural heritage can enhance economic opportunities, food security and environmental sustainability in desert-prone regions.

Recognition of the social elements embedded within Siwa Oasis’s agricultural heritage highlights the importance of community cohesion, gender roles and cultural practices. By acknowledging and preserving these aspects, initiatives can be developed to empower local communities, promote gender equality and preserve cultural identity. Such efforts can enhance social cohesion, strengthen cultural bonds and promote inclusivity in the development agenda of desert-prone areas.

This study contributes a novel conceptual framework that bridges multifunctionality concepts, sustainability principles and stakeholder perspectives to characterize agricultural heritage in desert-prone areas. It offers insights into the complex interplay of cultural, ecological, economic, social and scientific dimensions of agricultural heritage systems.

In the course of worldwide COVID-19, the phenomenon of corporate social responsibility (CSR) gained more importance and publicity. Many organizations made a significant contribution in dealing with the crisis situation and even increased their spending on the welfare activities. This study aims to evaluate the attitude of community, especially young generation, towards the CSR undertaken by the organization during the pandemic period.

Descriptive research design has been employed using purposive sampling for data collection through a structured questionnaire. A sample of 550 was taken, and pilot survey was conducted among 100 respondents before administering it at full scale. A model has been proposed and tested by using structural equation modelling in AMOS.

The results of the study show that compulsory provisions of CSR have enhanced the trust of community and made the organizations more responsive towards philanthropic, legal, ethical and economic responsibility. The respondents were found to be aware about the welfare activities carried out by the organizations and developed a positive attitude towards them.

First, the study is limited to examining the attitude of community towards CSR, especially young generation. Second, it is difficult to say whether outcomes of this study can be generalized for such other potential global crisis. Third, the study is based on the Carroll’s “CSR Pyramid” framework when other such frameworks and approaches could be available to analyse the impact of COVID-19-related CSR initiatives. Last, this study has been conducted only in the state of Maharashtra, and results may not be applicable to other states as well other countries.

The findings of the study may help the organizations to plan their activities in line with the amendments made time to time. This shall also help the regulating agencies to monitor and catch the wrongdoers and take appropriate action. Findings of such studies, based on public opinion, can also help the governments to make further amendments, time to time, in related acts. It can also be said that with the involvement of public/society, more transparency can be brought in the functioning of the organizations, especially in the context of CSR.

Earlier, most of the organizations were falsely recording the CSR expenditure in their balance sheets without actually spending it, but the amendment in act has made it compulsory for the organizations to follow it honestly. The pandemic period gave an opportunity to the society to evaluate the organizations on these parameters and to make their opinion about them in real time. Therefore, it is concluded that pandemic has created awareness in the society and significantly influenced their attitude about CSR activities. This shall also help the organizations feel a pressure in future while planning and implementing the activities under CSR.

The results of the study show contribution of the corporate towards social welfare during pandemic. The results would help the policymakers to monitor the execution of practices more closely and organizations to execute their strategies in a more effective manner.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.