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The agri-food industry is experiencing a revolutionary shift due to the introduction of Industry 4.0 technologies to improve efficiency, transparency and sustainability. The importance of agri-food supply chains (AFSC) in promoting sustainability is expanding as the globe struggles with issues including resource scarcity, climate change and population growth. In order to better understand how Industry 4.0 might improve sustainability in a world that is changing quickly, this work aims to focus on identifying various sustainability assessment factors influencing AFSC to increase overall sustainability, minimize resource consumption, cut waste and streamline operations.

Important sustainability assessment factors are identified from the past academic literature and are then validated using the fuzzy-Delphi method. A method called decision-making trial and evaluation laboratory (DEMATEL) is used to examine and analyze structural models with complex causal linkages. The results are then validated using sensitivity analysis.

The factors that emerged as the highest ranked for evaluating the sustainability of Industry 4.0 in AFSC are market competitiveness, and knowledge and skill development, followed by resource efficiency. Industry 4.0 technologies are essential for increasing the marketability of agricultural products because of the major implications of market competitiveness. The significance of knowledge and skill development draws attention to Industry 4.0’s contribution to the promotion of chances for farmers and agricultural employees to increase their capability.

By outlining the nexus between Industry 4.0 technologies and sustainability, the study presents a comprehensive framework that would be relevant for researchers, policymakers and industry stakeholders who want to leverage Industry 4.0 technology to build more sustainable AFSC in the future. The study findings can help the farmers or producers make sensible choices that adhere to sustainability standards and guarantee long-term financial viability.

The originality of this work lies in the identification of sustainability assessment factors especially for AFSC in the era of digitalization which has not been discussed previously.

Influencer transgressions can disappoint their followers. However, there is a lack of clarity about the effects of a false allegation on an influencer–follower relationship. Drawing from cognitive dissonance and moral reasoning theory, the current study aims to examine how this relationship is shaped across three time periods (before the allegation is leveled, after the allegation is leveled, and when the allegation is found to be baseless).

We study comments posted by followers of two falsely alleged social media influencers (SMI) on their YouTube and Instagram channels. Latent Dirichlet allocation (LDA) followed by netnography is used for thematic analysis. LDA is a social media topic modeling method that processes a statistically representative set of words to explain the tone and tenor of qualitative conversations. A sentiment analysis of the comments is done using SentiStrength.

When an allegation is leveled initially, the response from followers is overwhelmingly negative toward the influencer owing to moral coupling. However, when the allegations are proven to be false, the followers return to a positive opinion of the influencer, owing to feelings of dissonance and guilt.

The study contributes to the fields of influencer marketing, cognitive dissonance and moral reasoning. It highlights how endorsers can take advantage of the positive sentiment that arises once an accused SMI’s transgression is proven false.

This study introduces the concept of “Sentiment Reversal,” which is exhibited in the social media space. In this phenomenon, sentiments move from negative to positive toward the falsely accused SMI as they are vindicated of the previous charge.

Recent worldwide developments have altered how businesses operate. As a result, when making business decisions, the emphasis for many industries has shifted towards digital adoption to ensure sustainability, and the food supply chain is no exception. However, a substantial gap exists in assessing the barriers to a digitised food supply chain enabled by Industry 5.0 technologies. This study strives to bridge the gap by identifying and assessing the barriers to improved traceability.

For this study, a mixed method approach was used encompassing both qualitative and quantitative techniques, including an online survey, exploratory factor analysis (EFA), and the fuzzy evidential reasoning approach (FERA). The literature survey and expert opinion first yielded a list of 18 barriers, which were subsequently examined using EFA. As a result, four barriers were removed. The remaining 14 barriers were then assessed using FERA from the perspectives of the Technology, Organisation and Environment (TOE) framework. Further, a sensitivity analysis was performed to test the model’s reliability.

The present study resulted in the prioritisation of barriers from the TOE perspective. According to the findings, the top three barriers that impede the traceability of Industry 5.0-enabled digital food supply chains are Limited Digital and Physical Infrastructure, Inadequate Capital Investment, and the Intricate Supply Chain Framework.

The findings from this research will prove valuable for decision-makers, practitioners and policymakers in developing methods for improving traceability within the digital food supply chain. Concerned stakeholders may use the findings to identify and take immediate action for better decision-making.

This study’s originality lies in its position as one of the first to identify and examine the challenges to better traceability in an Industry 5.0-enabled digital food supply chain. It also adds value by broadening the TOE framework’s scope in the Industry 5.0-enabled digital food supply chain context.

This study focuses on the unsolved problem of the mechanism underlying the effect of big data analytics capabilities (BDAC) on organizational learning (OL) by shedding light on the roles of knowledge management (KM) and data-driven decision-making (DDDM) as mediators. Practitioners and academics have considered the underlying mechanism through which BDAC enhance OL.

This study proposes a framework by reviewing existing management literature and collecting data from 343 respondents. The model was tested using structural equation modeling and the bootstrapping method.

The results indicate that BDAC enhances OL, KM and DDDM. Furthermore, KM enhanced the DDDM and OL. In addition, DDDM enhances the OL. Finally, we verified that KM and DDDM mediated the link between BDAC and OL.

This study provides new insights into the mechanism underlying the effect of BDAC on OL by elucidating the mediating roles of DDDM and KM.

The thriving global tourism industry is a pivotal player in the global economy. It significantly contributes to a substantial carbon footprint through emissions from transportation, lodging, and infrastructure. In this study we have conducted a critical analysis of carbon emissions within the tourism industry using previous academic literature and institutional databases. Addressing the importance of measuring carbon footprints, the study delves into the definition of the carbon footprint in this sector, emphasizing the urgent need to comprehend and address its effects. This investigation also centers on the aspect of transportation, which is contributing to carbon emissions in the tourism industry. It explores variations based on transportation modes and discusses mitigation strategies, such as green transit. It also concentrates on accommodation, scrutinizing its environmental impact by evaluating carbon emissions from energy consumption, water usage, and waste generation in hotels and resorts. Emphasis is placed on promoting sustainable and green lodging facilities. Furthermore, this study examines the environmental impact of the tourism industry. Specifically, it examines the carbon footprint associated with infrastructure development. The analysis includes the impact of construction and development, resource consumption, and the role of communities and stakeholders in sustainable tourism planning. Furthermore, an assessment is conducted to examine the obstacles associated with adopting sustainable practices and assess the effectiveness of implemented mitigation strategies within the tourism industry. The concluding section outlines future perspectives and recommendations for research.

Spirituality serves as an ethical benchmark for shaping human and brand identity. Only a few studies have recently attempted to examine the impact of spiritual attributes on customer behavior. This study extends the current literature and aims to develop a research model to investigate spiritual brand attributes toward customers’ behavior. The study employs the stimulus–organism–response (SOR) model as a theoretical signpost to construct the research model. Driven by this chapter, future research opportunities are presented, and the opportunity for empirical research is also illustrated.

This paper aims to examine the structure and content of religious tourism destination image through the reviews of visitors to Makkah and Medina, two of the world’s most popular Muslim pilgrimage sites.

This is an exploratory netnographic study of the 913 reviews posted on TripAdvisor from 2018 to 2022. The structure (dimensions and attributes) and content (variables) comprising the construct of religious tourism destination image emerge through manual thematic analysis and confirmed through content analysis.

Religious tourism destination image is a three-dimensional – cognitive, affective and conative – construct comprising both religion-specific, generally sacred and secular variables in a single, indivisible crystallization of experience.

Destination marketing organizations and marketers of tourism enterprises should regularly analyze visitor reviews posted on social media and carefully manage all variables of the religious tourism destination image, specifically stressing the religious aspect.

To the best of the authors’ knowledge, this is the first systematic analysis of the structure and content of religious tourism destination image based on detailed consumer evaluations and unprompted storytelling.

The purpose of this paper is to investigate the moderating role of cultural factors (concern for face and stability of attribution) in the relationships among service recovery quality, postrecovery satisfaction and repurchase intention. Based on the politeness theory, this paper proposes a theoretical model for understanding how concern for face and stability of attribution may affect collectivists’ consumption behavior.

Data were collected in a field survey of 600 Hong Kong consumers who had experienced a telecommunications service failure. Partial least squares structural equation modeling (PLS-SEM) was used to test the theoretical hypotheses.

A cultural factor of concern for face is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction. Face also positively influences the relationship between postrecovery satisfaction and repurchase intention. Another cultural factor, stability of attribution, is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction and to negatively moderate the relationship between postrecovery satisfaction and repurchase intention.

This study contributes to the understanding of the relevance of concern for face and stability of attribution in collectivists’ consumption behavior. The findings have significant implications for managers in a position to exploit the cultural value mechanisms of collectivist consumers.

To the best of the authors’ knowledge, this has been the first research to examine the impact of concern for face and stability of attribution among service recovery quality, postrecovery satisfaction and repurchase intention.

During the past decade, marketing literature has focused on investigating the dark side of consumer emotions and their behavior. This demands further research for a more in-depth understanding. Hence, drawing on affective events theory, this study aims to examine employee incivility (rude and poor behavior) and its consequences on brand hate. Furthermore, the authors also introduced perceived justice as a moderator to buffer the impact of brand hate.

A scenario-based survey was used to collect data from 212 consumers from the airline industry. Structural equation modeling was performed through SmartPLS.

Findings revealed that employee incivility instigates brand hate. Results further showed that attitudinal brand hate influences brand opposition and detachment, whereas behavioral brand hate only influences brand detachment. Additionally, perceived justice moderates the negative relationship between employee incivility and brand hate. Overall, the results showed that employee incivility can trigger brand hate, and service recovery can reduce the negative impact of employee behavior on brand hate in the aviation sector.

This study contributes to brand hate and service recovery literature.

This study provides deep insights into how employee behavior can cause brand hate and how recovery strategies can help buffer its impact.

In the existing literature, limited studies have focused on organizational factors instigating brand hate. To the best of the authors’ knowledge, this study is the first to associate employee incivility and brand hate.