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The study investigates the effect of an uncivil comment made by a party representative on social media and tests whether it can lead to a change in observers' attitude toward the party.

Data are collected from 196 respondents using a scenario-based survey. Proposed model is tested using partial least square structural equation modeling (PLS-SEM).

It is found that individual's moral identity and issue involvement influence perceived civility of the online post, which in turn affects attitude toward the party as well as the individual. It is observed that for high partisans, effect of perceived civility on attitude toward the party is stronger compared to low partisans. Party's lack of responsiveness to address the uncivil comment from its representative increases party's incivility accountability and lowers the partisan attitude toward the party.

The study presents a novel understanding of how political party representatives can influence the image of the party by engaging in an uncivil discourse on social media. Results support that strong partisan would react more unfavorably indicating that loyalty toward the party cannot be taken for granted.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0084

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.

Consumer brand relationship literature has recently seen a surge of studies on brand hate, its antecedents and outcomes. Hate alone will not drive consumers to engage in negative electronic word-of-mouth (eWOM) and indicates the interplay of other social relationship factors that can strengthen the effect of brand hate on negative eWOM. The purpose of this study is to integrate the emerging concept of brand hate and perceived social media power with the theory of planned behavior (TPB) to expand the understanding of negative eWOM.

Data is collected through a survey conducted among university students based in the National Capital Region of Delhi in India. The research model is empirically tested using structural equation modeling in AMOSv23.

The three TPB dimensions, including brand attitude, subjective norms and individual’s propensity to anthropomorphize, are found to influence brand to hate significantly. The other perceived control factors included in the model, perceived homophily and social media self-efficacy, were found to affect perceived social media power, which, in turn, is crucial in predicting consumers’ engagement in negative eWOM behavior, both directly and through interaction with brand hate.

The study contributes to brand hate literature and offers a novel perspective by advocating the role of consumers’ propensity to anthropomorphize in augmenting feelings of brand hate.

This research is conducted in the context of beauty salons in India, to investigate how enhanced perceived acceptance in interpersonal relationships through consuming beauty salon services can generate narcissistic brand love among consumers via the mediation of brand happiness. It also investigates the moderating impact of consumer's anxious interpersonal attachment style and cynicism on the relationship between perceived salon brand-interpersonal acceptance goal congruence and salon brand happiness.

To test the hypothesized relationships, a survey was conducted among 225 regular consumers of beauty salon brands. The data were analyzed using Hayes' (2017) process macro in SPSS.

The results suggest that perceived goal congruence between beauty salon brand-interpersonal acceptance positively influences brand happiness, which in turn predicts consumer's narcissistic brand love. Consumer's anxious interpersonal attachment style positively moderates the effect of brand-interpersonal acceptance goal congruence on brand happiness, while cynicism negatively moderates the path.

Value of the study lies in extending interpersonal acceptance and rejection (IPAR) theory to the domain of consumer–salon brand relationship, to posit that if salon brands satisfy consumers' interpersonal acceptance goals, there is a potential for such happy consumers to love the salon brand, albeit narcissistically.

Deepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content.

The study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3.

The findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs.

The study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.

Consumer–brand relationship literature has seen a recent surge of work on the concept of brand hate. Considering that hate is not easily acknowledged, it is challenging to uncover the psychological mechanisms that underpin its development. Using the concept of “self” as over-arching theory, this study aims to uncover consumers’ psychological triggers for hating a brand by providing contextualized perspectives from the informants.

The authors use an interpretive approach focused on exploring the role of self in explaining the development of brand hate. Data is collected through 25 in-depth interviews and inductively analysed using the NVivo 12.

The findings of the study align with the motivational perspective of hate discussed in psychology literature. Six psychological strategies (coping, moral consciousness, ego defense, self-esteem protection, power reinstatement, and self-concept strengthening) cater to three motives of the self (self-preservation, self-defense, and self-enhancement).

The current study uses an interdisciplinary approach and draws perspectives from psychology, sociology and interpersonal relationship theories to study consumer brand hate. It uncovers the subconscious mechanisms that lead to the germination of brand hate and provides answers to unexplained and missing pieces in the existing literature. In particular, it offers a detailed perspective on how self-related motives can explain the psychology of brand hate.

The study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT).

We collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM).

Our findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools.

Our study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency.

Our study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators.

This paper aims to understand how strong brand attachment can intensify the feeling of perceived betrayal, leading to brand hate after a negative experience with the brand. The study further investigates how consumers make causal attributions for negative experiences when strong brand attachment exists. The moderating effect of a narcissistic personality in the dissemination of negative electronic word of mouth (eWOM) following brand hate is also tested.

The study uses a within-the-subject repeated measures experimental design. A total of 202 college students were exposed to two treatments (high versus no brand attachment), involving a situation of product failure of a smart phone brand. A total of 135 responses were used to compare the outcomes of the two treatments using multivariate analysis. The data of high brand attachment treatment (N = 202) were used to test the proposed research model using partial least square-structural equation modelling.

The results suggest that having a strong positive relationship with the brand can generate stronger feelings of perceived betrayal and brand hate after the brand transgresses the consumer’s expectations. The results indicate that resentful customers can resort to eWOM after feeling betrayed, even though the prior relationship with the brand was strong.

This paper extends the work on perceived betrayal to study brand hate and proposes that brand hate can arise even if there is a strong brand attachment. It contributes to the growing body of literature on brand hate and its possible antecedents. Additionally, the study poses some crucial managerial implications for the brand managers by suggesting that strong brand relationships not always ensure loyalty or commitment and can lead to consequences that are damaging for the brand equity.

Brands today operate in a dynamic business environment, which often requires them to take courageous actions, from taking a stand on controversial issues to responding to changing market needs. However, these actions are not merely strategic but also represent a unique aspect of the brands’ identity, which includes holding up to their core values and being resilient to social pressure. To better understand this positive virtue, the current study introduces the concept of brand bravery – a novel brand archetype that emulates the brand’s distinct identity. This study aims to conceptualize brand bravery and develop a psychometrically sound scale to measure it and investigate its relationship with positive brand relationship outcomes.

Two qualitative studies were conducted to identify the dimensions of perceived brand bravery. Three empirical studies develop and validate the proposed measurement scale and confirm the construct’s nomological validity by proposing a framework that explains the outcomes of perceived brand bravery.

Results from multiple studies support a seven-factor second-order reflective scale of perceived brand bravery, with dimensions altruism, bold, courageous, determined, enduring, fearless and gritty. The construct of brand bravery is found discriminant from other conceptually distinct but related brand attributes. Nomological validity tests further suggest that perceived brand bravery leads to positive consumer-related outcomes such as brand advocacy behaviors, positive attitude and consumer brand identification.

Brand bravery provides a vital roadmap to marketers who have sought to create a leading brand that can stay relevant in times of disruption. The multi-factor scale can help managers track, which dimension of the brand bravery scale is more relevant for shaping overall bravery perception.

The study introduces a novel brand attribute that has not been previously discussed beyond social and moral psychology literature. It conceptualizes brand bravery that will strengthen the understanding of this specific brand characteristic and provides a practical scale to measure brand bravery.

The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.