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Controversy has long swirled over the claim that Donald Trump's base has deeply rooted authoritarian tendencies, but Trump himself seems to have few doubts. Asked whether his stated wish to be dictator “on day one” of second term in office would repel voters, Trump said “I think a lot of people like it.” It is one of his invariable talking points that 74 million voters supported him in 2020, and he remains the unrivaled leader of the Republican Party, even as his rhetoric escalates to levels that cautious observers now routinely call fascistic.

Is Trump right that many people “like” his talk of dictatorship? If so, what does that mean empirically? Part of the answer to these questions was apparent early, in the results of the 2016 American National Election Study (ANES), which included survey questions that we had proposed which we drew from the aptly-named “Right-Wing Authoritarianism” scale. Posed to voters in 2012–2013 and again in 2016, those questions elicited striking responses.

In this chapter, we revisit those responses. We begin by exploring Trump's escalating anti-democratic rhetoric in the light of themes drawn from Max Weber and Theodor W. Adorno. We follow this with the text of the 2017 conference paper in which we first reported that 75% of Trump's voters supported him enthusiastically, mainly because they shared his prejudices, not because they were hurting economically. They hoped to “get rid” of troublemakers and “crush evil.” That wish, as we show in our conclusion, remains central to Trump's appeal.

This study employs S-D Logic to examine the hotel booking behaviors of individuals, with a focus on the impact of service customization on service cancellation. Additionally, the moderating role of social co-creation is explored to provide further insight.

The paper draws on booking data from two hotels: a resort hotel with 40,060 recorded bookings, including 11,122 cancellations, and bookings from a city hotel with 79,330 bookings, including 33,102 cancellations.

The result reveals that bookings with higher levels of initial customization, such as special requests, are more likely to be modified later and less likely to be canceled. Interestingly, while multi-adult bookings were found to have a higher cancellation rate than individual bookings, the effects of customization commitment were more pronounced for multi-adult bookings.

This paper is the first to establish a connection between service customization, the number of adults on a booking and the likelihood of cancellation, thus providing new empirical evidence for the emergence of customization effects in services. Additionally, the study identifies important contingencies based on the number of consumers in a booking.

The Pacific Island nation of Fiji, spanning 100s of islands, has been characterised by both geographical and ethnic divisions between, mainly, Indigenous Fijians and Fijians of Indian descent. The latter took shape in quite blatant forms in the island nation's historical tendency towards ethnic politics but has also been enacted across its sporting traditions. Today, while ethnic politics still exists to a degree, encouraged by ethnopolitical entrepreneurs, the reality is more nuanced. Divisions remain not only along the popularised lines of ethnicity but also across hierarchical, class and gender boundaries that have received somewhat less scholarly attention. This nuance is visible in the performance and packaging of Fijian sport and through the meanings that local people attach to it. This chapter, therefore, draws upon the experience of ethnographic fieldwork within and across Fijian subcultures with a focus on rugby and soccer. Inclusive of participant observation and interviews with diverse Fijian sporting stakeholders from differing intersections of local sport and society, the key threads above will be untangled to reveal a more three-dimensional and collective impression of contemporary Fiji.

Against social cognitive and social exchange theories, this research paper aims to investigate the significance and interaction between perceived knowledge, involvement, trust and brand community engagement in brand communities (BC).

BC participants (n = 503) completed a cross-sectional survey for this research. Analysis was performed using PLS-SEM via SmartPLS (v. 4.1.0.2) and the novel Necessary Condition Analysis (NCA).

An integrative KITE model with positive and significant relationships of key BC constructs was established. The perceived BC knowledge influenced involvement and engagement. Furthermore, the constructs of involvement and trust were discovered to have a positive and significant impact on engagement, with trust having a substantial effect on BC engagement. The indirect effects of the trust construct via the BC knowledge and BC involvement constructs were also significant.

This research advances the existing conceptual approaches by introducing knowledge as the key BC constructs. The study illustrates that members’ knowledge about a BC facilitates their involvement in the BCs. The vital role of trust is revealed in the KITE model, as it is significantly related to BC knowledge, BC involvement and BC engagement with at least medium to large effect sizes. Notably, the role of trust is enhanced as it is the only necessary must-have (instead of “should-have”) condition to achieve high levels of BC engagement. Furthermore, the KITE model provides insights for marketers to develop a valuable BC.

Despite the positive impact of professional learning communities (PLCs) in improving teaching practices, many teachers still struggle to effectively integrate information and communication technologies (ICTs) into their teaching and learning. Drawing from human capital theory and spillover effects, this paper examines how teachers PLCs can facilitate ICT integration.

Using a qualitative methodology, the researchers designed a phenomenological study. From semi-structured interviews, data were collected from 15 selected secondary school teachers from four selected secondary schools in Tanzania.

The study revealed that teachers use various strategies to enhance ICT integration in teaching practices, namely, community collaboration, practice-based approaches to ICT integration and the utilization of digital learning tools in instructional practices. Furthermore, the results showed several constraints on the ability of teachers’ PLCs to encourage ICT integration. These constraints were divided into three parts, i.e. major challenges at the macro, meso and micro levels.

The paper has the potential to inform policy and practice, particularly in the area of PLCs. Also, it helps to better understand the changing practices with ICTs through PLCs when there are insufficient resources for ICT integration.

To support teachers in using ICTs in their instructional practices, it is essential to build their capacities through PLCs to increase their confidence and competence in ICT integration.

The purpose of this paper is to explore the challenges of implementing accountable artificial intelligence (AI) systems in India, focusing on the need for algorithms to justify their decisions, especially in subjective and complex scenarios. By analyzing various government projects, documented biases and conducting empirical case studies and experiments, the study highlights the limitations of AI in recognizing the nuances of India’s unique social landscape. It aims to underscore the importance of integrating political philosophy to ensure that AI systems are held accountable within India’s sociopolitical context, urging policymakers to develop frameworks for responsible AI decision-making.

The research adopts a mixed-methods approach to address the five research questions. It begins with an extensive literature review, focusing on AI’s transformative potential, algorithmic bias and accountability in the Indian context. Data is collected from 15 AI use cases in health care, education and public safety, 13 government automated decision tools and five bias cases, including facial recognition and caste-based discrimination. Additionally, ten case studies and three experiments on ChatGPT are analyzed. Content analysis is used to interpret and categorize the data, identifying patterns and themes. Specific case studies and experiments on autocompletion in search engines further support the findings.

The study revealed significant limitations in current AI systems when applied to India’s complex socio-cultural landscape. Analyzing 15 AI applications and 13 government projects, the research identified multiple instances of algorithmic bias. Experiments with Google’s autocomplete and ChatGPT showed that these systems often reinforce social stereotypes and struggle with nuanced, subjective situations. The findings emphasize the accountability gap in AI-driven decisions, highlighting the need for rigorous oversight, particularly in welfare projects where errors could lead to severe consequences. The study recommends developing regulatory frameworks, improving AI design and raising public awareness to address these challenges.

In the context of complex societies like India, a pressing concern arises: who should assume responsibility for the repercussions stemming from algorithmic failures to comprehend subjective complexities? To this end, there exist no serious scholarly works toward which present paper tries to shed new insights. It draws upon insights from the corpus of political philosophy literature, encompassing both classical and contemporary notions of responsibility, and seeks to establish connections between these concepts and the unique sociopolitical structure of India. The work is unique in the focus of the paper and is original in the direction projected.

This paper aims to present a comprehensive overview of open-access publications by Indian non-profit organizations over the past two decades. The study explores the growth, licensing patterns, citations, authorship patterns and other parameters to understand the scholarly output.

The study involves data collection from OpenAlex scholarly catalog. Data analysis uses OpenRefine, a data carpentry tool, to examine and extract various aspects of scholarly output. A total of 89,149 scholarly outputs from 2004 to 2023 were analyzed using statistical and bibliometric methods.

The findings revealed a positive publication growth trend, with 57.74% open access. Gold OA dominates, with 69.61% of papers in 2023. Licensing patterns reveal that 63.75% of OA papers have licenses. Most papers have multiple authors, with 24.83% of over ten authors receiving 60.12% of citations. “Medknow” is the leading publisher, and “The Indian Journal of Ophthalmology” tops journals. Contributions from repositories like SSRN and PubMed are significant. The study also examines citation patterns across different OA types and identifies the top 30 research areas, emphasizing “Medicine” as the most prevalent.

The identified trends and patterns offer valuable insights for policymakers, researchers and organizations to enhance accessibility and impact. This study stresses sustained efforts for transparency and democratization of knowledge in the non-profit sector.

This study filled a gap in existing research by focusing on Indian non-profits, highlighting their roles and impacts often overlooked in scholarly literature. This study provides insights into the growth of open-access publications and their implications in the non-profit sector.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.