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This study aims to investigate the psychological mechanisms underpinning the impact of branded memes on brand love on social media.

Using a self-reported online survey, data from 479 verified Facebook users were analyzed using structural equation modeling to examine the interrelationships among sense of liberation, perceived trendiness, hedonic engagement, perceived humor, self-brand connection and brand love.

The results indicate that a sense of liberation significantly influences self-brand connection but does not directly affect perceived humor. Perceived trendiness is a key predictor of both perceived humor and self-brand connection. However, hedonic engagement has no substantial effect on perceived humor or self-brand connections. Importantly, perceived humor plays a crucial role in strengthening self-brand connections and fostering brand love.

To the best of the authors’ knowledge, this study is the first empirical exploration to showcase the consumer brand love formation process facilitated by branded memes, offering valuable insights into meme-marketing strategies. This highlights the importance of perceived humor and trendiness in enhancing brand love through social media platforms.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

This study aims to examine factors influencing potential customers’ intention to patronize Islamic banking products in Cameroon.

To achieve this, a structured questionnaire was used with 318 respondents, and 300 were usable for analysis with a respondent rate of 94%. The study used SEM-PLS to analyze the data.

The findings suggested that attitude, religious motivation, awareness, subjective norm and relative advantage significantly affect potential customers intention toward Islamic banking products, while perceived regulatory and perceived innovation are insignificant. Furthermore, attitude substantially mediates the relationship between religious motivation, awareness, subjective norm, relative advantage and perceived innovation.

However, this study focused on potential customers living in Muslim zones; future research should compare users and nonusers of Islamic banking products in both Muslim and non-Muslim zones to capture a big picture about customers’ perceptions of Islamic banking products in Cameroon.

The results of this study contribute to the literature by providing a new framework that combines the theories of planned behavior and diffusion of innovation theory and provides managerial implications at the level of Islamic finance operators. Meanwhile, this research offers some policy recommendations that can help boost the development of Islamic finance in Cameroon and promote financial inclusion.

To the best of the authors’ knowledge, this is the first research about potential customers’ intention to use Islamic banking products in Cameroon.

Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

This study uses a participatory action research (PAR) approach to evaluate the implementation and impact of the government of Tanzania health operation management information system (GoTHOMIS) on healthcare services in Tanzania. The primary objective is to assess the strengths, weaknesses and overall impact of GoTHOMIS.

The research uses a PAR methodology, involving active engagement with stakeholders from healthcare facilities, administrators and information technology experts. Data collection methods consist of key informant interviews and document analysis. The study strategically examines various aspects, including pre-implementation planning, challenges during implementation, system features and the strengths and weaknesses of GoTHOMIS. Furthermore, the focus extends to understanding the impact of GoTHOMIS on healthcare services.

The findings underscore critical areas for improvement, particularly in the pre-implementation planning stage. The study reveals issues related to a top-down approach and insufficient user participation, hindering the successful implementation of GoTHOMIS. Challenges such as inadequate infrastructure, power outages, missing diagnostic codes and compatibility issues with peripheral devices are identified. Despite these hurdles, GoTHOMIS exhibits strengths like improved accessibility, integration with the reporting system by the MTUHA government and automation of clinical tasks.

The study’s originality lies in applying a PAR approach to assess GoTHOMIS. This methodology provides valuable insights into the implementation challenges and suggests potential improvements. The study contributes to the existing body of knowledge by offering a detailed understanding of GoTHOMIS’s strengths and weaknesses, enhancing the potential for effective health management information systems.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This study explores how information volume affects crowdfunding success and identifies the signals – operational transparency, past crowdfunding experience, perceived project authenticity and perceived product quality – that moderate this relationship. The goal is to provide insights into managing information overload and enhancing the probability of funding success in various information volume contexts.

Data were collected from 2,681 Kickstarter campaigns and analyzed using fixed effects logit regression models.

The study reveals a curved relationship between information volume and funding success, moderated by factors such as operational transparency, crowdfunding experience, project authenticity and product quality.

This study provides fund-seekers with essential insights into disseminating information effectively.

This study contributes to the literature by elucidating the complex dynamics among information volume, signaling types and crowdfunding success, offering a nuanced understanding of how fund-seekers can optimize their campaigns for better outcomes.

The study aims to synthesize findings from over two decades of research, highlighting key trends, progress, innovations, methodologies and challenges in bioclimatic design strategies and their interconnection with building environmental performance across the world.

This systematic review examines advancements in bioclimatic design strategies aimed at enhancing the environmental performance of buildings from 2000 to 2023 (n = 1,069). The methodology/approach involves a comprehensive analysis of literature from the SCOPUS database using bibliometric analysis, identifying trends, thematic evolution, keyword clusters and pivotal strategies such as passive solar design, natural ventilation, green roofs and thermal mass utilization.

The review highlights significant progress in several areas, including improved simulation/modeling tools for passive solar design, advanced computational fluid dynamics models for natural ventilation optimization, and the integration of green roofs with photovoltaic systems for increased building energy efficiency. Additionally, the use of phase change materials and high-performance glazing has reduced heating and cooling loads, while real-time optimization technologies have enhanced building performance and led to energy savings.

The study recognizes limitations where the effectiveness of bioclimatic strategies varies across different climates. For example, passive solar design is highly effective in temperate climates but less so in tropical regions. Global differences in design preferences and building types and practices impact the applicability of bioclimatic strategies and traditional building methods in some cultures may not easily integrate with modern approaches, affecting their implementation and effectiveness. Furthermore, practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems, while social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

Practical implications highlight the potential for reduced reliance on artificial heating, cooling and lighting systems.

Social implications underscore the role of bioclimatic design in promoting sustainable construction practices.

This review offers a detailed analysis of bioclimatic design evolution, highlighting trends such as adaptive building designs and smart materials. This study serves as a crucial resource for architects, engineers and policymakers, advocating for innovative, climate-responsive design solutions to mitigate the environmental impact of the built environment and address challenges related to climate change and urbanization.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.