Human Behavior and Emerging Technologies最新文献

This study intends to conduct a bibliometric analysis of the literature on generative artificial intelligence (GenAI) in marketing. Moreover, it expounds the research foundations and emerging patterns associated with GenAI in marketing and formulates prospective research propositions. This study utilizes bibliometric analysis and a literature review to evaluate the scholarly contributions of publications, authors with the highest productivity, publications with significant impact, institutions, and nations. Three hundred and seventy-one Scopus and Web of Science database documents were retrieved and consolidated by eliminating duplicates. The analysis employed various techniques, including coword analysis, thematic representation, cocitations, coupling by clustering, and international collaborations. The research uses the Bibliometrix R package to merge the dataset and conduct the bibliometric analysis. The last 2 years, 2023 and 2024, stand out as the most productive years with a notable quantity of publications, reaching 107 in 2023 and 71 in 2024. The most influential papers revolve around advertising content, sentiment analysis, and text mining. The institution with the most influence in this field is the University of Colorado Boulder, and the country is the United States. Bibliographic coupling analysis proposed the presence of four thematic clusters: opinion and text mining, big data analytics, artificial intelligence in marketing, and user-generated content. The investigation is an enlightening resource for scholars researching GenAI within the marketing domain. It will benefit researchers to familiarize themselves with previous studies and current research in this field. It also offers valuable information on this area’s most promising articles, journals, and authors. Furthermore, it provides valuable insights into potential avenues for future investigations in this domain. Consequently, the findings of this study will be advantageous for aspiring scholars in this field to establish the direction of their research endeavors. This study primarily examines performance and an academic representation of GenAI’s role in marketing. It serves as the initial study to present GenAI’s current research positions and future directions in marketing through bibliometric analysis.

This research explores how technologies are adopted and used in the health field, focusing on how people integrate these innovations into their daily lives. Different models of technology adoption in the field of health are presented with evaluation methodologies to measure the adoption of technologies in healthcare, including qualitative and quantitative approaches. The challenges and barriers that may arise in the implementation of technologies in hospital and health environments are discussed; the importance of considering ethical, organizational, social, and legal aspects in this process is emphasized. It is essential to understand how users perceive the usefulness and ease of use of technology, in addition to considering the influence of social factors, previous experiences, and psychological aspects in this process. In summary, the article highlights the importance of thoroughly understanding the criteria that influence the adoption of technologies in the health field and underscores the need for comprehensive strategies that address both technical and human aspects to ensure successful adoption of these technologies.

The present research examined if active and passive social media uses are determined by different motivations and the extent to which these uses and motivations predict wellbeing. Two online surveys (total N = 480), one using a sample of adults and the other using an undergraduate sample, showed that active use has two components: self-presentational and other-oriented. In both studies, active self-presentational use was primarily motivated by attention seeking, whereas boredom and fear of missing out (FoMO) were the main motivators for passive use. Both active and passive uses were motivated by a desire for connection. In both studies, connection motives were associated with greater wellbeing, while disconnection motives (assessed in Study 2 only) were associated with poorer wellbeing. FoMO was the most consistent predictor of lower wellbeing, but Study 2 revealed that this association was largely explained by trait loneliness. Attention seeking predicted greater wellbeing and boredom predicted lower wellbeing among the adult but not the college student sample.

Children are keen users of digital technologies, and we studied the interaction of 60 children aged 4 to 10 years with YouTube videos played on a smartphone. We found that the 9- and 10-year-old children liked watching videos on a smartphone, while the younger ones preferred TV shows. Most of the children aged 4 to 6 years made accidental touches on the screen of the smartphone while watching a video on it, and such accidental touches sometimes paused or closed the video. Most children aged 7 years and more could use the voice search feature of YouTube to look for a specified video, use the different widgets of the website, and even skip through advertisement. The average attention span of the children while watching videos on a smartphone increased from 3 to 9 min between 4 and 10 years of age. We recommend videos meant for children have content to facilitate informal learning and nurture creativity in them, be of appropriate length, and avoid small objects and crowded scenes. Video streaming websites should have a “child mode” with a minimalistic user interface and support for voice-based interaction.

Based on a comprehensive literature review, this study highlights the critical role of the temporal dimension of facial dynamics in understanding facial expressions and improving the accuracy and robustness of automatic emotion recognition systems (machine-FER). While deep learning (DL) techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks offer significant advances, they face challenges such as gradient vanishing and overfitting, particularly in long and complex sequences. Vision transformers (ViTs) show promise but require integration with algorithms to mitigate spatial noise. Conventional machine learning (CML) methods like support vector machine (SVM) remain robust, especially in smaller datasets. The study underscores the importance of multimodal data synchronization (e.g., video, voice) in classifying emotions more accurately, reflecting both human and machine learning capabilities. It also addresses the limitations of current models, including cultural biases and the need for large, diverse datasets. The findings suggest that future research should focus on real-world conditions, integrating sequential multimodal data and employing supervised models based on theoretical and empirical frameworks. This approach is aimed at enhancing the understanding and classification of facial emotions, ensuring data quality and acceptable results through systematic human observations. The study provides valuable insights for selecting appropriate algorithms that are tailored to specific research objectives and contexts.

Despite the vast array of app choices available to children and evidence of a high prevalence of persuasive design features (e.g., rewards, character pressure, and aesthetic manipulation) within these apps, little is known about how, and to what extent, these design elements influence children’s digital app play. The current study investigated the effects of app persuasive design and children’s self-regulation, and their interaction, on children’s ability to disengage from digital devices. The study adopted a three-arm acute experimental design, wherein 73 children, aged 3–5 years, were randomly assigned to engage with one of three apps (of high, moderate, or low persuasive design), with a novel “digital disengagement” paradigm that measured the time to disengage from app play and researcher-rated degree of independent disengagement. Children’s self-regulation outside of digital contexts was also assessed. General linear models were used to compare the main effects of and interaction between app condition and child self-regulation on children’s digital disengagement. Given similar disengagement means in high and moderate conditions, these were collapsed and compared with the low condition. Significant interactions between persuasive design (moderate-high and low) and self-regulation (high and low) were found for disengagement time and degree of independent disengagement for one of the two self-regulation measures. Young children with higher self-regulation were able to disengage from the digital device regardless of persuasive design level (even at moderate-high levels). Children with lower self-regulation disengaged more easily and promptly under low persuasive design but took longer and needed more support to disengage when exposed to moderate-high persuasive design. This first-of-its-kind study offers novel insight into which children are more susceptible to extended digital engagement due to persuasive design. The study highlights the importance of considering the design elements within apps together with the child’s abilities to aid families make more informed digital choices.

Automated delivery robots (ADRs) are perceived as one of the solutions towards sustainable and efficient last-mile delivery process. However, research on their integration into public spaces has predominantly focused on interactions with intentional encounters in the case of users of delivery services, with less consideration given to nonusers’ interactions with ADRs. This paper contributes to the field by presenting two real-life case studies assessing nonusers’ experiences and attitudes towards ADRs. The first study investigates the acceptance of pedestrians to co-exist with ADRs in public spaces thanks to a survey and field observations, while the second examines the cyclist–ADR interaction supported by GNSS (global navigation satellite system) measurements and reported perceptions of the experiment. Both analyses revealed positive attitudes towards the coexistence with ADRs while emphasizing at the same time challenges that could hamper their deployment as well as the delivery process. These included for the pedestrian–ADR interactions how the crowd affects the navigation capabilities of ADRs and for the cyclist–ADR interactions the speed of the ADR as well as the width of the cycle lane. The paper concludes by highlighting the imperative for stakeholders to address issues of public space management and accessibility, with an emphasis on ensuring inclusivity for people with disabilities.

Interactions between consumers and companies are increasingly relying on technologies such as chatbots and voice assistants that are based on natural language processing (NLP) techniques. With the advent of more sophisticated technologies such as transformers and generative artificial intelligence, this trend will likely continue and further solidify. To our knowledge, this study is the first to systematically review the current scientific discourse on NLP-based technologies in the context of the customer journey and attempts to outline existing knowledge and identify gaps before the onset of a new era in NLP sophistication. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and co-occurrence analysis, we offer new and nuanced insights into the prevailing discourse. From a sample of 734 articles, 41 studies were selected and analyzed. Our findings shed light on the current research focus, exploring various technologies, concepts, and challenges. We also offer a starting point for how emerging NLP-based technologies could impact the customer journey, as well as future research directions.

The rise of social media has revolutionized global communication, enabling users and businesses to connect, advertise, and monitor competitors. However, this expansion has also fueled toxic behaviors like hate speech and harassment, exposing innocent users to harmful content while overwhelming human moderators and impacting their well-being. To address these challenges, artificial intelligence (AI) and natural language processing (NLP) have been explored as potential solutions. The aim of this paper is to study existing AI-based moderation approaches to understand which models have been used, their effectiveness, and the challenges they face. This work conducts a targeted systematic literature review of research efforts that present a technical approach to the topic while sharing model results and highlighting the challenges encountered. The findings reveal that AI-driven moderation shows promise by achieving high accuracy but has some issues that need to be addressed, such as dataset imbalance, obstacles and inconsistencies, bias, and misinterpretation of message meanings. By summarizing existing research efforts and identifying key gaps, this study provides insights into the strengths and weaknesses of current AI-based solutions for content moderation.

The cloud-based software-as-a-service (SaaS) model delivers corporate software to organizations as a service over the internet, minimizing investment in on-premises facilities and automatically adapting IT resources to meet demand variations. Integrating the two popular technology adoption frameworks, the technology acceptance model (TAM) and the technology, organization, and environment (TOE) framework, this study employs a structural equation modeling technique on a carefully chosen sample of 204 technology-intensive small and medium-sized enterprises (SMEs) in Sweden to investigate the effect of various antecedents on the intention and actual utilization of SaaS-based cloud applications. The results are counterintuitive regarding the relationship between perceived ease of use and intention and the inverse relationship between risk and trust. The central construct of TAM has an insignificant relationship with the intention to adopt SaaS applications, leading to substantial practical implications for Swedish SMEs. Similarly, a significant effect of trust with an insignificant impact of risk on intention challenges conventional wisdom. The novel integration of the two models also makes substantial theoretical contributions.