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This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

One of the most well-known, seminal models in the tourism field is the one proposed 50 years ago by Stanley C. Plog. His venturesomeness model has been widely cited in journal articles, textbooks, and has also been used as a reference for planning and designing tourism marketing projects. However, empirical research using Plog's psychographics has yielded varied results, some of which have corroborated his model, while others have found partial or no support for some postulates. Therefore, the purpose of this chapter is to offer an exhaustive review of 47 studies in the literature which have employed Plog's venturesomeness concept to examine travelers' personality traits, attitudes, and behavior, synthetizing empirical findings and drawing conclusions from the cumulative results. A discussion of the model's contribution to the current body of knowledge, managerial implications for tourism practitioners, and directions for future research are presented.

This study aims to analyze the moderating roles of familiarity, generation and gender on the impacts of coronavirus fear-uncertainty on the destination image and visiting intentions post-COVID-19.

This paper seeks to provide evidence for a research conclusion by conducting a survey of 431 potential travelers of various nationalities who visited Egypt. The data were examined using structural equation modeling with a multigroup analysis and PROCESS MACRO.

The findings indicated that the links between coronavirus fear-uncertainty, the destination image and intention to visit were significantly different from one gender and generation group to another. Moreover, it showed that destination familiarity moderates the negative effects of coronavirus fear-uncertainty on the destination image and intention to visit.

To the best of the authors’ knowledge, this paper is the first to explore the moderating role of destination familiarity, generation and gender in the effects post-COVID-19 of coronavirus fear-uncertainty on the destination image and travelers’ intention to visit using generational cohort theory and gender schema theory.

This study aims to construct a scale and identify the factors that might affect the implementation of gamification and Bloom's Digital Taxonomy-based assessment of students' learning, guided by the Goal-Setting Theory.

This study used a sequential exploratory mixed-methods design where data were collected qualitatively via interviews (n = 18) and field notes. Quantitative analysis was then applied via questionnaires (n = 96).

Four major themes emerged following thematic analysis of the observation notes and interview data: learning engagements; understandable learning goals; friendly competitions; and interactive learning feedback. A scale consisting of 32 items was developed based on the qualitative data. The quantitative data were then collected to evaluate the scale's factor structure using maximum likelihood exploratory factor analysis with Promax factor rotation. The final version of the scale had 29 items. The authors verified the alpha coefficients for each element and the entire hierarchy, ranging from 0.94 to 0.98 for the factors. The total scale was determined to be 0.96, and the Item Content Validity Index was considered valid.

The scale developed in this study is expected to inform future research and guide educators who wish to incorporate contemporary digital pedagogies in a different context. This study could also be beneficial to researchers who aspire to perform research on Goal-Setting Theory in the educational context.

This research explored how COVID-19 affects Shenzhen high school students' reading behavior and preference and their parents' attitudes toward reading during the lockdown.

This research adopted a qualitative approach to conduct one-on-one semi-structured interviews with parents of a boarding high school in Shenzhen, China. Thirteen parents were recruited through a purposeful sampling method, and NVivo12 software was used to analyze the results with a theme-based approach guided by the 5E instructional model.

The results revealed the effectiveness and problems of high school students' use of electronic resources and discovered changes in the reading behavior of high school students and their parents' attitudes during COVID-19.

There are few studies specifically on the reading behavior of boarding students from a parental view, especially in Asia. This research can fill the gaps in related research during COVID-19.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Behavioral threat assessment and intervention is a violence prevention strategy that has become widely used in US schools. Instead of relying on a zero tolerance disciplinary approach that often leads to school exclusion of students with disabilities, schools can use a multidisciplinary threat assessment team to assess the seriousness and context of a student's behavior and identify appropriate interventions and supports. This process is especially valuable for students with disabilities who are identified as threatening violence. This chapter describes an evidence-based model of threat assessment, the Comprehensive School Threat Assessment Guidelines (CSTAG), ¹ reviews some common misunderstandings of threat assessment, and presents a case example illustrating the value of threat assessment in preventing school exclusion, and provides services for a student with a disability.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This study aims to look at the integration of gamification in an e-learning model based on the technology acceptance model. The data was collected from respondents residing in India and elements of gamification (achievement, immersion and social) and personal characteristics of learners (self-efficacy, computer anxiety and enjoyment) and their impact on perceived ease of use (PEOU) and perceived usefulness (PU) were tested.

The data were collected from students and professionals who have ever played games during learning while using an e-learning module. Structural equation modeling using smart partial least square was used to create a model.

The findings showed that enjoyment affected both PEOU and PU and attitude toward e-learning. Achievement and social elements impacted attitude and the immersion element moderated the relation between enjoyment and PEOU and PU. These finally impact attitude and satisfaction, leading to higher intention to use e-learning platforms.

Because this study is very specific to the Indian context, a broad generalization requires further exploration in other cultural contexts. The absence of this exploration is one of the limitations of this study.

This study tested the GAMEFULQUEST suggested by Högberg et al. (2019) based on self-determination theory and its impact on the overall e-learning experience. The moderation of immersion has come out to be significant and achievement and social elements impacted attitude.

As independent travel has become increasingly popular in recent years, factors influencing users’ attitudes and behaviors should be explored. Besides, the rise of video platforms like YouTube, Bilibili and TikTok has led to the flourishing of user generated content (UGC), among which travel vlogs have become convenient and essential references for tourists before and while they travel on their own.

This research used quantitative analysis through a survey designed for young people, as most vlog viewers are in those age groups, to explore the attitudes and perceptions of vlog preference, attitudes and cognizance of travel vlogs among different gender, guided by the decision-making theory and preference theory with 455 participants.

Survey results showed that both genders have common choices regarding travel vlog preference, attitudes, sharing and cognizance. Yet, gender differences appear in some cognition of travel vlogs and specific preferences, such as information searching habits, trusty travel and information sources.

Existing studies focus on the influence of platforms on users, but scant studies focus on user perceptions of vlogs as a new information carrier for young people in East Asia, especially regarding gender differences, which is crucial in media-based marketing. This research provides practical suggestions to marketers and vloggers to attract and engage different genders of young people and theoretical implications on decision and preference theories.