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This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

Scholars of persuasion have long made a distinction between appeals to logic, emotion and authority- logos, ethos and pathos- but ideas developed to account for live face-to-face conversation processes must also be tested in new media. We aimed to test the effectiveness of these three strategies in one-to-one chats through different communication media.

With a 3 × 3 × 2 between-subject factorial design, we tested these three strategies in one-to-one chats (female–female or male–male pairs) through three communication media: face-to-face, Skype video or Skype text. The persuasion scenario was adapted from prior studies in which students were presented with the idea of requiring a comprehensive exam as part of their degree. The participants were all undergraduate students of a major university in USA.

Our results showed trivial differences between female–female and male–male conditions. The logos appeal worked best overall in persuading the participants to change their reported attitudes. Additionally, the explanations provided by the participants for their own opinions were most like the persuasion scripts in the logos condition compared to the other two appeal conditions. Separately, participants indicated some disapproval of the pathos appeal in the text-based chat condition, although this did not seem to make a difference in terms of actual attitude change.

One major limitation of our study is that our subjects are college students and therefore are not representative of Internet users in general. Future research should test these three types of persuasion strategies on people of diverse backgrounds. For example, while logos seems to be most effective strategy in persuading college students (at least in our study), pathos or ethos may be more effective when one attempts to persuade people of different backgrounds.

Although it is enough for a statistical test, our sample size is still relatively small due to constraints on time, personnel and funding. We also recognize that it is challenging both conceptually and empirically to compare the effectiveness of three persuasion strategies separately.

Our findings suggest it is helpful to use fact-checking tools to combat disinformation in cases where users may not have sufficient domain knowledge or may not realize the need to identify or examine the given information. Additionally, it may require more effort to negate the impact of the disinformation spread than correcting the information, as some users may not only believe false information but also may start to reason in ways similar to those presented in the disinformation messages.

Past studies on online persuasion have limitedly examined whether and how communication media and persuasion strategies interact in one-to-one persuasion sessions. Our experiment makes an attempt to close this gap by examining the persuasion process and outcome in three different communication media and with three different persuasion strategies.

This study investigated the attitudes and concerns of Saudi higher educational institution (HEI) academics about privacy and security in online teaching during the COVID-19 pandemic.

Online Questionnaire questionnaire was designed to explore Saudi HEI academic’s attitudes and concerns about privacy and security issues in online teaching. The questionnaire asked about attitudes and concerns held before the pandemic and since the pandemic. The questionnaire included four sections. At the beginning of the questionnaire, participants were asked what the phrase “online privacy and security” meant to them, to gain an initial understanding of what it meant to academics. A definition for what we intended for the survey was then provided: “that a person’s data, including their identity, is not accessible to anyone other than themselves and others whom they have authorised and that their computing devices work properly and are free from unauthorised interference” (based on my reading of a range of sources, e.g. Schatz et al., 2017; Steinberg, 2019; NCS; Windley, 2005). This was to ensure that participants did understand what I was asking about in subsequent sections.

This study investigated the attitudes and concerns of Saudi HEI academics about privacy and security in online teaching during the COVID-19 pandemic. The findings provide several key insights: Key aspects of online privacy and security for Saudi HEI academics: Saudi HEI academic’s notion of online privacy and security is about the protection of personal data, preventing unauthorized access to data and ensuring the confidentiality and integrity of data. This underscores the significance of robust measures to safeguard sensitive information in online teaching, but also the need to make academics aware of the other aspects of online privacy and security. Potential to improve policies and training about online privacy and security in Saudi HEIs: Although many participants were aware of the online privacy and security policies of their HEI, only a small percentage had received training in this area. Thus, there is a need to improve the development and dissemination of policies and to provide academics with appropriate training in this area and encourage them to take available training. Use of videoconferencing and chat technologies and cultural sensitivities: The study highlighted moderate levels of concern among Saudi HEI academics regarding the use of videoconferencing and online chat technologies, and their concerns about cultural factors around the use of these technologies. This emphasizes the need for online teaching and the growing use of technologies in such teaching to respect cultural norms and preferences, highlighting the importance of fostering a culturally sensitive approach to technology deployment and use. Surprising low webcam use: An unexpected finding is the low use of webcams by both academics and students during online teaching sessions, prompting a need for a deeper understanding of the dynamics surrounding webcam engagement in such sessions. This calls for a reevaluation of the effectiveness of webcam use in the teaching process and underscores the importance of exploring methods for enhancing engagement and interaction in online teaching. In summary, this paper investigated the attitudes and concerns about privacy and security in the online teaching of Saudi HEI academics during the coronavirus pandemic. The study reveals areas where further research and policy development can enhance the online teaching experience. As the education landscape continues to evolve, institutions must remain proactive in addressing the concerns of their academics while fostering a culturally sensitive approach to technology deployment.

One limitation of this study is the relatively small qualitative data sample, despite the adequate size of the sample including 36 academics from various Saudi Arabian HEIs for quantitative analysis. It was necessary to make the most of the open-ended questions optional – participants did not have to answer about concerns if they did not want to, as we did not want to make the questionnaire too long and onerous to complete. Consequently, the number of academics responding to the open-ended questions was limited, emphasizing the need for additional data and alternative research methods to further these issues. The study was focused on investigating the concerns of HEI Saudi academics, recognizing that the attitudes and concerns of academics in other countries may differ. Furthermore, the research also includes an exploration of the changes in academic attitudes and concerns before and since the COVID-19 pandemic, which will be the subject of further data analysis.

This research delves into Saudi HEI academics' perceptions and concerns regarding privacy and security in online education during the COVID-19 Pandemic. Notably, it highlights the moderate priority placed on online privacy and security, the unexpectedly low usage of webcams and the potential for enhancing policies and training. The study emphasizes the necessity for comprehensive measures to protect sensitive data and the importance of tailored policies for educators. It also underscores the need for a more nuanced understanding of webcam usage dynamics, offering valuable insights for institutions aiming to improve online education and address educators' concerns amidst evolving educational landscapes.

This paper aims first to describe the most prevalent teachers’ and students’ behaviors in synchronous online classes in emergency remote teaching; second, to discern behavior profiles and third, to investigate what features explain the observed behaviors.

An adapted COPUS observation protocol was employed to observe 292 online classes from 146 higher education teachers.

The most prevalent behaviors were: Presenting for teachers and Receiving for students, followed by Teachers Guiding and Students Talking to Class. Furthermore, cluster analysis showed two groups: Traditional and Interactive. The variables that better explained belonging to the Interactive lecture group were disciplinary area – social sciences and humanities –and teaching in technical institutions.

In a context where higher education institutions intend to project the lessons learned into post-pandemic learning experiences, this study provides observational evidence to realize the full potential expected from online and blended teaching and learning.

Despite the prevalence of synchronous online lectures during COVID-19, there is a paucity of observational studies on the actual behaviors that occurred in this context. Most research has been based on surveys and interviews. This study addresses this gap.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。

Drawing on the social presence theory, this study aims to explore how supplier–customer social media interactions influence supplier observers’ trust in the customers and what mechanisms contribute to variation in trust experience.

A total of 36 semi-structured interviews were conducted with Chinese suppliers using WeChat for business-to-business interactions. Data were analyzed in three steps: open coding, axial coding and selective coding.

Findings reveal that varied trust is based not only on the categories of social presence of interaction – whether social presence is embedded in informative interactions – but also on the perceived selectivity in social presence. Observer suppliers who experience selectivity during social and affective interactions create a perception of hidden information and an unhealthy relationship atmosphere, and report a sense of emotional vulnerability, thus eroding cognitive and affective trust.

The findings contribute new understandings to social presence theory by exploring the social presence of interactions in a supplier–supplier–customer triad and offer valuable insights into business-to-business social media literature by adopting a suppliers’ viewpoint to unpack the mechanisms of how social presence of interaction positively and negatively influences suppliers’ trust and behavioral responses.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

This study aims to understand how practitioners use their insights in software development models to share experiences within and between organizations.

This is a qualitative study of practitioners in software development projects, in large-, medium- or small-size businesses. It analyzes interview material in three-step iterations to understand reflexive practice when using software development models.

The study shows how work processes are based on team members’ experiences and common views. This study highlights the challenges of organizational learning in system development projects. Current practice is unreflective, habitual and lacks systematic ways to address recurring problems and share information within and between organizations. Learning is episodic and sporadic. Knowledge from previous experience is individual not organizational.

Software development teams and organizations tend to learn about, and adopt, software development models episodically. This research expands understanding of how organizational learning takes place within and between organizations with practitioners who participate in teams. Learnings show the potential for further research to determine how new curriculums might be formed for teaching software development model improvements.

This study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.

This quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.

The results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.

The research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.

This study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.

The purpose of this essay is to explore the ramifications of Web 3.0 on sports media and the desire for the ultimate fan experience.

The essay explains how Web 3.0 will influence (1) the social TV experience, (2) the drive for zero latency in sports streaming, (3) the desire for exclusive sports memorabilia and experiences, (4) artificial intelligence-driven content and (5) the potential decentralization of certain elements within the sports media ecosphere.

The core fan experience will still be recognizable, but AI, VR, blockchain and other elements will be infused within it.

Ramifications on sports media scholarships are offered.