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An intelligent personal assistant for personal computers (PCs) is a vital application for the current generation. The current computer personal assistant services checking frameworks are not proficient at removing significant data from PCs and long-range informal communication information.

The proposed verbalizers use long short-term memory to classify the user task and give proper guidelines to the users. The outcomes show that the proposed method determinedly handles heterogeneous information and improves precision. The main advantage of long short-term memory is that handle the long-term dependencies in the input data.

The proposed model gives the 22% mean absolute error. The proposed method reduces mean square error than support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP) and K-nearest neighbors (KNN).

This paper fulfills the necessity of intelligent personal assistant for PCs using verbalizer.

Digital voice assistants (DVAs) are revolutionising consumers’ interactions with technology and businesses. Whilst research on the adoption of these devices is rapidly expanding, few have explored post-adoption behaviour. To fill this gap, we investigate how functionality and human-like features shape customers’ emotions, engagement and loyalty towards DVAs.

The data were collected through a self-administered online survey from 509 DVA users. Structural equation modelling was employed for data analysis.

The results reveal that distinct human-like and functional factors of DVA independently explain customers’ positive emotions and engagement with DVAs. Positive emotions and engagement significantly impact customer loyalty to DVAs. The study shows that localisation of DVAs has a significant positive moderating influence on the service experience-customer engagement relationship but a negative moderating influence on the anthropomorphism-customer engagement relationship.

Unlike previous research, this study contributes to the literature by delving into post-adoption phenomena. It explains how DVAs’ human-like and functional attributes drive customers’ positive emotional responses, engagement and loyalty towards DVAs. The findings not only unveil new insights into the moderating role of localisation but also provide a crucial understanding regarding the boundary conditions of the influence of anthropomorphism and service experience on customer engagement.

The contemporary business environment is characterised by an increasing reliance on artificial intelligence, automation, optimisation, efficient communication and data-driven decision making. Based on the limited academic literature that examines the managerial perspective on enterprise chatbots, the paper aims to explore organisational needs and expectations for enterprise chatbots from a managerial perspective, assesses the relationship between managerial knowledge and managerial opinion regarding enterprise chatbots, and delivers a framework for integrating chatbots into the digital workforce.

The paper presents a quantitative design. An online, self-administered survey yielded 111 valid responses from managers in service and manufacturing organisations based on convenience and snowball sampling strategies. Given the nature of the data and the research questions, the research was conducted using principal component analysis, parallel analysis, correlation, internal consistency and difference in means tests.

This research explores the managerial perspective on enterprise chatbots from multiple perspectives (i.e., adoption, suitability, development requirements, benefits, barriers, performance and implications), presents a heat map of the average level of chatbot need across industries and business units, highlights the urgent need for education and training initiatives targeted at decision makers, and provides a strategic framework for successful chatbot implementation.

This study equips managers and practitioners dealing with enterprise chatbots with knowledge to effectively leverage the expected benefits of investing in this technology for their organisations. It offers direction for developers in designing chatbots that align with organisational expectations, capabilities and skills.

Insights for managers, researchers and chatbot developers are provided. The work complements the few academic studies that examine enterprise chatbots from a managerial perspective and enriches related commercial studies with more rigourous statistical analysis. The paper contributes to the ongoing discourse on decision-making in the context of technology development, integration and education.

This study aims to understand the extent of privacy concerns regarding voice-activated personal assistants (VAPAs) on Twitter. It investigates three key areas: (1) the effect of privacy-related press coverage on public sentiment and discussion volume; (2) the comparative negativity of privacy-focused conversations versus general conversations; and (3) the specific privacy-related topics that arise most frequently and their impact on sentiment and discussion volume.

A dataset of 441,427 tweets mentioning Amazon Alexa, Google Assistant, and Apple Siri from July 1, 2019 to June 30, 2021 were collected. Privacy-related press coverage has also been monitored. Sentiment analysis was conducted using the dictionary-based software LIWC and VADER, whereas text mining packages in R were used to identify privacy-related issues.

Negative privacy-related news significantly increases both negativity and volume in Twitter conversations, whereas positive news only boosts volume. Privacy-related tweets were notably more negative than general tweets. Specific keywords were found to either increase or decrease the sentiment and discussion volume. Additionally, a temporal evolution in sentiment, with general attitudes toward VAPAs becoming more positive, but privacy-specific discussions becoming more negative was observed.

This research augments the existing online privacy literature by employing text mining methodologies to gauge consumer sentiments regarding privacy concerns linked to VAPAs, a topic currently underexplored. Furthermore, this research uniquely integrates established theories from privacy calculus and social contract theory to deepen our analysis.

This study social based on cognitive theory (SCT), aims to better understand how transformational leadership affects team-level knowledge sharing and absorptive ability in the construction industry. It also examines the moderating influence of the AI-based virtual assistant on the indirect relationship between transformational leadership and team innovation through knowledge sharing and absorptive ability at the team level.

This study used a simple random sample approach to gather data from several small and medium-sized construction firms in Anhui Province, China. A total of 407 respondents, including 89 site engineers and 321 team members, provided their responses on a five-point Likert scale questionnaire.

The findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation. Unexpectedly, the findings showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.

This study adds a fresh perspective to the literature on construction management by examining team innovation driven by transformational leadership through an underlying mechanism. It is unique in that it uses the team adaptation theory to investigate the understudied relationship between transformational leadership and team innovation in the construction industry.

This study aims to determine how the attitudes toward artificial intelligence (AI) of religious tourists affect their AI self-efficacy and their engagement in AI. This study specifically intends to investigate the mediating role of AI self-efficacy in the relationship between attitudes toward AI and the engagement in AI of religious tourists. This study also seeks to identify the role of AI assistant use as a moderator in the relationship between attitudes toward AI and AI self-efficacy.

The data used in this study was gathered from a sample of 282 religious tourists who had just visited Karbala, central Iraq. Purposive sampling, which comprises a focused and systematic approach to data collection, was used after carefully assessing the distinctive characteristics and properties of the research population.

The results showed that attitudes to AI had a noticeable impact on AI self-efficacy, which, in turn, exerted a positive impact on engagement with AI. In addition, the use of AI assistants acted to positively moderate AI self-efficacy in terms of mediating the link between attitudes to AI and AI engagement.

The distinctive focus on religious tourists adds an original perspective to the existing literature, shedding light on how their attitudes towards AI impact not only their self-efficacy but also their engagement in dealing with AI. In addition, this study delves into the moderating role of AI assistant use, introducing a unique factor in understanding the complex interplay between attitudes, self-efficacy, and engagement in the context of religious tourism. The selection of Karbala, central Iraq, as this study site further adds originality, providing insights into a specific religious and cultural context.

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

This research investigates Hong Kong academic librarians' attitudes toward robotic process automation (RPA) and their willingness to learn this technology.

This qualitative study collected data through one-on-one semi-structured interviews conducted with video conferencing software. After participants received basic RPA information and three existing library application cases, they answered questions based on the interview guide. This research used the inductive thematic analysis method to analyze the collected data.

Regarding Hong Kong academic librarians' attitudes towards RPA, 19 themes were identified. Although all participants did not have previous knowledge of RPA, most showed positive attitudes toward implementing RPA in their libraries and some willingness to learn it. Besides, among all identified themes, negative attitudes mainly comprised “Affect” and “Cognition” factors, hindering RPA deployment in academic libraries.

This research helps librarians and RPA vendors make better decisions or strategies for implementing RPA for libraries, which has not been explored, especially in East Asia.

This paper analyses whether the humanisation of smart home speakers can improve users' attitudes towards covert information collection. Additionally, it examines the direct and indirect impact of trust, social presence and user's perceived surveillance on attitude towards covert information collection.

A total of 679 American users of smart home speakers are surveyed, and their responses are analysed using structural equation modelling. Mediating effects are also examined.

Humanisation increases social presence, improves users' attitude towards covert information collection and has a U-shaped effect on trust. A negative effect of humanisation on perceived surveillance is demonstrated. Social presence reduces perceived surveillance levels and improves users' attitude towards covert information collection.

We examine attitude towards covert information collection as a new outcome variable. This study contributes to the growing body of research on humanisation by providing new evidence of how humanisation helps improve users' attitude towards covert information collection and generates trust in the service provider. This research indicates the important role of social presence.