In this work we investigated the interplay between conversational user experience (UX) dimensions, conversational agent (CA) capabilities, and cognitive absorption (CGA) in human-AI interactions. Leveraging the Gricean maxims (quality, quantity, relevance and manner), its AI-specific extensions (benevolence, transparency and priority), our previously developed measurement scale called CASUX (Conversational Agent Scale for User Experience), and the Cognitive Absorption Theory; this research explores how different conversational capability agents influence users’ CGA. We undertook a cross-sectional comparative study, and analyzed using a mixed-methods approach by combining Partial-Least-Squares Structural Equation Modeling and Fuzzy-set Qualitative Comparative Analysis. Two custom-made CAs were developed using the Alexa skills kit, each representing distinct conversational capabilities (baseline vs. customized). Our findings reveal significant differences between CA types with proficiency emerging as the critical functional driver of CGA for customized CAs, whereas baseline CAs rely on social UX dimensions such as humanness, anthropomorphism, etiquette & mannerism, and personality.

Conversational agents are growing in popularity, and as such they must provide a good user experience and meet the needs of the users. Yet, how to measure the user experience in the conversational AI scenario remains an open and urgent question to be solved, which may hinder further empirical studies on human-agent interactions. In fact, there have been very few studies that explore how users perceive interacting with these conversational agents, which is important to ensure their sustainability. Accordingly, in this work, we used a subjective technique by following a measurement approach to develop a standardized measurement instrument/scale called the Conversational Agent Scale for User Experience. In terms of the methodology, we used a mixed-method approach involving an iterative process spanning across six different user studies (three qualitative and three quantitative) at different points of time for the purpose of dimension identification, item generation and subsequent item refinements. As a part of the qualitative studies we conducted a Systematic Literature Review, semi-structured interviews with users of conversational agents, and expert interviews. For the quantitative studies a lab-based experiment was performed for the pre-test, followed by two online surveys as a part of pilot testing and scale development. The qualitative studies initially identified a total of 13 distinct user experience dimensions and 418 measurement items. Finally, the Exploratory Factor Analysis as a part of the main survey resulted in 9 user experience dimensions (practicality perception, proficiency, humanness, sentiment, robustness, etiquette & mannerism, personality, anthropomorphism, and ease of use) and 34 measurement items. This was supplemented by conducting another Confirmatory Factor Analysis for establishing the reliability and validity of the proposed scale, together with checking the model-fit indices. All the 9 dimensions had sufficient validity, and a reasonable level of statistical reliability. Some of the user experience dimensions like humanness, personality, anthropomorphism, and etiquette & mannerism show the uniqueness of the conversational AI scenario from the traditional usage factors used commonly while evaluating graphical user interface-based systems. This work fills the gap of a lack of research on how to classify and measure the conversational experience of users and provides a reference for practitioners and designers in developing these agents and continuously improving the usage experience.

Physical closure of educational institutes worldwide due to the COVID-19 pandemic has resulted in the emergence of a new era of video-based learning. The current circumstances are unique that have forced students to use digital technologies for their learning purpose. However, the successful usage of such a system relies on the understanding of the adoption factors, for which an integrated model is proposed based on the Technology Acceptance Model and the Task Technology Fit Model. Additionally, this work considers the moderating effects of gender and digital inequality. Data are collected from 232 students all of whom have taken part in a full-semester video-based online learning course in times of the pandemic. A Partial Least Squares method is used for analyzing the data. Results show that video-based learning positively fits into the student’s perception and their actual use of the system. The effect of individual characteristics is more than the technology characteristics on the task-technology-fit chain. The integrated model can predict 64.6% of the variance in the final dependent variable: actual usage of video-based learning. Further, while the moderating effect of gender is found to be significant, that for digital inequality is non-significant. Finally, the theoretical and practical implications are discussed.

Studying computer programming requires not only an understanding of theories and concepts but also coding adeptness. Success in studying or conducting such a course is definitely a challenge. This paper proposes a systematic learning style recommendation. The model is designed to evaluate students’ attributes and ongoing or formative learning outcomes for suggesting the effective style-fit strategy that facilitates learners to enhance their learning performances in terms of knowledge and skill. A two-stage association analysis was designed and conducted on a dataset collected from IT major students who enrolled in the Introduction to Computer Programming course. The first stage of association rules is to analyze and discover important relationships amongst learning styles, students’ attribute, and learning performance. The second stage of moderation analysis is then applied to probe the moderation effect of the different learning preferences on the relationship between student attributes and learning achievement. Experiments expose many insights, for example, mathematics and logical thinking are powerful assets of success in computer programming study. Association rules can effectively identify associations of learning styles and the learning performance in terms of knowledge or skills. By moderation analysis, students in the “Excellent” cluster have a broad learning style than other students. Two types of significant moderators, the universal and specific, exemplify how lecturers can flexibly post style-fit teaching strategies for a class-wide and specific group, respectively.

Rapid enhancements in the Internet of Things (IoT) and other technologies have resulted in the emergence of various types of smart IT products such as the voice assistants (VAs). Accordingly, several attempts have been made through extant research for explaining the acceptance of these devices by using different models related to technology acceptance. In this article, an in-depth comparative analysis is conducted for determining which model best explains the user's acceptance of using VAs. The technology acceptance model (TAM), theory of planned behavior (TPB), unified theory of acceptance and use of technology (UTAUT), and value-based adoption model (VAM) are used for the purpose of comparison using data collected from 436 (275 potential and 161 actual) participants from the Amazon Mechanical Turk (MTurk) platform. A maximum-likelihood structural equation modelling approach is used for the purpose of hypothesis testing on a model-by-model basis, while for the purpose of comparison between the models, Hotelling's ${ T}^{ 2}$ test is used as the statistical measure. All the hypotheses across all the models are found to be true, though with varying degrees of ${\mathrm{ adjusted-}}{ R}^{ 2}$ values. VAM is found to have the greatest predictive power (${\mathrm{ adjusted-}}{ R}^{ 2} ={ 0}.{ 6}{ 83}$ ) and TAM has the least predictive power (${\mathrm{ adjusted-}}{ R}^{ 2} ={ 0}.{ 439}$ ) for behavioral intention (BI). Additionally, a multiple regression analysis is conducted for comparing each of the factors considered in the models in terms of their influence on BI. Results show that enjoyment has the greatest influence (31.24%), followed by subjective norms (15.43%). The effect of usefulness is found to be less (11.86%), which suggests the existence of some usability issues with the VAs. Finally, the research implications are discussed and suggestions are provided.

Across the globe, the increasing percentage of elderly people is a reason of growing concern among the research fraternity. A lot of research is currently going on in providing these elderly people with a smart environment or smart home that can promote their independence, overall sense of well-being and enable them to lead a better quality of life. The primary objective of this research paper is to provide with a concise systematic review that explores the influence these smart-homes can have on the living quality among the elderly. We identified a total of 4,512 initial publications related to smart-homes for the elderly across a variety of valid scientific databases. Out of these, 31 met our inclusion criterion to be included for the final review stage by 2 independent researchers. Depending upon the functionality we classify the smart-homes into 5 broad application areas that can serve the elderly. Our findings show that overall these elderly people have a positive attitude towards a smart-home, especially for the purpose of health-monitoring and independent assisted living. However, they also have serious concerns regarding their privacy and the security provided by such smart systems along with the fear of social-isolation that might happen due to an increased dependence on technology.

A new procedure is proposed for solving the problem of channel shortening (a.k.a. the TEQ problem) for discrete multi tone (DMT) systems. Channel shortening is accomplished by using an multi-input-single-output (MISO) adaptive filter bank at the receiver. Each input represents a "logical path" from the transmitter to the receiver The "paths" are derived either via oversampling, or by using multiple analog to digital converters (A/Ds) with different sampling delays, or from A/Ds connected to different receive antennas (as may be the case in a wireless system). It is shown that for a finite order linear time invariant channel, it is always possible (provided certain conditions are met) to find a FIR filter bank combiner such that the effective length of the channel is no more than the cyclic prefix.

A new procedure is proposed for estimating directions of arrival (DOA) of multipaths in multipath channels. Assuming finite support, a finite matrix description of the channel is derived. A sufficient condition for computing the DOAs is that the rank of this matrix be at least equal to the number of multipaths. This condition need not hold at all times, especially when the channel length in number of symbols is smaller than the number of multipaths. Usually this problem is solved via a so called spatial smoothing approach. But spatial smoothing requires a large array of antennas as it needs averaging over a number of subarrays (each subarray being larger than the number of significant multipaths). The new technique does not require extra antennas and computes the DOAs as long as the path delays are distinct.>

A new dual-input integrator/differentiator network is proposed; its time constant (T/sub 0/) is electronically tunable by the control voltage (V/sub x/) of a multiplier element. Digital control of T/sub 0/ by use of a DAC-multiplier combination has been examined.>