Investigating the Relationship between Personality and Technology Acceptance with a Focus on the Smartphone from a Gender Perspective: Results of an Exploratory Survey Study

Prediction of self-efficacy in recognizing deepfakes based on personality traits

Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - the realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual's self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people's personality traits influence their efficacy in recognizing deepfakes? Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults. Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it. Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits.

Technology Adoption in the Digital Entertainment Industry during the COVID-19 Pandemic: An Extended UTAUT2 Model for Online Theater Streaming

While the digitalization of products and services in the entertainment industry has gained momentum in the last decades, online theater streaming is a relatively new phenomenon boosted by the COVID-19 restrictions, which created new market opportunities—and demand—for theaters’ online presence. This study investigates a new online platform providing theater streaming services in Hungary from a customer-centric, technology acceptance point of view. The survey-based study is designed to examine which factors of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model are—and were—relevant in the under-researched live performance art sector of the digital entertainment industry under the unprecedented, coercive conditions of pandemic lockdowns. The results of the partial least squares structural equation modeling (PLS-SEM) show that habit is the most influential factor of theater webcasting adoption (before hedonic motivations and price value), suggesting that the new habits formed during the COVID-19 lockdowns might serve as a basis of a sustainable digital business model for theatres in the post-pandemic era as well. The analysis also tested for potential generational differences between cohorts of users, finding no significant ones, which suggests that, under this specific set of social, technology and market conditions, all generations react similarly and are equally relevant for widening the customer base. Keeping in mind some limitations (self-reported and cross-sectional data), these empirical results can not only enrich the scientific body of knowledge but can also serve as the basis of future marketing and communication strategies developed by partitioners.

To Use or Not to Use: Impact of Personality on the Intention of Using Gamified Learning Environments

Technology acceptance is essential for technology success. However, individual users are known to differ in their tendency to adopt and interact with new technologies. Among the individual differences, personality has been shown to be a predictor of users’ beliefs about technology acceptance. Gamification, on the other hand, has been shown to be a good solution to improve students’ motivation and engagement while learning. Despite the growing interest in gamification, less research attention has been paid to the effect of personality, specifically based on the Five Factor model (FFM), on gamification acceptance in learning environments. Therefore, this study develops a model to elucidate how personality traits affect students’ acceptance of gamified learning environments and their continuance intention to use these environments. In particular, the Technology Acceptance Model (TAM) was used to examine the factors affecting students’ intentions to use a gamified learning environment. To test the research hypotheses, eighty-three students participated in this study, where structural equation modeling via Partial Least Squares (PLS) was performed. The obtained results showed that the research model, based on TAM and FFM, provides a comprehensive understanding of the behaviors related to the acceptance and intention to use gamified learning environments, as follows: (1) usefulness is the most influential factor toward intention to use the gamified learning environment; (2) unexpectedly, perceived ease of use has no significant effect on perceived usefulness and behavioral attitudes toward the gamified learning environment; (3) extraversion affects students’ perceived ease of use of the gamified learning environment; (4) neuroticism affects students’ perceived usefulness of the gamified learning environment; and, (5) Openness affects students’ behavioral attitudes toward using the gamified learning environment. This study can contribute to the Human–Computer Interaction field by providing researchers and practitioners with insights into how to motivate different students’ personality characteristics to continue using gamified learning environments for each personality trait.

Exploring the Dimensions of Smartphone Distraction: Development, Validation, Measurement Invariance, and Latent Mean Differences of the Smartphone Distraction Scale (SDS)

Background: Distraction is a functional emotion regulation strategy utilized to relieve emotional distress. Within the attention economy perspective, distraction is increasingly associated with digital technology use, performance impairments and interference with higher-order cognitive processes. Research on smartphone distraction and its association with problematic smartphone use is still scarce and there is no available psychometric assessment tool to assess this cognitive and emotive process parsimoniously. Method: The present study reports the development and evaluation of the psychometric properties of the Smartphone Distraction Scale (SDS) through exploratory and confirmatory factor analysis, construct validity, gender invariance, and latent mean differences. The study was conducted in a sample of British university students (N = 1,001; M = 21.10 years, SD = 2.77). Results: The 16-item SDS was best conceptualized in a four-factor model solution comprising attention impulsiveness, online vigilance, emotion regulation, and multitasking. Construct validity was established using relevant psychosocial and mental health measures, with SDS scores being moderately associated with deficient self-regulation and problematic social media use. Gender measurement invariance was achieved at the configural, metric, and scalar levels, and latent mean differences indicated that females had significantly higher means than males across all four SDS latent factors. Discussion: The SDS presents with several strengths, including its theoretical grounding, relatively short length, and sound psychometric properties. The SDS enables the assessment of distraction, which appears to be one of the pathways to problematic smartphone use facilitating overuse and overreliance on smartphones for emotion regulation processes. The assessment of distraction in relation to problematic use in vulnerable populations may facilitate interventions that could encourage metacognition and benefit these groups by allowing sustained productivity in an increasingly disrupted work and social environment.