Technology Acceptance Literature Research Articles

Mobile app adoption comparison between U.S. and Chinese college students: information processing style and use frequency after download

Abstract Purpose This study examines similarities and differences in information processing of college mobile app adopters, as age peers, in China and the U.S., by using the heuristic-systematic model as the main theoretical framework. Method An online self-administered survey was conducted. Findings The results confirm that some peripheral factors affect personal factors. Some cultural orientations (power distance, indulgence, individualism, and uncertainty avoidance) influence app adopting behavior indirectly via information processing. Social norms significantly predict information processing and adopting behavior. Respondents share similar heuristic processing but show significant differences in systematic processing, which results in significantly different adopting behaviors. Implications Some cultural orientations affect app adopting behavior through information processing, but affect heuristic processing and systematic processing differently. Not all cultural orientations influence the decision-making process, and some orientations may be moderators instead of predictors. Social norms can create strong social motivation in app adoption. Respondents are capable of processing information so perceived behavioral control is not a significant influencer in the decision-making of app adoption. Respondents are different in systematic processing but not in heuristic processing, which calls attention to cross-cultural comparisons in terms of information processing, researchers should test at the dimensional or item level before comparing at the variable level. Value This study extends the heuristic-systematic model by connecting peripheral factors (national culture, social norms, and perceived behavioral control) and personal factors (information processing and behavior). This study also tests the special roles of social norms and perceived behavioral control, which originated from the theory of planned behavior, as peripheral factors, and enriches the literature on information processing of decision-making. This study introduces the possibility that respondents are more different in systematic processing than heuristic processing and cultural orientations affect heuristic processing and systematic processing in different ways, and also sheds light on technology acceptance literature in terms of non-adoption.

Teleworking antecedents: an exploration into availability bias as an impediment

AbstractTelework technologies have been known since the 1970s, yet their adoption levels remained low until Covid-19-related lockdowns and curfews. The known rational and non-rational technology acceptance theory and biases cannot fully explain this effect. One of the possible answers to fill this gap could be availability bias which has probably also affected the lag in adopting other technologies. To examine this phenomenon, we conducted a qualitative study with 22 interviews with individuals from different organizational backgrounds and telework adoption levels. Following a combination of inductive and deductive coding, we identified three key aspects of availability bias: intention, cognitive visibility, and cognitive transfer. The findings also allowed us to delineate this bias further from other biases, e.g., the status quo bias, and classical technology acceptance models, e.g., UTAUT. Thereby, this study examines a bias so far only very limitedly researched in the information systems and extends technology acceptance and cognitive bias literature. The findings should also enable practitioners to question their way of working and technology use more thoroughly.

IoT smart farming adoption intention under climate change: The gain and loss perspective

While Internet of Things (IoT) smart farming promises significant benefits in terms of enhancing efficiency, effectiveness, and productivity, and has witnessed increased adoption in agriculture in recent years, it still remains underutilised within the agricultural sector. Despite well-intentioned efforts by policymakers, the transformation of traditional agricultural systems into smart farms remains a formidable challenge. Drawing on technology acceptance literature and Prospect Theory (PT), this article explores, compares, and prioritizes the factors that influence IoT smart farming adoption, considering both the expected gains and losses. Survey data collected from 265 farmers in Thailand was analysed using structural equation modelling (SEM) to test our hypotheses. The results demonstrate that IoT smart farming is significantly influenced by both the perceived expected gains from its adoption and the perceived expected losses from climate change in the case of not adopting IoT smart farming. However, in contrast to PT, the impact of losses is not greater than that of gains. This article offers valuable insights that are essential for shaping sustainable farming practices and informing policy frameworks in the face of mounting challenges posed by climate change.

Consumer Behavioral Intention of Adopting Emerging Healthcare Technology

Gene repair is a new emerging healthcare technology garnering increasing attention. Despite the large potential future market for gene repair, a lack of research exists between the advancement of this innovation and consumer adoption intention. This study develops a theoretical model by contextualizing the unified theory of acceptance and use of technology (UTAUT) into gene repair applications. That model is extended to this specific context by integrating the relevant constructs of perceived risks and trust. Survey data allows empirically testing this new posited model through partial least square-structural equation modeling. Results show the effectiveness of the proposed model and explain the mechanism of consumer behavioral intention in the context of a gene repair application, a proxy of emerging healthcare technology. Specifically, findings show that both social influence and facilitating conditions positively correlate with consumer behavioral intention. In addition, research on trust is extended because performance expectancy and perceived risks fully mediate the correlation between trust and consumer behavioral intention. The findings of both direct and mediation effects contribute to the technology acceptance literature and provide practical implications on the promotion of emerging healthcare technologies.

PREDICTING OLDER ADULTS’ CONTINUED COMPUTER USE AFTER INITIAL ADOPTION

Abstract Digital technologies have the potential to help older adults in various aspects of their lives, but older adults need to adopt and keep using the new technologies to reap the benefits. Previous research on digital divide mainly focuses on adoption and acceptance of new technologies, and little is known about what might influence actual use and disuse after this initial stage. The current study modeled changes in constructs related to computer use after initial computer adoption and examined whether these changes predict continued use. We used data from the computer arm (N = 150, MAge = 76.15) of a 12-month field trial examining the potential benefits of computer use in older adults. Individual differences identified in the technology acceptance literature (perceived usefulness, ease of use, computer interest, computer self-efficacy, computer anxiety, quality of life, social isolation, and social support) were measured before (baseline), during (month 6), and after the intervention (posttest). Univariate and bivariate latent change score models examined changes in each predictor and their potential causal relationship with use. Results demonstrated large inter-individual differences in the change patterns of individual difference factors examined. Change in perceived usefulness, perceived ease of use, and computer interest, computer self-efficacy, and computer anxiety were correlated with but not predictive of change in use. Our findings demonstrate the limitation of popular constructs in technology acceptance literature in predicting continued use after initial adoption and point out important gaps in knowledge to be targeted in future investigations.

First impressions of a financial AI assistant: differences between high trust and low trust users.

Calibrating appropriate trust of non-expert users in artificial intelligence (AI) systems is a challenging yet crucial task. To align subjective levels of trust with the objective trustworthiness of a system, users need information about its strengths and weaknesses. The specific explanations that help individuals avoid over- or under-trust may vary depending on their initial perceptions of the system. In an online study, 127 participants watched a video of a financial AI assistant with varying degrees of decision agency. They generated 358 spontaneous text descriptions of the system and completed standard questionnaires from the Trust in Automation and Technology Acceptance literature (including perceived system competence, understandability, human-likeness, uncanniness, intention of developers, intention to use, and trust). Comparisons between a high trust and a low trust user group revealed significant differences in both open-ended and closed-ended answers. While high trust users characterized the AI assistant as more useful, competent, understandable, and humanlike, low trust users highlighted the system's uncanniness and potential dangers. Manipulating the AI assistant's agency had no influence on trust or intention to use. These findings are relevant for effective communication about AI and trust calibration of users who differ in their initial levels of trust.

Factors Influencing Consumer Acceptance of Mobile Payment during the COVID-19 Pandemic & Usage Continuance Intent: A Quantitative Study

The presence of COVID-19 has transformed the business sector’s paradigm and prompted a speedy consumption of mobile payment software systems of diverse ranges. Corporate sectors and businesses across the globe brought a shift to offer mobile payment methods; consequently, consumers were urged to maximize the use of mobile payment throughout the pandemic. The present research aims to investigate the factors that might influence consumers' intent to accept mobile payments and their relationships during COVID-19. The technology adoption model and the unified theory of acceptance and use of technology were employed in this proposed mobile payment adoption framework. A quantitative research approach was identified as a suitable method for this research. An online survey was administered, and 304 participants responded to the questionnaire. The results of the data analysis revealed statistically significant relationships and a positive impact of the factors perceived performance, social influence, consumers’ satisfaction, and perceived usefulness on consumers’ usage continuation intention. However, the results identified that factors such as transaction risk didn’t affect perceived usefulness, and financial transaction transparency didn’t affect consumers’ usage intention. This study makes a substantial contribution to the consumers’ technology acceptance literature in terms of validating a proposed theoretical framework that highlights the factors that influence consumers’ mobile payment usage intentions. As this study was conducted at a later stage of the COVID-19 pandemic, it adds value to the existing literature by providing insights to business managers on the factors influencing mobile payment usage. Considering the practical perspective, this study offers evidence of the essential elements that mobile payment service designers and marketers should consider. Doi: 10.28991/ESJ-2023-07-05-07 Full Text: PDF

The Unsung Heroes of Facebook Groups Moderation: A Case Study of Moderation Practices and Tools

Volunteer moderators have the power to shape society through their influence on online discourse. However, the growing scale of online interactions increasingly presents significant hurdles for meaningful moderation. Furthermore, there are only limited tools available to assist volunteers with their work. Our work aims to meaningfully explore the potential of AI-driven, automated moderation tools for social media to assist volunteer moderators. One key aspect is to investigate the degree to which tools must become personalizable and context-sensitive in order to not just delete unsavory content and ban trolls, but to adapt to the millions of online communities on social media mega-platforms that rely on volunteer moderation. In this study, we conduct semi-structured interviews with 26 Facebook Group moderators in order to better understand moderation tasks and their associated challenges. Through qualitative analysis of the interview data, we identify and address the most pressing themes in the challenges they face daily. Using interview insights, we conceptualize three tools with automated features that assist them in their most challenging tasks and problems. We then evaluate the tools for usability and acceptance using a survey drawing on the technology acceptance literature with 22 of the same moderators. Qualitative and descriptive analyses of the survey data show that context-sensitive, agency-maintaining tools in addition to trial experience are key to mass adoption by volunteer moderators in order to build trust in the validity of the moderation technology.

Predicting Older Adults' Continued Computer Use After Initial Adoption.

Sustained computer and internet use have the potential to help older adults in various aspects of their lives, making predicting sustained use a critical goal. However, some factors related to adoption and use (e.g., computer attitudes) change over time and with experience. To understand these dynamics, the current study modeled changes in constructs related to computer use after initial computer adoption and examined whether these changes predict continued use. We used data from the computer arm (N = 150, MAge = 76.15) of a 12-month field trial examining the potential benefits of computer use in older adults. Individual differences identified in the technology acceptance literature (perceived usefulness, ease of use, computer interest, computer self-efficacy, computer anxiety, quality of life, social isolation, and social support) were measured before (baseline), during (Month 6), and after the intervention (post-test). Univariate and bivariate latent change score models examined changes in each predictor and their potential causal relationship with use. Results demonstrated large interindividual differences in the change patterns of individual difference factors examined. Changes in perceived usefulness, perceived ease of use, computer interest, computer self-efficacy, and computer anxiety were correlated with but not predictive of change in use. Our findings demonstrate the limitation of popular constructs in technology acceptance literature in predicting continued use and point out important gaps in knowledge to be targeted in future investigations.

Surfing in virtual reality: An application of extended technology acceptance model with flow theory

This study aimed to develop a theoretical framework explaining how aspects of the technology acceptance model (TAM) and flow theory explain user attitudes and behavioral intentions in a virtual reality surfing experience. Using data from 251 participants, relationships between the TAM and flow theory concepts were tested with structural equation modeling (SEM). Results revealed that hedonic factors (experiencing a flow state) and utilitarian aspects (perceived usefulness and ease of use) drove intentions to adopt VR for virtual surfing. These findings support the argument that incorporating hedonic factors (i.e., flow experience) into the TAM increases its predictive power in understanding antecedents of consumer behavioral intentions toward new technology. These findings also contribute to the technology acceptance literature concerning VR technology in leisure activities. Regarding managerial implications, the study suggests that VR provides potential surfing consumers with a realistic, immersive, and enjoyable way to learn to surf. VR can apply to other leisure and tourism sectors requiring remote locations and increase their behavioral intentions to participate.

A status quo bias perspective on user resistance in building information modeling adoption – Towards a taxonomy

The architecture, engineering, and construction (AEC) industry is currently undergoing a rapid IT-based change due to digital transformation – a change that presents both new opportunities and new challenges. Technologies and methodologies such as building information modeling (BIM) are intended to assist with this transformation and to simplify work processes. However, the use of BIM is fraught with barriers and reservations. We thus investigated the resistance that many users have to using BIM, thereby building on the status quo bias (SQB) perspective and the technology acceptance literature from research on information systems (IS). We furthermore developed a taxonomy that identifies the SQB in BIM adoption. To that end, we ran a quantitative study (n = 155) in the architectural domain, whose results indicate the presence both of different biases and of a strong resistance to BIM. Based on our taxonomy, we discuss valuable directions for future scientific work and provide initial recommendations for the AEC industry.

Green Information System (GIS) Model in the Conference Sector: Exploring Attendees' Adoption Behaviors for Conference Apps.

IntroductionAcademic conferences are carbon-intensive. Conference travel by air contributes to huge amount of global greenhouse gas emissions, which can be reduced by adopting green information systems (green IS or GIS). This study aims to investigate attendees’ behaviors for adopting green IS (such as conference apps) from an ecological perspective.MethodsFor this research, a survey method was used. Survey instrument had 29 items adapted from existing scales. Data was collected using Mturk’s panel of respondents on online Qualtrics. Exploratory and confirmatory factor analysis were conducted on 403 responses for data analysis and validation of the results using SPSS AMOS ver. 24.FindingsThis study provides empirical evidence to add ecological beliefs as a key predictor of green IS adoption models. Findings demonstrate that attitudes and ecological beliefs play an essential role in conference app adoption, in comparison with performance expectations, social impacts, and facilitation conditions, as presented in the traditional technology acceptance literature. Findings assist conference owners develop essential communication strategies for green attendees.

A critical evaluation of mobile guided tour apps: Motivators and inhibitors for tour guides and customers

This paper identifies the motivators and inhibitors for using mobile tour guiding apps from the perspective of tour guides and customers. The study provides insights into the challenges of designing and developing apps through the evaluation of an existing tour guiding app and makes recommendations for further improvement. In-depth interviews were undertaken with tour guides from the WeGoTrip App as well as a sample of their customers. This study has important implications for app designers in terms of usability of such apps. It provides insights into the readiness of guides to adopt and promote such new developments, as well as the experiences of potential customers. The paper contributes to technology acceptance literature in the under-researched area of mobile app technology acceptance. The focus is on the motivators/inhibitors of guides and tourists to use/re-use mobile apps in the context of guided tours.

The dynamics of electric vehicle acceptance in corporate fleets: Evidence from Germany

Electric vehicles as part of corporate fleets play a key role in reducing CO2 emissions. However, business practice has shown that employees often refrain from using these newly procured fleet vehicles. The aim of this paper is therefore to explain the drivers and barriers of acceptance of electric vehicles on the employee's level. Based on a Grounded Theory approach, the authors extracted five main determinants from interviews with 16 experts. They were cross-checked with the literature including technology acceptance literature and electric vehicle research. To understand the dynamics of acceptance, panel data was collected in a longitudinal study with three waves in Germany. The panel data was analyzed using partial least squares (PLS) path modelling. Results show that employees' environmental concern as an attitudinal factor is the only short-term determinant triggering the first electric vehicle usage. Later, the influence of environmental concern disappears, while the impact of product-related determinants, such as enjoyment, ease of use, perceived risks, and relative advantage increases. Furthermore, a mediating effect of enjoyment emerges between ease of use on the intention to use an electric vehicle. Several important implications for decision-makers in management and policy are deduced to support short- and long-term usage of electric vehicles in corporate fleets.

Transforming the automotive retail: Drivers for customers' omnichannel BOPS (Buy Online & Pick up in Store) behavior

This study aims to identify the drivers in BOPS (Buy Online & Pick up in Store) usage intention of omnichannel consumers in the automotive retail sector. The study proposes a conceptual framework, extended UTAUT-TTF, synthesizing various theoretical perspectives and revealing the mechanism behind consumer behavior. The research surveyed 436 potential customers in South Korea, incorporating a factorial survey method. The findings suggest that Performance expectancy, Effort expectancy, and Facilitating conditions have a mediation effect through Task-technology fit affecting Usage intention. The results also support the influences of decisive factors in predicting the consumers' preferences to use BOPS. Social influence, however, did not present a significant effect on Usage intention. Personal innovativeness, selected for the extension of UTAUT, was the strongest predictor for the customer's BOPS usage intention. The research contributes to future technology acceptance literature, proposing that UTAUT-TTF can serve an essential role in predicting consumer behavior.

Acceptance of Consumer-Oriented Health Information Technologies (CHITs): Integrating Technology Acceptance Model with Perceived Risk

This paper focused on understanding the growing demand for consumer-oriented health information technologies (CHITs) wearable and adult healthcare management apps. This study utilised the Technology Acceptance Model (TAM) and integrated the concept of perceived risk. The structural Equation Modelling (SEM) technique was applied to test the research hypotheses based on the 450 quantitative responses. This study confirms significant relationships between perceived usefulness, perceived ease of use, perceived risk, attitude, behavioural intention, and actual intention in using CHITs. The findings also showed no evidence to conclude that age and education influenced respondents perceived usefulness and perceived ease of the CHITs. This study incorporated the perceived risk to fill a gap in the literature and broaden the current TAM theoretical application in the public health setting. The study findings fill the health-related technology acceptance literature gap and broaden TAM's present application in the public health setting.

Preserving the legacy – Why do professional soccer clubs (not) adopt innovative process technologies? A grounded theory study

Technologies such as Robotic Process Automation or Artificial Intelligence are becoming increasingly recognized as essential elements and engines of digital transformation in businesses today. However, especially in the professional sports industry, the adoption of these technologies appears to be lagging behind. This paper aims to provide a deeper perspective and understanding of the drivers and inhibitors of the adoption and use of innovative process technologies in professional sports. To this end, we conducted a grounded theory study of data collected from two German professional soccer clubs: one from the first league (Bundesliga) and one from the third league (3. Liga). Our study shows that adoption depends upon expected efficiency and effectiveness gains, perceived ease of use, perceived (positive) market pull, and perceived (negative) supporter perception. While the first three constructs are well-known in the technology acceptance literature, the latter two are comparably new in this research stream. Therefore, we discuss especially the impact of perceived positive market pull (i.e., a pull from clubs’ sponsors and competition) and perceived negative supporter perception (i.e., the fear that customers might lose loyalty due to increasing process digitalization) on the adoption and use of innovative process technologies in professional sports.

“Baby, you can drive my car”: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles

Artificial intelligence (AI)-powered autonomous vehicles (AVs) are one of the most highly anticipated technological advancements of our time, with potentially wide-ranging social implications in terms of driver/passenger safety, equity and environmental aspects. However, most consumers feel reluctant towards the adoption of AI-powered AVs. To analyse user acceptance of AI-powered AVs, we need to understand the related psychological, social and cognitive factors. To do so, we established a conceptual model based on the technology acceptance literature and considered how performance and effort expectancy, social recognition, hedonism technology security and privacy concerns influence both technology trust and user well-being as mediators that subsequently influence the behavioural intention of the use of AI-powered AVs. We used user innovativeness as a moderator, and we performed a survey in France. Our results from the structural equation modelling largely support the positive relationship between the behavioural intention to use AI-powered AVs and performance-/effort expectancy, social recognition, well-being, hedonism and technology trust, as well as security. On the other hand, privacy concerns negatively influence technology trust.

The Moderating Effects of Leader-Member Exchange for Technology Acceptance

Within the technology acceptance literature, the issue of top management support and commitment has been studied extensively; however, the issue of leadership per se has not been addressed directly. A missing piece of the leadership puzzle as it relates to technology acceptance is an exploration of how top management support gets translated in the organizational hierarchy. This study introduces leader-member exchange (LMX) to better understand this missing piece. Specifically, this research explores the role direct supervisors play in the acceptance process by end users based on the moderated model of LMX and supervisor influence. The empirical test results in the field setting show that LMX is a significant moderator for most of the technology acceptance variables within organizations. The study explores the role of the quality of the relationship between supervisors and employees as end users. It also highlights the role of LMX and supervisor influence as a conduit for the acceptance process among end users in the organization.

Employees’ intention to use artificial intelligence

As the world is at the beginning of a Fourth Industrial Revolution, using and applying artificial intelligence (AI) has become a significant task for organizations and individuals. In this study, based on the technology acceptance model and literature on trust at organizations, we investigated factors that influence employees’ intention to use AI. We hypothesized that perceived usefulness of AI and trust would increase employees’ intention to use AI, and examined the role of perceived organizational support on the effects of perceived usefulness on trust. Using an experimental study, we found that Korean employees with high levels of perceived usefulness of AI reported higher levels of cognitive trust and affective trust than their counterparts. Cognitive trust and affective trust fully mediated the effects of perceived usefulness of AI on intention to use it, and the effects remained significant even after accounting for the effects of age, gender, and prior experience of using AI. We also found that perceived organizational support moderated the effect of perceived usefulness of AI on affective trust such that its effect was positive only for those with low perceived organizational support. For those with high perceived organizational support, affective trust toward AI did not vary depending on perceived usefulness of AI. Based on these findings, we discussed implications, limitations, and recommendations for future research.