Sage Journals: Discover world-class research

Abstract

This study examines the factors influencing Vietnamese journalists’ adoption of Artificial Intelligence in journalism. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, it hypothesizes that performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, regulatory support, and technology affinity and incorporating additional trust, regulatory support, and technology affinity have significant impacts on journalists’ adoption of technology. Questionnaires were administered to 238 journalists in the context of the Vietnamese press. The findings showed the significant impact of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, regulatory support, and technology affinity on journalists’ adoption of AI in journalism. These results expand the UTAUT framework and offer a more comprehensive understanding of technology adoption in journalism. Practically, the study provides valuable insights for practitioners and policymakers in the journalism industry. Understanding the determinants of journalists’ adoption can inform the development of targeted interventions, training programs, and regulatory frameworks to facilitate the integration of AI technologies into journalistic practices. However, the study has limitations, and future research should replicate the findings in diverse cultural and organizational contexts while considering additional moderating factors.

Plain Language Summary

This research investigates why Vietnamese journalists increasingly use Artificial Intelligence (AI) in their work. Journalism AI can range from automated news writing to the analysis of large datasets for investigative reporting. This study is based on a well-established model, the Unified Theory of Acceptance and Use of Technology (UTAUT), which helps us understand how and why people decide to use new technologies. In simple terms, the study examined various factors that might influence a journalist’s decision to use AI. These factors include Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral intention, Trust, Regulatory Support, and Technology Affinity. The researchers surveyed 238 Vietnamese journalists to understand these factors. They found that all of these factors significantly impact whether journalists decide to use AI. For example, if journalists trust AI and find it easy to use, they are more likely to adopt it in their work. Similarly, if they see others using AI and have the necessary support and resources, they are encouraged to use it. These findings are important because they help us understand what encourages journalists to use AI. This information is valuable for those who train journalists, develop journalism technology, or make policies related to media and technology. By understanding what influences journalists’ decisions, they can create better training programs, develop user-friendly AI tools, and create supportive policies. While this study offers valuable insights, it is specific to the Vietnamese context. The authors suggest that future research should examine different countries and consider other factors that might influence the use of AI in journalism. This will help build a more global understanding of how AI is changing journalism worldwide.

Keywords

artificial intelligence journalism technology adoption trust unified theory of acceptance and use of technology (UTAUT)

Introduction

Artificial intelligence (AI) has undergone significant advancements, overcoming initial limitations in data processing and human-like cognition since its inception in the mid-1950s, (Y. Wang et al., 2019). The ever-evolving definition of AI tools, combined with the integration of technologies such as the Internet of Things and big data, has resulted in a surge in research, particularly in design and performance (Pan et al., 2019; Romanova et al., 2021; J. Wang et al., 2021). However, the successful adoption and use of AI tools remain challenging for organizations (Brown et al., 2010; Hong et al., 2014; Venkatesh et al., 2016), often due to insufficient infrastructure and inadequate training.

The media and journalism sectors have rapidly adapted to technological changes, with AI gaining increasing attention (Chou et al., 2021; Guzman & Lewis, 2020; Lindén, 2017). AI’s adoption in these sectors has numerous benefits, including enhancing journalists’ efficiency and competitiveness for media organizations (Broussard et al., 2019; Túñez-López et al., 2021). However, issues such as the evolving role of journalists, personalized content delivery, training requirements, and work organization warrant further investigation (Kothari & Cruikshank, 2022; Parratt-Fernández et al., 2021).

Although newsrooms worldwide explore AI adoption for improving information sourcing, organization, and distribution, a gap exists between resource-rich organizations and those with limited means (Newman et al., 2020). As AI transforms newsrooms into large-scale economies, understanding the adoption and response to technological advances in low-income countries is crucial (Kothari & Cruikshank, 2022). While some Southeast Asian countries have started integrating AI technologies, research is scarce on AI adoption and its implications in these regions.

In recent years, AI’s potential to revolutionize the media sector has garnered attention in Asia, particularly in Vietnam (NDO, 2022). AI implementation can benefit Vietnamese journalism by enhancing reporting accuracy and providing cost-effective alternatives for financially constrained media firms (Minh Thu, 2022). The increasing use of AI in Vietnam’s media sector is strengthening its international presence. This study aims to identify the factors influencing AI adoption in Vietnamese press agencies, thereby addressing the research gap on AI use in Asian journalism, specifically in Vietnam. The findings will contribute to the theoretical understanding of AI application in media journalism and provide practical suggestions for AI adoption in the Vietnamese press and countries with similar contexts.

Literature Reviews

Artificial Intelligence Technology in Journalism

The application of artificial intelligence (AI) technology in journalism has seen a significant increase in recent years, with various terms used to describe this integration, such as “computational journalism” (S. Cohen et al., 2011), “algorithmic journalism” (Anderson, 2013), and “automated journalism” (Nickolas, 2019). Using algorithms to analyze data from various sources, translate text into audio and video, and determine sentiment are examples of how AI is implemented in newsrooms (Calvo Rubio & Ufarte Ruiz, 2021). The New York Times, The Washington Post, and the Associated Press are examples of traditional news organizations that have effectively integrated AI efforts in their newsrooms (Chan-Olmsted, 2019). Chinese newsrooms, like the state-run Xinhua News Agency, also use AI tools to produce news through platforms like Media Brain, which autogenerates news bulletins (Yu & Huang, 2021).

AI has facilitated the development of news proposal systems (Helberger, 2019; Túñez-López et al., 2021) and has become a crucial technology for exploring data, analyzing trends, identifying topics of interest, and verifying data (Latar, 2015; Noain-Sánchez, 2022). Journalists increasingly use social networks, websites, and online forums to search for news on the internet, allowing them to quickly explore news events (Parratt-Fernández et al., 2021). AI applications in data analysis serve to create content and understand readers (Chou et al., 2021). Charlie (Charlie, 2019) found that machine learning, automation, and data processing are the primary AI technologies used in news production, which comprises gathering, creating, and spreading the news. AI has made mass media more competitive in today’s fragmented media landscape (Chou et al., 2021).

However, AI application in news production faces several limitations. AI models are often designed for specific stories, requiring re-creation and training for new projects, which prevents cost amortization (Stray, 2019). Investigative journalism projects using computer vision necessitate significant investments in technology infrastructure and skilled personnel (de-Lima-Santos & Mesquita, 2021). Furthermore, AI models often use outdated and biased datasets, raising ethical concerns (Guzman & Lewis, 2020). AI implementation in newsrooms also requires substantial expenses (Broussard et al., 2019).

While AI is not a panacea for journalism, it is a novel instrument that necessitates that news agency personnel gain a deeper understanding to promote and advance newsroom AI skills. Power dynamics between stakeholders, AI enforcement tools, and adherence to legal and ethical standards must be explicitly considered (Broussard et al., 2019). AI tools and algorithms may reflect the values and biases of their developers, and technology companies have little incentive for transparency regarding algorithmic bias (Bird et al., 2016). Research has revealed societal gender biases (Bolukbasi et al., 2016), racial biases (Buolamwini & Gebru, 2018; Campolo & Crawford, 2020) or foment violence, spread falsehoods, to diminish self-esteem (Haugen, 2023), and more in AI systems. Such biases can pose challenges when applying AI systems across different countries or cultures, as the encoded values may not automatically adjust to new contexts. Consequently, the study of AI usage in journalism, both in theory and practice, should consider diverse cultural contexts worldwide.

The Unified Theory of Acceptance and Use of Technology (UTAUT) in Media Journalism

UTAUT has been a focal point in media journalism research, with studies examining the influence of factors such as effort expectancy, performance expectancy, and social influence. Additionally, research has explored the evolution track and future research trends based on UTAUT and its application to journalists and technology adaptation (Ahadzadeh et al., 2021; Peng & Miller, 2021; C. T. Pham & Thi Nguyet, 2023). UTAUT has been used to investigate every stage of technology adoption, from pre-adoption through post-adoption use. Performance expectancy, effort expectancy, social influence, and facilitating conditions are among the fundamental ideas of UTAUT (Venkatesh et al., 2003). These concepts have to do with whether someone thinks using a system will improve their job performance, how simple it is to use, whether they believe important others think they should use the new system, and whether they think there is an organizational and technological infrastructure in place to support system use.

The research agenda presents four primary research opportunities: antecedents/determinants of UTAUT constructs (Morris & Venkatesh, 2010; Morris et al., 2005; Venkatesh et al., 2004), interventions, moderators of UTAUT relationships, new predictors, and consequences. It is crucial to note that UTAUT has been a robust general theoretical model, with its constructs proving predictive in various contexts and technologies (Venkatesh et al., 2012, 2016; Xu et al., 2017).

Venkatesh (2022) discusses the utilization of UTAUT as a theoretical basis for examining antecedents/determinants tailored to specific technologies. This examination framework encompasses individual characteristics such as personality (Thong, 1999); technology characteristics like quality (Venkatesh et al., 2007); environmental characteristics, including the culture of innovation (Venkatesh & Bala, 2008); and interventions such as training (Venkatesh et al., 2016). Individual factors like risk-seeking and desire to learn (Venkatesh, 2000) play a significant role in adopting and using AI tools. Technology characteristics, including perceptions of model errors and AI model transparency, can impact UTAUT predictors (Venkatesh, 1999). Environmental factors like organizational climate promoting innovation can also influence AI tool adoption and usage. Interventions, including training, can be studied to evaluate their effects on adoption and use (Venkatesh & Bala, 2008).

The UTAUT model has undergone many investigations to ensure its validity, and the findings have consistently demonstrated that it is a viable and trustworthy model for forecasting technology adoption. For additional empirical research, Bervell and Umar (2017) suggested comparing the proposed full UTAUT model with non-linear relationships and modifiers to the original UTAUT model. The UTAUT model was a superior technology acceptance and used predictor compared to other competing models. Jacob and Pattusamy (2020) also developed empirical support for the UTAUT model using data from Germany and India. The research discovered that the UTAUT model was a trustworthy indicator of technological adoption and utilization in both nations.

Additionally, Abdullahi et al. (2021) did a study to evaluate language teachers at public institutions in North-Central Nigeria’s awareness of and integration of e-learning amid the COVID-19 pandemic. The UTAUT model was superior to eight theories/models in empirical testing and validation. Empirical testing has shown that the UTAUT model is ideal for other widely used rival models. It is a solid and accurate model for estimating technological adoption and utilization. It will take further research to refine the idea and address its flaws.

Based on these discussions, this study uses UTAUT as a theoretical framework to suggest possible research topics in individual characteristics, technical characteristics, environmental characteristics, and interventions. The recommendations in this study could help organizations positively affect the adoption of AI tools and contribute to the adoption literature and a springboard for additional research (Venkatesh, 2022; Zhang & Venkatesh, 2018).

Research Hypothesis Development and Model

The UTAUT highlights the key factors influencing adoption intention and behavior and enables researchers to examine potential moderator effects that can amplify or limit the impact of key factors. Based on UTAUT, Performance expectance (PE), Effort Expectancy (EE), and Social Influence (SI) significantly affect individual intention to use. Facilitating Conditions (FC), and Behavioral intention significantly affects individual behavior toward new technologies (Venkatesh et al., 2003; Williams et al., 2015). Accordingly, the present study was to test the following hypotheses:

H1: Performance expectancy (PE) significantly affects the behavioral intention (BI) to use AI in journalism.

H2: Effort Expectancy (EE) significantly affects the behavioral intention to use AI in journalism.

H3: Social Influence (SI) significantly affects the behavioral intention to use AI in journalism.

H4: Facilitating Conditions (FC) significantly affect the behavior of AI Adoption (AA) in journalism.

H5: Behavioral intention (BI) significantly affects the behavior of AI Adoption in journalism.

This study seeks to determine what factors significantly encourage journalists to use AI rather than attempting to replicate the UTAUT studies. As a result, the study model (see Figure 1) incorporates the following three additional constructs: trust (TR), regulatory support (RS), and technology affinity (TA). In which:

Figure 1.

Conceptual model.

Trust is a crucial factor that influences behavioral intention. Several studies have shown that trust significantly affects consumers’ attitudes and behavioral intentions toward technology adoption (e.g., Latifa & Zakaria, 2020; C. T. Pham & Thi Nguyet, 2023; Zhu et al., 2021). Trust helps consumers overcome perceptions of risk and uncertainty related to technology adoption and enhances their acceptance and intention. Other factors, such as social influence and performance expectancy, influence behavior intention, but trust plays a leading role (Stojanović et al., 2023). Therefore, this study to test the hypothesis is:

H6: Trust (TR) significantly affects the behavioral intention to use AI in journalism.

Regulatory support plays a significant role in influencing new technology adoption behavior. Studies have shown that regulatory support influences the intention to adopt new technology (Koster & Borgman, 2020; Kumar Bhardwaj et al., 2021; Matias & Hernandez, 2021). Regulatory agencies’ policies and rules are thought to influence the diffusion of new technology (Zhao et al., 2016). In some cases, regulators may engage in a regulatory deal where a reduced monitoring probability is granted in exchange for adopting new technology (Coria & Villegas-Palacio, 2014). Regulatory support can also reduce compliance costs and address data breaches and fraud concerns, making it easier for firms to adopt new technology (Bondio et al., 2018). Additionally, Press management regulations heavily influence Vietnam’s press environment (C. Pham & Ta, 2022). Therefore, the research hypothesis here is as follows:

H7: Regulatory support (RS) significantly affects the behavior of AI Adoption in journalism.

Technology affinity refers to an individual’s natural inclination toward technology and their comfort level. It is a significant factor in influencing new technology adoption behavior. Studies have shown that individuals with a higher level of technology affinity are more likely to adopt new technologies than those with a lower level of affinity. Technology affinity can modify the influence of classical technology acceptance model (TAM) variables on behavioral adoption intention of new technology (Aldás-Manzano et al., 2009). Additionally, technology affinity affects adoption intention indirectly via attitude (Trautwein et al., 2021). The adoption of new technology is also influenced by social influence, based on users’ direct and indirect purposes and the system’s perceived usefulness (Hasgall et al., 2018). Additionally, technology affinity has been found to play a significant role in predicting journalists’ future intention of technology use (C. T. Pham & Thi Nguyet, 2023). Overall, technology affinity plays a crucial role in shaping an individual’s attitude toward and willingness to adopt new technology, and it could be hypothesized that:

H8: Technology affinity (TA) significantly affects the behavior of AI Adoption in journalism.

UTAUT is a widely used model to understand technology adoption. However, some studies do not consider or do not find out the effect of all four moderators of gender, age, experience, and voluntariness when applying the UTAUT (e.g., Afrizal & Wallang, 2021; Dutta & Sarma, 2021; Jacob & Pattusamy, 2020). The inclusion or exclusion of moderators in UTAUT studies may depend on the research question and context. In this study, we do not consider the moderator effect of gender, age, experience, and voluntariness because this study is not longitudinal, and AI has not been popularized in the Vietnamese press; incapable of capturing increasing levels of user experience or assessing the context of use (Venkatesh et al., 2003).

Method

Questionnaire Design

To gather data, we employed a questionnaire. UTAUT constructs, Trust, Regulatory support, and Technology affinity were selected as the primary constructs of the research model from measurement constructs created in relevant studies (Aldás-Manzano et al., 2009; Kumar Bhardwaj et al., 2021; C. T. Pham & Thi Nguyet, 2023; Venkatesh & Zhang, 2010; Venkatesh et al., 2003; Zhu et al., 2021).

Six journalism and communication specialists were consulted after we had prepared the questionnaire to enhance its semantics and content validity. A pre-test with 25 journalists was conducted after expert revision to ensure the questionnaire’s phrasing, completeness, sequencing, and other potential errors. Following input from respondents, the questionnaire was slightly revised to improve its clarity and thoroughness. The assessment tool utilized in this study had 32 items in the final questionnaire, with 9 components in our research model (see Appendix 1). The first section’s questions were all scored using a 5-point Likert scale, from “strongly disagree” to “strongly agree,” for each response. Basic information about the respondent’s attributes, such as age, gender, and duration of journalism employment, was also provided.

Data Analysis

The Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach was employed to examine the conceptual model, utilizing SmartPLS software version 4 (Ringle et al., 2022). The analysis followed a two-step procedure, beginning with evaluating the measurement model to ensure validity and reliability, then examining the structural model to test the proposed hypotheses (Hair et al., 2017). The measurement model defines the methods for measuring each construct, while the structural model delineates the relationships between these constructs in the overall model.

PLS-SEM has gained widespread acceptance as a multivariate method frequently used in social science research (Khan et al., 2018). The choice of PLS-SEM for this study is based on its ability to simultaneously analyze both the measurement and structural models, resulting in more precise estimations (Al-Saedi et al., 2020). Data collected via the questionnaire was entered into an Excel spreadsheet and subsequently imported into SmartPLS for analysis, allowing for the evaluation of the research model and the hypothesized relationships between constructs.

Data Collection and Participants

Most of the study’s participants were journalists from Hanoi, Vietnam, who were chosen using convenience sampling. To gather information, we spoke with them in person. All participants agreed before data was collected, and they were assured that their answers would be kept private and used exclusively for research. From October 2022 to December 2022, data was gathered. We gathered 238 surveys in all. We took into account the Partial Least Squares SEM (PLS-SEM) sample size recommendations; as a result, 188 observations are needed to achieve an 80% statistical power to detect R2 values of at least 0.1 with 1% probability of error (Hair et al., 2017, p. 48) when the measurement and structural models include eight independent variables. Consequently, the study’s sample size was sufficient.

The 238 participants’ demographic and employment-related details are shown in Table 1. The group’s gender composition is primarily male (57.1%), with females accounting for less than half of the total (42.9%). Most of the participants, or 34.4%, are between the ages of 30 and 39 when the age range of the participants is considered. 18.1% of the group is 40 to 49 years old, and a close second, 19.3%, is 50 or older. After that, 28.2% of the participants are between the ages of 22 and 29, indicating a workforce that is still quite young. This demonstrates a good representation of all age groups in the sample, with a bit of bias toward younger ages. The statistics also shed light on how long the individuals worked. Most of the group (38.2%) have been employed for 1 to 2 years, while 35.7% have been employed for 3 to 5 years. The lowest group is six years or more of employment (26.1%). According to this breakdown, most of the workforce has only five years of experience or less.

Table 1.

Participants’ Information (N = 238).

Demographic information	Count	Percent
Gender
Male	136	57.1
Female	102	42.9
Age
22–29	67	28.2
30–39	82	34.4
40–49	43	18.1
≥50	46	19.3
Length of time worked
1–2 years	91	38.2
3–5 years	85	35.7
≥6 years	62	26.1
The most type of AI adoption in working
Text to Speech and Speech to Text	94	39.5
Video, Image, and text processing	88	37.0
Content creation	18	7.6
Content editing	27	11.3
Other	11	4.6

The many types of AI adoption at work are detailed in Table 1. Text-to-Speech and Speech-to-Text technology is the group’s most used type of AI, employed by 39.5% of the participants. Video, picture, and text processing, used by 37.0%, are next. Other kinds of AI technology adoption have fallen significantly. Only 7.6% of participants utilize AI for content production; compared to 11.3% who use content editing, 4.6% of respondents said they used other AI systems. According to the data, this group mainly uses text-to-speech and video, image, and text processing; other AI techniques are employed far less frequently.

Results

Common Method Bias and Multicollinearity

We calculated the Variance Inflation Factor (VIF) values to ensure the acquired data didn’t have common method bias (CMB). If all of the VIFs in the outer model (see Table 1) and all of the VIFs in the inner model (see Table 2) are equal to or lower than 3.3 as a result of a thorough collinearity test, the model is thought to be free of CMB (Kock, 2015; Kock & Lynn, 2012). The results showed that All VIFs had values lower than 3.3; hence, the data gathered had no CMB-related problems. Table 2’s VIF value data reveals that all VIF coefficients are below 3. As a result, according to Hair et al. (2019), there was no multicollinearity in this research model.

Table 2.

Collinearity Statistics—Inner Model.

Inner paths	BI→AA	EE→BI	FC→BI	PE→BI	RS→AA	SI→BI	TA→AA	TR→BI
Variance Inflation Factor (VIF)	1.743	1.872	1.282	2.003	1.465	2.065	1.829	1.658

Reliability and Validity

At first, the reliability and validity were checked for all constructs. Internal consistency usually indicates reliability, measured by Cronbach’s alpha, composite reliability rho_c and rho_a. According to the findings (see Table 3), Cronbach’s alpha, Composite reliability rho_c, and rho_a were greater than .7, indicating that all scales fulfilled reliability standards (Hair et al., 2012, 2017).

Table 3.

Construct Reliability and Validity.

Constructs	Items	Factor loadings	Cronbach’s alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)	VIF—outer model
AA	AA1	0.842	0.720	0.731	0.843	0.642	1.564
	AA2	0.827					1.486
	AA3	0.730					1.299
BI	BI1	0.836	0.774	0.774	0.869	0.689	1.619
	BI2	0.836					1.678
	BI3	0.817					1.505
EE	EE1	0.757	0.755	0.758	0.844	0.576	1.450
	EE2	0.778					1.485
	EE3	0.781					1.487
	EE4	0.719					1.380
FC	FC1	0.626	0.745	0.823	0.827	0.548	1.366
	FC2	0.661					1.456
	FC3	0.841					1.412
	FC4	0.809					1.465
PE	PE1	0.802	0.812	0.812	0.876	0.639	1.675
	PE2	0.823					1.868
	PE3	0.778					1.545
	PE4	0.794					1.666
RS	RS1	0.794	0.764	0.776	0.863	0.677	1.573
	RS2	0.818					1.476
	RS3	0.856					1.624
SI	SI1	0.826	0.773	0.780	0.868	0.687	1.572
	SI2	0.802					1.538
	SI3	0.858					1.669
TA	TA1	0.771	0.821	0.822	0.882	0.650	1.538
	TA2	0.819					1.726
	TA3	0.823					1.866
	TA4	0.811					1.810
TR	TR1	0.809	0.826	0.827	0.885	0.657	1.843
	TR2	0.807					1.686
	TR3	0.835					1.972
	TR4	0.792					1.608

The concept of convergent validity is the measurement of conceptual similarity across items. To investigate the concurrent validity, this study investigated factor loadings and average variance extracted (AVE). According to the findings in Table 3, the AVE values varied from 0.548 to 0.689, exceeding the recommended value of 0.5 (Hair et al., 2017). Only FC1 and FC2 factor loadings were lower than the recommended value of 0.7, ranging from 0.6 to 0.7, but in social research studies, and this value can be accepted (Hair et al., 2012; Hulland, 1999), so most items of factor loadings have satisfied the requirements, according to Table 3. These findings enable convergent validity to be established.

This study uses the Heterotrait-Monotrait ratio (HTMT) criterion to evaluate the discriminant validity. The HTMT has been proven to perform better than the prior criteria for the measurement of discriminant validity (Henseler et al., 2015). Table 4 indicates that the HTMT criterion is met because all values were below the suggested threshold of 0.9 (Hair et al., 2017).

Table 4.

Heterotrait-Monotrait Ratio (HTMT) Matrix.

Construct	AA	BI	EE	FC	PE	RS	SI	TA
BI	0.836
EE	0.862	0.783
FC	0.412	0.386	0.528
PE	0.841	0.818	0.691	0.370
RS	0.748	0.637	0.626	0.239	0.603
SI	0.786	0.774	0.820	0.505	0.756	0.552
TA	0.816	0.781	0.721	0.375	0.782	0.663	0.733
TR	0.684	0.812	0.584	0.220	0.721	0.620	0.63	0.686

Structural Model Assessment

The structural model is the next stage after the validated measurement model. To find the coefficient of determination and the path coefficients, a bootstrapping technique with 5,000 additional samples is required (Hair et al., 2017). According to the results, Q² values of AA = 0.334 and BI = 0.400 from the construct cross-validated redundancy and PLS Predict showed that the model had a stronger predictive potential (see Table 5), and the PLS-path model had a moderate to sizeable predictive relevance (Hair et al., 2019). The robustness of the prediction model was evaluated by the holdout test’s Q² values (AA = 0.534, BI = 0.572) from the PLS predict (Hair et al., 2017). These findings demonstrated that the PLS-SEM model demonstrates acceptable predictive relevance and accuracy for AA and BI. This shows that the model could be able to predict the values of AA and BI if it makes use of the relationships between the independent variables.

Table 5.

PLS Predict and Construct Cross-Validated Redundancy.

Construct	Q² (=1 − SSE/SSO)	Q² predict
AA	0.334	0.534
BI	0.400	0.572

As indicated in Table 6 and Figure 2, the structural model of the assessment indicates hypotheses testing. The findings indicated that PE, EE, and SI significantly impact AI adoptions’ behavioral intention to use in journalism. Thus, H1 (β = .245, t = 3.554), H2 (β = .223, t = 3.717), H3 (β = .148, t = 2.345) are supported. Furthermore, the results pointed out that Trust significantly positively affects behavioral intention to use AI in journalism. Hence, H6 is supported (β = .327, t = 5.312). Moreover, the results revealed that perceived FC, BI, RS, and TA significantly positively affect AI adoption in journalism. Therefore, H4 (β = .109, t = 2.211), H5 (β = .291, t = 4.202), H7 (β = .257, t = 3.944), and H8 (β = .276, t = 3.621) are supported.

Table 6.

Hypothesis Testing.

Hypothesis	PLS Paths	Beta-values (β)	t-Value	p-Values	Decision	f-square
H1	PE → BI	.245	3.554	.000	Supported	0.075
H2	EE → BI	.223	3.717	.000	Supported	0.068
H3	SI → BI	.148	2.345	.019	Supported	0.027
H4	FC → AA	.109	2.211	.027	Supported	0.022
H5	BI → AA	.291	4.202	.000	Supported	0.103
H6	TR → BI	.327	5.312	.000	Supported	0.160
H7	RS → AA	.257	3.944	.000	Supported	0.098
H8	TA → AA	.276	3.621	.000	Supported	0.089

The effect size (f-square) quantifies each predictor’s relative contribution to the dependent variable’s variance. It is employed to ascertain the relationships’ practical importance in the model (J. Cohen, 1988). The results showed that FC, BI, RS, and TA have a medium effect on AA, whereas PE, EE, and SI have a medium effect on BI, and TR has a larger effect on BI, according to the f-square values in Table 6.

Figure 2.

Structural model.

Importance-Performance Map Analysis (IPMA)

As an advanced method of PLS-SEM, the importance-performance map analysis (IPMA) is used in this study, with BI and AA as the goal variables. The IPMA technique, which considers the average value of latent variables and associated indicators (i.e., performance measure), can be used to understand better PLS-SEM results (Hair et al., 2017; Ringle & Sarstedt, 2016). The findings of the IPMA are shown in Figures 3 and 4. All of the independent variables’ significance and effectiveness were evaluated. The results of IPMA in Figure 3 indicate that TR has the highest importance for predicting the behavioral intention to use, with the highest effect and performance scores. Concerning the importance of measures in AI adoption, Figure 4 shows that BI is the most important factor impacting AI adoption, followed by TA and RS.

Figure 3.

IPMA for behavioral intention.

Figure 4.

IPMA for AI adoption.

Discussion

The research problem addressed in this study was understanding the factors influencing journalists’ adoption of AI in journalism. The study aimed to test the hypotheses related to the impact of Performance expectancy (PE), Effort expectancy (EE), Social influence (SI), Facilitating conditions (FC), Behavioral intention (BI), Trust (TR), Regulatory support (RS), and Technology affinity (TA) on the behavioral intention and adoption of AI in journalism.

First, we found support for the hypothesis that PE, EE, and SI significantly affect the behavioral intention to use AI in journalism. FC and BI significantly affect the behavior of AI adoption in journalism. This finding aligns with previous studies utilizing the UTAUT framework (Ahadzadeh et al., 2021; Peng & Miller, 2021; Venkatesh et al., 2003; Williams et al., 2015). This finding indicates that how journalists view the pros and cons of adopting AI affects their decision to use AI technologies, and it is essential to consider the impact of social networks, peers, and coworkers on journalists’ adoption choices. Besides that, the organizational and technological environment's capacity to provide the appropriate tools, infrastructure, and support is vital in enabling journalists to use AI technologies.

In addition to the core determinants proposed by the UTAUT model, this study incorporated three additional constructs: Trust, Regulatory support, and Technology affinity, to comprehensively examine the factors influencing journalists’ adoption of AI in journalism. Regarding Trust, our findings confirmed its significant impact on the behavioral intention to use AI in journalism. This result is consistent with prior studies that have emphasized the role of Trust in technology adoption (Latifa & Zakaria, 2020; C. T. Pham & Thi Nguyet, 2023; Zhu et al., 2021). Trust enables journalists to overcome perceptions of risk and uncertainty associated with AI adoption, thus enhancing their acceptance and intention to adopt AI technologies.

Furthermore, the hypothesis that RS significantly affects the behavior of AI adoption in journalism received empirical support. This finding suggests that the policies and rules set by regulatory agencies influence the diffusion and adoption of AI technologies in journalism. It aligns with previous research highlighting the impact of regulatory support on technology adoption (Koster & Borgman, 2020; Kumar Bhardwaj et al., 2021; Matias & Hernandez, 2021). Regulatory support can reduce compliance costs, address data breaches and fraud concerns, and create an environment conducive to adopting new technologies.

Additionally, we found that TA significantly affects the behavior of AI adoption in journalism. This result underscores the role of individuals’ natural inclination toward technology and their comfort level in shaping their adoption behavior. It is consistent with prior research highlighting the influence of technology affinity on technology adoption (Aldás-Manzano et al., 2009; Trautwein et al., 2021). Journalists with a higher level of technology affinity are more likely to adopt AI technologies, as it modifies the impact of traditional technology acceptance factors on their adoption intentions.

This study fills several research gaps in the existing literature. First, it contributes to understanding AI adoption in the specific context of journalism, especially in developing journalism like Vietnam. While previous research has examined technology adoption in various domains, the present study focuses on AI adoption within journalism, providing insights into the unique factors influencing journalists’ adoption decisions. Second, by adding new constructs, this study broadens the UTAUT framework. This study includes trust, regulatory support, and technical affinity in a complete model to explain journalists’ use of AI in journalism. These new components emphasize the significance of regulatory elements, human propensity toward technology, and trust in influencing adoption behavior.

This research contributes to the theoretical understanding of AI adoption in journalism by extending and integrating the insights from UTAUT (Venkatesh et al., 2003). One key theoretical contribution of this study is the identification of TR, RS, and TA as significant factors influencing AI adoption in journalism. While previous research on technology adoption has acknowledged the importance of TR (Latifa & Zakaria, 2020; C. T. Pham & Thi Nguyet, 2023; Stojanović et al., 2023; Zhu et al., 2021), RS (Koster & Borgman, 2020; Kumar Bhardwaj et al., 2021; Matias & Hernandez, 2021), and TA (Hasgall et al., 2018; Trautwein et al., 2021) in various contexts, this study highlights their relevance in journalism, where issues of accuracy, transparency, and ethical use of technology are paramount. This finding calls for further investigation into the boundary conditions of the UTAUT model and the exploration of context-specific factors that may influence technology adoption in journalism.

The practical contributions of this study are manifold, providing valuable insights for AI developers, media organizations, and policymakers. By identifying the factors influencing journalists’ intentions to adopt AI tools and their actual adoption behavior, this study offers guidance for developing and promoting AI tools in journalism. For AI developers, the study highlights the importance of designing AI tools that are easy to use and that demonstrate their performance benefits to journalists. By focusing on these factors, developers can create AI tools more likely to be embraced by journalists. Building trust among journalists by ensuring AI tools’ transparency, accuracy, and reliability is crucial for widespread adoption (de-Lima-Santos & Mesquita, 2021; Guzman & Lewis, 2020; Haugen, 2023).

Media organizations can leverage the findings of this study to foster a supportive environment for AI adoption. By providing training and support to improve journalists’ technology affinity and encouraging the use of AI through social influence, media organizations can promote a culture of innovation and acceptance of AI in journalism. Finally, policymakers can use the insights from this study to inform the development of regulations and guidelines that promote transparency, accountability, and ethical use of AI tools in journalism (Broussard et al., 2019).

Conclusion

This study contributes significantly to the theoretical framework of AI adoption in journalism, particularly within the context of Vietnamese journalism. Integrating core constructs from the UTAUT model with additional constructs of trust, regulatory support, and technology affinity offers a comprehensive understanding of the factors influencing journalists’ adoption of AI. The results underscore the importance of perceived benefits, ease of use, social influence, and organizational support in shaping journalists’ behavioral intentions toward AI adoption. The significant roles of trust, regulatory support, and individual technology affinity were highlighted, emphasizing how these elements can facilitate or hinder the adoption process. These findings enrich the UTAUT framework and provide practical insights for AI developers, media organizations, and policymakers to develop strategies to encourage AI adoption in journalism.

However, this study has several limitations that pave the way for future research. The specific focus on Vietnamese journalism limits the generalizability of the findings, highlighting the need for replication studies in diverse cultural and organizational settings to validate the results. The study’s cross-sectional design constrains the establishment of causality, suggesting that future research could employ longitudinal or experimental methods to obtain more robust evidence. Additionally, reliance on self-reported data raises concerns about potential biases, indicating the need for future studies to incorporate objective measures of AI adoption. The study also overlooks potential influencing factors such as perceived risk, job characteristics, and organizational culture, which could be crucial in understanding AI adoption in journalism. Future research may collect qualitative data from interviews to make the data more interpretive. Lastly, the study did not examine the moderating effects of demographic factors such as gender, age, experience, and voluntariness. Addressing these aspects in future research could provide a more comprehensive understanding of the adoption process and help tailor AI tools and policies more effectively to suit the diverse needs of journalists.

Footnotes

I would like to send great thanks to the boards of directors at the surveyed news agencies. I also owe thanks to the participants involved in this study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iDs

Ta Thi Nguyet Trang

Pham Chien Thang

Le Dinh Hai

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the Figshare repository,

References

Abdullahi

Haruna Iliyasu

Rabo

Bara’atu

(2021). Assessment of awareness and intergration of E-learning amid COVID-19 pandemic among language lecturers in public universities, North-Central Nigeria. Arts Literature & Linguistics, 79(1), 20–20. https://doi.org/10.47119/IJRP100791620212012

Afrizal

Wallang

(2021). Attitude on intention to use e-government in Indonesia. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 435–441. https://doi.org/10.11591/IJEECS.V22.I1.PP435-441

Ahadzadeh

A. S.

S. L.

Ong

F. S.

Deng

(2021). The mediating influence of the unified theory of acceptance and use of technology on the relationship between internal health locus of control and mobile health adoption: Cross-sectional study. Journal of Medical Internet Research, 23(12), e28086. https://doi.org/10.2196/28086

Aldás-Manzano

Ruiz-Mafé

Sanz-Blas

(2009). Exploring individual personality factors as drivers of M-shopping acceptance. Industrial Management & Data Systems, 109(6), 739–757. https://doi.org/10.1108/02635570910968018

Al-Saedi

Al-Emran

Ramayah

Abusham

(2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 101293. https://doi.org/10.1016/J.TECHSOC.2020.101293

Anderson

C. W.

(2013). Towards a sociology of computational and algorithmic journalism. New Media and Society, 15(7), 1005–1021. https://doi.org/10.1177/1461444812465137

Bervell

Umar

I. N.

(2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6471–6490. https://doi.org/10.12973/EJMSTE/78076

Bird

Barocas

Crawford

Diaz

Wallach

(2016). Exploring or exploiting? Social and ethical implications of autonomous experimentation in AI. Workshop on Fairness, Accountability, and Transparency in Machine Learning. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2846909

Bolukbasi

Chang

K.-W.

Zou

J. Y.

Saligrama

Kalai

A. T.

(2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings [30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain]. Advances in Neural Information Processing Systems, 29. https://code.google.com/archive/p/word2vec/

10.

Bondio

Shahnazari

McHugh

(2018). The technology of the middle class: Understanding the fulfilment of adoption intentions in Queensland’s rapid uptake residential solar photovoltaics market. Renewable and Sustainable Energy Reviews, 93, 642–651. https://doi.org/10.1016/J.RSER.2018.05.035

11.

Broussard

Diakopoulos

Guzman

A. L.

Abebe

Dupagne

Chuan

C. H.

(2019). Artificial Intelligence and Journalism. Journalism and Mass Communication Quarterly, 96(3), 673–695. https://doi.org/10.1177/1077699019859901/ASSET/1077699019859901.FP.PNG_V03

12.

Brown

Dennis

Venkatesh

(2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54. https://doi.org/10.2753/MIS0742-1222270201

13.

Buolamwini

Gebru

(2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Friedler

S. A.

Wilson

(Eds.), Proceedings of Machine Learning Research (Vol. 81, pp. 77–91). PMLR. https://proceedings.mlr.press/v81/buolamwini18a.html

14.

Calvo Rubio

L. M.

Ufarte Ruiz

M. J

. (2021). Artificial intelligence and journalism: Systematic review of scientific production in web of science and scopus (2008–2019). Communication and Society, 34(2), 159–176. https://doi.org/10.15581/003.34.2.159-176

15.

Campolo

Crawford

(2020). Enchanted determinism: Power without responsibility in artificial intelligence. Engaging Science, Technology, and Society, 6, 1–19. https://doi.org/10.17351/ESTS2020.277

16.

Chan-Olmsted

S. M.

(2019). A review of artificial intelligence adoptions in the media industry. JMM International Journal on Media Management, 21(3–4), 193–215. https://doi.org/10.1080/14241277.2019.1695619

17.

Charlie

(2019, November 18). New powers, new responsibilities. A global survey of journalism and artificial intelligence. https://blogs.lse.ac.uk/polis/2019/11/18/new-powers-new-responsibilities/

18.

Chou

K. C.

Liao

C. S.

Chiang

T. H. C.

(2021). The application and development of artificial intelligence in taiwan’s news communication. Taiwan Journal of East Asian Studies, 18(1), 43–88. https://doi.org/10.6163/TJEAS.202106_18(1).0002

19.

Cohen

(1988). Set correlation and contingency tables. Applied Psychological Measurement, 12(4), 425–434. https://doi.org/10.1177/014662168801200410

20.

Cohen

Hamilton

J. T.

Turner

(2011). Computational journalism. Communications of the ACM, 54(10), 66–71. https://doi.org/10.1145/2001269.2001288

21.

Coria

Villegas-Palacio

(2014). Regulatory dealing: Technology adoption versus enforcement stringency of emission taxes. Contemporary Economic Policy, 32(2), 451–473. https://doi.org/10.1111/COEP.12013

22.

de-Lima-Santos

M. F.

Mesquita

(2021). Data journalism beyond technological determinism. Journalism Studies, 22(11), 1416–1435. https://doi.org/10.1080/1461670X.2021.1944279

23.

Dutta

Sarma

M. K.

(2021). Adoption of digital innovations: A special referenceto routinised incremental innovations. Review of Market Integration, 13(2–3), 73–97. https://doi.org/10.1177/09749292211073636

24.

Guzman

A. L.

Lewis

S. C.

(2020). Artificial intelligence and communication: A Human–Machine Communication research agenda. New Media and Society, 22(1), 70–86. https://doi.org/10.1177/1461444819858691

25.

Hair

J. F.

Hult

G. T.

Ringle

Sarstedt

(2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.

26.

Hair

J. F.

Risher

J. J.

Sarstedt

Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

27.

Hair

J. F.

Sarstedt

Pieper

T. M.

Ringle

C. M.

(2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340. https://doi.org/10.1016/J.LRP.2012.09.008

28.

Hasgall

Ahituv

Barzilai

Voloch

Steiner

O. L.

(2018). Traffic control in a smart intersection by an algorithm with social priorities. Contemporary Engineering Sciences, 11(31), 1511. https://doi.org/10.12988/ces.2018.83126

29.

Haugen

(2023). The power of one. Little, Brown and Company.

30.

Helberger

(2019). On the democratic role of news recommenders. Digital Journalism, 7(8), 993–1012. https://doi.org/10.1080/21670811.2019.1623700

31.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/S11747-014-0403-8

32.

Hong

Chan

F. K. Y.

Thong

J. Y. L.

Chasalow

L. C.

Dhillon

(2014). A framework and guidelines for context-specific theorizing in information systems research. Information Systems Research, 25(1), 111–136. https://doi.org/10.1287/isre.2013.0501

33.

Hulland

(1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7

34.

Jacob

Pattusamy

(2020). Examining the inter-relationships of UTAUT constructs in mobile Internet use in India and Germany. Journal of Electronic Commerce in Organizations, 18(2), 36–48. https://doi.org/10.4018/JECO.2020040103

35.

Khan

I. U.

Hameed

Islam

Sheikh

Khan

S. U.

(2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964–978. https://doi.org/10.1016/J.TELE.2017.09.009

36.

Kock

(2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/IJEC.2015100101

37.

Kock

Lynn

G. S.

(2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1JAIS.00302

38.

Koster

Borgman

(2020). New kid on the block! Understanding blockchain adoption in the public sector [53rd Hawaii International Conference on System Sciences, Maui, Hawaii]. Hawaii International Conference on System Sciences 2020 (HICSS-53). https://aisel.aisnet.org/hicss-53/dg/blockchain/3

39.

Kothari

Cruikshank

S. A.

(2022). Artificial intelligence and journalism: An agenda for journalism research in Africa. African Journalism Studies, 43(1), 17–33. https://doi.org/10.1080/23743670.2021.1999840

40.

Kumar Bhardwaj

Garg

Gajpal

. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021, 5537395. https://doi.org/10.1155/2021/5537395

41.

Latar

N. L.

(2015). The robot journalist in the age of social physics: The end of human journalism? In Gali

(Ed.), The new world of transitioned media (pp. 65–80). Springer. https://doi.org/10.1007/978-3-319-09009-2_6

42.

Latifa

M. I.

Zakaria

(2020). Factors determine the behavioural intention in adopting the blockchain technology by Malaysian public sector officers. Journal of Advanced Research in Business and Management Studies, 20(1), 34–43. https://doi.org/10.37934/ARBMS.20.1.3443

43.

Lindén

C.-G.

(2017). Algorithms for journalism: The future of news work. The Journal of Media Innovations, 4(1), 60–76. https://doi.org/10.5617/JMI.V4I1.2420

44.

Matias

J. B.

Hernandez

A. A.

(2021). Cloud computing adoption intention by MSMEs in the Philippines. Global Business Review, 22(3), 612–633. https://doi.org/10.1177/0972150918818262

45.

Minh Thu. (2022, January 18). Trí tuệ nhân tạo: Cơ hội phát triển cho báo chí thời đại công nghệ | Truyền thông | Vietnam+ (VietnamPlus). Vietnamplus. https://www.vietnamplus.vn/tri-tue-nhan-tao-co-hoi-phat-trien-cho-bao-chi-thoi-dai-cong-nghe/767154.vnp

46.

Morris

M. G.

Venkatesh

(2010). Job characteristics and job satisfaction: understanding the role of enterprise resource planning system implementation. MIS Quarterly: Management Information Systems, 34(1), 143–161. https://doi.org/10.2307/20721418

47.

Morris

M. G.

Venkatesh

Ackerman

P. L.

(2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69–84. https://doi.org/10.1109/TEM.2004.839967

48.

NDO. (2022, June 11). Sử dụng trí tuệ nhân tạo trong hoạt động báo chí: Thực tiễn và các vấn đề pháp lý. Nhan Dan Newspaper. https://nhandan.vn/su-dung-tri-tue-nhan-tao-trong-hoat-dong-bao-chi-thuc-tien-va-cac-van-de-phap-ly-post700920.html

49.

Newman

Fletcher

Schulz

Simge

Nielsen

R. K.

(2020). Journalism, media, and technology trends and predictions 2020. Reuters Institute Digital News Report. https://www.digitalnewsreport.org/publications/2020/journalism-media-and-technology-trends-and-predictions-2020

50.

Nickolas

(2019). Automating the news: How algorithms are rewriting the media. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674976986

51.

Noain-Sánchez

(2022). Addressing the Impact of Artificial Intelligence on Journalism: The perception of experts, journalists and academics. Communication and Society, 35(3), 105–121. https://doi.org/10.15581/003.35.3.105-121

52.

Pan

Ding

Yang

(2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners: A technology transfer perspective. International Journal of Production Research, 57(18), 5801–5820. https://doi.org/10.1080/00207543.2018.1550272

53.

Parratt-Fernández

Mayoral-Sánchez

Mera-Fernández

(2021). The application of artificial intelligence to journalism: an analysis of academic production. Profesional de La Información, 30(3), e300317. https://doi.org/10.3145/EPI.2021.MAY.17

54.

Peng

Miller

(2021). An examination of how social and technological perceptions predict social media news use on WeChat. Journalism Practice, 17(3), 554–573. https://doi.org/10.1080/17512786.2021.1925948

55.

Pham

C. T.

Thi Nguyet

T. T.

(2023). Determinants of blockchain adoption in news media platforms: A perspective from the Vietnamese press industry. Heliyon, 9(1), e12747. https://doi.org/10.1016/J.HELIYON.2022.E12747

56.

Pham

(2022). Using media to influence consumer attitudes to domestic goods in Vietnam by framing public interest: A media framing effect analysis. SAGE Open, 12(4), 1–14. https://doi.org/10.1177/21582440221138247

57.

Ringle

C. M.

Sarstedt

(2016). Gain more insight from your PLS-SEM results the importance-performance map analysis. Industrial Management and Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449/FULL/XML

58.

Ringle

C. M.

Wende

Becker

J.-M.

(2022). SmartPLS 4. Oststeinbek: SmartPLS (4.0.9.2). https://smartpls.com/

59.

Romanova

Stoyan

Pankratov

Litvinchev

Avramov

Chernobryvko

Yanchevskyi

Mozgova

Bennell

(2021). Optimal layout of ellipses and its application for additive manufacturing. International Journal of Production Research, 59(2), 560–575. https://doi.org/10.1080/00207543.2019.1697836

60.

Stojanović

Radenković

Popović

Mitrović

Bogdanović

(2023). A readiness assessment framework for the adoption of 5G based smart-living services. Information Systems and E-Business Management, 21389–413. https://doi.org/10.1007/S10257-023-00625-3

61.

Stray

(2019). Making artificial intelligence work for investigative journalism. Digital Journalism, 7(8), 1076–1097. https://doi.org/10.1080/21670811.2019.1630289

62.

Thong

J. Y. L.

(1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187–214. https://doi.org/10.1080/07421222.1999.11518227

63.

Trautwein

Lindenmeier

Arnold

. (2021). The effects of technology affinity, prior customer journey experience, and brand familiarity on the acceptance of smart service innovations. Journal of Service Management Research, 5(1), 36–49. https://doi.org/10.15358/2511-8676-2021-1-36

64.

Túñez-López

J. M.

Ceide

C. F.

Vaz-álvarez

(2021). Impact of artificial intelligence on journalism: Transformations in the company, products, contents and professional profile. Communication & Society, 34(1), 177–193. https://doi.org/10.15581/003.34.1.177-193

65.

Venkatesh

(1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly: Management Information Systems, 23(2), 239–260. https://doi.org/10.2307/249753

66.

Venkatesh

(2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872

67.

Venkatesh

(2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. https://doi.org/10.1007/S10479-020-03918-9

68.

Venkatesh

Bala

(2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/J.1540-5915.2008.00192.X

69.

Venkatesh

Bala

Sambamurthy

(2016). Implementation of an information and communication technology in a developing country: A multimethod longitudinal study in a bank in India. Information Systems Research, 27(3), 558–579. https://doi.org/10.1287/isre.2016.0638

70.

Venkatesh

Davis

F. D.

Morris

M. G.

(2007). Dead or Alive? The Development, Trajectory and Future of Technology Adoption Research. Journal of the Association for Information Systems, 8(4), 267–286. https://doi.org/10.17705/1JAIS.00120

71.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540

72.

Venkatesh

Morris

M. G.

Sykes

T. A.

Ackerman

P. L.

(2004). Individual Reactions to New Technologies in the Workplace: The Role of Gender as a Psychological Construct. Journal of Applied Social Psychology, 34(3), 445–467. https://doi.org/10.1111/J.1559-1816.2004.TB02556.X

73.

Venkatesh

Thong

J. Y. L.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412

74.

Venkatesh

Zhang

(2010). Unified theory of acceptance and use of technology: U.S. vs. China. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198X.2010.10856507

75.

Wang

Liu

Deng

Wang

Liu

Deng

Wang

(2021). Research trend of the unified theory of acceptance and use of technology theory: A bibliometric analysis. Sustainability, 14(1), 10. https://doi.org/10.3390/SU14010010

76.

Wang

Lin

Zhong

R. Y.

(2019). IoT-enabled cloud-based additive manufacturing platform to support rapid product development. International Journal of Production Research, 57(12), 3975–3991. https://doi.org/10.1080/00207543.2018.1516905

77.

Williams

M. D.

Rana

N. P.

Dwivedi

Y. K.

(2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–448. https://doi.org/10.1108/JEIM-09-2014-0088/FULL/XML

78.

Thong

J. Y. L.

Tam

K. Y.

(2017). Winning back technology disadopters: Testing a technology readoption model in the context of mobile internet services. Journal of Management Information Systems, 34(1), 102–140. https://doi.org/10.1080/07421222.2017.1297172

79.

Huang

(2021). Friend or foe? Human journalists’ perspectives on artificial intelligence in Chinese media outlets. Chinese Journal of Communication, 14(4), 409–429. https://doi.org/10.1080/17544750.2021.1915832

80.

Zhang

Venkatesh

(2018). From design principles to impacts: A theoretical framework and research agenda. AIS Transactions on Human-Computer Interaction, 10(2), 105–128. https://doi.org/10.17705/1thci.00106

81.

Zhao

Lee

K. C.

Kwon

Frederick

P. D.

Onega

T. L.

Piepkorn

M. W.

Knezevich

Barnhill

R. L.

Elder

D. E.

Elmore

J. G.

(2016). The self-reported use of immunostains and cytogenetic testing in the diagnosis of melanoma by practicing U.S. pathologists of 10 selected states. Journal of Cutaneous Pathology, 43(6), 492–497. https://doi.org/10.1111/CUP.12705

82.

Zhu

Y. Q.

Azizah

A. H.

Hsiao

(2021). Examining multi-dimensional trust of technology in citizens’ adoption of e-voting in developing countries. Information Development, 37(2), 193–208. https://doi.org/10.1177/0266666920902819

Understanding the Adoption of Artificial Intelligence in Journalism: An Empirical Study in Vietnam

Abstract

Plain Language Summary

Keywords

Introduction

Literature Reviews

Artificial Intelligence Technology in Journalism

The Unified Theory of Acceptance and Use of Technology (UTAUT) in Media Journalism

Research Hypothesis Development and Model

Method

Questionnaire Design

Data Analysis

Data Collection and Participants

Results

Common Method Bias and Multicollinearity

Reliability and Validity

Structural Model Assessment

Importance-Performance Map Analysis (IPMA)

Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Data Availability Statement

References