The prospect of digital human communication for organizational purposes

Introduction

The role artificial intelligence (AI) will play in future of the workplace is rapidly evolving (Srnicek, 2017). A considerable amount of research and speculation has focused on how AI may automate and potentially eliminate certain jobs (McKinsey and Company, 2022). However, beyond the ability of AI to complete work tasks, the role of AI in interpersonal communication has the potential to profoundly change how people communicate (Hancock et al., 2020). Even though AI is being used extensively in supporting organizational communication, it has garnered less attention (Androutsopoulou et al., 2019). For example, many customer service applications have shifted the initial customer contact online and in call centers through the utilization of AI-enabled chatbots (Poola, 2017). The use of AI-enabled chatbot technology in business applications is now the focus of more research (Rust and Huang, 2014; Pizzi et al., 2021).

Beyond chatbots, AI has already imbedded itself in written communication by auto-correcting spelling errors, highlighting grammatical errors, and suggesting more efficient ways to phrase a sentence. Additionally, many households regularly utilize adaptive AI communication through Amazon's Alexa or Apple's Siri (Hepp, 2020). More recently, there has been a great deal of attention on how AI may help write news stories or academic papers using ChatGPT (Graves, 2023). Furthermore, Graves (2023) notes the potential for AI functionality such as Chat CPT to personalize communication in the voice of those utilizing the tool by mining the communication text of users and speaking in their voice will likely be a future enhancement.

The evolving use of AI in communication technology has the potential to make communication more targeted, efficient, and available to users by providing access to communication in ways that have not previously been utilized (Hancock et al., 2020). One such way is with a digital human. According to Deloitte (2022), digital humans may be the next step in organizational communication, where an avatar or the image of someone representing the organization shares information with those observing the communication. This technology has the potential to humanize the use of technology in communication by using a human's imagination, body language, and voice cues while being easy to access and available for 24 h a day and 7 days a week. However, technology is only as valuable as users are willing to accept it (Davis, 1989).

The purpose of this study is to determine how effectively users perceive digital human technology when it is used for a corporate announcement compared with other more traditional communication modes, i.e., in-person or email communication. In this study, a corporate announcement is delivered in person, via email, and by a digital human across three groups of respondents in separate surveys. Quantitative and qualitative data are analyzed to assess how effective respondents felt each communication mode was in delivering the corporate announcement and whether that impacted their perception of organizational support. Analyzing these data on user perception provides researchers and practitioners with valuable data on this new technology which can be used to draw insights into the potential for further adoption. Drawing on prior research in communication technology perception and acceptance, the article considers what lessons can be learned and applied as digital human technology evolves. This research is important due to the critical role corporate communication plays in organizational behavior (Robbins and Judge, 2009).

The evolution of communication technology

Myers and Myers (1982) posit that organizational communication is the tool that binds employees and drives organizational behavior. Organizational communication is used to communicate corporate mission, goals, and culture and build organizational support and cohesion (Zaccaro et al., 2001; Verburg et al., 2013). Technology-mediated communication can be a substitute for leadership, providing structural support when a leader cannot be present (Hoch and Kozlowski, 2014; Newman et al., 2020). Therefore, organizational communication is critical for an organization's success.

Technology and the growth of computer-mediated communication technology have an important role in the delivery of organizational communication. Computer-mediated communication originated with mainframe computers communicating with each other, which, then, evolved into simple text-based messages sent from user to user in mainframes and eventually evolving into email (Walther, 1996). Developing concurrently with the evolution of different computer-mediated communication modes was a dramatic improvement in the availability of the internet, making computer-mediated communication more effective (Armbruster and Wimmer, 1992). As these technologies were being developed, broadband technology was also becoming more accessible, allowing for computer-mediated communication to be transmitted 50 times faster than early broadband technology. This established a technology infrastructure that could support additional communication technology development (Savage and Waldman, 2005). The development of high-speed, affordable internet service led to an explosion in the number of technology-enabled communication tools from social media to online meeting services such as Zoom and WebEx (Newman and Golpalkrishnan, 2022), to the integration of AI into computer-mediated communication with chatbot-based tools (Shankar, 2018).

There is currently a great deal of research on chatbots and the role they can play in different business applications from customer service (Pizzi et al., 2021) to corporate communication and training to support human resource management (Majumder and Mondal, 2021). IBM defines a chatbot as “a computer program that uses artificial intelligence (AI) and natural language processing (NLP) to understand customer questions and automate responses to them, simulating human conversation” (https://www.ibm.com/topics/chatbots). A literature review from Mygland et al. (2021) found that chatbots can assist humanlike conversation by navigating and facilitating a process, providing assistance or information, and ensuring there is privacy in communication. In a business application, a chatbot can provide a lower cost service delivery model than live customer service agents and support online shopping experiences and answer questions or make suggestions to support the customer experience (Pizzi et al., 2021). In other business applications such as human resource management, chatbots can support predictable inquiries and can help organizations gain data and insight into areas of inquiry (Taule et al., 2022).

However, as the use and application of chatbots have grown, this does not mean users are completely satisfied with their experience. Research has found that user dissatisfaction with chatbots can originate from the chatbots' perceived lack of understanding and their inability to display emotion (Lin et al., 2022). Because of this, one of the next evolutions in communication technology will be the use of digital humans to assist with corporate communications (Deloitte, 2022).

“A digital human can be defined as a life-like being, powered by artificial intelligence (AI), with the capability of conversing, communicating, and creating an emotional connection, like any other human being” (Silva and Bonetti, 2021, p. 1). Digital human technology combining realistic or real human images and AI got off to an auspicious start as “deep fakes” which simulated realistic communication, oftentimes related to the news which generated conversation and outrage (Westerlund, 2019). In a more positive application, digital humans can assist in delivering important organizational communication that enables human connections (Deloitte, 2022). For example, the company UNEEQ (2023) advises that digital humans can be used for a variety of applications from customer service and financial advising to being a wellness coach. 2mee Ltd. and IBM have developed technology that allows users to create a digital version of them to communicate quickly with customers (IBM, 2023).

In a customer service application, using digital human communication can increase engagement and clickthrough rates by 87% (IBM, 2023). Silva and Bonetti (2021) completed a study on retail engagement that showed respondents were likely to choose to interact with digital human avatars when shopping. Those surveyed felt strongly about the ability of the digital human to positively interact and have a social presence with the customers. Research emerging on digital human communication technology has shown some early success in customer service; however, how this engagement translates to other business applications is still being determined.

Acceptance of digital human technology

How broadly technology is used and accepted is a major theme of technology research (e.g., Lee et al., 2003; Chuttur, 2009; Vahdat et al., 2021). When subjects are exposed to new technologies, they make an appraisal of the benefits of using that technology. Users' decisions on whether new technology may be personally beneficial can be explained by the appraisal theory (Scherer et al., 2001). According to the appraisal theory, employees make an appraisal when being exposed to a new technology that their experience with the application is either positive, neutral, or negative (Zhang and Provost, 2019). This appraisal is based on experience and environmental factors that may influence the user's perception (Jokinen and Silvennoinen, 2020).

Recently, employees' appraisal and acceptance of new technology for communication have varied based on the application and experience (Lin, 2003). We have seen pushback on the use of Zoom for video conferencing, as employees suffer from “Zoom fatigue” resulting in a negative perception of the technology (Newman and Golpalkrishnan, 2022). Park et al. (2014) found that technology acceptance for workplace communication technology is tied to the perceived ease of use and the usefulness of a technology. In retail applications, users were influenced by how realistic and pleasant their experience was when interacting with communication technology (Silva and Bonetti, 2021). This assessment of utility is likely what caused the rapid growth and use of mobile devices for communications through “apps” over the last 15 years (Min et al., 2021).

The Uses and Gratification Theory (U&G) helps explain the process users go through when determining whether they will have a positive or negative perception of different communication media based on the users' communication needs and the gratification they anticipate getting from the communication media (Blumler and Katz, 1974). The U&G posits that the motivation to use certain communication media can be driven by cognitive, affective, personal, and social needs as well as the need to reduce tension (Katz et al., 1973). U&G has been applied to help explain the process users go through when evaluating new communication technology (Ruggiero, 2000). For example, a study by Chen (2011) concluded that users were motivated to use Twitter as a communication media based on their perceived communication needs and satisfaction with that communication media. U&G has also been applied to users deciding whether there is a benefit to using AI-driven communication technology such as learning platforms (Chang et al., 2022), chatbots (Rese et al., 2020), and AI assistants (Xie et al., 2023).

In addition, the technology acceptance model (TAM) plays a prominent role in predicting how well users would be motivated to adopt and use technology innovation (Davis, 1989). In the model, users' perception of the utility of a new technology influences how they perceive its effectiveness which drives their willingness to use the technology (Kim et al., 2010). Several studies focused on the acceptance of new communication technology innovation have evaluated the influence of the perceived usefulness of the technology in driving adoption, from cell phones (Kwon and Chidambaram, 2000), email (Szajna, 1996) to social media (Rauniar et al., 2014), Zoom (Djojo et al., 2021), chatbots (Dosovitsky and Bunge, 2021), and intelligent voice assistants (Yin and Wu, 2023).

Communication technology is now evolving again with the application of AI-driven chatbots for customer support and corporate communication. System developers are driven to make technology as realistic and human-like as possible to encourage use and acceptance (Carmigniani et al., 2011). Organizations are currently investing nearly $100 billion in AI technology communication which can simulate humans (Statisica, 2022). These investments have been accelerated by individuals' experiences with COVID-19, which has helped develop more positive attitudes toward the usefulness of communication technology (Ishaq et al., 2021). This makes the creation of digital human technology the next frontier of technology-driven communication tools for organizations to explore (McKinsey and Company, 2022).

Even though AI has been introduced into technology communication, there are few quantitative studies that have assessed employees' willingness to accept AI technology in the workplace (Choi, 2021). However, the use of AI in communication is “advancing rapidly, with potentially critical impacts in areas”, such as personal relationships and relationships with organizations (Hancock et al., 2020, p. 97). Choi (2021) suggested that individual factors such as motivation and ability may impact employees' perception of AI.

Thus, as digital human communication technology emerges, it's acceptance will be based on it's perceived ease of use, how enjoyable the interaction felt and if it fills and communication needs. Due to the increased use of communication technology and limited research in this area, an exploratory study was conducted to collect data regarding users' perceptions of the effectiveness of digital human technology for organizational communication. This led us to the following hypotheses:

H1: Users will have mixed reactions regarding the effectiveness of digital human technology when communicating a corporate announcement.

H2: Since users are being exposed to a new form of technology, they will find more traditional communication methods, such as email and live communication as more effective than digital human.

H3: Users will be satisfied with all three modes of communication.

H4: Users who receive corporate communication in person or via email will perceive organizational support as higher than those who receive it from the digital human.

Methods

A quasi experimental study was used to assess the current perception and utility of digital human technology. A corporate communication announcing the merger of two companies was developed (see Appendix A). The respondents were undergraduate and graduate students in business programs at US universities based in the southeast. The 374 participants were divided into three groups. The first group of 131 respondents received the message with a digital human speaking the script (https://youtu.be/1ekvq9aYoXc). The second group (130) received an email with the exact same content, and the third group (113) was in-person communication making the same corporate announcement. Once the script was communicated, the respondents in each group completed a survey regarding various constructs on the effectiveness of the communication and perception of organizational support from the resulting communication. The demographics of the respondents are presented in Table 1, Study demographics.

TABLE 1

Table 1. Study demographics.

The survey instrument used in this study consisted of three sections as follows: (a) demographic information, (b) survey scales, and (c) a collection of qualitative data. The respondents were first asked to assess their perception of the effectiveness of each communication mode based on Newman et al. (2020) Perception of Leaders' Effective Use of Communication on seven-point Likert scale. The scale assessed whether the communication each respondent received was clear and the message was understood. It also assessed the respondents' confidence in the communication and whether or not the respondents thought the communication mode was acceptable for the message being communicated.

Next, respondents were asked on a 10-point differentiation scale, how satisfied they were with the communication they received. The measure was “on a scale of 0 to 10 how satisfied were you with the communication you received?”.

Respondents were then asked to assess how they perceived the organizations' support after receiving the communication. The 8-item perceived organization support scale by Eisenberger et al. (1986) was used for this assessment. The scale items included items like whether the organization cares about the employees' wellbeing, whether the organization considers the employees' goals and values, and whether or not the organization values the employees' opinion.

Finally, respondents were asked to provide qualitative responses on why the specific communication mode they were evaluating was effective and why they felt that way. The qualitative analysis was conducted in multiple ways. First, an inductive coding system was created by the researchers to categorize the sentiment of each response regarding effectiveness, as positive, neutral, or negative. Within each of these categories, themes were identified as to why a respondent perceived the communication in that way. To complete this analysis, the qualitative research process outlined by Thomas (2006) was followed to code and establish themes. In addition, qualitative data were analyzed for sentiment using tools developed by Soper (2023).

Analysis and results

To analyze these data, the mean results were computed for each communication method. ANOVA was, then, run to determine differences between the respondents' view on the perceived communication effectiveness based on the communication mode they received (F = 8.58, 2,368, p < 0.01). Bonferroni was also leveraged for post hoc analysis to validate differences between the groups.

As shown in Table 2, a comparison of the perceived communication effectiveness scale across modes shows the difference between the communication delivered by the digital human and a memo as 0.704, which indicates that the respondents found the memo to be more effective than the digital human. In addition, the in-person communication was found less effective than the memo (−0.55). There was not a statistically significant difference seen between the communication being delivered in person and by the digital human.

TABLE 2

Table 2. Comparison of the perceived communication effectiveness scale across modes.

Hypothesis 1

Hypothesis 1 was supported. The results indicate that the respondents did have mixed reactions regarding the use of digital technology to deliver a corporate announcement. For each communication mode, respondents were asked whether the digital human communication mode was effective and provided qualitative responses for why they rated the communication that way. A total of 91 respondents provided qualitative data. Iterative analysis was conducted to identify the tone of each comment and categorized into positive, neutral, or negative. The respondents demonstrated mixed reactions to digital human communication: 30 responses were positive, 26 were neutral, and 35 were negative. Within each tone, themes were then identified.

Positive responses included the feeling that “I believe the digital human would be effective to bridge and communication lags”, “I think using a digital human for this type of corporate communication can be effective”, and “I think this could work for corporate communication because sometimes the person who needs to communicate is not always available so for them to be able to record the message ahead of time will save money and time”. Examples of neutral responses included “it could be effective, but I would prefer a real person” and “it could be effective for some people, but not everyone”. Negative responses included “robots wouldn't be able to communicate effectively”, “it felt impersonal for the huge change that is occurring”, and “to me this is missing the human emotional connection that is had in a live speech”.

Second, data were reviewed using the Daniel Soper sentiment analysis to measure the overall sentiment for the digital human communication delivery. The result of the analysis was the digital human corporate communication and was in the neutral range (−6.5) on a scale of 100 to −100. This result was similar to the sentiment score from the in-person communication (−15.9) and email communication (−20.3).

Hypothesis 2

Hypothesis 2 was supported. The data results showed us that respondents felt that the in-person and email modes of communication were slightly more effective than digital humans. The mean results (on a 7 point Likert scale) were (a) email memo received 5.88, (b) in-person communication 5.33, and (c) digital human, 5.17 for communication effectiveness. Next, ANOVA was run against the perceived communication effectiveness scale to determine if there was a statistically significant difference between the three communication modes (F = 8.58, 2,368, p < 0.01). For post hoc, we ran Bonferroni. As shown in Table 2, the comparison of the perceived communication effectiveness scale across the modes shows the difference between the communication delivered by the digital human and the email memo as 0.704, which indicates that the respondents found the memo to be statistically more effective than the digital human. In addition, the in-person communication was found less effective than the email memo (−0.55). There was not a statistically significant difference between in-person communication and digital human (see Table 2).

Further analysis was completed using one-way ANOVA, isolating the measure “Do you think using a < insert communication treatment> for this type of corporate communication can be effective?” When looking at only this question, there was a significant difference among the three groups (F = 35.267, df = 2,368, p < 0.001). The same result occurred in post hoc testing using Bonferroni, Tukey, and Dunnett tests.

As shown in Table 3, a comparison of the perceived communication effectiveness measure across modes shows that receiving the communication in person was perceived as more effective than the digital human (0.908^*) when only asking the single measure of perceived communication effectiveness. Additionally, comparing the perceived effectiveness of the email memo vs. digital human, the email communication was perceived as more effective when evaluating the single measure (1.125^*).

TABLE 3

Table 3. Comparison of the perceived communication effectiveness measure across modes.

Hypothesis 3

Hypothesis 3 was supported. The results indicate that the respondents were satisfied with all three modes of communication. This was evaluated by comparing the overall rating of satisfaction on the differentiation scale which asked how satisfied respondents were with the communication mode received on a scale of 0 to 10 (10 being completely satisfied). All three communication modes resulted in a degree of satisfaction: 8.13 for the e-mail memo, 7.24 for in-person communication, and 6.50 for the digital human communication.

Further analysis was conducted with one-way ANOVA, and while respondents were satisfied with each communication mode, there was a statistically significant difference among the three modes with the degree of satisfaction (F = 14.218, 2,365, p < 0.001). The results in Table 4, differential scale results, show that the email communication was preferred over the in-person (-0.890^*) and digital human technology (1.629^*). There was not a statistically significant difference in the level of satisfaction between the communication delivered in person and by the digital human.

TABLE 4

Table 4. Differential scale results.

Hypothesis 4

Hypothesis 4 was not proven when comparing groups and scales. Respondents did not perceive higher organizational support when communication was given in person or by email. ANOVA analysis of the degree of perceived organizational support after receiving the communication did not result in a statistically significant difference between the three groups (F = 2.181, 2,368 p = 0.114).

Discussion and theoretical implications

How artificial intelligence may be used in the workplace in future is currently being explored. The results from this study provide interesting insights into the potential of digital human technology in corporate communication. This study advances our understanding of the utility of digital human communication for organizational communication. The results from comparing respondents' perception of the effectiveness of an organizational announcement using digital human technology and the respondent's satisfaction with the communication mode showed no statistical difference between the level of satisfaction and perceived effectiveness of in-person communication vs. the digital human, while the email memo was perceived as more effective than both the in-person and digital human communication for the corporate announcement. Additionally, no communication modality impacted the respondent's perception of organizational support.

Leveraging digital humans for organizational communication

Organizational communication is fundamental to the success of companies (Myers and Myers, 1982). These results provide significant implications for the future potential of digital human communication. The fact that all three communication modes had a positive level of satisfaction, and there was no statistical difference between the three in the level of perceived organization support provided, introduces the possibility that organizations could start leveraging digital human communication for more corporate announcements and other types of communications.

This is important because there are many potential advantages to leveraging digital human communication. Digital human communication is potentially more efficient than having large group meetings where leaders may need to travel to attend, and there will always be employees who will not be available at the time of the meeting. Digital human communication technology can help solve this issue with messages being available for 24 h a day and 7 days a week and can improve the employees' experience. For global offices and remote employees, this communication mode can help employees feel more connected to the organization and use time more efficiently.

Companies that are able to successfully deploy digital human communication technology may be able to substantially improve the communication experience when combining the technology with interactive artificial intelligence. This will allow employees to have access to ask questions to leaders in ways they are not able to ask today. In the example of a corporate announcement, the employee watching the digital human would be able to ask questions directly to a C-Suite leader. The digital human would be able to provide responses to anticipated responses directly to the employee. If a question asked is outside of the options programmed, the digital human could acknowledge the question and provide a response that it is a good question but not provide a direct response. Employee questions that are not able to be answered could then be reported to an internal communication resource, who could collect and revise responses or follow up with the employee asking the question.

Perceived value of digital human communication

Prominent themes in the qualitative data from respondents who felt the digital human communication was effective can be leveraged for organizations, considering this communication mode in future. The advantages cited were the availability of the digital human communication, the efficiency of the communication, and the richness of digital human technology. Responses in the qualitative data illustrate the future potential of this communication mode, “the digital human communication helps to cover the areas that the human is limited. This system is programmed to work automatically and repeatedly, and the information would be available to everyone in real time”. Another added “today's workplaces are going digital and remote working has become the new way. Digital communication is fast, easier, and cheaper”.

Regarding the efficiency and richness of digital human communication media, one respondent noted that “the digital human will save time and we will get faster answers,” and another noted that it “is effective because with that much information, people will not read a document or email with all that information. I feel they would skim it vs. watching a video”.

Perceived limitations of digital human communication

Respondents who did not feel the digital human communication was effective resulted in three prominent themes as follows: they did not like the technology, they would have preferred the communication in-person, and they thought the communication was impersonal. Understanding these themes help organizations strategize around future use. For example, if company leaders provided context for why they are leveraging the digital human communication modality, such as the inability to travel to every location and meet with employees in person but wanted to talk directly to everyone, they may be able to mitigate some of those who did not like the communication because they wanted it in person. As well, using the leaders' actual voice for the announcement and any AI-generated questions and answers may make the technology “feel” more personable.

Theoretical implications

The results from this study also advance our knowledge of the U&G theory and technology acceptance model for how users perceive the utility of AI-driven communication technology. The U&G theory posits that users who perceive a benefit and gain or gratification when using new communication media will positively impact their perception of the media. The technology acceptance model focuses on the perceived usefulness of new technology and how it influences users' perception. The influence of these theories was observed in our qualitative data analysis. Analysis of positive themes or those who enjoyed their interaction with the digital human technology was followed with positive comments on the use of the technology and a willingness to continue using or acknowledgment of the potential future use of the communication technology. Conversely, those who did not enjoy their interaction saw little use for the technology. These data show the need for a thoughtful deployment of digital human communication technology for organizations who plan to utilize it in future.

Deploying digital human communication

Furthermore, the data from this study reinforce the importance of change in management when rolling out new communication technology starting with conveying why the change is being made and the expected benefit to new users. For example, the advantages of the digital human communication being always available and able to reach people globally when users can dedicate time to listen to the message should be explained to employees. Leaders should also consider a change in management process to provide support for the communication technology since most organizational change and transformation fail (Kotter, 2012), and the risk of failure also applies to changes to organizational communication norms (Suchan, 2006). There is a risk that the employee will perceive communication as poorly executed and this can reduce employee trust, organizational commitment and alignment to the organization (Society of Human Resource Management, 2023).

To successfully execute technology change, it is suggested that the organizations evaluate its ecosystem to understand what resistance to the technology change may be received (Adner and Kapoor, 2016). Once the ecosystem is understood and if there is a good match for leveraging digital human communication, Goodman and Griffith (1991) provide five steps to execute technology change as follows: socialization, commitment, rewards, feedback and adjustment, and diffusion. Additionally, Suchan (2006) offers a model for changes in communication norms which include understanding the current organizational environment, determining the reason for changes, recruiting change agents, creating structure and skills as needed, and aligning the new systems to support the business need.

Combining communication modes for important organizational communications should also be strongly considered. Communicating messages in person or with a digital human can help promote emotion, excitement, and a tone of a message that an email cannot. Additionally, email communication can be interpreted differently by different receivers (Ellis, 2021). However, email has the advantages of being able to document important strategic decisions, policies, and ensure everyone has received the same message (Ellis, 2021).

The advantages of using multiple modes of communication inclusive of digital humans were also reflected in our data with many respondents liking the email communication because it provided documentation they could reference, while others said they would never read an email, that there was too much information presented, and therefore preferring the digital human. A digital human would be useful for communication for auditory learners, visual learners would prefer an email. The email also provides reference documentation and maybe an advantageous approach combining the strengths of both modalities.

Future research and limitations

This was the first study of its kind for such communication by measuring the perception of the effectiveness of digital human communication and comparing that perception with other communication modes and with that come some limitations. First, this study focused on a corporate announcement related to a merger. Future studies should test other types of corporate communication. Some of the negative results in the reaction to the communication was based on respondents' resistance to a merger which may have skewed their perspective on the communication effectiveness. The experiment included undergraduate and graduate students, with an average age of 30.52 years, receiving the digital human communication. It is possible that older populations may have been more resistant to the digital human and younger populations could have been more open to the technology. The communication was provided without any context. Potentially, if respondents' perception would have been different and if they were told that leaders were not available for in-person communication or that they are working virtually.

These limitations start the research agenda for future exploration regarding the effectiveness and utility of digital human communication using different messages across different age groups and in different industries. In addition, future research could expand to build out question and answer capabilities driven by AI.

The strategic future of many organizations will make an assessment on how AI-enabled activities can be leveraged to improve delivery and efficiency. Exploring how organizations can leverage AI and digital humans to make corporate communication more available, efficient, and consistent may have great value in future. The results from this study show that there is an open window to explore the use of digital human communication within an organizational context.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by Rollins College IRB. The patients/participants provided their written informed consent to participate in this study.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Acknowledgments

The authors would like to acknowledge Sherry Kragler for her review and suggestions in the process of writing and editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2023.1200985/full#supplementary-material

References

Adner, R., and Kapoor, R. (2016). Right Time Wrong Tech, Harvard Business Review. (2016).

Androutsopoulou, A., Karacapilidis, N., Loukis, E., and Charladies, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 36, 358–367. doi: 10.1016/j.giq.2018.10.001

CrossRef Full Text | Google Scholar

Armbruster, H., and Wimmer, K. (1992). Broadband multimedia applications using ATM networks: high-performance computing, high-capacity storage, and high-speed communication. IEEE J. Sel. Areas Commun. 10, 1382–1396. doi: 10.1109/49.184868

CrossRef Full Text | Google Scholar

Blumler, J. G., and Katz, E. (1974). “The uses of mass communications: current perspectives on gratifications research,” in Sage Annual Reviews of Communication Research Volume III. Beverly Hills, CA: Sage Publications. p. 318. Available online at: https://eric.ed.gov/?id=ED119208

Google Scholar

Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., and Ivkovic, M. (2011). Augmented reality technologies, systems and applications. Multimed. Tools Appl. 51, 341–377. doi: 10.1007/s11042-010-0660-6

CrossRef Full Text | Google Scholar

Chang, Y., Lee, S., Wong, S. F., and Jeong, S. P. (2022). AI-powered learning application use and gratification: an integrative model. Inf. Technol. People. 35, 2115–2139. doi: 10.1108/ITP-09-2020-0632

CrossRef Full Text | Google Scholar

Chen, G. M. (2011). Tweet this: a uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Comput. Human Behav. 27, 755–762. doi: 10.1016/j.chb.2010.10.023

CrossRef Full Text | Google Scholar

Choi, Y. (2021). A study of employee acceptance of artificial intelligence technology. Eur. J. Manag. Bus. 30, 318–330. doi: 10.1108/EJMBE-06-2020-0158

CrossRef Full Text | Google Scholar

Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. All Sprouts Content. p. 290. Available online at: http://aisel.aisnet.org/sprouts_all/290

Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340. doi: 10.2307/249008

CrossRef Full Text | Google Scholar

Deloitte. (2022). Delivering the Human Experience in the Digital World. Available online at: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/consultancy/deloitte-uk-future-of-sales-and-service-deloitte-2022.pdf

Djojo, B. W., Hafizh, W., Gui, A., Shaharudin, M. S., and Karmawan, I. G. M. (2021). “Analysist Acceptance of Video Conference at Zoom Application using Technology Acceptance Model,” in 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE). Piscataway, NJ: IEEE. p. 18–23. doi: 10.1109/ICITACEE53184.2021.9617485

CrossRef Full Text | Google Scholar

Dosovitsky, G., and Bunge, E. L. (2021). Bonding with bot: user feedback on a chatbot for social isolation. Front. Digital Health. 138, 735053. doi: 10.3389/fdgth.2021.735053

PubMed Abstract | CrossRef Full Text | Google Scholar

Eisenberger, R., Huntington, R., Hutchison, S., and Sowa, D. (1986). Perceived organizational support. J. Appl. Psychol. 71, 500–507.

Google Scholar

Ellis, C. (2021). Stop, Does that Message Really Need to be an Email? Harvard Business Review. (2021).

Goodman, P. S., and Griffith, T. L. (1991). A process approach to the implementation of new technology. J. Eng. Technol. Manag. 8, 261–285. doi: 10.1016/0923-4748(91)90014-I

PubMed Abstract | CrossRef Full Text | Google Scholar

Graves, C. (2023). Generative AI Can Help You Tailor Messaging to Specific Audiences. Brighton, Massachusetts: Harvard Business Review. (2023). Available online at: https://hbr.org/2023/02/generative-ai-can-help-you-tailor-messaging-to-specific-audiences (accessed May 15, 2023).

Hancock, J. T., Naaman, M., and Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. J. Comput-Mediat Comm. 25, 89–100. doi: 10.1093/jcmc/zmz022

CrossRef Full Text | Google Scholar

Hepp, A. (2020). Artificial companions, social bots and work bots: communicative robots as research objects of media and communication studies. Media, Culture Soc. 42, 1410–1426. doi: 10.1177/0163443720916412

CrossRef Full Text | Google Scholar

Hoch, J. E., and Kozlowski, S. W. (2014). Leading virtual teams: Hierarchical leadership, structural supports, and shared team leadership. J. Appl. Psychol. 99, 390–403. doi: 10.1037/a0030264

PubMed Abstract | CrossRef Full Text | Google Scholar

IBM. (2023). Humanizing Digital Communication. Available online at: https://www.ibm.com/case-studies/2mee

Google Scholar

Ishaq, E., Bashir, S., Zakariya, R., and Sarwar, A. (2021). Technology acceptance behavior and feedback loop: exploring reverse causality of TAM in Post-COVID-19 scenario. Front. Psychol. 12, 682507. doi: 10.3389/fpsyg.2021.682507

PubMed Abstract | CrossRef Full Text | Google Scholar

Jokinen, J. P. P., and Silvennoinen, J. (2020). “The appraisal theory of emotion in human-computer interaction,” in Emotions in Technology Design: From Experience to Ethics, eds R. Rousi, J. Leikas, and P. Saariluoma (Springer International Publishing), 27–39. doi: 10.1007/978-3-030-53483-7_3

CrossRef Full Text | Google Scholar

Katz, E., Haas, H., and Gurevitch, M. (1973). On the use of the mass media for important things. Am. Sociol. Rev. 164–181. doi: 10.2307/2094393

CrossRef Full Text | Google Scholar

Kim, C., Mirusmonov, M., and Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Comput. Human Behav. 26, 310–322. doi: 10.1016/j.chb.2009.10.013

CrossRef Full Text | Google Scholar

Kotter, J. P. (2012). Leading Change. Brighton, Massachusetts: Harvard Business Press.

Google Scholar

Kwon, H. S., and Chidambaram, L. (2000). “A test of the technology acceptance model: The case of cellular telephone adoption,” in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. Piscataway, NJ: IEEE. p. 7.

PubMed Abstract | Google Scholar

Lee, Y., Kozar, K. A., and Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Commun. Assoc. Inf. Syst. 12, 50. doi: 10.17705/1CAIS.01250

CrossRef Full Text | Google Scholar

Lin, C. A. (2003). An interactive communication technology adoption model. Commun. Theory. 13, 345–365. doi: 10.1111/j.1468-2885.2003.tb00296.x

CrossRef Full Text | Google Scholar

Lin, X., Shao, B., and Wang, X. (2022). Employees' perceptions of chatbots in B2B marketing: Affordances vs. disaffordances. Indust. Market. Manag. 101, 45–56. doi: 10.1016/j.indmarman.2021.11.016

CrossRef Full Text | Google Scholar

Majumder, S., and Mondal, A. (2021). Are chatbots really useful for human resource management?. Int. J. Speech Technol. 24, 1–9. doi: 10.1007/s10772-021-09834-y

CrossRef Full Text | Google Scholar

McKinsey Company (2022). The State of AU in 2022-and Half a Decade in Review. Available online at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review (accessed February 5, 2023).

Min, S., So, K. K. F., and Jeong, M. (2021). “Consumer adoption of the Uber mobile application: insights from diffusion of innovation theory and technology acceptance model,” in Future of Tourism Marketing. Oxfordshire: Routledge. p. 2–1.5 doi: 10.4324/9781003176039-2

CrossRef Full Text | Google Scholar

Myers, M. T., and Myers, G. E. (1982). Managing by Communication – An Organizational Approach. New York: McGraw-Hill Book Company.

Google Scholar

Mygland, M. J., Schibbye, M., Pappas, I. O., and Vassilakopoulou, P. (2021). “Affordances in human-chatbot interaction: a review of the literature,” in Responsible AI and Analytics for an Ethical and Inclusive Digitized Society: 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021 (Galway: Springer International Publishing), 3–17.

Google Scholar

Newman, S. A., Ford, R. C., and Marshall, G. W. (2020). Virtual team leader communication: Employee perception and organizational reality. Int. J. Business Commun. 57, 452–473. doi: 10.1177/2329488419829895

PubMed Abstract | CrossRef Full Text | Google Scholar

Newman, S. A., and Golpalkrishnan, D. (2022). Does using video enabled communication positively impact the perception of effective communication? SAM Adv. Manag. J. 87,45–57.

PubMed Abstract | Google Scholar

Park, N., Rhoads, M., Hou, J., and Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Comput. Hum. Behav. 39, 118–127. doi: 10.1016/j.chb.2014.05.048

CrossRef Full Text | Google Scholar

Pizzi, G., Scarpi, D., and Pantano, E. (2021). Artificial intelligence and the new forms of interaction: who has the control when interacting with a chatbot?. J. Bus. Res. 129, 878–890. doi: 10.1016/j.jbusres.2020.11.006

CrossRef Full Text | Google Scholar

Poola, I. (2017). Making artificial intelligence (AI) and disrupted business intelligence (BI) truly conversational with humanity touch, automated descriptions and talking bots. Int. J. Adv. Res. Ideas Innovations Technol. 3, 573−577.

Google Scholar

Rauniar, R., Rawski, G., Yang, J., and Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. J. Enterprise Infor. Manag. 27, 6–30. doi: 10.1108/JEIM-04-2012-0011

CrossRef Full Text | Google Scholar

Rese, A., Ganster, L., and Baier, D. (2020). Chatbots in retailers' customer communication: how to measure their acceptance? J. Retailing Consumer Serv. 56, 102176. doi: 10.1016/j.jretconser.2020.102176

CrossRef Full Text | Google Scholar

Robbins, S. P., and Judge, T. (2009). Organizational Behavior. Cape Town City: Pearson South Africa.

Google Scholar

Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass Commun. Soc. 3, 3–37. doi: 10.1207/S15327825MCS0301_02

PubMed Abstract | CrossRef Full Text | Google Scholar

Rust, R. T., and Huang, M. H. (2014). The service revolution and the transformation of marketing science. Market. Sci. 33, 206–221. doi: 10.1287/mksc.2013.0836

CrossRef Full Text | Google Scholar

Savage, S. J., and Waldman, D. (2005). Broadband Internet access, awareness, and use: Analysis of United States household data. Telecommunications Policy. 29, 615–633. doi: 10.1016/j.telpol.2005.06.001

CrossRef Full Text | Google Scholar

Scherer, K. R., Shorr, A., and Johnstone, T. (2001). Appraisal Processes in Emotion: Theory, Methods, Research. Canary, NC: Oxford University Press.

Google Scholar

Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. J. Retail. 94, 6–11. doi: 10.1016/S0022-4359(18)30076-9

CrossRef Full Text | Google Scholar

Silva, E. S., and Bonetti, F. (2021). Digital humans in fashion: will consumers interact?. J. Retail. Consum. Serv. 60, 102430. doi: 10.1016/j.jretconser.2020.102430

CrossRef Full Text | Google Scholar

Society of Human Resource Management (2023). “Managing Organizational Communication,” in Society of Human Resource Management. Available online at: https://www.shrm.org/resourcesandtools/tools-and-samples/toolkits/pages/managingorganizationalcommunication.aspx (accessed March 22, 2023).

Google Scholar

Soper, D. (2023). Sentiment Analyzer. Available online at: https://www.danielsoper.com/sentimentanalysis/default.aspx (accessed March 20, 2023).

Google Scholar

Srnicek, N. (2017). Platform Capitalism. Hoboken, NJ: John Wiley and Sons.

Google Scholar

Statisica (2022). Available online at: https://www.statista.com/statistics/941137/ai-investment-and-funding-worldwide/ (accessed March 17, 2023).

Suchan, J. (2006). Changing organizational communication practices and norms: A framework: JBTC. J. Business Technical Commun. 20, 5–47. doi: 10.1177/1050651905281038

CrossRef Full Text | Google Scholar

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Manage. Sci. 42, 85–92. doi: 10.1287/mnsc.42.1.85

PubMed Abstract | CrossRef Full Text | Google Scholar

Taule, T., Følstad, A., and Fostervold, K. I. (2022). How Can a Chatbot Support Human Resource Management? Exploring the Operational Interplay. In Chatbot Research and Design: 5th International Workshop, CONVERSATIONS 2021 Virtual Event, November 23–24, 2021. Cham: Springer International Publishing. p. 73–89. doi: 10.1007/978-3-030-94890-0_5

CrossRef Full Text | Google Scholar

Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. Am. J. Evaluat. 27, 237–246. doi: 10.1177/1098214005283748

CrossRef Full Text | Google Scholar

UNEEQ (2023). Revolutionize Customer Experiences with Scalable Human Connections. Available online at: https://digitalhumans.com/ (accessed March 20, 2023).

Google Scholar

Vahdat, A., Alizadeh, A., Quach, S., and Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Market. J 29, 187–197. doi: 10.1016/j.ausmj.2020.01.002

CrossRef Full Text | Google Scholar

Verburg, R. M., Bosch-Sijtsema, P., and Vartiainen, M. (2013). Getting it done: critical success factors for project managers in virtual work settings. Int. J. Project Manag. 31, 68–79. doi: 10.1016/j.ijproman.2012.04.005

CrossRef Full Text | Google Scholar

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Commun. Res. 23, 3–43.

PubMed Abstract | Google Scholar

Westerlund, M. (2019). The emergence of deepfake technology: a review. Technol. Innovat. Manag. Rev. 9, 39–52. doi: 10.22215/timreview/1282

PubMed Abstract | CrossRef Full Text | Google Scholar

Xie, Y., Zhao, S., Zhou, P., and Liang, C. (2023). Understanding continued use intention of AI assistants. J. Comp. Inform. Syst. 1–14. doi: 10.1080/08874417.2023.2167134

CrossRef Full Text | Google Scholar

Yin, B., and Wu, S.-Q. (2023). Enhancing organizational communication via intelligent voice assistant for knowledge workers: the role of perceived supervisor support, psychological capital, and employee wellbeing. Front. Commun. 7, 1084703. doi: 10.3389/fcomm.2022.1084703

CrossRef Full Text | Google Scholar

Zaccaro, S. J., Rittman, A. L., and Marks, M. A. (2001). Team leadership. Leadership Q. 12, 451–483 doi: 10.1016/S1048-9843(01)00093-5

CrossRef Full Text | Google Scholar

Zhang, B., and Provost, W. (2019). “Automatic recognition of self-reported and perceived emotions,” in Computer Vision and Pattern Recognition, Multimodal Behavior Analysis in the Wild, Alameda-Pineda, X, Ricci, E., Sebe, N., (eds). Cambridge, MA: Academic Press. p. 443–470. doi: 10.1016/B978-0-12-814601-9.00027-4

CrossRef Full Text | Google Scholar