An individual’s underlying personality traits influence how often they use conversational artificial intelligence. This largely depends on whether you feel the tool enhances your social status and how confident you are in operating it. Researchers recently mapped how an individual’s psychological tendencies relate to early adoption of text-based machine learning tools. The study was published in the Journal of Psychology.
Psychological frameworks often divide human behavior into five broad categories, collectively known as the five-factor model. This system categorizes personality into extraversion, openness, conscientiousness, agreeableness, and neuroticism. These characteristics serve as a psychological baseline for relationships. These shape how people interact with their environment, overcome unexpected challenges, and adopt new habits throughout their lives.
When new digital tools emerge, behavioral researchers often look to these personality profiles to understand who will adopt the technology first. Extraversion reflects sociability and a desire to interact with the outside world. Openness includes high curiosity, creativity, and a willingness to try new experiences. Integrity refers to people who are highly organized, disciplined, and focused on practical results.
Although the five-factor model also includes agreeableness and neuroticism, researchers often study these two traits separately in the context of technology. Highly collaborative people prioritize group harmony and usually wait until a program becomes mainstream before trying it out. People high in neuroticism tend to overestimate the risks associated with new systems and are hesitant to implement unfamiliar software.
Because artificially intelligent chatbots are still relatively unpredictable, researchers focused strictly on the characteristics most relevant to early technology adoption. Extraversion, openness, and conscientiousness generally reflect novelty-seeking behavior, positive thinking, and goal-directed behavior. These active characteristics typically match the profile of early technology users looking to gain an edge in their daily work.
To accurately analyze technology adoption, researchers combine these internal personality traits with external behavioral frameworks. One common framework is the technology acceptance model. This model assesses how external environmental pressures and internal technological beliefs persuade people to incorporate certain software into their daily lives.
Two important factors in this acceptance model are social image and computer self-efficacy. Social image refers to an individual’s personal belief that using new technology will make them appear better or more visible to their colleagues. Computer self-efficacy represents the level of confidence an individual has regarding his or her ability to operate a program effectively without the need for outside help.
Artificial intelligence chat programs have become a new and popular category of digital utilities. Programs like ChatGPT only became publicly available in late 2022, so there is still a lack of empirical data on people actively using them. Researchers Tingjun Deng and Dake Wang from Shanghai Jiao Tong University in China designed a project to discover how intrinsic personality differences and extrinsic social cognition relate to user habits.
In March 2023, Deng, Wang and colleagues surveyed 784 participants across China. The researchers limited their sample to individuals who had already tried ChatGPT at least once in their lives. The participant group consisted primarily of university students and young professionals from an eastern coastal city.
Participants answered a series of psychological and behavioral questions using a standardized five-point scale. The survey measured respondents’ levels of innate extraversion, openness, and conscientiousness. We also asked them to rate their personal confidence in using chatbots, their perception of how using the software affects their social standing, and their actual frequency of use.
After data collection, the researchers used structural equation modeling to analyze responses. This statistical method allows researchers to test multiple relationships between different variables simultaneously. The goal was to see whether personality traits directly influence software use, or whether social prestige or technological independence act as a stepping stone between personality and behavior.
The analysis revealed that extraversion was the only trait measured that had a direct, positive association with general chatbot usage. People high in extraversion reported logging into applications more often. Extraverts tend to seek social interaction and may treat interactions using the responsive language model as an extension of their natural conversational habits.
However, two other characteristics influenced usage primarily through indirect channels. The direct association between openness and frequency of use was not statistically significant. The lack of a direct relationship also applies to the relationship between conscientiousness and general frequency of use.
The researchers suggested several potential mechanisms for these missing direct links. Highly open people often value imaginative and highly original ideas in their daily pursuits. Because text generators output aggregated responses based on existing data patterns, the generated text may feel too conventional to satisfy even the most creative users.
Another point of friction can exist for highly conscientious people. Conscientious people are careful and methodical, and often prefer tools that are completely reliable and predictable. Early versions of text generators were sometimes prone to factual errors, which may have caused disciplined users to retreat to familiar, tried-and-true search methods.
Although openness and honesty were not directly related to use, they were still connected to technology through indirect channels. All three personality traits showed strong positive associations with social image. Extraverts, highly open people, and highly conscientious people were all more likely to believe that mastering advanced digital tools would make them appear more tech-savvy and improve their standing among their peers.
This enhanced social image served as a bridge for action. When users felt that the chatbot enhanced their reputation, their confidence in using the tool increased. This increase in computer self-efficacy was strongly related to how often they ended up logging into the program to complete real-world tasks.
The relationship between social prestige and technological trust depends largely on how a group communicates about new software. When a colleague insists on a particular tool, their verbal encouragement can act as a powerful source of motivation. Hearing friends and colleagues praise a program can reduce a user’s initial anxiety and make the learning curve much less intimidating.
Watching others succeed also provides a type of vicarious experience. People who see their colleagues using chatbots to quickly complete tasks are much more likely to believe they can achieve the exact same results. This environmental feedback loops back into their own self-assessment, steadily increasing their overall technical confidence.
The researchers noted that this process forms a chain of relationships. The user’s personality influences the desire to look good in front of others. That desire for social status correlates with a willingness to practice and feel competent. Ultimately, tested abilities lead to consistent usage.
Some personality traits had clear limitations in building technical confidence. For example, extroverts did not show a direct increase in computer self-efficacy. Extroverts typically rely on facial expressions, tone of voice, and physical gestures to communicate successfully, so strictly text-based interfaces can limit their natural confidence. Highly open people also didn’t see a direct increase in their technical confidence. This is probably because their strength lies in creative exploration rather than mechanical software mastery.
Conversely, conscientiousness showed a stronger relationship with computer self-efficacy. Their structured learning approach may have helped them quickly understand the software’s advanced features. Once these people felt fully capable, application usage increased at a steady rate.
Software developers and technology marketers often study these behavioral paths to improve their products. By understanding how different personality types react to new interfaces, companies can tailor user training modules to reduce initial anxiety and build technical confidence. Future public campaigns may focus on social prestige, a tool that appeals to status-conscious consumers.
This dataset has several notable limitations that should be addressed in future research. This study relied on a completely cross-sectional design, meaning the researchers observed behavior at a single point in time. Because of this structural setting, the researchers were only able to identify mathematical relationships between variables, rather than proving that one factor directly caused another.
Reliance on self-reported data also introduces subjective bias. Participants may overestimate or underestimate how much time they actually spend chatting with the program each day on average. Future research may combine software tracking metrics with psychological profiles to gather fully objective usage statistics.
The participant demographic was relatively narrow, focusing primarily on young respondents living in China who had already adopted technology. Including a wider range of ages, geographic locations, and language groups will help determine whether these behavioral trends hold true globally. Direct comparisons between active users and those who refuse to use technology may provide a deeper understanding of digital resistance.
The study, “The Association between Personality Traits and ChatGPT Usage: The Dual Mediating Role of Social Image and Computer Self-Efficacy,” was authored by Tingjun Deng, Dake Wang, Jiaojiao Ma, Tian Wang, Benqian Li, Talib Hussain, Yongjie Yue, and Pengcheng Wang.
