In a groundbreaking exploration into the realm of artificial intelligence and consumer interactions, researchers Mohamed Abd-El-Salam and Hassan Youssef deliver profound insights into how chatbot interactivity influences customer behavior. Their study, set among Egyptian consumers, offers a meticulous dissection of how chatbots can reshape user experience, trust, and ultimately, purchase intentions. This research not only uncovers behavioral patterns but also presents a sophisticated statistical model underscoring the dynamics between chatbot features and user perceptions, marking a pivotal step in understanding AI-driven marketing tools.
The demographic profile of the study participants plays a crucial role in contextualizing the findings. The sample consisted of 366 Egyptians, predominantly female, representing nearly 61% of respondents, an important factor noting gender disparities in tech engagement. Most participants were young adults, with almost 30% between the ages of 30 to 40 and about a quarter under 20 years old, shedding light on generational attitudes towards chatbot use. Income levels further revealed that nearly half of these consumers earn between 5000 to 10,000 Egyptian pounds, providing a snapshot of economically middle-income users who are potential prime targets for chatbot-driven commerce.
One technical cornerstone of any behavioral study involves ensuring the data’s integrity and validity. Through rigorous normality testing, the researchers established that the dataset largely violates normal distribution assumptions, except for variables measuring information quality. This nuanced discovery signals the need for advanced statistical tools adept at handling non-normal distributions, such as Partial Least Squares Structural Equation Modeling (PLS-SEM), which the study expertly employs to unravel complex, interrelated constructs.
Common Method Bias (CMB), a potential pitfall in survey research, was scrutinized using Harman’s one-factor test. The recorded CMB value stood comfortably below the critical threshold of 50%, at just over 36%. This statistically alleviates concerns over artificial inflation or deflation of relationships among variables due to shared measurement methods, fortifying the study’s overarching validity and enhancing confidence in the subsequent analytical findings.
Crucial to multivariate analytical modeling, the evaluation of multicollinearity was conducted by computing Variance Inflation Factor (VIF) values for all latent variables. All VIF scores fell below the accepted ceiling of 5, confirming that predictor variables were sufficiently independent to provide reliable parameter estimates. This ensures that subsequent path analyses in the structural model could proceed free from the distortions excessive collinearity might introduce.
The refinement of measurement instruments was addressed by systematically removing items demonstrating poor factor loadings, which is a meticulous process guaranteeing that each construct in the model truly reflects its intended theoretical dimension. This purge ensures that the constructs measuring concepts such as perceived ease of use, perceived usefulness, and trust are robust and capable of eliciting accurate consumer attitudes toward chatbots.
Convergent validity assessment demonstrated compelling internal consistency within the constructs. Factor loadings and Composite Reliability (CR) exceeded 0.7, while Average Variance Extracted (AVE) surpassed 0.5 across the board, affirming that item sets converge adequately to measure their respective latent constructs. Rigorous verification at this stage means that the constructs are reliable aggregations of their indicators, a prerequisite for any credible structural equation modeling.
Discriminant validity was established by examining the Heterotrait-Monotrait Ratio (HTMT), positioned below the cautious threshold of 0.9 among all construct pairs. These findings confirm that the constructs are uniquely measuring different facets of chatbot interactivity and consumer response, which is crucial for drawing valid conclusions about how these factors distinctly influence consumer behavior metrics like purchase intention and trust.
The study’s structural model, diagrammed meticulously using PLS-SEM, orchestrates a web of interactions illustrating the inner relationships between stimuli such as control, responsiveness, personalization, and information quality, and responses like perceived ease of use (PEU), perceived usefulness (PU), attitude toward chatbots (ATT), trust, reliance, resistance, and ultimately purchase intention (PI). The model’s path coefficients offer a precise quantification of these relationships, enabling a nuanced understanding of which chatbot features most effectively drive consumer engagement.
The calculated coefficients of determination (R² values) portray a model with modest explanatory power, with notable percentages for perceived ease of use (15.31%), perceived usefulness (13.67%), and attitude toward chatbots (14.97%). Though these figures represent moderate predictive capability, they signify meaningful connections warranting attention from both academia and the marketing industry. The lower R² values for constructs such as trust, reliance, and resistance suggest these are complex psychological constructs influenced by additional external factors beyond chatbot interactivity.
Stone–Geisser Q² values further affirm the predictive relevance of the model, exceeding benchmarks for the endogenous constructs. Particularly, variables like perceived ease of use, perceived usefulness, and attitude toward chatbots register high Q² values, indicating that the model successfully approximates how consumers process their experience with chatbots. This predictive quality bodes well for marketers seeking to optimize AI interfaces for enhanced user satisfaction and commercial success.
Effect size evaluations via f² revealed varying impacts among independent variables, with effect sizes ranging from small to medium when benchmarked against conventional thresholds. These findings provide a scaled measure of how substantially each predictor influences outcomes, guiding future chatbot design decisions to emphasize elements with the most potent influence on consumer attitudes and purchasing behavior.
The synergy between perceived ease of use and perceived usefulness emerged as a dominant driver of positive attitudes toward chatbots. This aligns with foundational technology acceptance models that posit user-friendly interfaces and functional usefulness as critical to adoption. The study further elaborates on how personalization intensifies this effect by fostering a sense of uniqueness and control in interactions, enhancing user trust and willingness to rely on chatbot-mediated purchase processes.
Intriguingly, the study highlights the dual role of trust and resistance. Trust emerges as a vital mediator positively affecting purchase intentions, whereas resistance, which includes skepticism or hesitation, acts as a deterrent. The nuanced interplay between these factors underscores the delicate balance marketers must achieve when deploying chatbots, ensuring transparency and reliability to mitigate user resistance and encourage reliance.
This investigation is particularly relevant in a contemporary digital commerce landscape where AI-driven chatbots are proliferating. By grounding their analysis in robust empirical data and advanced statistical techniques, Abd-El-Salam and Youssef present an invaluable blueprint for integrating chatbot technologies in ways that resonate with consumers, offering actionable insights into how interactivity and user-centric design underpin successful chatbot engagement strategies.
Beyond the specifics of Egyptian consumer behavior, this study’s methodological rigor and theoretical clarity offer transferable lessons internationally. It advances our understanding of how AI interfaces can be leveraged not simply as tools but as dynamic agents that shape psychological, emotional, and behavioral responses, thereby influencing economic outcomes in an increasingly digital marketplace.
The research points toward future avenues of inquiry, notably the potential moderating roles of culture, demographic variables, and evolving AI capabilities. Further exploration could deepen insights into how chatbot interactivity blends with human element substitutes or complements, shaping long-term consumer loyalty and brand perception in a digital-first era.
In sum, this pioneering study sheds light on the multifaceted relationship between chatbot interactivity and consumer behavior, illuminating the path for more intelligent, empathetic, and effective AI consumer interfaces. It challenges marketers and developers alike to reimagine chatbots not just as informational tools but as interactive partners that engage, build trust, and drive purchase decisions, thus heralding a new era in AI-driven commerce.
Subject of Research: Consumer behavior in relation to chatbot interactivity among Egyptian customers
Article Title: Investigating the impact of chatbot interactivity on consumer behavior
Article References:
Mohamed Abd-El-Salam, E., Hassan Youssef, A. Investigating the impact of chatbot interactivity on consumer behavior. Humanit Soc Sci Commun 13, 996 (2026). https://doi.org/10.1057/s41599-026-08081-3
Image Credits: AI Generated
DOI: https://doi.org/10.1057/s41599-026-08081-3
