The expansion of electronic commerce and the development of digital platforms have fundamentally transformed the ways in which consumers interact with products and services, thereby creating favorable conditions for the emergence of impulse-buying behaviors in online environments. Consumer behavior has long been a central area of marketing research, and understanding it requires simultaneous attention to the factors influencing product selection, decision-making, purchasing, consumption, and evaluation. Consumer decisions are not always based on rational and preplanned processes; rather, psychological, social, environmental, and situational factors may alter the course of decision-making (Kotler, Armstrong, & Saunders, 2000). Within this framework, impulse buying is defined as an immediate, emotional, and unplanned behavior accompanied by a powerful and relatively enduring urge to purchase, which may arise in the absence of any prior purchase intention (De Korneel et al., 2009; Sharma, Sivakumaran, & Marshall, 2010). Recent empirical studies have also shown that environmental stimuli, including time pressure, limited product availability, economic benefits, and social influence, facilitate online impulse buying through emotional responses involving arousal and pleasure (Ngo et al., 2024; S2). These findings highlight the relationship between external conditions and consumers’ internal mechanisms. The present extended abstract is based on the study entitled “Developing and Testing a Model of Iraqi Consumers’ Impulse-Buying Behaviors in the Digital Environment with Emphasis on Intergenerational Differences,” and its data and findings have been directly derived from that study (S1).
The significance of this phenomenon is particularly pronounced in digital environments because the interval between product exposure and transaction completion is extremely short. The continuous presentation of advertisements, discounts, and commercial offers blurs the boundary between planned purchasing and reactive buying. In addition, generation represents a potentially important variable for explaining differences in consumers’ responses to digital stimuli. A generation refers to a group of individuals born during a particular period and raised under relatively similar historical and technological conditions (McCrindle & Wolfinger, 2010). Given the growing importance of online shopping and the participation of different generations in digital environments, the central question was whether the factors shaping impulse-buying behavior operate in the same manner across different generational groups. Accordingly, the main research problem was to identify and test the factors shaping the impulse-buying behaviors of Iraqi consumers in the digital environment and to examine whether this pattern differs significantly among younger, middle-aged, and older consumer groups.
The purpose of this study was to develop and test a model of Iraqi consumers’ impulse-buying behaviors in the digital environment, with particular emphasis on the role of intergenerational differences. The research was applied in terms of purpose and quantitative and descriptive–survey-based in terms of methodology. The statistical population comprised all consumers and buyers in Iraq with prior experience purchasing from online platforms. Because no comprehensive sampling frame was available, nonprobability convenience sampling was employed. A total of 250 electronic questionnaires were distributed, of which 200 complete and valid questionnaires—representing a response rate of 80%—were retained for analysis after data screening. The data-collection instrument was a 28-item questionnaire operationalizing the constructs of product-related, individual–cognitive, social–media, technological–platform, and economic–temporal factors, as well as impulse-buying behavior. Partial least squares structural equation modeling (PLS-SEM) was used to test the conceptual model. To examine intergroup differences, respondents were classified into three age groups. Participants who had taken part in the qualitative phase were excluded from the quantitative sample in order to preserve the independence of the data.
The results of the measurement-model assessment demonstrated the satisfactory quality of the research instrument. The overall Cronbach’s alpha coefficient was 0.82, exceeding the conventional threshold of 0.70 and confirming the instrument’s reliability. The average variance extracted (AVE) values for all constructs ranged from 0.54 to 0.63, while composite reliability (CR) values ranged from 0.81 to 0.88. Both indices exceeded the accepted criteria of 0.50 and 0.70, respectively, thereby confirming convergent validity. Discriminant validity was assessed using the Fornell–Larcker criterion. The square root of the AVE for each construct, ranging from 0.73 to 0.79, was greater than its correlations with the other constructs, indicating adequate construct distinctiveness.
In the structural model, all five research hypotheses were supported at the 95% confidence level using bootstrapping with 5,000 resamples. Individual–cognitive factors exerted the strongest effect on impulse-buying behavior, with a path coefficient of 0.35 and a t-statistic of 5.21. These were followed by technological–platform factors (β = 0.31; t = 4.56), product-related factors (β = 0.27; t = 3.84), economic–temporal factors (β = 0.22; t = 3.11), and social–media factors (β = 0.18; t = 2.67). The positive direction of all path coefficients indicated that improvements in each of these dimensions increase the likelihood of online impulse buying. The coefficient of determination (R²) for online impulse-buying behavior was 0.62, while the adjusted R² was 0.61. This indicates that the five dimensions included in the model explained approximately 62% of the variance in impulse-buying behavior. The model-fit indices were also satisfactory: SRMR = 0.061, below the recommended threshold of 0.08; NFI = 0.91, above the minimum acceptable value of 0.90; and RMS_theta = 0.089, below the recommended threshold of 0.12. These results confirmed the model’s compatibility with the empirical data.
The findings provide a multidimensional account of digital impulse-buying behavior. This behavior is not merely the consequence of a single stimulus; rather, it results from the synergy between external conditions and consumers’ internal processes. This pattern is consistent with international evidence concerning the simultaneous role of external stimuli and emotional responses in online impulse buying (Ngo et al., 2024; S2). The predominance of individual–cognitive factors demonstrates that even in highly technological environments, emotion, intrinsic motivation, and information-processing styles remain central to purchasing decisions. Two consumers exposed to the same stimulus may therefore display entirely different responses. The second-place position of technological–platform factors indicates that ease of use, interface design, and payment speed reduce friction throughout the decision-making process and shorten the distance between purchase intention and actual behavior. Although the effect of social–media factors was statistically significant, it was weaker than the effects of the other dimensions. This finding suggests that social media content and user recommendations function as links in the chain of purchase stimulation rather than as its sole determinants. This result is also consistent with evidence indicating that social influence is effective when operating alongside other stimuli (Ngo et al., 2024).
With respect to intergenerational differences, contrary to the theoretical expectation that differences in lived experience and technological familiarity would lead to distinct behavioral patterns, the comparison of the younger, middle-aged, and older groups revealed no statistically significant differences in the study variables. This finding may indicate a process of behavioral convergence across generations. Online shopping has evolved from a behavior primarily associated with young and technologically proficient users into an aspect of everyday life for a broad range of consumers. Shared experience with digital platforms may therefore have reduced the initial age-related differences among generational groups. Consequently, different generations do not necessarily constitute entirely distinct markets for electronic commerce.
From a practical perspective, the findings offer online-store managers and digital marketers in the Iraqi market several actionable implications. The design of purchasing offers should be grounded in an understanding of consumers’ cognitive and emotional needs and responses. A simple, fast, and seamless user experience should be placed at the center of platform development. Product features should be presented in a manner that enables consumers to understand the product’s value within a short period of time. Economic and temporal stimuli should be used authentically and proportionately to the product’s actual value. In addition, the potential of social media should be activated through an integrated strategy aligned with other elements of digital marketing.
Overall, by confirming all research hypotheses, explaining 62% of the variance in online impulse-buying behavior, and demonstrating satisfactory model fit, the present study shows that the proposed model provides a coherent and empirically supported framework for explaining consumers’ impulse-buying behavior in the digital environment. The model can serve as a basis for developing marketing and customer-experience strategies in Iraq’s online market. Nevertheless, a proportion of the variance in impulse-buying behavior may still be attributable to factors not included in the current model (S1).