Introduction and Purpose: The rapid expansion of digital markets and e-commerce has transformed the ways in which consumers interact with businesses, platforms, payment systems, and service providers. Alongside the benefits of accessibility, speed, convenience, and scalability, digital marketplaces have also created new opportunities for consumer deviant behavior. Consumer deviant behavior refers to intentional actions by customers that violate accepted rules, regulations, procedures, or norms governing market exchanges. In e-commerce environments, such behaviors may include abuse of return policies, fraudulent claims, fake reviews, malicious clicking, creation of multiple or fraudulent accounts, misuse of promotional incentives, identity manipulation, and payment-related fraud. These behaviors are not limited to direct financial losses; they may also increase monitoring and operational costs, disrupt organizational processes, undermine consumer trust, and weaken the sustainability of the digital ecosystem. The increasing scale of e-commerce transactions in Iran makes the development of effective prevention mechanisms particularly important. Although previous studies have examined the motives, individual characteristics, situational factors, moral justification mechanisms, deterrence, anonymity, opportunity structures, and technological controls associated with consumer deviance, the literature provides limited explanation of how these mechanisms can be integrated into a coherent and context-sensitive prevention process. Three major gaps are particularly important: a theoretical gap concerning the integration of cognitive, deterrence, and opportunity-based mechanisms; an empirical and practical gap concerning the design and combination of prevention strategies; and a contextual gap arising from the direct transfer of prevention models developed in other cultural, institutional, technological, and economic settings. Accordingly, this study aims to develop a native, context-sensitive model for preventing consumer deviant behavior in Iranian e-commerce businesses from the perspective of professional experts.
Methodology: The study employed a qualitative research design using the systematic grounded theory approach developed by Strauss and Corbin. The selection of grounded theory was appropriate because the purpose of the study was not merely to identify isolated factors, but to explain how causal and contextual conditions, cognitive and cultural mechanisms, technological capabilities, institutional conditions, prevention strategies, and consequences are connected within a process of prevention. Sixteen experts were selected through theoretical sampling from four professional groups: university faculty members with expertise in marketing management, business management, law, criminology, and sociology; managers and specialists working in e-commerce platforms and businesses; experts in cybersecurity and fraud prevention; and judges and lawyers specializing in cybercrime and electronic commerce. Participants were selected according to the emerging conceptual requirements of the study rather than according to statistical proportionality. Data were collected through semi-structured interviews and analyzed using open, axial, and selective coding. The initial theoretical framework was informed by moral disengagement theory, deterrence theory, and routine activity theory; however, these theories were used only as sensitizing frameworks, and the final categories and relationships were determined by the empirical data. The analysis resulted in 247 initial codes, which were consolidated into 161 unique concepts and 28 sub-categories. These were integrated into seven main categories. Theoretical saturation was assessed through the adequacy of category properties and dimensions, stabilization of relationships, comparison across expert groups, and the absence of new evidence capable of substantially altering the emerging theoretical structure. Trustworthiness was strengthened through member checking, audit-trail documentation, reflexive memoing, and systematic attention to discrepant evidence.
Findings: The findings produced a multidimensional and process-oriented model of native consumer deviance prevention. Seven main categories emerged from the data: targeted formulation of consumer deviant behaviors; cognitive-cultural mechanisms of justification; perceived monitoring and deterrence mechanisms; technological strategies for opportunity control; platform-level preventive policy design; positive prevention and social capital development; and institutional challenges and coordination-oriented strategies. The final paradigm model was organized around six components: causal conditions, the core phenomenon, contextual conditions, intervening conditions, strategies, and consequences. The causal conditions included perceived injustice, relative anonymity in digital environments, economic pressures, and cultural norms that may facilitate deviant behavior. The core phenomenon concerned the need for context-sensitive prevention of consumer deviance in Iranian e-commerce businesses. Contextual conditions included characteristics of the digital environment, limited legal awareness, restricted regulatory structures, and consumer culture. Intervening conditions consisted of weaknesses in control infrastructures, legal constraints, technical limitations of platform systems, and implementation barriers. The findings suggest that effective prevention requires simultaneous attention to the opportunity structure of deviance and to the cognitive processes that make deviant behavior appear acceptable or justifiable. Experts particularly emphasized cultural notions associated with “cleverness,” perceptions of unfairness, and the relative anonymity of online interactions. Prevention strategies therefore extend beyond punitive control and include technological monitoring, authentication, detection of abnormal behavioral patterns, transparent and balanced platform policies, user education, deterrent mechanisms, positive prevention, trust-building, and institutional coordination. Abuse of return policies was the most frequently referenced behavior in the qualitative material; however, this frequency indicates prominence within expert narratives rather than population prevalence or definitive managerial priority.
Discussion and Conclusion: The study conceptualizes native prevention not as the rejection of international prevention tools, but as their context-sensitive combination, prioritization, adaptation, and implementation within Iran’s cultural, economic, technological, organizational, and institutional environment. The proposed model indicates that prevention is a multilevel process in which causal conditions activate the prevention problem, contextual conditions shape the range of feasible responses, and intervening conditions facilitate or constrain implementation. Multidimensional strategies subsequently seek to reduce opportunities for deviance, increase perceived monitoring and deterrence, improve procedural safeguards, strengthen platform governance, influence social norms, and enhance coordination among relevant institutions. The principal contribution of the study is therefore the development of an integrated prevention logic rather than the identification of entirely unique prevention instruments. Measures such as authentication, behavioral monitoring, transparent communication, warning mechanisms, return-policy redesign, and suspicious-pattern detection may be broadly applicable across countries, but their effectiveness depends on how they are combined and implemented within the local context. The model highlights that factors such as cultural interpretations of rule compliance, perceived injustice, institutional trust, infrastructure limitations, and coordination capacity should be considered when designing preventive interventions for Iranian e-commerce platforms. Because the present study represents the qualitative phase of a broader research project and relies on expert perspectives rather than direct population-level consumer data, the model should be understood as a qualitative, context-bound theoretical explanation. Its components and relationships should not be interpreted as statistically tested causal effects or as estimates of the prevalence of consumer deviant behavior. Future quantitative and empirical studies are required to validate the proposed model, assess the relative importance of its components, and examine its applicability across different types of e-commerce platforms and consumer groups.