University of Isfahan New Marketing Research Journal 2228-7744 16 1 2026 03 21 Developing a Model of Factors Influencing Customer Online Purchase Intention: A Meta-Synthesis Approach Developing a Model of Factors Influencing Customer Online Purchase Intention: A Meta-Synthesis Approach 95 132 30276 10.22108/nmrj.2026.145884.3213 FA Hoda Salehi Kian M.Sc., Department of MBA, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran Bozorgmehr Ashrafi Associate professor, Department of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran Hosein Seyedi Assistant professor, Department of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran Neda Salehi Kian M.Sc., Department of MBA, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran Journal Article 2025 07 15 The widespread adoption of the Internet in the 21st century coupled with the increasing use of computers and mobile technologies has significantly heightened consumers' inclination and intention to make online purchases. This study aimed to identify, rank, and develop a conceptual model of the factors influencing customers' online purchase intentions. From a research perspective, the study adopted an applied approach and employed a mixed-methods design (qualitative and quantitative) with its primary innovation being the integration of these methodologies. In the qualitative phase, a meta-synthesis method was utilized. English and Persian articles related to online purchase intentions published from 2010 to May 2025 (Gregorian calendar) and 1390 to Ordibehesht 1404 (Iranian calendar) were systematically reviewed. After multiple screening stages—considering relevance based on article titles, abstracts, and content, publication language (English or Persian), study type, publication period, accessibility, and methodological quality assessed using the CASP checklist—55 articles were selected from an initial pool of 932 studies. In the quantitative phase, the Shannon entropy technique was employed to prioritize the identified factors. The analysis resulted in the extraction of 90 unique codes, 22 concepts, and 8 overarching categories, which were ranked based on their significance. The main categories identified included individual factors, technical and security factors, motivational factors, marketing-related factors, product-related factors, supportive and communicative factors, social factors, and AI-based factors. Notably, the Shannon entropy analysis revealed that "customer trust", "perceived risk", and "perceived ease of use" ranked as the top three factors, indicating their substantial influence on customers' online purchase intentions. Ultimately, a comprehensive model of the factors affecting online purchase intention was developed. The findings of this study offer valuable insights for scholars and practitioners in the field of electronic commerce. Introduction Rapid advancements in information technology coupled with the widespread adoption of the Internet and digital tools have fundamentally transformed consumer purchasing behavior. Online shopping has emerged as a prominent manifestation of this transformation, becoming an integral part of daily life and a primary channel for fulfilling consumer needs. This shift has not only altered traditional purchasing patterns, but has also introduced new forms of consumer behavior in digital environments. As a result, electronic commerce represents a tangible expression of the information and communication technology revolution within the economic sphere, reshaping both the structure and substance of commercial processes. In recent years, e-commerce firms have experienced remarkable growth, alongside a significant increase in the number of online consumers. While online shopping offers numerous advantages, consumers' purchase intentions in digital contexts are continually influenced by a diverse range of technological, informational, psychological, and contextual factors. Understanding these factors is crucial for managers and marketers as insights into online purchase intentions can inform strategic marketing decisions, demand forecasting, customer segmentation, user experience enhancement, and customer loyalty development. Conversely, a lack of knowledge about these determinants can lead to ineffective marketing strategies, lost competitive opportunities, and the failure of online services. Although a growing body of research has examined the factors influencing online purchase intentions, existing studies remain fragmented and, in some cases, yield inconsistent findings. Previous research has often concentrated on isolated dimensions, such as user-generated content, artificial intelligence technologies, or digital literacy, without providing a comprehensive and integrated analytical framework. Consequently, the literature lacks a systematic, multidimensional, and unified model capable of coherently explaining online purchase intentions. This fragmentation highlights a significant theoretical gap between the current state of scattered and unsystematic knowledge and the desired development of an integrated, evidence-based framework. Addressing this gap was the central focus of the present study. The primary objective was to systematically identify and prioritize the factors influencing consumers' online purchase intentions through a mixed-methods approach. Specifically, this study employed a meta-synthesis method to integrate and reinterpret findings from previous research, thereby extracting a comprehensive set of key determinants. Following this, the Shannon entropy technique was applied to quantitatively evaluate and rank the relative importance of the identified factors. The novelty of this research lay in the combination of meta-synthesis with Shannon entropy, leading to the development of a robust and evidence-based framework for understanding online purchase intentions. While the study was limited by its reliance on previously published research, this methodological integration facilitated a deeper and more systematic understanding of the phenomenon. Accordingly, this study aimed to address the following research questions: What factors influence consumers’ online purchase intentions? How can these factors be prioritized based on their relative significance? And can a comprehensive, systematic model of online purchase intention be developed to support strategic decision-making in e-commerce contexts? Materials & Methods The present study aimed to systematically identify and prioritize the factors influencing consumers' online purchase intentions through an evidence-based synthesis of prior research. This research was applied in nature and utilized a mixed-methods design concerning data and analytical logic. Specifically, a qualitative meta-synthesis approach was employed to integrate and reinterpret findings from existing studies followed by the application of the Shannon entropy method as a quantitative technique for determining the relative importance of the identified factors. Research Design and Data Sources Meta-synthesis is an advanced qualitative research method that seeks to conceptually integrate findings from multiple studies addressing a common phenomenon. Unlike traditional systematic reviews, which primarily summarize results, meta-synthesis focuses on uncovering latent concepts, reinterpreting empirical evidence, and generating a comprehensive and coherent theoretical framework. This approach is particularly well-suited for research domains, such as e-commerce and consumer behavior, where existing studies are numerous, fragmented, and occasionally contradictory. The population of this study consisted of published scientific articles related to online purchase intention. To ensure methodological rigor and credibility of the included studies, the Critical Appraisal Skills Programme (CASP) checklist was utilized as a quality assessment tool. The meta-synthesis process adhered to the 7-step framework proposed by Sandelowski and Barroso (2006), which provided a structured and transparent procedure for qualitative synthesis. Step 1: Formulating the Research Questions The 1st step involved defining clear and focused research questions to guide the entire synthesis process. This phase specified the research phenomenon, target population, temporal scope, and methodological approach. Accordingly, the central research question was formulated as follows: What factors influence consumers' online purchase intentions? This question was operationalized based on 4 parameters: the research focus (identifying and modeling influencing factors), the study population (published scholarly research), the time frame (from 2010 to May 2025), and the data collection method (documentary analysis of scientific literature). Step 2: Systematic Literature Search In the 2nd step, a comprehensive and systematic search of relevant literature was conducted. International databases, such as ScienceDirect, Wiley Online Library, Taylor & Francis, Sage Journals, Emerald, ProQuest, IEEE Xplore, PubMed, and ERIC were searched, alongside Iranian databases, including Magiran, NoorMags, SID, the Comprehensive Humanities Portal, Ganj, and Elmnet. A range of Persian and English keywords related to online purchase intention was employed. This initial search resulted in the identification of 932 potentially relevant articles. Step 3: Screening and Selecting Studies The 3rd step focused on screening and selecting eligible studies based on predefined inclusion and exclusion criteria. Articles were assessed in sequence based on title, abstract, full text, and methodological quality, utilizing the CASP checklist. Inclusion criteria comprised relevance to online purchase intention, publication within the specified time frame, availability of full text, publication in either Persian or English, and classification as peer-reviewed research articles. Studies that did not align with the research objectives or failed to meet quality standards were excluded. Ultimately, 55 articles were retained for final analysis. Step 4: Data Extraction In this 4th step, relevant information was systematically extracted from the selected articles. The extracted data included the authors' names, publication years, and all reported factors related to online purchase intentions. These data formed the foundation for subsequent coding and categorization. A comprehensive database of extracted concepts was developed to ensure transparency and traceability of findings. Step 5: Data Analysis and Synthesis The 5th step involved qualitative content analysis and synthesis of the extracted data. Initially, open coding was used to identify unique factors across studies, resulting in 90 non-redundant codes. These codes were then grouped into 22 concepts based on semantic similarity, which were further integrated into 8 overarching categories: motivational factors, technical and security factors, social factors, marketing factors, supportive and relational factors, product-related factors, individual factors, and artificial intelligence–based factors. This stage highlighted that prior research primarily addressed isolated dimensions of online purchase intention, revealing the lack of a comprehensive and systematic framework in the literature. Step 6: Quality Control and Reliability Assessment To ensure the reliability of the coding and categorization process, inter-coder agreement was assessed by using Cohen’s Kappa coefficient. A subset of extracted codes was independently classified by an expert reviewer, who was unaware of the researcher’s categorizations. The resulting Kappa value of 0.89 calculated by using SPSS at a significance level of 0.001 indicated a high level of agreement and confirmed the robustness of the qualitative synthesis. Step 7: Presentation of Results and Quantitative Prioritization In the final step, the synthesized findings were quantitatively analyzed by using the Shannon entropy method to determine the relative importance of the identified factors. First, a decision matrix was constructed based on the frequency of each code across the selected studies. The matrix was then normalized and entropy values were calculated to measure the degree of uncertainty associated with each factor. Subsequently, entropy weights were computed with higher weights indicating greater importance. This procedure enabled the objective and data-driven prioritization of factors influencing online purchase intentions. By integrating qualitative meta-synthesis with Shannon entropy, this mixed-methods approach provided a comprehensive, reliable, and evidence-based framework for understanding and prioritizing the determinants of online purchase intention. The resulting model offers both theoretical advancements and practical guidance for researchers and practitioners in the field of electronic commerce. Research Findings Results of the Meta-Synthesis Process Study Selection: A total of 55 high-quality articles were analyzed to derive evidence related to consumers’ online purchase intention. Identification of Codes: Through content analysis, 90 non-redundant codes were identified, each representing distinct factors influencing online purchase intention. Categorization: Codes were clustered into broader concepts and integrated into overarching categories, resulting in a multi-level framework: Categories: Technological, psychological, social, marketing-related, and individual factors. Framework: Presented in Table 1, illustrating the comprehensive representation of influencing factors. Results of the Shannon Entropy Analysis Methodology: The Shannon entropy method was applied to determine the relative importance of the identified factors based on their frequency and distribution. Findings: Customer Trust: Ranked highest indicating it significantly influenced online purchase intention. Perceived Risk: Second most influential reflecting concerns about uncertainty and security in online transactions. Perceived Ease of Use: Ranked third emphasizing the need for user-friendly interfaces. Product Price & Perceived Usefulness: Tied for fourth place highlighting the role of economic considerations and perceived functional benefits. User Privacy Protection, Perceived Enjoyment, & Perceived Value: Found equal importance suggesting experiential and value-based aspects influenced purchase intentions. Discussion of Results & Conclusion Hierarchical Influence: The results indicated that customer trust was paramount in shaping online purchasing behavior. Risk and Usability Factors: Perceived risk and ease of use also played critical roles with implications for e-commerce strategies. Experiential Aspects: The inclusion of experiential factors pointed to a shift in understanding consumer behavior in online environments. Summary of Findings: The study highlighted the complexity of factors influencing online purchase intentions, establishing a clear hierarchy. Implications for Practice: Understanding these factors can help online retailers enhance their strategies to build consumer trust and address perceived risks. Future Research Directions: Further studies could explore the evolving factors influencing online purchasing as technology and consumer behaviors change over time. The widespread adoption of the Internet in the 21st century coupled with the increasing use of computers and mobile technologies has significantly heightened consumers' inclination and intention to make online purchases. This study aimed to identify, rank, and develop a conceptual model of the factors influencing customers' online purchase intentions. From a research perspective, the study adopted an applied approach and employed a mixed-methods design (qualitative and quantitative) with its primary innovation being the integration of these methodologies. In the qualitative phase, a meta-synthesis method was utilized. English and Persian articles related to online purchase intentions published from 2010 to May 2025 (Gregorian calendar) and 1390 to Ordibehesht 1404 (Iranian calendar) were systematically reviewed. After multiple screening stages—considering relevance based on article titles, abstracts, and content, publication language (English or Persian), study type, publication period, accessibility, and methodological quality assessed using the CASP checklist—55 articles were selected from an initial pool of 932 studies. In the quantitative phase, the Shannon entropy technique was employed to prioritize the identified factors. The analysis resulted in the extraction of 90 unique codes, 22 concepts, and 8 overarching categories, which were ranked based on their significance. The main categories identified included individual factors, technical and security factors, motivational factors, marketing-related factors, product-related factors, supportive and communicative factors, social factors, and AI-based factors. Notably, the Shannon entropy analysis revealed that "customer trust", "perceived risk", and "perceived ease of use" ranked as the top three factors, indicating their substantial influence on customers' online purchase intentions. Ultimately, a comprehensive model of the factors affecting online purchase intention was developed. The findings of this study offer valuable insights for scholars and practitioners in the field of electronic commerce. Introduction Rapid advancements in information technology coupled with the widespread adoption of the Internet and digital tools have fundamentally transformed consumer purchasing behavior. Online shopping has emerged as a prominent manifestation of this transformation, becoming an integral part of daily life and a primary channel for fulfilling consumer needs. This shift has not only altered traditional purchasing patterns, but has also introduced new forms of consumer behavior in digital environments. As a result, electronic commerce represents a tangible expression of the information and communication technology revolution within the economic sphere, reshaping both the structure and substance of commercial processes. In recent years, e-commerce firms have experienced remarkable growth, alongside a significant increase in the number of online consumers. While online shopping offers numerous advantages, consumers' purchase intentions in digital contexts are continually influenced by a diverse range of technological, informational, psychological, and contextual factors. Understanding these factors is crucial for managers and marketers as insights into online purchase intentions can inform strategic marketing decisions, demand forecasting, customer segmentation, user experience enhancement, and customer loyalty development. Conversely, a lack of knowledge about these determinants can lead to ineffective marketing strategies, lost competitive opportunities, and the failure of online services. Although a growing body of research has examined the factors influencing online purchase intentions, existing studies remain fragmented and, in some cases, yield inconsistent findings. Previous research has often concentrated on isolated dimensions, such as user-generated content, artificial intelligence technologies, or digital literacy, without providing a comprehensive and integrated analytical framework. Consequently, the literature lacks a systematic, multidimensional, and unified model capable of coherently explaining online purchase intentions. This fragmentation highlights a significant theoretical gap between the current state of scattered and unsystematic knowledge and the desired development of an integrated, evidence-based framework. Addressing this gap was the central focus of the present study. The primary objective was to systematically identify and prioritize the factors influencing consumers' online purchase intentions through a mixed-methods approach. Specifically, this study employed a meta-synthesis method to integrate and reinterpret findings from previous research, thereby extracting a comprehensive set of key determinants. Following this, the Shannon entropy technique was applied to quantitatively evaluate and rank the relative importance of the identified factors. The novelty of this research lay in the combination of meta-synthesis with Shannon entropy, leading to the development of a robust and evidence-based framework for understanding online purchase intentions. While the study was limited by its reliance on previously published research, this methodological integration facilitated a deeper and more systematic understanding of the phenomenon. Accordingly, this study aimed to address the following research questions: What factors influence consumers’ online purchase intentions? How can these factors be prioritized based on their relative significance? And can a comprehensive, systematic model of online purchase intention be developed to support strategic decision-making in e-commerce contexts? Materials & Methods The present study aimed to systematically identify and prioritize the factors influencing consumers' online purchase intentions through an evidence-based synthesis of prior research. This research was applied in nature and utilized a mixed-methods design concerning data and analytical logic. Specifically, a qualitative meta-synthesis approach was employed to integrate and reinterpret findings from existing studies followed by the application of the Shannon entropy method as a quantitative technique for determining the relative importance of the identified factors. Research Design and Data Sources Meta-synthesis is an advanced qualitative research method that seeks to conceptually integrate findings from multiple studies addressing a common phenomenon. Unlike traditional systematic reviews, which primarily summarize results, meta-synthesis focuses on uncovering latent concepts, reinterpreting empirical evidence, and generating a comprehensive and coherent theoretical framework. This approach is particularly well-suited for research domains, such as e-commerce and consumer behavior, where existing studies are numerous, fragmented, and occasionally contradictory. The population of this study consisted of published scientific articles related to online purchase intention. To ensure methodological rigor and credibility of the included studies, the Critical Appraisal Skills Programme (CASP) checklist was utilized as a quality assessment tool. The meta-synthesis process adhered to the 7-step framework proposed by Sandelowski and Barroso (2006), which provided a structured and transparent procedure for qualitative synthesis. Step 1: Formulating the Research Questions The 1st step involved defining clear and focused research questions to guide the entire synthesis process. This phase specified the research phenomenon, target population, temporal scope, and methodological approach. Accordingly, the central research question was formulated as follows: What factors influence consumers' online purchase intentions? This question was operationalized based on 4 parameters: the research focus (identifying and modeling influencing factors), the study population (published scholarly research), the time frame (from 2010 to May 2025), and the data collection method (documentary analysis of scientific literature). Step 2: Systematic Literature Search In the 2nd step, a comprehensive and systematic search of relevant literature was conducted. International databases, such as ScienceDirect, Wiley Online Library, Taylor & Francis, Sage Journals, Emerald, ProQuest, IEEE Xplore, PubMed, and ERIC were searched, alongside Iranian databases, including Magiran, NoorMags, SID, the Comprehensive Humanities Portal, Ganj, and Elmnet. A range of Persian and English keywords related to online purchase intention was employed. This initial search resulted in the identification of 932 potentially relevant articles. Step 3: Screening and Selecting Studies The 3rd step focused on screening and selecting eligible studies based on predefined inclusion and exclusion criteria. Articles were assessed in sequence based on title, abstract, full text, and methodological quality, utilizing the CASP checklist. Inclusion criteria comprised relevance to online purchase intention, publication within the specified time frame, availability of full text, publication in either Persian or English, and classification as peer-reviewed research articles. Studies that did not align with the research objectives or failed to meet quality standards were excluded. Ultimately, 55 articles were retained for final analysis. Step 4: Data Extraction In this 4th step, relevant information was systematically extracted from the selected articles. The extracted data included the authors' names, publication years, and all reported factors related to online purchase intentions. These data formed the foundation for subsequent coding and categorization. A comprehensive database of extracted concepts was developed to ensure transparency and traceability of findings. Step 5: Data Analysis and Synthesis The 5th step involved qualitative content analysis and synthesis of the extracted data. Initially, open coding was used to identify unique factors across studies, resulting in 90 non-redundant codes. These codes were then grouped into 22 concepts based on semantic similarity, which were further integrated into 8 overarching categories: motivational factors, technical and security factors, social factors, marketing factors, supportive and relational factors, product-related factors, individual factors, and artificial intelligence–based factors. This stage highlighted that prior research primarily addressed isolated dimensions of online purchase intention, revealing the lack of a comprehensive and systematic framework in the literature. Step 6: Quality Control and Reliability Assessment To ensure the reliability of the coding and categorization process, inter-coder agreement was assessed by using Cohen’s Kappa coefficient. A subset of extracted codes was independently classified by an expert reviewer, who was unaware of the researcher’s categorizations. The resulting Kappa value of 0.89 calculated by using SPSS at a significance level of 0.001 indicated a high level of agreement and confirmed the robustness of the qualitative synthesis. Step 7: Presentation of Results and Quantitative Prioritization In the final step, the synthesized findings were quantitatively analyzed by using the Shannon entropy method to determine the relative importance of the identified factors. First, a decision matrix was constructed based on the frequency of each code across the selected studies. The matrix was then normalized and entropy values were calculated to measure the degree of uncertainty associated with each factor. Subsequently, entropy weights were computed with higher weights indicating greater importance. This procedure enabled the objective and data-driven prioritization of factors influencing online purchase intentions. By integrating qualitative meta-synthesis with Shannon entropy, this mixed-methods approach provided a comprehensive, reliable, and evidence-based framework for understanding and prioritizing the determinants of online purchase intention. The resulting model offers both theoretical advancements and practical guidance for researchers and practitioners in the field of electronic commerce. Research Findings Results of the Meta-Synthesis Process Study Selection: A total of 55 high-quality articles were analyzed to derive evidence related to consumers’ online purchase intention. Identification of Codes: Through content analysis, 90 non-redundant codes were identified, each representing distinct factors influencing online purchase intention. Categorization: Codes were clustered into broader concepts and integrated into overarching categories, resulting in a multi-level framework: Categories: Technological, psychological, social, marketing-related, and individual factors. Framework: Presented in Table 1, illustrating the comprehensive representation of influencing factors. Results of the Shannon Entropy Analysis Methodology: The Shannon entropy method was applied to determine the relative importance of the identified factors based on their frequency and distribution. Findings: Customer Trust: Ranked highest indicating it significantly influenced online purchase intention. Perceived Risk: Second most influential reflecting concerns about uncertainty and security in online transactions. Perceived Ease of Use: Ranked third emphasizing the need for user-friendly interfaces. Product Price & Perceived Usefulness: Tied for fourth place highlighting the role of economic considerations and perceived functional benefits. User Privacy Protection, Perceived Enjoyment, & Perceived Value: Found equal importance suggesting experiential and value-based aspects influenced purchase intentions. Discussion of Results & Conclusion Hierarchical Influence: The results indicated that customer trust was paramount in shaping online purchasing behavior. Risk and Usability Factors: Perceived risk and ease of use also played critical roles with implications for e-commerce strategies. Experiential Aspects: The inclusion of experiential factors pointed to a shift in understanding consumer behavior in online environments. Summary of Findings: The study highlighted the complexity of factors influencing online purchase intentions, establishing a clear hierarchy. Implications for Practice: Understanding these factors can help online retailers enhance their strategies to build consumer trust and address perceived risks. Future Research Directions: Further studies could explore the evolving factors influencing online purchasing as technology and consumer behaviors change over time.

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