University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Psychographic Segmentation of Global Online Retailing Based on Online Shopping Motivations and Online Shopping Behaviors Psychographic Segmentation of Global Online Retailing Based on Online Shopping Motivations and Online Shopping Behaviors 1 26 29757 10.22108/nmrj.2025.144863.3177 FA Fatemeh Ranjbar M.Sc., Department of Business Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran Manijeh Bahrainizad Associate professor, Department of Business Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran Journal Article 2025 04 13 The primary objective of this research was to psychographically segment online retail customers based on their purchasing motivations and behaviors. This study was classified as applied research in terms of its purpose and descriptive-survey research regarding data collection. The statistical population comprised individuals, who had engaged in online shopping from various stores at least once. A questionnaire served as the data collection tool for this research. To analyze the data, self-organizing map methods based on artificial neural networks were utilized, along with the Viscovery SOMine software. The findings indicated that online retail customers could be segmented into three distinct clusters, each characterized by varying demographic traits, motivations, and behaviors influencing their shopping experiences. The first cluster named "Balanced Futurist" primarily sought efficiency, focusing on optimizing their purchases when visiting online stores. The second cluster consisted of "Professional Pragmatists", individuals, who prominently leveraged utilitarian motivations for online shopping, viewing their purchases as practical necessities and allocating specific time for them. The third cluster known as "Pleasure-Seeking Explorers" comprised customers with extensive online shopping experience. Their hedonic motivations coupled with an exploratory attitude toward online shopping had driven them to seek new and satisfying experiences. Introduction The growth of information technology and the rapid expansion of Internet usage have given rise to a new form of retail transactions: Internet retailing. This evolution has made online shopping a daily activity for people worldwide (Yu et al., 2019). The continuous growth of online shopping can be attributed to the widespread adoption and penetration of Internet technology (Rose et al., 2011). When discussing e-commerce, many people immediately think of platforms like Amazon or eBay. A 2021 survey revealed that nearly two-thirds (63%) of social media shoppers had globally made an unplanned (impulsive) purchase through these channels. Additionally, approximately a quarter (23%) of respondents had reported making an impulsive purchase on social media, while 14% had planned their purchases (Statista, 2021). Customer segmentation involved dividing the customer base into distinct groups that shared similar characteristics, such as demographics, interests, behaviors, or locations. This process enabled businesses to focus their marketing efforts and resources on valuable and loyal customers, ultimately helping them achieve their business goals. Segmentation could be conducted using demographic, geographic, behavioral, and psychographic data (Zhou et al., 2014). When customers experienced hedonic motivation while browsing the web, they were more likely to extend their visit duration and return to the same website. Both utilitarian and hedonic motivations significantly influenced repurchase intentions in business-to-consumer e-commerce. As such, researchers have suggested that these motivations have a direct and positive impact on the intention to continue using and purchasing from websites or social media platforms. Given the crucial role of online retailers in the global e-commerce landscape, it is essential to examine consumer behavior in the online context to understand how consumers differ from one another worldwide. Previous studies have not yet explored the segmentation of online retail consumers by integrating psychological criteria, such as online shopping motivations (e.g., hedonic and utilitarian motivations), with behavioral variables (e.g., online shopping intention, online search intention, online impulse buying, and online consumer engagement), as well as demographic and geographical factors on a global scale. Therefore, this study aimed to fill this gap, which was essential for advancing marketing science in the realm of online shopping. This research addressed the following questions: How do different groups of online retail consumers vary based on their shopping motivations, online shopping behaviors, and demographic characteristics? What are the profiles of the distinct consumer segments based on their shopping motivations and online behaviors? Materials & Methods This research was classified as applied in terms of purpose and as descriptive survey research in terms of methodology. The study involved a comprehensive and accurate review of previous literature and the use of secondary data related to the subject. Two primary online shopping motivations—utilitarian motivation and hedonic motivation—along with four online shopping behaviors (online search intention, online purchase intention, online impulse buying, and online consumer involvement) were identified. The collected data were categorized and labeled based on online shopping motivations, behaviors, and demographic characteristics using a data mining approach that employed self-organizing maps and artificial neural networks. The statistical population for this study consisted of individuals worldwide, who had at least one experience with online shopping. Given the unlimited population size and a sampling error of 5%, the Cochran formula determined the minimum sample size to be 384 participants. To enhance reliability, the questionnaire was distributed to 2,110 individuals, resulting in 810 complete and usable responses for analysis. Due to the inability to access a list of buyers from the selected retailers, a non-random, convenience sampling method was employed. Data collection was facilitated through a questionnaire designed and administered via the Google Forms platform, which was also used for statistical analysis. The link to the questionnaire was distributed through social networks, such as Facebook and Instagram, targeting customers, who had made purchases from online retailers like Amazon, eBay, and Alibaba. To enhance both the number of respondents and the diversity of the statistical sample, we also utilized a swap service that facilitated the exchange of respondents between different studies. The primary data collection tool for this study was a questionnaire, which was reviewed and approved by marketing professors for content validity. It was structured into the three sections of demographic information, motivations, and online shopping behaviors and was distributed to respondents through convenience sampling. To assess the construct validity (instrument validity) of the questionnaire, confirmatory factor analysis was employed. To identify potential clusters of online retail customers relevant to this research, self-organizing maps based on artificial neural networks were utilized with the analysis conducted using Viscovery SOMine version 8.0.1 software. The software visualization capabilities are evident in the patterns produced by the self-organizing maps. For this analysis, 1,000 neurons were selected for the input layer to determine the network dimensions. The training speed was optimized to achieve the most accurate output state for the final results. Additionally, the elasticity parameter of network training set to 0.5 allowed for a more detailed display of the output map structure. The software presented the output maps in dimensions of 33×29 after 27 iterations based on the input commands. Research Findings Online retail customers were categorized into three distinct clusters. The following describes each of these clusters. Profile of the First Segment: Balanced Prospective Customers This segment represented the largest group of online retail customers, accounting for over half (58.9%) of the total. Within this segment, the frequency of female customers (62.9%) surpassed that of male customers. Most individuals in this group were between 18 and 30 years old with a predominance of European customers. The majority of customers in this segment had over five years of online shopping experience. Typically, these customers spent less than one hour per week browsing online stores and shopped at these retailers once or twice a month. Amazon was the preferred platform for this segment with 57.2% of customers using it, making it the most popular online store among them (0.95%). Additionally, most customers in this segment accessed online stores primarily through mobile phones. This segment was driven more by utilitarian motives. Overall, their online shopping behaviors were less pronounced compared to those of the other two segments. To encourage more frequent online shopping, it was essential to activate behaviors that significantly impacted this group. The most influential online shopping behaviors for this segment included online shopping intention, online search intention, online consumer engagement, and online impulse buying. This cluster was characterized by a demographic focus on females, young adults, and college students with incomes below $500. Customers in this group prioritized convenience in online shopping and possessed over 5 years of experience in this domain. Since they primarily entered online stores to enhance efficiency and optimize their purchases, the name “Balanced Futures” was chosen to reflect their balanced shopping behavior and emphasis on convenience. Profile of the Second Segment: Professional and Pragmatic Customers This segment represented the second largest group of online retail customers. Within this cluster, the proportion of male customers (57.1%) exceeded that of female customers. Most individuals in this segment fell within the age range of 31 to 40 years, with a majority being of Asian descent and residing in Europe. The customers in this segment had significant experience with over 5 years of shopping with online retailers. Typically, these customers spent 1-3 hours per week browsing online stores and made purchases from these retailers 1-2 times per month. Amazon was the most frequently used online store among this segment with a relative frequency of 38.8%; it was the preferred platform for 95.6% of customers in this group. Furthermore, the majority of customers in this segment accessed online stores using their mobile phones. In this cluster, utilitarian motives were more prominent, while hedonic motives were less significant compared to the other two segments. Regarding the online shopping behaviors that influenced this segment, the following behaviors were notable: online shopping intention (4.226), online search intention (3.999), online consumer engagement (2.754), and online impulse buying (2.299). It is important to highlight that online shopping intention with a score of 4.226 had the most substantial impact on this segment compared to the other groups. This cluster was comprised of men, middle-aged individuals, and employees with a monthly income between $2,000 and $5,000, who primarily engaged in online shopping driven by utilitarian motives. They viewed their purchases as practical necessities and allocated specific time for shopping. The name “Professional Pragmatist” reflected their meticulous, planned approach to online shopping. Profile of the Third Segment: Hedonistic Explorers This segment was the smallest among online retail customers. Within this group, the proportion of women (58.7%) was higher than that of men. Most customers were aged between 18 and 30 years, predominantly of Asian descent with many residing in Oceania. Nearly all customers in this segment had over 5 years of experience shopping with online retailers. Typically, they spent 1-3 hours per week browsing online stores and made purchases from these retailers once or twice a month. The online platforms most frequently used by this segment were Amazon and Alibaba (27.5%), with Alibaba being the most popular choice at 56.9%. Most customers in this segment accessed online stores primarily through mobile phones. In this cluster, utilitarian motives were more prominent than hedonic motives although the overall score for utilitarian motives was lower than those of the other two segments. Conversely, hedonic motives scored higher in this cluster compared to the others. Overall, online shopping behaviors were particularly pronounced among this segment. Customers in this group were primarily female, students, and of Asian descent with lower incomes. Their extensive online shopping experience combined with hedonic motivations encouraged them to seek new and satisfying experiences during their shopping journeys. Therefore, the name "Hedonistic Explorer" was chosen for this cluster, reflecting their blend of exploratory behavior and hedonistic motivations. Discussion of Results & Conclusion Online retail managers and marketers should prioritize understanding customers' online shopping motivations and behaviors as crucial elements of their marketing strategies. Factors like convenience of shopping, access to a diverse range of products, ability to compare prices, and opportunity to take advantage of special discounts significantly shape customers' online shopping behavior and influence their decision-making processes. Therefore, it is recommended that online retailers tailor their programs to align with the characteristics of different customer segments, thereby providing an easy, secure, and appealing shopping experience. To enhance outcomes and offer more effective recommendations, this study referenced previous research on customers' online shopping motivations and behaviors. It is important to acknowledge that all research had its limitations. The neural network-based segmentation approach employed in this study required a large sample size to achieve valid and reliable results, particularly given the global diversity of the statistical population. The vast range of customers worldwide complicated data collection, making it a time-consuming process that presented numerous challenges and significantly extended the duration of the research. Additionally, the lengthy nature of completing the research questionnaire coupled with the inclusion of sensitive questions, such as nationality and country of residence, may have led to reluctance among respondents to participate fully. The extensive number of demographic questions, which were necessary for identifying specific online customer behaviors, may have also caused some participants to lose focus while completing the questionnaire. The primary objective of this research was to psychographically segment online retail customers based on their purchasing motivations and behaviors. This study was classified as applied research in terms of its purpose and descriptive-survey research regarding data collection. The statistical population comprised individuals, who had engaged in online shopping from various stores at least once. A questionnaire served as the data collection tool for this research. To analyze the data, self-organizing map methods based on artificial neural networks were utilized, along with the Viscovery SOMine software. The findings indicated that online retail customers could be segmented into three distinct clusters, each characterized by varying demographic traits, motivations, and behaviors influencing their shopping experiences. The first cluster named "Balanced Futurist" primarily sought efficiency, focusing on optimizing their purchases when visiting online stores. The second cluster consisted of "Professional Pragmatists", individuals, who prominently leveraged utilitarian motivations for online shopping, viewing their purchases as practical necessities and allocating specific time for them. The third cluster known as "Pleasure-Seeking Explorers" comprised customers with extensive online shopping experience. Their hedonic motivations coupled with an exploratory attitude toward online shopping had driven them to seek new and satisfying experiences. Introduction The growth of information technology and the rapid expansion of Internet usage have given rise to a new form of retail transactions: Internet retailing. This evolution has made online shopping a daily activity for people worldwide (Yu et al., 2019). The continuous growth of online shopping can be attributed to the widespread adoption and penetration of Internet technology (Rose et al., 2011). When discussing e-commerce, many people immediately think of platforms like Amazon or eBay. A 2021 survey revealed that nearly two-thirds (63%) of social media shoppers had globally made an unplanned (impulsive) purchase through these channels. Additionally, approximately a quarter (23%) of respondents had reported making an impulsive purchase on social media, while 14% had planned their purchases (Statista, 2021). Customer segmentation involved dividing the customer base into distinct groups that shared similar characteristics, such as demographics, interests, behaviors, or locations. This process enabled businesses to focus their marketing efforts and resources on valuable and loyal customers, ultimately helping them achieve their business goals. Segmentation could be conducted using demographic, geographic, behavioral, and psychographic data (Zhou et al., 2014). When customers experienced hedonic motivation while browsing the web, they were more likely to extend their visit duration and return to the same website. Both utilitarian and hedonic motivations significantly influenced repurchase intentions in business-to-consumer e-commerce. As such, researchers have suggested that these motivations have a direct and positive impact on the intention to continue using and purchasing from websites or social media platforms. Given the crucial role of online retailers in the global e-commerce landscape, it is essential to examine consumer behavior in the online context to understand how consumers differ from one another worldwide. Previous studies have not yet explored the segmentation of online retail consumers by integrating psychological criteria, such as online shopping motivations (e.g., hedonic and utilitarian motivations), with behavioral variables (e.g., online shopping intention, online search intention, online impulse buying, and online consumer engagement), as well as demographic and geographical factors on a global scale. Therefore, this study aimed to fill this gap, which was essential for advancing marketing science in the realm of online shopping. This research addressed the following questions: How do different groups of online retail consumers vary based on their shopping motivations, online shopping behaviors, and demographic characteristics? What are the profiles of the distinct consumer segments based on their shopping motivations and online behaviors? Materials & Methods This research was classified as applied in terms of purpose and as descriptive survey research in terms of methodology. The study involved a comprehensive and accurate review of previous literature and the use of secondary data related to the subject. Two primary online shopping motivations—utilitarian motivation and hedonic motivation—along with four online shopping behaviors (online search intention, online purchase intention, online impulse buying, and online consumer involvement) were identified. The collected data were categorized and labeled based on online shopping motivations, behaviors, and demographic characteristics using a data mining approach that employed self-organizing maps and artificial neural networks. The statistical population for this study consisted of individuals worldwide, who had at least one experience with online shopping. Given the unlimited population size and a sampling error of 5%, the Cochran formula determined the minimum sample size to be 384 participants. To enhance reliability, the questionnaire was distributed to 2,110 individuals, resulting in 810 complete and usable responses for analysis. Due to the inability to access a list of buyers from the selected retailers, a non-random, convenience sampling method was employed. Data collection was facilitated through a questionnaire designed and administered via the Google Forms platform, which was also used for statistical analysis. The link to the questionnaire was distributed through social networks, such as Facebook and Instagram, targeting customers, who had made purchases from online retailers like Amazon, eBay, and Alibaba. To enhance both the number of respondents and the diversity of the statistical sample, we also utilized a swap service that facilitated the exchange of respondents between different studies. The primary data collection tool for this study was a questionnaire, which was reviewed and approved by marketing professors for content validity. It was structured into the three sections of demographic information, motivations, and online shopping behaviors and was distributed to respondents through convenience sampling. To assess the construct validity (instrument validity) of the questionnaire, confirmatory factor analysis was employed. To identify potential clusters of online retail customers relevant to this research, self-organizing maps based on artificial neural networks were utilized with the analysis conducted using Viscovery SOMine version 8.0.1 software. The software visualization capabilities are evident in the patterns produced by the self-organizing maps. For this analysis, 1,000 neurons were selected for the input layer to determine the network dimensions. The training speed was optimized to achieve the most accurate output state for the final results. Additionally, the elasticity parameter of network training set to 0.5 allowed for a more detailed display of the output map structure. The software presented the output maps in dimensions of 33×29 after 27 iterations based on the input commands. Research Findings Online retail customers were categorized into three distinct clusters. The following describes each of these clusters. Profile of the First Segment: Balanced Prospective Customers This segment represented the largest group of online retail customers, accounting for over half (58.9%) of the total. Within this segment, the frequency of female customers (62.9%) surpassed that of male customers. Most individuals in this group were between 18 and 30 years old with a predominance of European customers. The majority of customers in this segment had over five years of online shopping experience. Typically, these customers spent less than one hour per week browsing online stores and shopped at these retailers once or twice a month. Amazon was the preferred platform for this segment with 57.2% of customers using it, making it the most popular online store among them (0.95%). Additionally, most customers in this segment accessed online stores primarily through mobile phones. This segment was driven more by utilitarian motives. Overall, their online shopping behaviors were less pronounced compared to those of the other two segments. To encourage more frequent online shopping, it was essential to activate behaviors that significantly impacted this group. The most influential online shopping behaviors for this segment included online shopping intention, online search intention, online consumer engagement, and online impulse buying. This cluster was characterized by a demographic focus on females, young adults, and college students with incomes below $500. Customers in this group prioritized convenience in online shopping and possessed over 5 years of experience in this domain. Since they primarily entered online stores to enhance efficiency and optimize their purchases, the name “Balanced Futures” was chosen to reflect their balanced shopping behavior and emphasis on convenience. Profile of the Second Segment: Professional and Pragmatic Customers This segment represented the second largest group of online retail customers. Within this cluster, the proportion of male customers (57.1%) exceeded that of female customers. Most individuals in this segment fell within the age range of 31 to 40 years, with a majority being of Asian descent and residing in Europe. The customers in this segment had significant experience with over 5 years of shopping with online retailers. Typically, these customers spent 1-3 hours per week browsing online stores and made purchases from these retailers 1-2 times per month. Amazon was the most frequently used online store among this segment with a relative frequency of 38.8%; it was the preferred platform for 95.6% of customers in this group. Furthermore, the majority of customers in this segment accessed online stores using their mobile phones. In this cluster, utilitarian motives were more prominent, while hedonic motives were less significant compared to the other two segments. Regarding the online shopping behaviors that influenced this segment, the following behaviors were notable: online shopping intention (4.226), online search intention (3.999), online consumer engagement (2.754), and online impulse buying (2.299). It is important to highlight that online shopping intention with a score of 4.226 had the most substantial impact on this segment compared to the other groups. This cluster was comprised of men, middle-aged individuals, and employees with a monthly income between $2,000 and $5,000, who primarily engaged in online shopping driven by utilitarian motives. They viewed their purchases as practical necessities and allocated specific time for shopping. The name “Professional Pragmatist” reflected their meticulous, planned approach to online shopping. Profile of the Third Segment: Hedonistic Explorers This segment was the smallest among online retail customers. Within this group, the proportion of women (58.7%) was higher than that of men. Most customers were aged between 18 and 30 years, predominantly of Asian descent with many residing in Oceania. Nearly all customers in this segment had over 5 years of experience shopping with online retailers. Typically, they spent 1-3 hours per week browsing online stores and made purchases from these retailers once or twice a month. The online platforms most frequently used by this segment were Amazon and Alibaba (27.5%), with Alibaba being the most popular choice at 56.9%. Most customers in this segment accessed online stores primarily through mobile phones. In this cluster, utilitarian motives were more prominent than hedonic motives although the overall score for utilitarian motives was lower than those of the other two segments. Conversely, hedonic motives scored higher in this cluster compared to the others. Overall, online shopping behaviors were particularly pronounced among this segment. Customers in this group were primarily female, students, and of Asian descent with lower incomes. Their extensive online shopping experience combined with hedonic motivations encouraged them to seek new and satisfying experiences during their shopping journeys. Therefore, the name "Hedonistic Explorer" was chosen for this cluster, reflecting their blend of exploratory behavior and hedonistic motivations. Discussion of Results & Conclusion Online retail managers and marketers should prioritize understanding customers' online shopping motivations and behaviors as crucial elements of their marketing strategies. Factors like convenience of shopping, access to a diverse range of products, ability to compare prices, and opportunity to take advantage of special discounts significantly shape customers' online shopping behavior and influence their decision-making processes. Therefore, it is recommended that online retailers tailor their programs to align with the characteristics of different customer segments, thereby providing an easy, secure, and appealing shopping experience. To enhance outcomes and offer more effective recommendations, this study referenced previous research on customers' online shopping motivations and behaviors. It is important to acknowledge that all research had its limitations. The neural network-based segmentation approach employed in this study required a large sample size to achieve valid and reliable results, particularly given the global diversity of the statistical population. The vast range of customers worldwide complicated data collection, making it a time-consuming process that presented numerous challenges and significantly extended the duration of the research. Additionally, the lengthy nature of completing the research questionnaire coupled with the inclusion of sensitive questions, such as nationality and country of residence, may have led to reluctance among respondents to participate fully. The extensive number of demographic questions, which were necessary for identifying specific online customer behaviors, may have also caused some participants to lose focus while completing the questionnaire.

https://nmrj.ui.ac.ir/article_29757_1cf6a3561ba80787dd2d6e7eb3c3c20f.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Meta-Synthesis of Service Recovery Studies with a Cultural Approach Meta-Synthesis of Service Recovery Studies with a Cultural Approach 27 48 29782 10.22108/nmrj.2025.145140.3186 FA Shahriar Azizi Associate professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran Shahram Ferdosi Ph.D. graduate, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran Journal Article 2025 05 30 This study aimed to address the following questions: What are the characteristics of the service recovery model within the framework of cultural components? What mechanisms underpin service recovery in relation to these components? What are the relationships among the factors constituting service recovery within this framework? To achieve these objectives, a meta-synthesis method was employed to review and analyze the existing research literature. This method facilitated the identification and formulation of concepts that offered a more comprehensive understanding of the phenomenon under investigation. The steps of this method included defining research questions, systematically reviewing the literature, searching for and selecting relevant articles, extracting information, and analyzing and integrating qualitative findings. The resulting model from this holistic approach to service recovery comprised 42 codes, 15 concepts, and 5 categories. Unlike previous studies, this research moved beyond simply affirming that cultural components influence service recovery—a conclusion that is often taken for granted. What made this research innovative was its comprehensive model that elucidated the relationship between culture and service recovery, providing greater clarity on how these two concepts were interconnected. Introduction Service failure and subsequent recovery are foundational pillars in contemporary marketing research. The dynamic interplay between these two phenomena significantly influences customer satisfaction, loyalty, and, ultimately, an organization's long-term viability. In an increasingly competitive global marketplace, the ability to effectively address service failures is not merely a reactive measure but a proactive strategy for relationship building and brand enhancement. Despite the widely acknowledged importance of service recovery in transforming dissatisfied customers into loyal advocates, a persistent gap remains: many organizations consistently fail to meet customer expectations during the critical complaint resolution process. This research aimed to systematically address this gap by exploring how cultural elements intricately shape and influence service recovery processes. Our objective extended beyond merely recognizing the presence of cultural influence; we aimed to provide a comprehensive, integrated, and actionable model of culturally informed service recovery through a robust meta-synthesis approach. By meticulously synthesizing qualitative studies, we offered nuanced insights into how specific cultural values, dimensions, and societal norms affect customers' recovery expectations, their perceptions of justice, their evaluations of organizational responses, and ultimately, the effectiveness of recovery outcomes. This meta-synthesis sought to bridge a significant theoretical divide, transforming disparate findings into a cohesive framework that highlighted the mediating and moderating roles of culture at various stages of the service recovery journey. Our work not only enhances theoretical understanding, but also provides practical guidance for organizations operating in diverse, multicultural environments. Materials & Methods To achieve a comprehensive understanding of the cultural nuances in service recovery, we employed a qualitative meta-synthesis method. This approach was particularly well-suited to our research question as it allowed for the systematic review, aggregation, and interpretation of findings from multiple qualitative studies. By transcending the limitations of individual studies, this method generated new theoretical insights and fostered a more holistic understanding of the phenomenon. Unlike quantitative meta-analysis, which aggregated numerical data, qualitative meta-synthesis focused on synthesizing conceptual findings, themes, and interpretations from primary qualitative research. This method enabled the identification of overarching patterns, discrepancies, and deeper meanings embedded within diverse qualitative datasets, providing a richer and more nuanced understanding of the complex relationship between culture and service recovery. Our methodology adhered strictly to the rigorous, widely recognized, and iterative seven-step framework for qualitative meta-synthesis proposed by Sandelowski and Barroso (2007). This structured approach ensured methodological transparency, rigor, and reproducibility, thereby enhancing the credibility and trustworthiness of our synthesized findings. Each step was executed with meticulous attention to detail and outlined as forming research questions, conducting a systematic review, screening abstracts, selecting relevant studies, extracting information, analyzing and synthesizing qualitative findings, and quality control. Research Findings Previous research indicated that culture in the service recovery literature could be categorized into two main types: employee/organizational culture and customer culture. Customer culture was often analyzed using Hofstede’s model, which focused on dimensions, such as power distance, uncertainty avoidance, individualism versus collectivism, and masculinity versus femininity. Some studies also examined organizational culture, highlighting aspects ike commitment to quality, compassion, empathy, and responsibility. Employee culture had been reviewed from a national perspective, again utilizing Hofstede’s framework. The influence of culture played a moderating role in service recovery research with many studies identifying culture as a significant factor affecting the relationship between service recovery and customer satisfaction. Additionally, culture shaped customers’ pre-recovery expectations, which were influenced by advertising, past experiences, and social recommendations. Both organizational and employee cultures significantly impacted the recovery process; organizations that emphasized empathy and responsibility typically experienced better outcomes. Research indicated that the service recovery process encompassed both financial actions (e.g., discounts, compensation) and non-financial actions (e.g., apologies, expressions of empathy). Cultural factors shaped preferences for recovery approaches and the effectiveness of employee responses. Furthermore, after the recovery process, cultural elements continued to moderate customer evaluations and satisfaction, influencing outcomes, such as loyalty and word-of-mouth recommendations. Thus, cultural factors impacted expectations before recovery, behaviors during the process, and reactions to the outcomes, highlighting why service recovery could not be regarded as universally effective across different cultural contexts. Discussion of Results & Conclusion Meta-synthesis studies serve various purposes and this research specifically examined the culturally related components of service failure recovery. The selected articles aimed to introduce new themes and dimensions concerning service recovery elements from a cultural perspective. A review of previous studies indicated that cultural factors in the service recovery process, as well as the utilization of cultural differences to optimize and enhance recovery strategies, had not received adequate attention. It was essential for service staff to continuously revise and improve their service delivery processes, taking into account the evolving needs and expectations of their customers. Additionally, organizations had to establish infrastructures that not only provided flawless and accurate services initially, but also facilitated quick and efficient recovery in the event of a service failure. Speed in service recovery was regarded as a critical factor from the consumer's perspective. This study aimed to address the following questions: What are the characteristics of the service recovery model within the framework of cultural components? What mechanisms underpin service recovery in relation to these components? What are the relationships among the factors constituting service recovery within this framework? To achieve these objectives, a meta-synthesis method was employed to review and analyze the existing research literature. This method facilitated the identification and formulation of concepts that offered a more comprehensive understanding of the phenomenon under investigation. The steps of this method included defining research questions, systematically reviewing the literature, searching for and selecting relevant articles, extracting information, and analyzing and integrating qualitative findings. The resulting model from this holistic approach to service recovery comprised 42 codes, 15 concepts, and 5 categories. Unlike previous studies, this research moved beyond simply affirming that cultural components influence service recovery—a conclusion that is often taken for granted. What made this research innovative was its comprehensive model that elucidated the relationship between culture and service recovery, providing greater clarity on how these two concepts were interconnected. Introduction Service failure and subsequent recovery are foundational pillars in contemporary marketing research. The dynamic interplay between these two phenomena significantly influences customer satisfaction, loyalty, and, ultimately, an organization's long-term viability. In an increasingly competitive global marketplace, the ability to effectively address service failures is not merely a reactive measure but a proactive strategy for relationship building and brand enhancement. Despite the widely acknowledged importance of service recovery in transforming dissatisfied customers into loyal advocates, a persistent gap remains: many organizations consistently fail to meet customer expectations during the critical complaint resolution process. This research aimed to systematically address this gap by exploring how cultural elements intricately shape and influence service recovery processes. Our objective extended beyond merely recognizing the presence of cultural influence; we aimed to provide a comprehensive, integrated, and actionable model of culturally informed service recovery through a robust meta-synthesis approach. By meticulously synthesizing qualitative studies, we offered nuanced insights into how specific cultural values, dimensions, and societal norms affect customers' recovery expectations, their perceptions of justice, their evaluations of organizational responses, and ultimately, the effectiveness of recovery outcomes. This meta-synthesis sought to bridge a significant theoretical divide, transforming disparate findings into a cohesive framework that highlighted the mediating and moderating roles of culture at various stages of the service recovery journey. Our work not only enhances theoretical understanding, but also provides practical guidance for organizations operating in diverse, multicultural environments. Materials & Methods To achieve a comprehensive understanding of the cultural nuances in service recovery, we employed a qualitative meta-synthesis method. This approach was particularly well-suited to our research question as it allowed for the systematic review, aggregation, and interpretation of findings from multiple qualitative studies. By transcending the limitations of individual studies, this method generated new theoretical insights and fostered a more holistic understanding of the phenomenon. Unlike quantitative meta-analysis, which aggregated numerical data, qualitative meta-synthesis focused on synthesizing conceptual findings, themes, and interpretations from primary qualitative research. This method enabled the identification of overarching patterns, discrepancies, and deeper meanings embedded within diverse qualitative datasets, providing a richer and more nuanced understanding of the complex relationship between culture and service recovery. Our methodology adhered strictly to the rigorous, widely recognized, and iterative seven-step framework for qualitative meta-synthesis proposed by Sandelowski and Barroso (2007). This structured approach ensured methodological transparency, rigor, and reproducibility, thereby enhancing the credibility and trustworthiness of our synthesized findings. Each step was executed with meticulous attention to detail and outlined as forming research questions, conducting a systematic review, screening abstracts, selecting relevant studies, extracting information, analyzing and synthesizing qualitative findings, and quality control. Research Findings Previous research indicated that culture in the service recovery literature could be categorized into two main types: employee/organizational culture and customer culture. Customer culture was often analyzed using Hofstede’s model, which focused on dimensions, such as power distance, uncertainty avoidance, individualism versus collectivism, and masculinity versus femininity. Some studies also examined organizational culture, highlighting aspects ike commitment to quality, compassion, empathy, and responsibility. Employee culture had been reviewed from a national perspective, again utilizing Hofstede’s framework. The influence of culture played a moderating role in service recovery research with many studies identifying culture as a significant factor affecting the relationship between service recovery and customer satisfaction. Additionally, culture shaped customers’ pre-recovery expectations, which were influenced by advertising, past experiences, and social recommendations. Both organizational and employee cultures significantly impacted the recovery process; organizations that emphasized empathy and responsibility typically experienced better outcomes. Research indicated that the service recovery process encompassed both financial actions (e.g., discounts, compensation) and non-financial actions (e.g., apologies, expressions of empathy). Cultural factors shaped preferences for recovery approaches and the effectiveness of employee responses. Furthermore, after the recovery process, cultural elements continued to moderate customer evaluations and satisfaction, influencing outcomes, such as loyalty and word-of-mouth recommendations. Thus, cultural factors impacted expectations before recovery, behaviors during the process, and reactions to the outcomes, highlighting why service recovery could not be regarded as universally effective across different cultural contexts. Discussion of Results & Conclusion Meta-synthesis studies serve various purposes and this research specifically examined the culturally related components of service failure recovery. The selected articles aimed to introduce new themes and dimensions concerning service recovery elements from a cultural perspective. A review of previous studies indicated that cultural factors in the service recovery process, as well as the utilization of cultural differences to optimize and enhance recovery strategies, had not received adequate attention. It was essential for service staff to continuously revise and improve their service delivery processes, taking into account the evolving needs and expectations of their customers. Additionally, organizations had to establish infrastructures that not only provided flawless and accurate services initially, but also facilitated quick and efficient recovery in the event of a service failure. Speed in service recovery was regarded as a critical factor from the consumer's perspective.

https://nmrj.ui.ac.ir/article_29782_88f5f1204b76fa0531fcf047d60a3b5c.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Developing a Demarketing Model to Improve Environmental Sustainability Behaviors in the Electricity Company Developing a Demarketing Model to Improve Environmental Sustainability Behaviors in the Electricity Company 49 76 29904 10.22108/nmrj.2025.146044.3222 FA Abdullah Saedi Assistant professor, Department of Management, Faculty of Management and Economics, Lorestan University, Khorramabad Branch, Lorestan, Iran Journal Article 2025 07 20 Demarketing serves as a strategic approach to managing demand for overused products and has the potential to encourage customers to adopt more responsible patterns and behaviors aligned with environmental sustainability. This study aimed to develop an anti-growth marketing model designed to enhance environmentally sustainable behaviors. It employed a qualitative methodology within an inductive paradigm characterized as descriptive-survey and fundamental in nature. The statistical population of the study comprised 16 experts, including university professors and electricity company managers selected by using purposive sampling based on the principle of theoretical sufficiency. Data collection was conducted through content analysis with semi-structured interviews designed to elicit the experts' views and opinions on the phenomenon of Demarketing. The gathered data were subsequently analyzed using MAXQDA software. To ensure the validity and reliability of the data collection instruments, content validity, as well as inter-coder and intra-coder reliability, was assessed. The findings revealed that the research model encompassed effective factors, such as legal requirements, stakeholder interests, and unstable procedures, strategies, including reverse marketing mix, supply and inventory management, and highlighting negative information, and consequences at various levels: individual (awareness, support for sustainable products, and active participation), organizational (strategic positioning, combating greenwashing, and enhancing brand image), and environmental (improving public health, preserving biodiversity, and mitigating climate change). Introduction A closer examination of the lives of humans and other creatures reveals just how fundamentally their survival depends on the environment. Clean water, fresh air, and sufficient oxygen are essential needs provided by the resources available in nature (Carvalhais et al, 2025). Unfortunately, human activities have led to significant environmental degradation, posing a threat greater than that to any other species. This crisis of environmental destruction will inevitably endanger all forms of life (Wang et al, 2025). Thus, protecting and preserving this invaluable asset—the environment—is a responsibility shared by all inhabitants of the Earth. Imam Ali (AS) stated, “Man is never allowed to consider himself free, unbridled, and detached from nature; he has no right to seek comfort through the pollution and destruction of nature.” This hadith underscores the importance of environmental stewardship, a value recognized in both Islamic teachings and ancient philosophies (Valizadeh & Nojomi, 2017). In recent decades, concepts like green human resources, green marketing, the Internet of Energy, and green supply chains have gained prominence in management and organizational literature aimed at fostering responsible and sustainable behaviors that mitigate negative environmental impacts (Kahia et al, 2024). Conversely, another marketing strategy that organizations employ in response to environmental concerns is anti-growth marketing. This approach is adopted for various reasons, including the reduction of pollution, curtailing excessive consumption, managing resource limitations, and maintaining brand image (Yoon et al, 2024). Anti-growth marketing stands in stark contrast to traditional marketing, which primarily seeks to increase demand and consumption. This strategy intentionally reduces demand for various reasons, including promoting environmentally sustainable behaviors, curbing excessive resource consumption, and ensuring compliance with laws and regulations (Ahmad & Guzmán, 2021). Demarketing can be viewed as the intentional rejection of customers whose values do not align with those of the organization (Hall & Wood, 2021). The urgency to protect and preserve the environment is more pronounced than ever, despite advancements in science and technology. Issues like population growth, uncontrolled development, environmental degradation, and pollution—along with the repercussions of industrialization and consumerism—have significantly heightened environmental challenges and concerns (Kumar & Kumar, 2023). Consequently, the optimal use of resources and the reduction of waste have become essential principles for both governments and organizations. Achieving a balance between human needs and environmental protection through strategies like market reduction ensures that our efforts to fulfill those needs do not compromise environmental quality, thereby maintaining ecosystem sustainability for future generations (Salem & Al-Ethaw, 2023). Materials & Methods This research employed a qualitative approach within an inductive paradigm characterized as descriptive-survey and fundamental in purpose. The statistical population comprised 16 experts, including university professors and electricity company managers, who were selected based on the principle of theoretical adequacy through purposive sampling. Data collection utilized a content analysis method. After reviewing relevant texts, semi-structured interview questions were developed to elicit the experts' views and opinions on the phenomenon of anti-growth marketing. The collected data were subsequently analyzed using MAXQDA software. To ensure the validity and reliability of the data collection instruments, both content validity and inter- and intra-coder reliability assessments were conducted. Research Findings The study successfully developed an anti-growth marketing model aimed at enhancing environmental sustainability behaviors. Through qualitative analysis involving 16 experts from academia and the electricity sector, the findings highlighted several key components: Effective Factors Legal Requirements: Government policies and regulations played a crucial role in stimulating anti-growth marketing practices, particularly in controlling pollutant emissions. Stakeholder Interests: Engaging various stakeholders could create a supportive environment for sustainable practices. Unstable Procedures: Factors like natural disasters and political instability could significantly impact demand and supply chains. Strategies Reverse Marketing Mix: Adjusting traditional marketing strategies to emphasize sustainability Supply and Inventory Management: Efficiently managing resources to minimize waste and overconsumption Highlighting Negative Information: Educating consumers about the environmental impacts of excessive consumption Consequences Individual Level: Increased awareness, support for sustainable products, and enhanced cooperation among individuals Organizational Level: Improved strategic positioning, reduced greenwashing, and a stronger brand image Environmental Level: Positive impacts, including enhanced public health, biodiversity preservation, and reduced climate change effects The findings indicated that adopting Demarketing strategies could effectively promote responsible consumption behaviors, ultimately contributing to sustainable development and mitigating the adverse effects of climate change. Discussion of Results & Conclusion Promoting sustainable and responsible environmental behaviors is crucial for reducing pollution and fostering a healthier environment for future generations. Environmental sustainability is not only vital for human survival and well-being, but also plays a significant role in mental health, as well as economic and social development. The global perspective on environmental sustainability is evolving, with sustainability defined as meeting current human needs without jeopardizing the ability of future generations to meet their own. Adopting strategies like Demarketing to encourage responsible behaviors can significantly contribute to sustainable development and mitigate the adverse effects of climate change. By enhancing awareness, changing habits, and establishing appropriate infrastructure, we can work towards achieving this essential goal of a sustainable environment. This study aimed to develop an anti-growth marketing model to promote environmentally sustainable behaviors. The research model informed by data analysis encompassed effective factors, strategies, and consequences at individual, organizational, and environmental levels as illustrated in Figure 1. The findings indicated that legal requirements could serve as a powerful catalyst for anti-growth marketing. Governments can support environmental organizations by enforcing strict policies and regulations concerning pollutant emissions and other environmental issues. In conjunction with these legal requirements, organizations can utilize anti-growth marketing to enhance customer awareness about excessive consumption and environmental risks, thereby effectively reducing demand. The research conducted by Salem et al. (2021) further supports the notion that government regulations are instrumental in implementing anti-growth marketing strategies. Additionally, factors like scarcity, natural disasters, political changes, wars, and global pandemics can disrupt supply chains, leading to customer loss and damage to brand reputation. Demarketing serves as a strategic approach to managing demand for overused products and has the potential to encourage customers to adopt more responsible patterns and behaviors aligned with environmental sustainability. This study aimed to develop an anti-growth marketing model designed to enhance environmentally sustainable behaviors. It employed a qualitative methodology within an inductive paradigm characterized as descriptive-survey and fundamental in nature. The statistical population of the study comprised 16 experts, including university professors and electricity company managers selected by using purposive sampling based on the principle of theoretical sufficiency. Data collection was conducted through content analysis with semi-structured interviews designed to elicit the experts' views and opinions on the phenomenon of Demarketing. The gathered data were subsequently analyzed using MAXQDA software. To ensure the validity and reliability of the data collection instruments, content validity, as well as inter-coder and intra-coder reliability, was assessed. The findings revealed that the research model encompassed effective factors, such as legal requirements, stakeholder interests, and unstable procedures, strategies, including reverse marketing mix, supply and inventory management, and highlighting negative information, and consequences at various levels: individual (awareness, support for sustainable products, and active participation), organizational (strategic positioning, combating greenwashing, and enhancing brand image), and environmental (improving public health, preserving biodiversity, and mitigating climate change). Introduction A closer examination of the lives of humans and other creatures reveals just how fundamentally their survival depends on the environment. Clean water, fresh air, and sufficient oxygen are essential needs provided by the resources available in nature (Carvalhais et al, 2025). Unfortunately, human activities have led to significant environmental degradation, posing a threat greater than that to any other species. This crisis of environmental destruction will inevitably endanger all forms of life (Wang et al, 2025). Thus, protecting and preserving this invaluable asset—the environment—is a responsibility shared by all inhabitants of the Earth. Imam Ali (AS) stated, “Man is never allowed to consider himself free, unbridled, and detached from nature; he has no right to seek comfort through the pollution and destruction of nature.” This hadith underscores the importance of environmental stewardship, a value recognized in both Islamic teachings and ancient philosophies (Valizadeh & Nojomi, 2017). In recent decades, concepts like green human resources, green marketing, the Internet of Energy, and green supply chains have gained prominence in management and organizational literature aimed at fostering responsible and sustainable behaviors that mitigate negative environmental impacts (Kahia et al, 2024). Conversely, another marketing strategy that organizations employ in response to environmental concerns is anti-growth marketing. This approach is adopted for various reasons, including the reduction of pollution, curtailing excessive consumption, managing resource limitations, and maintaining brand image (Yoon et al, 2024). Anti-growth marketing stands in stark contrast to traditional marketing, which primarily seeks to increase demand and consumption. This strategy intentionally reduces demand for various reasons, including promoting environmentally sustainable behaviors, curbing excessive resource consumption, and ensuring compliance with laws and regulations (Ahmad & Guzmán, 2021). Demarketing can be viewed as the intentional rejection of customers whose values do not align with those of the organization (Hall & Wood, 2021). The urgency to protect and preserve the environment is more pronounced than ever, despite advancements in science and technology. Issues like population growth, uncontrolled development, environmental degradation, and pollution—along with the repercussions of industrialization and consumerism—have significantly heightened environmental challenges and concerns (Kumar & Kumar, 2023). Consequently, the optimal use of resources and the reduction of waste have become essential principles for both governments and organizations. Achieving a balance between human needs and environmental protection through strategies like market reduction ensures that our efforts to fulfill those needs do not compromise environmental quality, thereby maintaining ecosystem sustainability for future generations (Salem & Al-Ethaw, 2023). Materials & Methods This research employed a qualitative approach within an inductive paradigm characterized as descriptive-survey and fundamental in purpose. The statistical population comprised 16 experts, including university professors and electricity company managers, who were selected based on the principle of theoretical adequacy through purposive sampling. Data collection utilized a content analysis method. After reviewing relevant texts, semi-structured interview questions were developed to elicit the experts' views and opinions on the phenomenon of anti-growth marketing. The collected data were subsequently analyzed using MAXQDA software. To ensure the validity and reliability of the data collection instruments, both content validity and inter- and intra-coder reliability assessments were conducted. Research Findings The study successfully developed an anti-growth marketing model aimed at enhancing environmental sustainability behaviors. Through qualitative analysis involving 16 experts from academia and the electricity sector, the findings highlighted several key components: Effective Factors Legal Requirements: Government policies and regulations played a crucial role in stimulating anti-growth marketing practices, particularly in controlling pollutant emissions. Stakeholder Interests: Engaging various stakeholders could create a supportive environment for sustainable practices. Unstable Procedures: Factors like natural disasters and political instability could significantly impact demand and supply chains. Strategies Reverse Marketing Mix: Adjusting traditional marketing strategies to emphasize sustainability Supply and Inventory Management: Efficiently managing resources to minimize waste and overconsumption Highlighting Negative Information: Educating consumers about the environmental impacts of excessive consumption Consequences Individual Level: Increased awareness, support for sustainable products, and enhanced cooperation among individuals Organizational Level: Improved strategic positioning, reduced greenwashing, and a stronger brand image Environmental Level: Positive impacts, including enhanced public health, biodiversity preservation, and reduced climate change effects The findings indicated that adopting Demarketing strategies could effectively promote responsible consumption behaviors, ultimately contributing to sustainable development and mitigating the adverse effects of climate change. Discussion of Results & Conclusion Promoting sustainable and responsible environmental behaviors is crucial for reducing pollution and fostering a healthier environment for future generations. Environmental sustainability is not only vital for human survival and well-being, but also plays a significant role in mental health, as well as economic and social development. The global perspective on environmental sustainability is evolving, with sustainability defined as meeting current human needs without jeopardizing the ability of future generations to meet their own. Adopting strategies like Demarketing to encourage responsible behaviors can significantly contribute to sustainable development and mitigate the adverse effects of climate change. By enhancing awareness, changing habits, and establishing appropriate infrastructure, we can work towards achieving this essential goal of a sustainable environment. This study aimed to develop an anti-growth marketing model to promote environmentally sustainable behaviors. The research model informed by data analysis encompassed effective factors, strategies, and consequences at individual, organizational, and environmental levels as illustrated in Figure 1. The findings indicated that legal requirements could serve as a powerful catalyst for anti-growth marketing. Governments can support environmental organizations by enforcing strict policies and regulations concerning pollutant emissions and other environmental issues. In conjunction with these legal requirements, organizations can utilize anti-growth marketing to enhance customer awareness about excessive consumption and environmental risks, thereby effectively reducing demand. The research conducted by Salem et al. (2021) further supports the notion that government regulations are instrumental in implementing anti-growth marketing strategies. Additionally, factors like scarcity, natural disasters, political changes, wars, and global pandemics can disrupt supply chains, leading to customer loss and damage to brand reputation.

https://nmrj.ui.ac.ir/article_29904_c353c7cf9c9a1f6ca6ac4c443aa4f82d.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Developing a Model for Formulating Sustainable Marketing Strategies in the Iranian Food Industry (Case Study: Protein Product Manufacturers) Developing a Model for Formulating Sustainable Marketing Strategies in the Iranian Food Industry (Case Study: Protein Product Manufacturers) 77 104 29941 10.22108/nmrj.2025.145879.3212 FA Raheleh Jalalniya Ph.D. student in Business Policy Management, Department of Business Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran Mohsen Akbari Professor, Department of Business Administration, Faculty of Management and Economics, University of Guilan, Rasht, Iran Journal Article 2025 07 08 In response to global shifts towards sustainable development and the mounting environmental, social, and institutional pressures on the food industry, sustainable marketing has emerged as a strategic approach to meet stakeholder expectations and enhance corporate competitiveness. This study aimed to design and present a comprehensive model for formulating sustainable marketing strategies within Iran’s food industry. Methodologically, the research was classified as a non-experimental, descriptive study, employing a cross-sectional survey design. The participant community comprised theoretical experts (university professors in marketing management) and practical experts (senior managers from protein product manufacturing companies). Participants were purposefully selected through theoretical sampling based on the criteria like expertise, professional reputation, specialized knowledge, diversity of perspectives, and motivation to participate. A total of 15 experts contributed to the study. Data collection was conducted through semi-structured interviews and a fuzzy Delphi questionnaire. Qualitative data were analyzed using grounded theory, while quantitative data were assessed with the fuzzy Delphi technique. In the qualitative phase, data validity was confirmed based on the criteria of credibility, transferability, confirmability, and dependability with expert verification. The reliability of the qualitative analysis evaluated by using the Holsti method yielded an agreement coefficient of 0.802, indicating satisfactory analytical reliability. The developed model identified causal conditions—such as transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing—that influenced the core phenomenon (sustainable marketing strategy). This phenomenon, along with contextual conditions (government digitalization, facilitation of smart governance, macro environment, and market) and intervening conditions (structural and regulatory challenges in the sustainable marketing support system), impacted strategies and actions (operational sustainable marketing strategies). Ultimately, these strategies led to economic, social, cultural, and environmental outcomes. The proposed model offered a comprehensive representation of the factors and internal mechanisms driving the development of sustainable marketing in the food industry of Iran. Introduction In today’s world, sustainability has emerged as the prevailing paradigm influencing policymaking, managerial decision-making, and strategic orientations. Environmental pressures, challenges related to food security, shifting consumption patterns, limited natural resources, and growing social inequalities have increasingly underscored the necessity to rethink traditional economic approaches (Kaur et al., 2025). In this context, sustainable marketing has become a pivotal driver of business transformation, integrating economic benefits, social responsibility, and environmental protection into a cohesive strategic framework. This approach transcends mere sales and market expansion, aiming instead to enhance shared value among companies, consumers, society, and the environment (Aung et al., 2025). The primary objective of this research was to develop a comprehensive model for formulating sustainable marketing strategies in Iran’s food industry. This model identified the key factors that influenced the success of such strategies and provided a coherent framework for managers to achieve economic goals while fulfilling social and environmental responsibilities. The proposed model encompassed dimensions, such as green branding, environmental innovation, resource management, green marketing information systems, and adaptation to local Iranian conditions, offering practical strategies for implementation. The findings were anticipated to address theoretical gaps in the field of sustainable marketing from a localized perspective and serve as an effective tool for food industry managers and policymakers to refine marketing strategies and evaluate the sustainability performance of their companies. Materials & Methods This study was an applied-developmental research endeavor aimed at presenting a model for formulating sustainable marketing strategies in Iran’s food industry. Methodologically, it was classified as a non-experimental (descriptive) study conducted through a cross-sectional survey approach. The qualitative research community comprised both theoretical experts (professors of marketing management) and practical experts (managers from protein product manufacturing companies). Participants were selected based on 5 criteria: key expertise, professional reputation, theoretical knowledge, diversity of perspectives, and motivation to engage. Data were collected through semi-structured interviews and a fuzzy Delphi questionnaire. For data analysis, the grounded theory method was utilized alongside MAXQDA software. Subsequently, the identified indicators were screened and validated using the fuzzy Delphi method and MATLAB software. Research Findings In the qualitative phase of this research, a total of 15 participants were involved, comprising 5 university professors and 10 experts from Iran’s food industry. The gender distribution included 10 male and 5 female participants. Age-wise, 1 participant was under 45 years old, 8 were between 46 and 55, and 6 were over 56. In terms of educational background, 2 participants held master’s degrees and 13 ones possessed Ph.D. degrees. Regarding professional experience, 6 and 9 participants had between 10 and 15 and over 16 years of experience, respectively. The grounded theory analysis revealed the following key findings: Causal Conditions: These included transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing. Contextual Conditions: These comprised government digitalization and facilitation of smart governance, as well as the macro environment and market dynamics. Core Phenomenon: The sustainable marketing strategy was identified as the central theme. Intervening Conditions: Structural and regulatory challenges within the sustainable marketing support system were noted as intervening factors. Strategies and Actions: These were represented by operational sustainable marketing strategies. Outcomes: The outcomes included economic and marketing results, social and cultural impacts, and environmental benefits. Subsequently, the research indicators were validated using the fuzzy Delphi method. The results indicated that the difference in mean scores between two consecutive rounds was less than 0.2 for all indicators. This finding demonstrated the relative stability of expert opinions and a sufficient level of consensus in their evaluations. Therefore, it could be confidently stated that the conditions for concluding the Delphi rounds were met, finalizing the Delphi process at this stage. Discussion of Results & Conclusion This study aimed to develop a model for formulating sustainable marketing strategies within Iran’s food industry, specifically focusing on protein product manufacturers. The proposed model identified causal conditions—such as transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing—that directly influenced the core phenomenon: sustainable marketing strategy. Furthermore, the findings indicated that the core phenomenon in conjunction with contextual conditions—including government digitalization, facilitation of smart governance, and the macro environment—and intervening conditions, such as structural and regulatory challenges in the sustainable marketing support system, significantly impacted strategies and actions, specifically operational sustainable marketing strategies. Ultimately, the results demonstrated that the strategies and actions formulated within this sustainable marketing framework directly led to economic and marketing outcomes, as well as social, cultural, and environmental benefits. Based on these findings, it is recommended that companies prioritize circular and sustainable economy practices by minimizing waste, adopting green financing, and optimizing marketing costs through digital media and smart packaging. Marketing messages should reflect cultural values and foster a sense of social belonging. Additionally, employing experiential marketing, ensuring informational transparency, and obtaining environmental certifications can strengthen consumer trust and encourage sustainable purchasing behaviors. Conducting environmental cost-benefit analyses and aligning production processes with global green standards can also facilitate entry into export markets. Furthermore, developing environmental standards and enhancing regional collaborations can improve the position of Iran’s food industry in sustainable exports. In response to global shifts towards sustainable development and the mounting environmental, social, and institutional pressures on the food industry, sustainable marketing has emerged as a strategic approach to meet stakeholder expectations and enhance corporate competitiveness. This study aimed to design and present a comprehensive model for formulating sustainable marketing strategies within Iran’s food industry. Methodologically, the research was classified as a non-experimental, descriptive study, employing a cross-sectional survey design. The participant community comprised theoretical experts (university professors in marketing management) and practical experts (senior managers from protein product manufacturing companies). Participants were purposefully selected through theoretical sampling based on the criteria like expertise, professional reputation, specialized knowledge, diversity of perspectives, and motivation to participate. A total of 15 experts contributed to the study. Data collection was conducted through semi-structured interviews and a fuzzy Delphi questionnaire. Qualitative data were analyzed using grounded theory, while quantitative data were assessed with the fuzzy Delphi technique. In the qualitative phase, data validity was confirmed based on the criteria of credibility, transferability, confirmability, and dependability with expert verification. The reliability of the qualitative analysis evaluated by using the Holsti method yielded an agreement coefficient of 0.802, indicating satisfactory analytical reliability. The developed model identified causal conditions—such as transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing—that influenced the core phenomenon (sustainable marketing strategy). This phenomenon, along with contextual conditions (government digitalization, facilitation of smart governance, macro environment, and market) and intervening conditions (structural and regulatory challenges in the sustainable marketing support system), impacted strategies and actions (operational sustainable marketing strategies). Ultimately, these strategies led to economic, social, cultural, and environmental outcomes. The proposed model offered a comprehensive representation of the factors and internal mechanisms driving the development of sustainable marketing in the food industry of Iran. Introduction In today’s world, sustainability has emerged as the prevailing paradigm influencing policymaking, managerial decision-making, and strategic orientations. Environmental pressures, challenges related to food security, shifting consumption patterns, limited natural resources, and growing social inequalities have increasingly underscored the necessity to rethink traditional economic approaches (Kaur et al., 2025). In this context, sustainable marketing has become a pivotal driver of business transformation, integrating economic benefits, social responsibility, and environmental protection into a cohesive strategic framework. This approach transcends mere sales and market expansion, aiming instead to enhance shared value among companies, consumers, society, and the environment (Aung et al., 2025). The primary objective of this research was to develop a comprehensive model for formulating sustainable marketing strategies in Iran’s food industry. This model identified the key factors that influenced the success of such strategies and provided a coherent framework for managers to achieve economic goals while fulfilling social and environmental responsibilities. The proposed model encompassed dimensions, such as green branding, environmental innovation, resource management, green marketing information systems, and adaptation to local Iranian conditions, offering practical strategies for implementation. The findings were anticipated to address theoretical gaps in the field of sustainable marketing from a localized perspective and serve as an effective tool for food industry managers and policymakers to refine marketing strategies and evaluate the sustainability performance of their companies. Materials & Methods This study was an applied-developmental research endeavor aimed at presenting a model for formulating sustainable marketing strategies in Iran’s food industry. Methodologically, it was classified as a non-experimental (descriptive) study conducted through a cross-sectional survey approach. The qualitative research community comprised both theoretical experts (professors of marketing management) and practical experts (managers from protein product manufacturing companies). Participants were selected based on 5 criteria: key expertise, professional reputation, theoretical knowledge, diversity of perspectives, and motivation to engage. Data were collected through semi-structured interviews and a fuzzy Delphi questionnaire. For data analysis, the grounded theory method was utilized alongside MAXQDA software. Subsequently, the identified indicators were screened and validated using the fuzzy Delphi method and MATLAB software. Research Findings In the qualitative phase of this research, a total of 15 participants were involved, comprising 5 university professors and 10 experts from Iran’s food industry. The gender distribution included 10 male and 5 female participants. Age-wise, 1 participant was under 45 years old, 8 were between 46 and 55, and 6 were over 56. In terms of educational background, 2 participants held master’s degrees and 13 ones possessed Ph.D. degrees. Regarding professional experience, 6 and 9 participants had between 10 and 15 and over 16 years of experience, respectively. The grounded theory analysis revealed the following key findings: Causal Conditions: These included transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing. Contextual Conditions: These comprised government digitalization and facilitation of smart governance, as well as the macro environment and market dynamics. Core Phenomenon: The sustainable marketing strategy was identified as the central theme. Intervening Conditions: Structural and regulatory challenges within the sustainable marketing support system were noted as intervening factors. Strategies and Actions: These were represented by operational sustainable marketing strategies. Outcomes: The outcomes included economic and marketing results, social and cultural impacts, and environmental benefits. Subsequently, the research indicators were validated using the fuzzy Delphi method. The results indicated that the difference in mean scores between two consecutive rounds was less than 0.2 for all indicators. This finding demonstrated the relative stability of expert opinions and a sufficient level of consensus in their evaluations. Therefore, it could be confidently stated that the conditions for concluding the Delphi rounds were met, finalizing the Delphi process at this stage. Discussion of Results & Conclusion This study aimed to develop a model for formulating sustainable marketing strategies within Iran’s food industry, specifically focusing on protein product manufacturers. The proposed model identified causal conditions—such as transformative technology, a circular and sustainable economy, sustainable consumer psychology, and intelligent environmental management in sustainable marketing—that directly influenced the core phenomenon: sustainable marketing strategy. Furthermore, the findings indicated that the core phenomenon in conjunction with contextual conditions—including government digitalization, facilitation of smart governance, and the macro environment—and intervening conditions, such as structural and regulatory challenges in the sustainable marketing support system, significantly impacted strategies and actions, specifically operational sustainable marketing strategies. Ultimately, the results demonstrated that the strategies and actions formulated within this sustainable marketing framework directly led to economic and marketing outcomes, as well as social, cultural, and environmental benefits. Based on these findings, it is recommended that companies prioritize circular and sustainable economy practices by minimizing waste, adopting green financing, and optimizing marketing costs through digital media and smart packaging. Marketing messages should reflect cultural values and foster a sense of social belonging. Additionally, employing experiential marketing, ensuring informational transparency, and obtaining environmental certifications can strengthen consumer trust and encourage sustainable purchasing behaviors. Conducting environmental cost-benefit analyses and aligning production processes with global green standards can also facilitate entry into export markets. Furthermore, developing environmental standards and enhancing regional collaborations can improve the position of Iran’s food industry in sustainable exports.

https://nmrj.ui.ac.ir/article_29941_41dbf1dcef4be90a018003e5848acfa3.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Explaining a Conceptual Model of Artificial Intelligence Applications in Digital Marketing with an Emphasis on Enhancing Consumer Loyalty: A Mixed-Methods Approach Explaining a Conceptual Model of Artificial Intelligence Applications in Digital Marketing with an Emphasis on Enhancing Consumer Loyalty: A Mixed-Methods Approach 105 132 29826 10.22108/nmrj.2025.145405.3194 FA Afshin Alipour Assistant professor, Department of Business Management, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Ali Naeimi Khondabi Master's student, Department of Business Management, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Mohammadreza Zolghadr Master's student, Department of Business Management, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Journal Article 2025 05 27 This research aimed to design, validate, and elucidate a conceptual model for harnessing Artificial Intelligence (AI) in digital marketing, specifically to enhance consumer loyalty. The study was conducted in 4 structured phases. First, key components were identified through a systematic literature review (meta-synthesis) of 53 academic sources. In the second phase, the fuzzy Delphi method was utilized with 10 industry experts to validate the relevance of the content and achieve expert consensus. The third phase employed Interpretive Structural Modeling (ISM) to analyze and structure the causal relationships among 26 identified components. Finally, MICMAC analysis was used to categorize these components based on their driving power and dependence. The resulting model integrated both technological enablers—such as supervised, unsupervised, and reinforcement learning, Natural Language Processing (NLP), Large Language Models (LLMs), recommender systems, and Graph Neural Networks (GNNs)—and human-centric psychological dimensions, including flow experience, perceived value, satisfaction, trust, and consumer engagement, across 4 hierarchical levels. The findings indicated that foundational elements like “Reinforcement Learning” and “Natural Language Processing” served as primary drivers, while behavioral and attitudinal outcomes, such as “loyalty”, “brand advocacy”, and “eWOM” ranked at the top of the model. The model's innovation lay in its structured synthesis of data-driven AI technologies and human perception layers—a perspective often overlooked in previous frameworks. Practical implications were discussed, providing marketers with guidelines for deploying AI-based tools, such as recommender engines, real-time pricing algorithms, and sentiment analysis through NLP. The study concluded with recommendations for future research on industry-specific applications (e.g., fintech, edtech, tourism) and the ethical considerations surrounding AI-driven marketing decisions. Introduction Rapid advancement of digital transformation has fundamentally altered the modern marketing landscape. With the emergence of sophisticated technologies, such as machine learning, cloud computing, the Internet of Things (IoT), and particularly Artificial Intelligence (AI), marketing has become increasingly data-driven and experience-oriented. As a cornerstone of the fourth industrial revolution, AI enables organizations to automate processes, gain deep insights into consumer behavior, predict preferences, and personalize interactions in real time. This transformation has significantly reshaped how companies engage with consumers, devise strategies, and cultivate long-term loyalty. In today’s highly competitive and fast-evolving market, consumers demand immediacy, relevance, and personalization. AI technologies facilitate companies in meeting these expectations by analyzing vast amounts of consumer data and generating insights that inform tailored marketing efforts. For instance, recommendation systems on platforms like Amazon and Netflix, chatbots utilizing natural language processing, and predictive analytics employed by financial institutions exemplify AI's pervasive influence. Despite this growth, there remains a notable absence of an integrated model that synthesizes these diverse AI applications into a coherent framework while considering psychological, experiential, and ethical dimensions. Current research often focuses on specific AI tools in marketing; some studies examine predictive models for customer churn, while others investigate NLP in sentiment analysis. While these inquiries are valuable, they frequently overlook the broader context—how various AI elements interconnect to impact customer loyalty. Loyalty is a multifaceted concept shaped not only by repeated transactions, but also by attitudinal factors, such as trust, perceived value, and brand advocacy. The role of AI in fostering these deeper loyalty outcomes is yet to be clearly defined. Addressing this gap is crucial both academically and practically. From an academic perspective, it enriches marketing theory by integrating technological and behavioral dimensions. Practically, it provides guidance for practitioners seeking to leverage AI responsibly and effectively. The primary research question guiding this study was: What are the key applications of AI in digital marketing and how can these be organized into a conceptual framework that elucidates their role in enhancing consumer loyalty? By answering this question, the study aimed to advance the theory of AI-enabled marketing and present a structured, practical model that aligned advanced technologies with human-centered values. Materials & Methods Research Design This study employed a qualitative, exploratory, and applied research design aimed at developing a conceptual model. This approach was particularly well-suited for topics that remained underexplored and required the construction of a grounded framework rather than mere hypothesis testing. The methodological process was executed in 3 phases: meta-synthesis, Delphi validation, and structural modeling using ISM and MICMAC techniques. Phase 1: Meta-Synthesis The first stage involved a systematic literature review conducted through the Scopus, ScienceDirect, and Emerald databases. The inclusion criteria specified publications from 2015 to 2025 that were indexed in reputable journals and explicitly focused on AI applications in marketing or consumer behavior. A total of 53 articles met these criteria. Utilizing MAXQDA software, a 3-stage coding process (open, axial, and selective) was implemented, resulting in the extraction of 26 components categorized into technological, experiential, and socio-ethical dimensions. This meta-synthesis ensured comprehensive coverage of both empirical and conceptual contributions. Phase 2: Delphi Method To validate the identified components, the Delphi method was employed with a panel of 10 experts, comprising both academic researchers and senior industry professionals. The Delphi technique was chosen for its effectiveness in achieving consensus on complex, multi-dimensional constructs. Two iterative rounds of surveys were conducted, yielding a high reliability coefficient (Cohen’s Kappa = 0.82) and indicating strong agreement among the experts. The panel confirmed the relevance of the 26 components and suggested two refinements: (1) incorporating “AI-driven responses to competitor strategies” within the context of reinforcement learning and (2) including “sentiment-informed CSR initiatives”. These additions underscored the dynamic and ethical dimensions of AI in marketing. Phase 3: ISM and MICMAC Analyses Interpretive Structural Modeling (ISM) was employed to map the relationships among the identified components and construct a hierarchical model. This approach clarified which elements served as foundational drivers and which were outcomes. Complementing ISM, MICMAC analysis was utilized to classify the components based on their driving and dependence power. The combined analysis revealed 4 categories: driving forces (e.g., reinforcement learning), linkage factors (e.g., transparency), dependent outcomes (e.g., loyalty), and relatively autonomous elements (e.g., multi-sensory engagement). Together, these methodological phases ensured rigor by integrating breadth (literature synthesis), depth (expert validation), and structure (hierarchical modeling). Research Findings Core Components and Hierarchical Layers The validated model comprised 26 components organized into 4 hierarchical layers: Technological Enablers: This layer included supervised and unsupervised learning, reinforcement learning, Natural Language Processing (NLP), Large Language Models (LLMs), generative AI models, recommender systems, and graph neural networks. Together, these elements formed the infrastructural backbone that facilitated advanced data analysis, prediction, and personalization. Consumer Experience and Perception: This layer encompassed constructs, such as flow experience, perceived value, consumer satisfaction, trust, algorithmic transparency, human-like interaction, and multi-sensory engagement. These factors mediated the relationship between technological enablers and outcomes related to loyalty. Socio-Ethical Considerations: This layer was defined by Corporate Social Responsibility (CSR), ethical issues in LLMs, fairness, privacy, and consumer engagement in CSR, reflecting the growing demand for responsible and ethical AI practices. Behavioral Outcomes: At the pinnacle of the model were attitudinal loyalty, behavioral loyalty, electronic word-of-mouth, and brand advocacy, and consumer recommendation intentions— outcomes that organizations valued most. ISM–MICMAC Results The hierarchical analysis yielded the following classifications: Drivers: Reinforcement learning, LLMs, and recommender systems served as critical initiators within the model. Linkage Factors: Transparency, flow experience, and NLP-driven sentiment analysis mediated the relationship between technological enablers and behavioral outcomes. Dependents: Loyalty measures, consumer satisfaction, and electronic word-of-mouth emerged as dependent outcomes. Autonomous Elements: Peripheral factors, such as multi-sensory engagement and AI-assisted user-generated content, exerted comparatively lower influence. This analysis highlighted a clear causal pathway: technological foundations shaped consumer experiences, which were moderated by socio-ethical considerations and, in turn, drove loyalty-related behaviors. Discussion of Results & Conclusion Hypothesis Validation The study provided empirical support for 6 hypotheses: Supervised learning enhances the predictive accuracy of consumer behavior. Unsupervised learning facilitates segmentation and the identification of hidden patterns. Reinforcement learning enables adaptive, real-time decision-making. Flow experience supported by AI positively influences consumer loyalty. AI-driven personalization of perceived value strengthens both attitudinal and behavioral loyalty. CSR initiatives informed by sentiment analysis reinforce brand trust and advocacy. Theoretical Contributions This study contributed to the literature by proposing a holistic conceptual model that integrated AI technologies with consumer psychological constructs and ethical considerations. Unlike prior fragmented research, the model introduced a layered structure that systematically connected technological enablers to loyalty outcomes. This integration enhanced our theoretical understanding of how AI-driven personalization and responsible data practices jointly shaped sustainable consumer relationships. Managerial Implications Recommendation Systems: Organizations should implement advanced AI engines (e.g., matrix factorization and deep learning-based recommenders) to provide highly tailored consumer experiences. Sentiment Analysis: NLP models, such as BERT and GPT-4, can be utilized to decode consumer emotions, thereby informing CSR strategies and enhancing communication effectiveness. Experience Design: AI can facilitate immersive and adaptive digital experiences that foster consumer engagement and promote flow states. Ethical AI Practices: Managers must prioritize transparency, fairness, and privacy in AI applications to ensure long-term consumer trust and loyalty. Limitations and Future Research A key limitation of this study was its reliance on expert judgment. Empirical validation using large-scale consumer datasets is essential to strengthen the robustness of the proposed model. Future research should adopt quantitative approaches, such as Structural Equation Modeling (SEM) or longitudinal designs. Additionally, sector-specific adaptations (e.g., fintech, healthcare, and education) can enhance external validity. Cross-cultural comparisons would further elucidate how cultural contexts moderate AI-driven loyalty formation. Finally, the ethical challenges associated with LLMs—including bias, misinformation, and privacy risks—warrant deeper scholarly investigation. Conclusion This research presented a structured conceptual model that integrated the technological, experiential, and socio-ethical dimensions of AI-driven digital marketing, positioning consumer loyalty as the ultimate outcome. The model offered both theoretical insights and practical guidance, emphasizing the necessity of aligning AI tools with human values and social responsibility. In doing so, it establishes a foundation for future empirical investigations and for responsible application of AI in managerial practice. This research aimed to design, validate, and elucidate a conceptual model for harnessing Artificial Intelligence (AI) in digital marketing, specifically to enhance consumer loyalty. The study was conducted in 4 structured phases. First, key components were identified through a systematic literature review (meta-synthesis) of 53 academic sources. In the second phase, the fuzzy Delphi method was utilized with 10 industry experts to validate the relevance of the content and achieve expert consensus. The third phase employed Interpretive Structural Modeling (ISM) to analyze and structure the causal relationships among 26 identified components. Finally, MICMAC analysis was used to categorize these components based on their driving power and dependence. The resulting model integrated both technological enablers—such as supervised, unsupervised, and reinforcement learning, Natural Language Processing (NLP), Large Language Models (LLMs), recommender systems, and Graph Neural Networks (GNNs)—and human-centric psychological dimensions, including flow experience, perceived value, satisfaction, trust, and consumer engagement, across 4 hierarchical levels. The findings indicated that foundational elements like “Reinforcement Learning” and “Natural Language Processing” served as primary drivers, while behavioral and attitudinal outcomes, such as “loyalty”, “brand advocacy”, and “eWOM” ranked at the top of the model. The model's innovation lay in its structured synthesis of data-driven AI technologies and human perception layers—a perspective often overlooked in previous frameworks. Practical implications were discussed, providing marketers with guidelines for deploying AI-based tools, such as recommender engines, real-time pricing algorithms, and sentiment analysis through NLP. The study concluded with recommendations for future research on industry-specific applications (e.g., fintech, edtech, tourism) and the ethical considerations surrounding AI-driven marketing decisions. Introduction Rapid advancement of digital transformation has fundamentally altered the modern marketing landscape. With the emergence of sophisticated technologies, such as machine learning, cloud computing, the Internet of Things (IoT), and particularly Artificial Intelligence (AI), marketing has become increasingly data-driven and experience-oriented. As a cornerstone of the fourth industrial revolution, AI enables organizations to automate processes, gain deep insights into consumer behavior, predict preferences, and personalize interactions in real time. This transformation has significantly reshaped how companies engage with consumers, devise strategies, and cultivate long-term loyalty. In today’s highly competitive and fast-evolving market, consumers demand immediacy, relevance, and personalization. AI technologies facilitate companies in meeting these expectations by analyzing vast amounts of consumer data and generating insights that inform tailored marketing efforts. For instance, recommendation systems on platforms like Amazon and Netflix, chatbots utilizing natural language processing, and predictive analytics employed by financial institutions exemplify AI's pervasive influence. Despite this growth, there remains a notable absence of an integrated model that synthesizes these diverse AI applications into a coherent framework while considering psychological, experiential, and ethical dimensions. Current research often focuses on specific AI tools in marketing; some studies examine predictive models for customer churn, while others investigate NLP in sentiment analysis. While these inquiries are valuable, they frequently overlook the broader context—how various AI elements interconnect to impact customer loyalty. Loyalty is a multifaceted concept shaped not only by repeated transactions, but also by attitudinal factors, such as trust, perceived value, and brand advocacy. The role of AI in fostering these deeper loyalty outcomes is yet to be clearly defined. Addressing this gap is crucial both academically and practically. From an academic perspective, it enriches marketing theory by integrating technological and behavioral dimensions. Practically, it provides guidance for practitioners seeking to leverage AI responsibly and effectively. The primary research question guiding this study was: What are the key applications of AI in digital marketing and how can these be organized into a conceptual framework that elucidates their role in enhancing consumer loyalty? By answering this question, the study aimed to advance the theory of AI-enabled marketing and present a structured, practical model that aligned advanced technologies with human-centered values. Materials & Methods Research Design This study employed a qualitative, exploratory, and applied research design aimed at developing a conceptual model. This approach was particularly well-suited for topics that remained underexplored and required the construction of a grounded framework rather than mere hypothesis testing. The methodological process was executed in 3 phases: meta-synthesis, Delphi validation, and structural modeling using ISM and MICMAC techniques. Phase 1: Meta-Synthesis The first stage involved a systematic literature review conducted through the Scopus, ScienceDirect, and Emerald databases. The inclusion criteria specified publications from 2015 to 2025 that were indexed in reputable journals and explicitly focused on AI applications in marketing or consumer behavior. A total of 53 articles met these criteria. Utilizing MAXQDA software, a 3-stage coding process (open, axial, and selective) was implemented, resulting in the extraction of 26 components categorized into technological, experiential, and socio-ethical dimensions. This meta-synthesis ensured comprehensive coverage of both empirical and conceptual contributions. Phase 2: Delphi Method To validate the identified components, the Delphi method was employed with a panel of 10 experts, comprising both academic researchers and senior industry professionals. The Delphi technique was chosen for its effectiveness in achieving consensus on complex, multi-dimensional constructs. Two iterative rounds of surveys were conducted, yielding a high reliability coefficient (Cohen’s Kappa = 0.82) and indicating strong agreement among the experts. The panel confirmed the relevance of the 26 components and suggested two refinements: (1) incorporating “AI-driven responses to competitor strategies” within the context of reinforcement learning and (2) including “sentiment-informed CSR initiatives”. These additions underscored the dynamic and ethical dimensions of AI in marketing. Phase 3: ISM and MICMAC Analyses Interpretive Structural Modeling (ISM) was employed to map the relationships among the identified components and construct a hierarchical model. This approach clarified which elements served as foundational drivers and which were outcomes. Complementing ISM, MICMAC analysis was utilized to classify the components based on their driving and dependence power. The combined analysis revealed 4 categories: driving forces (e.g., reinforcement learning), linkage factors (e.g., transparency), dependent outcomes (e.g., loyalty), and relatively autonomous elements (e.g., multi-sensory engagement). Together, these methodological phases ensured rigor by integrating breadth (literature synthesis), depth (expert validation), and structure (hierarchical modeling). Research Findings Core Components and Hierarchical Layers The validated model comprised 26 components organized into 4 hierarchical layers: Technological Enablers: This layer included supervised and unsupervised learning, reinforcement learning, Natural Language Processing (NLP), Large Language Models (LLMs), generative AI models, recommender systems, and graph neural networks. Together, these elements formed the infrastructural backbone that facilitated advanced data analysis, prediction, and personalization. Consumer Experience and Perception: This layer encompassed constructs, such as flow experience, perceived value, consumer satisfaction, trust, algorithmic transparency, human-like interaction, and multi-sensory engagement. These factors mediated the relationship between technological enablers and outcomes related to loyalty. Socio-Ethical Considerations: This layer was defined by Corporate Social Responsibility (CSR), ethical issues in LLMs, fairness, privacy, and consumer engagement in CSR, reflecting the growing demand for responsible and ethical AI practices. Behavioral Outcomes: At the pinnacle of the model were attitudinal loyalty, behavioral loyalty, electronic word-of-mouth, and brand advocacy, and consumer recommendation intentions— outcomes that organizations valued most. ISM–MICMAC Results The hierarchical analysis yielded the following classifications: Drivers: Reinforcement learning, LLMs, and recommender systems served as critical initiators within the model. Linkage Factors: Transparency, flow experience, and NLP-driven sentiment analysis mediated the relationship between technological enablers and behavioral outcomes. Dependents: Loyalty measures, consumer satisfaction, and electronic word-of-mouth emerged as dependent outcomes. Autonomous Elements: Peripheral factors, such as multi-sensory engagement and AI-assisted user-generated content, exerted comparatively lower influence. This analysis highlighted a clear causal pathway: technological foundations shaped consumer experiences, which were moderated by socio-ethical considerations and, in turn, drove loyalty-related behaviors. Discussion of Results & Conclusion Hypothesis Validation The study provided empirical support for 6 hypotheses: Supervised learning enhances the predictive accuracy of consumer behavior. Unsupervised learning facilitates segmentation and the identification of hidden patterns. Reinforcement learning enables adaptive, real-time decision-making. Flow experience supported by AI positively influences consumer loyalty. AI-driven personalization of perceived value strengthens both attitudinal and behavioral loyalty. CSR initiatives informed by sentiment analysis reinforce brand trust and advocacy. Theoretical Contributions This study contributed to the literature by proposing a holistic conceptual model that integrated AI technologies with consumer psychological constructs and ethical considerations. Unlike prior fragmented research, the model introduced a layered structure that systematically connected technological enablers to loyalty outcomes. This integration enhanced our theoretical understanding of how AI-driven personalization and responsible data practices jointly shaped sustainable consumer relationships. Managerial Implications Recommendation Systems: Organizations should implement advanced AI engines (e.g., matrix factorization and deep learning-based recommenders) to provide highly tailored consumer experiences. Sentiment Analysis: NLP models, such as BERT and GPT-4, can be utilized to decode consumer emotions, thereby informing CSR strategies and enhancing communication effectiveness. Experience Design: AI can facilitate immersive and adaptive digital experiences that foster consumer engagement and promote flow states. Ethical AI Practices: Managers must prioritize transparency, fairness, and privacy in AI applications to ensure long-term consumer trust and loyalty. Limitations and Future Research A key limitation of this study was its reliance on expert judgment. Empirical validation using large-scale consumer datasets is essential to strengthen the robustness of the proposed model. Future research should adopt quantitative approaches, such as Structural Equation Modeling (SEM) or longitudinal designs. Additionally, sector-specific adaptations (e.g., fintech, healthcare, and education) can enhance external validity. Cross-cultural comparisons would further elucidate how cultural contexts moderate AI-driven loyalty formation. Finally, the ethical challenges associated with LLMs—including bias, misinformation, and privacy risks—warrant deeper scholarly investigation. Conclusion This research presented a structured conceptual model that integrated the technological, experiential, and socio-ethical dimensions of AI-driven digital marketing, positioning consumer loyalty as the ultimate outcome. The model offered both theoretical insights and practical guidance, emphasizing the necessity of aligning AI tools with human values and social responsibility. In doing so, it establishes a foundation for future empirical investigations and for responsible application of AI in managerial practice.

https://nmrj.ui.ac.ir/article_29826_7ae3c62418b833a1604f021fdab6c6f5.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Developing a Conceptual Model for the Implementation of Industry 4.0 Technologies in the Supply Chain: A Grounded Theory Approach Developing a Conceptual Model for the Implementation of Industry 4.0 Technologies in the Supply Chain: A Grounded Theory Approach 133 160 29987 10.22108/nmrj.2025.145929.3216 FA Sanaz Shafiee Assistant professor, Department of Business Management and Information Technology Management, Faculty of Management, Payame Noor University, Tehran, Iran. Journal Article 2025 07 12 In recent decades, digital transformation and emergence of technologies associated with Industry 4.0 have fundamentally reshaped the structure, performance, and competitiveness of supply chains across various industries. This study aimed to identify and elucidate the role of Industry 4.0 technologies in the smartification of supply chains. The research adopted a qualitative approach grounded in Grounded Theory. Data were collected through semi-structured interviews with 10 experts from both academia and industry. For data analysis, we employed Strauss and Corbin’s 3-stage coding method—open, axial, and selective coding. Subsequently, Interpretive Structural Modeling (ISM) was used to hierarchically structure the key concepts and MICMAC analysis was conducted to assess the position of each component based on its driving power and dependency. The findings indicated that the successful implementation of Industry 4.0 technologies in supply chains necessitated the availability of information infrastructure, formulation of a digital strategy, enhancement of human resource skills, and overcoming of cultural resistance within organizations. Additionally, Industry 4.0 technologies were identified as dependent variables in the model with their effectiveness in improving productivity, flexibility, and responsiveness of the supply chain contingent upon the realization of lower-level components. MICMAC analysis further highlighted factors, such as market pressure, digital strategy, and technological infrastructure, as key driving forces in the transformation of supply chains. The practical implications of this research included the development of digital transformation roadmaps, formulation of organizational policies for technology adoption, re-engineering of supply chain processes, enhancement of employees' digital skills, and establishment of national policies related to Industry 4.0. Introduction In recent decades, rapid advancement of digital transformation and emergence of Industry 4.0 technologies have fundamentally reshaped supply chain structures, performance, and competitiveness (Emon & Khan, 2025; Ostadi et al., 2024). Increasing global competition, volatile markets, shorter product life cycles, and the demand for resilient and adaptive operations have compelled organizations to rethink traditional supply chain management and transition toward digital and intelligent ecosystems (Al Mashalah et al., 2022). Industry 4.0 often referred to as the 4th industrial revolution, integrates technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data analytics, Blockchain, Augmented and Virtual Reality, Advanced Robotics, and Cloud Computing. These innovations facilitate real-time connectivity, automation, and data-driven decision-making throughout the supply chain (George, 2024; Huang et al., 2023). While these technologies enhance productivity, transparency, and agility, their implementation presents significant challenges, particularly in developing economies. Barriers like a lack of digital infrastructure, inadequate integration of legacy systems, insufficient human resource capabilities, and cultural resistance to technological change remain critical obstacles (Reaidy et al., 2024). In response to these challenges, this study aimed to identify and explain the key factors influencing the successful deployment of Industry 4.0 technologies in manufacturing supply chains. The research sought to address the following questions: (1) What role do Industry 4.0 technologies play in the digitalization of supply chains? (2) What are the barriers to their implementation? and (3) How can a conceptual model be developed to describe the relationships among these factors? Materials & Methods This research employed a qualitative, exploratory-applied design within the interpretive paradigm by using Grounded Theory (Strauss and Corbin) as its primary methodological framework. Data were collected through semi-structured interviews with 10 experts, including academic scholars and industrial managers in the fields of manufacturing and digital transformation. The interview questions were developed based on a systematic literature review and refined through consultations with experts. Purposeful and judgmental sampling techniques were utilized to ensure a diverse representation of expertise. Data saturation was achieved after the 10th interview. The qualitative data were analyzed by using a 3-stage coding process: open, axial, and selective coding. During the open coding phase, 60 primary concepts were extracted from the interview transcripts. These concepts were then categorized into 6 axial categories that represented causal, contextual, intervening, strategic, and consequential conditions. Subsequently, the core category—digitalization of the supply chain through Industry 4.0 technologies—was identified. To validate and structure the relationships among these categories, Interpretive Structural Modeling (ISM) and MICMAC analysis were employed. 11 key variables were selected for ISM modeling and their pairwise influences were analyzed to determine hierarchical levels. The MICMAC analysis further classified the variables based on their driving power and degree of dependence, identifying independent, linkage, and dependent factors. This methodological integration facilitated the development of a robust, multilevel conceptual model that illustrated the dynamic interdependencies among organizational, technological, and human enablers of Industry 4.0 implementation. Research Findings The results indicated that the successful implementation of Industry 4.0 technologies in manufacturing supply chains relied on a series of interconnected organizational, technological, and human factors. The ISM analysis identified 6 hierarchical levels. The foundational layer consisted of market pressure and competition followed by the need for transparency, a shortage of digital skills, and cultural resistance. The mid-level included technological infrastructure and digital strategy, while the upper levels encompassed employee training, systems integration, and ultimately the adoption of Industry 4.0 technologies. These factors led to enhanced productivity, cost reduction, flexibility, and responsiveness. The MICMAC analysis classified "market pressure", "digital strategy", and "IT infrastructure" as driving (independent) variables, while "integration", "skills", and "training" were identified as linkage variables—both influencing and being influenced by other factors. The dependent variables—"productivity improvement", "cost reduction", and "supply chain flexibility"—represented the ultimate performance outcomes. Furthermore, the study found that technological adoption alone was insufficient; it required complementary enablers, such as management commitment, employee empowerment, and a supportive digital culture. The results also highlighted that technologies like AI, IoT, Big Data, and Blockchain could significantly enhance real-time monitoring, traceability, decision-making accuracy, and transparency. However, their effectiveness was contingent upon organizational readiness, infrastructure maturity, and strategic alignment. Discussion of Results & Conclusion The study concluded that Industry 4.0 technologies served as critical yet dependent enablers in the digital transformation of supply chains. Their effectiveness hinged on the prior establishment of a coherent digital strategy, a robust technological infrastructure, and a digitally skilled and motivated workforce. The ISM–MICMAC-based conceptual model–illustrated that digital transformation followed a hierarchical cause-and-effect progression: from strategic and infrastructural readiness to technological implementation, culminating in performance outcomes. 5 major barriers were identified: (1) the absence of a comprehensive digital roadmap, (2) inadequate IT infrastructure, (3) a shortage of digital skills, (4) cultural resistance to change, and (5) insufficient structured training programs. Addressing these barriers necessitated an integrated approach that encompassed strategic governance, investment in infrastructure, process re-engineering, and human capacity building. Practically, the findings offer managerial guidelines for designing effective digital transformation roadmaps, developing training and motivation systems, and formulating organizational policies to enhance readiness for Industry 4.0 adoption. Theoretically, this research contributes to the growing body of literature on smart supply chains by providing a contextually grounded conceptual model that bridges the gap between technological potential and organizational capability. Future research could extend this work by quantitatively validating the proposed model across various industries and regions. In recent decades, digital transformation and emergence of technologies associated with Industry 4.0 have fundamentally reshaped the structure, performance, and competitiveness of supply chains across various industries. This study aimed to identify and elucidate the role of Industry 4.0 technologies in the smartification of supply chains. The research adopted a qualitative approach grounded in Grounded Theory. Data were collected through semi-structured interviews with 10 experts from both academia and industry. For data analysis, we employed Strauss and Corbin’s 3-stage coding method—open, axial, and selective coding. Subsequently, Interpretive Structural Modeling (ISM) was used to hierarchically structure the key concepts and MICMAC analysis was conducted to assess the position of each component based on its driving power and dependency. The findings indicated that the successful implementation of Industry 4.0 technologies in supply chains necessitated the availability of information infrastructure, formulation of a digital strategy, enhancement of human resource skills, and overcoming of cultural resistance within organizations. Additionally, Industry 4.0 technologies were identified as dependent variables in the model with their effectiveness in improving productivity, flexibility, and responsiveness of the supply chain contingent upon the realization of lower-level components. MICMAC analysis further highlighted factors, such as market pressure, digital strategy, and technological infrastructure, as key driving forces in the transformation of supply chains. The practical implications of this research included the development of digital transformation roadmaps, formulation of organizational policies for technology adoption, re-engineering of supply chain processes, enhancement of employees' digital skills, and establishment of national policies related to Industry 4.0. Introduction In recent decades, rapid advancement of digital transformation and emergence of Industry 4.0 technologies have fundamentally reshaped supply chain structures, performance, and competitiveness (Emon & Khan, 2025; Ostadi et al., 2024). Increasing global competition, volatile markets, shorter product life cycles, and the demand for resilient and adaptive operations have compelled organizations to rethink traditional supply chain management and transition toward digital and intelligent ecosystems (Al Mashalah et al., 2022). Industry 4.0 often referred to as the 4th industrial revolution, integrates technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data analytics, Blockchain, Augmented and Virtual Reality, Advanced Robotics, and Cloud Computing. These innovations facilitate real-time connectivity, automation, and data-driven decision-making throughout the supply chain (George, 2024; Huang et al., 2023). While these technologies enhance productivity, transparency, and agility, their implementation presents significant challenges, particularly in developing economies. Barriers like a lack of digital infrastructure, inadequate integration of legacy systems, insufficient human resource capabilities, and cultural resistance to technological change remain critical obstacles (Reaidy et al., 2024). In response to these challenges, this study aimed to identify and explain the key factors influencing the successful deployment of Industry 4.0 technologies in manufacturing supply chains. The research sought to address the following questions: (1) What role do Industry 4.0 technologies play in the digitalization of supply chains? (2) What are the barriers to their implementation? and (3) How can a conceptual model be developed to describe the relationships among these factors? Materials & Methods This research employed a qualitative, exploratory-applied design within the interpretive paradigm by using Grounded Theory (Strauss and Corbin) as its primary methodological framework. Data were collected through semi-structured interviews with 10 experts, including academic scholars and industrial managers in the fields of manufacturing and digital transformation. The interview questions were developed based on a systematic literature review and refined through consultations with experts. Purposeful and judgmental sampling techniques were utilized to ensure a diverse representation of expertise. Data saturation was achieved after the 10th interview. The qualitative data were analyzed by using a 3-stage coding process: open, axial, and selective coding. During the open coding phase, 60 primary concepts were extracted from the interview transcripts. These concepts were then categorized into 6 axial categories that represented causal, contextual, intervening, strategic, and consequential conditions. Subsequently, the core category—digitalization of the supply chain through Industry 4.0 technologies—was identified. To validate and structure the relationships among these categories, Interpretive Structural Modeling (ISM) and MICMAC analysis were employed. 11 key variables were selected for ISM modeling and their pairwise influences were analyzed to determine hierarchical levels. The MICMAC analysis further classified the variables based on their driving power and degree of dependence, identifying independent, linkage, and dependent factors. This methodological integration facilitated the development of a robust, multilevel conceptual model that illustrated the dynamic interdependencies among organizational, technological, and human enablers of Industry 4.0 implementation. Research Findings The results indicated that the successful implementation of Industry 4.0 technologies in manufacturing supply chains relied on a series of interconnected organizational, technological, and human factors. The ISM analysis identified 6 hierarchical levels. The foundational layer consisted of market pressure and competition followed by the need for transparency, a shortage of digital skills, and cultural resistance. The mid-level included technological infrastructure and digital strategy, while the upper levels encompassed employee training, systems integration, and ultimately the adoption of Industry 4.0 technologies. These factors led to enhanced productivity, cost reduction, flexibility, and responsiveness. The MICMAC analysis classified "market pressure", "digital strategy", and "IT infrastructure" as driving (independent) variables, while "integration", "skills", and "training" were identified as linkage variables—both influencing and being influenced by other factors. The dependent variables—"productivity improvement", "cost reduction", and "supply chain flexibility"—represented the ultimate performance outcomes. Furthermore, the study found that technological adoption alone was insufficient; it required complementary enablers, such as management commitment, employee empowerment, and a supportive digital culture. The results also highlighted that technologies like AI, IoT, Big Data, and Blockchain could significantly enhance real-time monitoring, traceability, decision-making accuracy, and transparency. However, their effectiveness was contingent upon organizational readiness, infrastructure maturity, and strategic alignment. Discussion of Results & Conclusion The study concluded that Industry 4.0 technologies served as critical yet dependent enablers in the digital transformation of supply chains. Their effectiveness hinged on the prior establishment of a coherent digital strategy, a robust technological infrastructure, and a digitally skilled and motivated workforce. The ISM–MICMAC-based conceptual model–illustrated that digital transformation followed a hierarchical cause-and-effect progression: from strategic and infrastructural readiness to technological implementation, culminating in performance outcomes. 5 major barriers were identified: (1) the absence of a comprehensive digital roadmap, (2) inadequate IT infrastructure, (3) a shortage of digital skills, (4) cultural resistance to change, and (5) insufficient structured training programs. Addressing these barriers necessitated an integrated approach that encompassed strategic governance, investment in infrastructure, process re-engineering, and human capacity building. Practically, the findings offer managerial guidelines for designing effective digital transformation roadmaps, developing training and motivation systems, and formulating organizational policies to enhance readiness for Industry 4.0 adoption. Theoretically, this research contributes to the growing body of literature on smart supply chains by providing a contextually grounded conceptual model that bridges the gap between technological potential and organizational capability. Future research could extend this work by quantitatively validating the proposed model across various industries and regions.

https://nmrj.ui.ac.ir/article_29987_551fa6e7793c2d41510917fcfd5f5d0c.pdf

University of Isfahan New Marketing Research Journal 2228-7744 15 3 2025 11 22 Exploring the Role of Discounting in Creating Cognitive Dissonance: A Study of Business Owners in the Fashion and Apparel Industry Exploring the Role of Discounting in Creating Cognitive Dissonance: A Study of Business Owners in the Fashion and Apparel Industry 161 182 30029 10.22108/nmrj.2025.145574.3203 FA Reza Ameli Ph.D. candidate, Department of Marketing and Market Development, Faculty of Business Management, College of Management, University of Tehran, Kish Campus, Iran Mohsen Nazari Professor, Department of Marketing and Market Development, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran 0000-0003-2861-2052 Reza Shafei Associate Professor, Department of Business Management, Faculty of Management, University of Kurdistan, ‎Sanandaj, Iran Journal Article 2025 06 06 Discounting is a pivotal marketing strategy in the fashion and apparel industry, capable of both stimulating purchases and inducing cognitive dissonance among consumers. This study investigated how discounts trigger cognitive dissonance in customers within this sector. Using grounded theory methodology (following the Strauss and Corbin’s approach), we conducted 13 semi-structured interviews with managers, store owners, and industry experts, employing purposive sampling to achieve theoretical saturation. The interview data were analyzed through open, axial, and selective coding. The findings indicated that frequent and substantial discounting could lead customers to question the inherent value of products, resulting in cognitive dissonance. From the analysis, 5 core categories emerged: factors contributing to cognitive dissonance, contextual factors (facilitators), intervening factors (either facilitators or inhibitors), responses to dissonance (adaptation strategies), and consequences of cognitive dissonance. By integrating these categories, the study developed a conceptual model that illustrated the mechanisms underlying cognitive dissonance in the discount shopping experience and strategies for managing it. The results were compared with the existing literature and practical recommendations were proposed to assist businesses in mitigating the adverse effects of cognitive dissonance. Introduction Discounting is a prevalent marketing strategy in the fashion and apparel industry as designed to stimulate customer purchases, accelerate inventory turnover, and maintain competitiveness in markets characterized by rapid trend cycles and high product substitutability. While discounts often achieve their intended commercial objectives by increasing sales volume and attracting price-sensitive consumers, they can also lead to unintended psychological and behavioral consequences—most notably cognitive dissonance. This phenomenon refers to the internal conflict that arises when there is a discrepancy between an individual's beliefs, expectations, and actions. In the context of consumer behavior, cognitive dissonance frequently emerges post-purchase, particularly when customers begin to question whether their choices have been optimal. The fashion and apparel industry is particularly vulnerable to this issue due to the symbolic and expressive nature of clothing. Consumers often purchase apparel not just for functional or economic reasons, but also for social identity, emotional satisfaction, and self-expression. Consequently, when products are offered at a discount—especially steep or frequent discounts—customers may start to doubt the true value and authenticity of the product, integrity of the brand, and quality of their purchasing decisions. For instance, significant discounts may lead consumers to question whether the original price has been artificially inflated, whether the product's quality is compromised, or whether the brand is struggling in the market. Despite extensive research on discount strategies as tools for influencing consumer behavior, the psychological consequences of price reductions—particularly in the realm of fashion consumption—have received relatively little scholarly attention. This study aimed to fill this gap by examining how discounting practices contribute to the development of cognitive dissonance among customers in the fashion and apparel industry, as well as how customers respond to and manage the dissonance they experience. This research focused on developing a conceptual framework that linked discounting practices to cognitive dissonance. Specifically, it investigated the contributing factors, contextual conditions, intervening influences, behavioral responses, and potential outcomes associated with dissonance in the discount shopping experience. By doing so, the study sought to provide both theoretical insights and practical recommendations for fashion brands and retailers aiming to optimize their discount strategies while maintaining customer trust and satisfaction. Materials & Methods To explore this phenomenon in depth, the study employed Grounded Theory methodology following the systematic approach outlined by Strauss and Corbin (1998). Grounded Theory is particularly well-suited for examining complex, process-oriented social interactions and developing conceptual models derived directly from empirical data rather than testing preconceived hypotheses. This approach aligned with the exploratory nature of the research, which aimed to understand how and why cognitive dissonance arose in the context of discount-driven purchasing. Data were collected through 13 semi-structured interviews with individuals possessing extensive experience and insight into consumer behavior within the fashion and apparel market. Participants included store managers, brand owners, marketing consultants, and retail supervisors. A purposive sampling technique was employed to ensure that respondents had relevant knowledge of consumer interactions, pricing strategies, and brand communication practices. Sampling continued until theoretical saturation was achieved, indicating that additional interviews no longer provided new conceptual insights. The interview data were analyzed using a 3-stage coding process: Open Coding: Key concepts were identified and labeled based on participants’ descriptions of consumer behaviors, emotional reactions, discount practices, and brand strategies. Axial Coding: Concepts were grouped into categories and subcategories with relationships among them examined to identify patterns and linkages. Selective Coding: A core category was identified, leading to the formation of a conceptual model that integrated all categories and illustrated the process of cognitive dissonance in discount-driven consumer experiences. Throughout the analysis, the constant comparative method was utilized to refine categories and ensure that the emerging theory accurately reflected participants' perspectives and realities of the marketplace. Research Findings The data revealed that discounting practices—particularly frequent and substantial price reductions—could significantly impact customers’ perceptions of value and authenticity. When customers encountered heavily discounted apparel, they might begin to question whether the initial price was artificially inflated or if the product was of inferior quality. These doubts signified the onset of cognitive dissonance, where an internal conflict arose between the desire to secure a good deal and uncertainty regarding the true worth of the product. The analysis resulted in the emergence of 5 core categories: Factors Contributing to Cognitive Dissonance: These included discrepancies between perceived value and price, inconsistencies in brand positioning, uncertainties about product quality, influences from social comparisons, and the symbolic significance of clothing in personal identity expression. Contextual Factors (Facilitators): Market-wide practices, such as seasonal sales, clearance cycles, intense competition, and price wars, were identified as conditions that normalized discounting and increased consumer exposure to reduced-price items, thereby shaping expectations and heightening the likelihood of dissonance. Intervening Factors: These included trust in the brand, prior purchasing experiences (both positive and negative), word-of-mouth recommendations, and consumer’s knowledge of fashion pricing. These factors could either exacerbate or alleviate the dissonance process. Consumer Responses and Adaptation Strategies: Customers employed various strategies to alleviate the discomfort associated with dissonance. These included rationalizing (e.g., “I needed this item anyway”), justifying through comparison (e.g., “It’s still cheaper than other brands”), seeking reassurance from others, avoiding thoughts about the price, or returning the product in some cases. Consequences of Cognitive Dissonance: The outcomes of unresolved dissonance could be significant, leading to reduced customer satisfaction, diminished trust in the brand, reluctance to make future purchases, negative word-of-mouth, and brand switching in severe cases. The final conceptual model developed in this study illustrated the dynamic and interactive nature of cognitive dissonance in discount-driven purchases, highlighting the pathway from discount exposure to emotional reactions, decision-making strategies, and long-term outcomes in customer-brand relationships. Discussion of Results & Conclusion The findings of this study revealed that while discounts could effectively drive short-term sales, they also posed the risk of triggering cognitive dissonance, which might adversely impact customer satisfaction, trust, and loyalty if not managed properly. An overreliance on discounts could undermine a brand’s value proposition and diminish its perceived authenticity in the eyes of consumers. Therefore, fashion retailers must adopt strategic, transparent, and value-aligned discount practices to mitigate negative psychological effects. Practical recommendations include: Limiting the frequency of substantial discounts to maintain price integrity. Clearly communicating the rationale behind discounts (e.g., seasonal changes and inventory rotation). Enhancing brand storytelling to reinforce product value beyond mere pricing. Training sales staff to reassure customers by emphasizing quality and brand identity. Monitoring post-purchase feedback to identify early signs of cognitive dissonance. By integrating psychological insights into the formulation of discount strategies, fashion brands can strike a balance between achieving commercial objectives and ensuring the sustainability of long-term customer relationships. Discounting is a pivotal marketing strategy in the fashion and apparel industry, capable of both stimulating purchases and inducing cognitive dissonance among consumers. This study investigated how discounts trigger cognitive dissonance in customers within this sector. Using grounded theory methodology (following the Strauss and Corbin’s approach), we conducted 13 semi-structured interviews with managers, store owners, and industry experts, employing purposive sampling to achieve theoretical saturation. The interview data were analyzed through open, axial, and selective coding. The findings indicated that frequent and substantial discounting could lead customers to question the inherent value of products, resulting in cognitive dissonance. From the analysis, 5 core categories emerged: factors contributing to cognitive dissonance, contextual factors (facilitators), intervening factors (either facilitators or inhibitors), responses to dissonance (adaptation strategies), and consequences of cognitive dissonance. By integrating these categories, the study developed a conceptual model that illustrated the mechanisms underlying cognitive dissonance in the discount shopping experience and strategies for managing it. The results were compared with the existing literature and practical recommendations were proposed to assist businesses in mitigating the adverse effects of cognitive dissonance. Introduction Discounting is a prevalent marketing strategy in the fashion and apparel industry as designed to stimulate customer purchases, accelerate inventory turnover, and maintain competitiveness in markets characterized by rapid trend cycles and high product substitutability. While discounts often achieve their intended commercial objectives by increasing sales volume and attracting price-sensitive consumers, they can also lead to unintended psychological and behavioral consequences—most notably cognitive dissonance. This phenomenon refers to the internal conflict that arises when there is a discrepancy between an individual's beliefs, expectations, and actions. In the context of consumer behavior, cognitive dissonance frequently emerges post-purchase, particularly when customers begin to question whether their choices have been optimal. The fashion and apparel industry is particularly vulnerable to this issue due to the symbolic and expressive nature of clothing. Consumers often purchase apparel not just for functional or economic reasons, but also for social identity, emotional satisfaction, and self-expression. Consequently, when products are offered at a discount—especially steep or frequent discounts—customers may start to doubt the true value and authenticity of the product, integrity of the brand, and quality of their purchasing decisions. For instance, significant discounts may lead consumers to question whether the original price has been artificially inflated, whether the product's quality is compromised, or whether the brand is struggling in the market. Despite extensive research on discount strategies as tools for influencing consumer behavior, the psychological consequences of price reductions—particularly in the realm of fashion consumption—have received relatively little scholarly attention. This study aimed to fill this gap by examining how discounting practices contribute to the development of cognitive dissonance among customers in the fashion and apparel industry, as well as how customers respond to and manage the dissonance they experience. This research focused on developing a conceptual framework that linked discounting practices to cognitive dissonance. Specifically, it investigated the contributing factors, contextual conditions, intervening influences, behavioral responses, and potential outcomes associated with dissonance in the discount shopping experience. By doing so, the study sought to provide both theoretical insights and practical recommendations for fashion brands and retailers aiming to optimize their discount strategies while maintaining customer trust and satisfaction. Materials & Methods To explore this phenomenon in depth, the study employed Grounded Theory methodology following the systematic approach outlined by Strauss and Corbin (1998). Grounded Theory is particularly well-suited for examining complex, process-oriented social interactions and developing conceptual models derived directly from empirical data rather than testing preconceived hypotheses. This approach aligned with the exploratory nature of the research, which aimed to understand how and why cognitive dissonance arose in the context of discount-driven purchasing. Data were collected through 13 semi-structured interviews with individuals possessing extensive experience and insight into consumer behavior within the fashion and apparel market. Participants included store managers, brand owners, marketing consultants, and retail supervisors. A purposive sampling technique was employed to ensure that respondents had relevant knowledge of consumer interactions, pricing strategies, and brand communication practices. Sampling continued until theoretical saturation was achieved, indicating that additional interviews no longer provided new conceptual insights. The interview data were analyzed using a 3-stage coding process: Open Coding: Key concepts were identified and labeled based on participants’ descriptions of consumer behaviors, emotional reactions, discount practices, and brand strategies. Axial Coding: Concepts were grouped into categories and subcategories with relationships among them examined to identify patterns and linkages. Selective Coding: A core category was identified, leading to the formation of a conceptual model that integrated all categories and illustrated the process of cognitive dissonance in discount-driven consumer experiences. Throughout the analysis, the constant comparative method was utilized to refine categories and ensure that the emerging theory accurately reflected participants' perspectives and realities of the marketplace. Research Findings The data revealed that discounting practices—particularly frequent and substantial price reductions—could significantly impact customers’ perceptions of value and authenticity. When customers encountered heavily discounted apparel, they might begin to question whether the initial price was artificially inflated or if the product was of inferior quality. These doubts signified the onset of cognitive dissonance, where an internal conflict arose between the desire to secure a good deal and uncertainty regarding the true worth of the product. The analysis resulted in the emergence of 5 core categories: Factors Contributing to Cognitive Dissonance: These included discrepancies between perceived value and price, inconsistencies in brand positioning, uncertainties about product quality, influences from social comparisons, and the symbolic significance of clothing in personal identity expression. Contextual Factors (Facilitators): Market-wide practices, such as seasonal sales, clearance cycles, intense competition, and price wars, were identified as conditions that normalized discounting and increased consumer exposure to reduced-price items, thereby shaping expectations and heightening the likelihood of dissonance. Intervening Factors: These included trust in the brand, prior purchasing experiences (both positive and negative), word-of-mouth recommendations, and consumer’s knowledge of fashion pricing. These factors could either exacerbate or alleviate the dissonance process. Consumer Responses and Adaptation Strategies: Customers employed various strategies to alleviate the discomfort associated with dissonance. These included rationalizing (e.g., “I needed this item anyway”), justifying through comparison (e.g., “It’s still cheaper than other brands”), seeking reassurance from others, avoiding thoughts about the price, or returning the product in some cases. Consequences of Cognitive Dissonance: The outcomes of unresolved dissonance could be significant, leading to reduced customer satisfaction, diminished trust in the brand, reluctance to make future purchases, negative word-of-mouth, and brand switching in severe cases. The final conceptual model developed in this study illustrated the dynamic and interactive nature of cognitive dissonance in discount-driven purchases, highlighting the pathway from discount exposure to emotional reactions, decision-making strategies, and long-term outcomes in customer-brand relationships. Discussion of Results & Conclusion The findings of this study revealed that while discounts could effectively drive short-term sales, they also posed the risk of triggering cognitive dissonance, which might adversely impact customer satisfaction, trust, and loyalty if not managed properly. An overreliance on discounts could undermine a brand’s value proposition and diminish its perceived authenticity in the eyes of consumers. Therefore, fashion retailers must adopt strategic, transparent, and value-aligned discount practices to mitigate negative psychological effects. Practical recommendations include: Limiting the frequency of substantial discounts to maintain price integrity. Clearly communicating the rationale behind discounts (e.g., seasonal changes and inventory rotation). Enhancing brand storytelling to reinforce product value beyond mere pricing. Training sales staff to reassure customers by emphasizing quality and brand identity. Monitoring post-purchase feedback to identify early signs of cognitive dissonance. By integrating psychological insights into the formulation of discount strategies, fashion brands can strike a balance between achieving commercial objectives and ensuring the sustainability of long-term customer relationships.

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