| چکیده انگلیسی مقاله |
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 w:as char:acterized 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. |