دانلود مقاله بازاریابی شبکه اجتماعی و رفتار خرید مصرف کننده
چکیده
1. مقدمه
2. مرور مطالعات پیشین و توسعه فرضیه ها
3. روش تحقیق
4. نتایج
5. بحث
6. نتیجه گیری، پیامدهای مدیریتی، محدودیت ها و پیشنهادات
پیوست A
منابع
Abstract
1. Introduction
2. Literature Review and Hypotheses Development
3. Research Method
4. Results
5. Discussion
6. Conclusions, Managerial Implications, Limitations, and Suggestions
Appendix A
References
چکیده
هدف این مقاله این است که نشان دهد چگونه بازاریابی شبکه اجتماعی (SNM) می تواند بر رفتار خرید مصرف کنندگان (CPB) تأثیر بگذارد. ما از ترکیب مدل‌سازی معادلات ساختاری (SEM) و رویکردهای یادگیری ماشین بدون نظارت به عنوان یک روش نوآورانه استفاده کردیم. جامعه آماری این مطالعه کاربرانی هستند که در مجارستان زندگی می کنند و از فیس بوک مارکت استفاده می کنند. این تحقیق از روش نمونه گیری در دسترس برای غلبه بر سوگیری استفاده می کند. از 475 نظرسنجی توزیع شده، در مجموع 466 پاسخ دهنده با موفقیت کل نظرسنجی را با نرخ پاسخ 98.1 درصد پر کردند. نتایج نشان داد که تمامی ابعاد بازاریابی شبکه‌های اجتماعی مانند سرگرمی، سفارشی‌سازی، تعامل، WoM و روند، بر رفتار خرید مصرف‌کننده (CPB) در بازار فیسبوک تأثیر مثبت و معناداری داشته است. علاوه بر این، ما از خوشه‌بندی سلسله مراتبی و الگوریتم‌های K-means بدون نظارت برای خوشه‌بندی مصرف‌کنندگان استفاده کردیم. نتایج نشان می دهد که پاسخ دهندگان این پژوهش را می توان بر اساس رفتارهای مربوط به ویژگی های جمعیت شناختی در 9 گروه مختلف دسته بندی کرد. این بدان معناست که می توان از استراتژی های متمایز برای خوشه های مختلف استفاده کرد. در این میان، مدیران بازاریابی می توانند گزینه ها، محصولات و خدمات مختلفی را برای هر گروه ارائه دهند. این مطالعه از این جهت اهمیت بالایی دارد که از بسته‌های plspm و Matrixpls در R برای نشان دادن قدرت پیش‌بینی مدل استفاده کرده است. در همین حال، ما از الگوریتم‌های یادگیری ماشینی بدون نظارت برای خوشه‌بندی رفتارهای مصرف‌کننده استفاده کردیم.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors.
Introduction
With the advent of social networks, a lot of changes have happened in the marketplace. Nowadays, social networks (SN) have become the preferred platform of shopping for many consumers. Social networks make interactive communication among users and create substantial opportunities for marketers to connect with consumers [1].
Facebook is the prime social network service in the world and a tool that has become an important part of consumers’ lives [2]. Facebook users, especially, tend to create commercial groups that allow them to conduct business. This kind of group that enables users to conduct consumer-to-consumer commercial activities is called a marketplace [3]. The marketplace is a kind of group which Facebook users create to sell their items. Many developed and developing countries are using social media platforms for purchasing products. COVID-19 has also significantly impacted the influence to purchase products in marketplaces. Moreover, popular social networks, such as Facebook and Twitter, are used by marketers to draw attention to their products and services and reach out to the customers [1,4]. Social networks marketing (SNM) has the potential to optimize the customer experience and journey [5], provide connection with customers [6], lower the marketing cost [7], and enable marketers to send messages to millions of consumers simultaneously [8]. Therefore, social network marketing is going to be more popular in every country, and it is not surprising that social networks are one of the most important tools to encourage the consumption of products.
Results
Measurement Models
The reliability of the questionnaire was evaluated by Cronbach’s alpha, composite reliability, Dillon–Goldstein’s rho and by checking the first and second eigenvalues of the indicators’ correlation matrix (Table 2). Some researchers suggest 0.7 and above as the favorable point for Cronbach’s alpha [69,71–74] and DG rho [75]. As the value of these coefficients is higher than 0.7, it means that the reliability of the research is confirmed. The first eigenvalue should be much larger than 1, whereas the second eigenvalue should be smaller than 1 [75]. The outer loading values were above the 0.7 thresholds [76]. Meanwhile, the AVE (block communality) scores were above the threshold of 0.50 (Table 2), showing the internal consistency of the measurement model [77,78]. Figure 2 shows that all items have an acceptable outer loadings level based on the graphical outer loading figure (Plspm package with R).
Discriminant validity was assessed at the construct level by the Heterotrait–Monotrait ratio (HTMT), as shown in Table 3. Values less than 0.9 are considered favorable for this index [79]. To assess the discriminant validity of items, cross-loadings were used by adopting the plspm package with R (see Figure 3) which show reliable results and confirmed the discriminant validity in the items level.