Keywords

Social Networks Marketing, Consumer Purchase Behavior, IPMA Matrix, Strategic View, Microeconomics.

Introduction

2020 has been a challenging year for various businesses. The challenges that different businesses have experienced this year reveal the need to pay more attention to e-commerce (Khahro et al., 2021). Regarding the statistics of January 2020, 3.8 billion people worldwide use various social networks, and numerous brands have invested in at any rate one social network for their strategic advertising goals to influence CPB (Sreejesh et al., 2020).

Based on the latest statistics in March 2021, the number of Facebook users in Hungary has reached 7.17 million, which shows that Facebook is the most prevalent social media in Hungary by 95.89% in 2020 (Statista, 2021; Statcounter, 2020). It seems that, based on these statistics, Facebook has offered a good chance for online businesses in Hungary to affect CPB, and this has been one of the important reasons for doing this research. The current article seeks to provide a strategic perspective on CPB in Hungary and uses the IPMA matrix to offer a suitable answer to the main research question. RQ: “What is the position of SNM dimensions and items in terms of importance and performance in the Hungarian online community (Facebook)?

Literature Review

Social networks and particularly Facebook, have provided a great opportunity for numerous businesses to attain marketing goals (Eger et al., 2020). Using social media, especially SNM, is an important approach for online businesses to research marketing goals (Hegyesné Görgényi et al., 2017; Salamzadeh & Markovic, 2018).

SNM approach is developing intensely, especially in years to come, but this field still lacks a comprehensive definition agreed by the majority of experts (Ibrahim et al., 2020; Opresnik et al., 2020). Some researchers reflect it just as using social media platforms for Marketing; however, some scholars have defined it distinctively by focusing on many aspects of this phenomenon, including but not limited to entertainment, customization, interaction, word of mouth, and trend (Kim & Ko, 2010; Salamzadeh et al., 2013; Doshmanli et al., 2018).

At large, SNM increases competition and improves the quality of services provided, which eventually affects loyalty and CPB (Chen et al., 2017). Strategic factors that affect online CPB help marketers in developing social media marketing strategies. The special characteristics and unique opportunities available to consumers and marketers in the social media space make CPB more complex. Previous studies based on various theories, including the theory of consumption values (Wu et al., 2018), technology acceptance model (Fu et al., 2018) and WOM theory (Wang & Herrando, 2019) have examined the factors affecting online CPB on social networks.

The participation of consumers in social networks generates value and value co-creation, which eventually affects the CPB. In fact, by sharing information on social networks, consumers have a significant role in online business policy and increasing competition (Moghadamzadeh et al., 2020). Consequently, understanding online CPB is very significant for the success of online businesses in e-commerce and plays a vital role in marketing strategies (Lin et al., 2019).

Methodology

The methodology of the present study is focused on IPMA matrix that serve to answer the research question. The statistical population of the study included users on the Facebook platform who have done online purchasing. The distribution of the online questionnaire was done through the Facebook platform in the first three months of 2021. To receive desirable feedback from respondents, online links were posted and shared on Facebook. Finally, 419 completed questionnaires were obtained in the analysis. Prior to the formal data collection process, a pilot study was conducted for confirming the content validity and reliability from 25 sample size. In the research sample, 60.8% and 39.2% of the respondents were men and women, in the respective order. The maximum number of respondents (74.4%) were in the age groups of 23 to 37 years. Most of the respondents (33.7%) spent one to two hours every day on Facebook different pages.

Twenty-five items (Appendix 1) on a five-point Likert scale from “Strongly disagree = 1” to “Strongly agree = 5” were used. CPB was measured using seven items modified from a study by Turban et al. (2008). SNM dimensions were measured using eighteen items modified from a study by Kim and Ko (2010). ICC coefficient was performed regarding consistency and absolute agreement to confirm the questionnaire's content validity.

Reliabiliy & Validity

All Cronbach values, CR and rho_A values were above the threshold of 0.7, showing internal consistency and reliability (Salamzadeh et al., 2013; Sanchez, 2013). All the outer loading values were significant and above 0.6. Meanwhile, AVE scores were above 0.50. The AVE and outer loadings values also showed the measurement model’s convergent validity (Hair et al., 2019; Nguyen, 2019; Ebrahimi et al., 2020). VIF values for items were below the threshold value of 5, indicating that collinearity is not an issue. (Ebrahimi et al., 2021). All the HTMT ratios were below 0.85, signifying that the measurement model reached discriminant validity (Henseler et al., 2015).

Results

IPMA is a very useful analytical tool in PLS-SEM; it explicitly spreads the standard path coefficient estimates in a more practical approach (Ringle & Sarstedt, 2016; Ebrahimi et al., 2019; Gonda et al., 2020). More precisely, IPMA gives a contrast of importance (i.e. total effect of predecessor constructs in predicting a target construct) and performance (i.e. average latent variable scores). Therefore, IPMA is useful for classifying predecessors which have a relatively low performance but high importance for the target constructs. In this research, our target construct is that CPB predicted by five predecessors (Figures 1 & 2).

Figure 1 Ipma Matrix with Target of Customer Purchase Behaviour (Focused on Variables)

Figure 2 Ipma Matrix With Target of Customer Purchase Behaviour (Focused on Items)

It is depicted that “Interaction” has the highest importance score of 0.296; if Facebook channels increase their interaction by one unit point, its overall CPB will increase by 0.296. Additionally, our findings disclose that the lowest performance (81.027) is linked to interaction, revealing an excellent opportunity for development in this area (Table 1).

Conclusion

As stated, the “interaction” dimension of SNM has the highest importance among the dimensions of SNM. After the interaction, the strategic analysis results emphasize the “trend” as an important dimension, which also reveals the need for managerial attention to this dimension. The critical fact is that, in the analysis of items of this dimension, the TRE1 item “It is a leading branding by using Facebook” is of the highest importance. After this item, TRE2 is also very important, which shows the strategic role of trend in the process of influencing CPB in the Hungarian online community. The important point is that by considering the current performance of the SNM dimensions, we find that all the dimensions of the SNM in the Hungarian online community are in good working order. This significant finding indicates that online businesses in Hungary are well aware of the importance of investing in customers in online communities, particularly Facebook. Though in terms of importance, it is possible to modify, improve and increase the impact on CPB, in terms of performance, it can be mentioned that SNM is in ideal condition in Hungary and, according to the published reports, online businesses have invested a lot in this area.

Appendix

Items of Questionnaire

Social networks marketing

Entertainment

ENT 1: The contents on Facebook are believed to be thought-provoking.

ENT 2: Using Facebook is exciting for Hungarian consumers.

ENT 3: Gathering data on brands and products through Facebook is fun for Hungarian consumers.

ENT 4: Using Facebook spend time easily.

Customization

CUS 1: Looking for tailored data on Facebook is possible

CUS 2: Facebook offers customized services.

CUS 3: Facebook offers sparkling feed data that users are interested in.

CUS 4: Using Facebook is easy and delightful for Hungarian consumers.

CUS 5: Facebook is everywhere.

Interaction

INT 1: Conveying opinions through Facebook is easy.

INT 2: Exchange opinions or conversation with other users through Facebook is easy.

INT 3: Two-way interaction through Facebook is done easily.

INT 4: Sharing data with other users through Facebook is done easily.

Word of mouth

WOM 1: I like to share information on brands, products, or services from Facebook with my friends.

WOM 2: I like uploading contents from Facebook on my page, blog or microblog.

WOM 3: I like sharing thoughts on brands, items, or services acquired from Facebook with my friends.

Trend

TRE 1: It is a leading branding by using Facebook

TRE 2: Contents on Facebook are fresh and hot.

Consumer Purchase Behavior

CPB 1: I am familiar with online search and purchase on Facebook.

CPB 2: I constantly use the online purchase of Facebook.

CPB 3: I have used Facebook online purchase many times.

CPB 4: Online purchase is a good idea for modern life.

CPB 5: Based on my experience, online purchase is safe.

CPB 6: Online purchase saves me time.

CPB 7: Online purchase provides many options for comparing price and quality.

References

1. Chen, Y.S., Lin, C.K, Lin, C.Y, Chuang, H.M., & Wang, L.C. (2017). Electronic commerce marketing-based social networks in evaluating competitive advantages using SORM. International Journal of Social and Humanistic Computing, 2(3/4), 261-277.
2. Doshmanli, M., Salamzadeh, Y., & Salamzadeh, A. (2018). Development of SMEs in an emerging economy: does corporate social responsibility matter?. International Journal of Management and Enterprise Development, 17(2), 168-191.
3. Ebrahimi, P., Ahmadi, M., Gholampour, A., & Alipour, H.R. (2019). CRM performance and development of media entrepreneurship in digital, social media and mobile commerce. International Journal of Emerging Markets, 16 (1), 25-50.
4. Ebrahimi, P., Hajmohammadi, A., & Khajeheian, D. (2020). Place branding and moderating role of social media. Current Issues in Tourism, 23(14), 1723-1731.
5. Ebrahimi, P., Soleimani, M., Kot, S., Fekete-Farkas, M., & Alipour, H. (2021). COVID-19 crisis and online businesses resilience: A Moderated Mediation Model. European Journal of International Management, In press.
6. Eger, L., Egerová, D., Tomczyk, L., Krysto?, M., & Czeglédi, C. (2020). Facebook for public relations in the higher education field: A study from four countries Czechia, Slovakia, Poland and Hungary. Journal of Marketing for Higher Education.
7. Fu, S., Yan, Q., & Feng, G.C. (2018). Who will attract you? Similarity effect among users on online purchase intention of movie tickets in the social shopping context. International Journal of Information Management, 40, 88-102.
8. Gonda, G., Gorgenyi-Hegyes, E., Nathan, R.J., & Fekete-Farkas, M. (2020). Competitive factors of fashion retail sector with special focus on SMEs. Economies, 8, 95.
9. Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
10. Hegyesné Görgényi, E., Máté, B., Vafaei, S.A., & Farkasné Fekete, M. (2017). The role of social media in gastronomy industry. Applied Studies in Agribusiness and Commerce, 11(3-4), 95-103.
11. Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
12. Ho, T.N., Wu, W.Y., Nguyen, P.T., & Chen, H.C. (2019). The Moderating Effects for the Relationships between Green Customer Value, Green Brand Equity and Behavioral Intention. Academy of Strategic Management Journal, 18(4), 1-21.
13. Ibrahim, B., Aljarah, A., & Ababneh, B. (2020). Do social media marketing activities enhance consumer perception of brands? A meta-analytic examination. Journal of Promotion Management, 26(4), 544-568.
14. Khahro, S.H., Hassan, S., Zainun, N.Y.B., & Javed, Y. (2021). Digital Transformation and E-Commerce in Construction Industry: A Prospective Assessment. Academy of Strategic Management Journal, 20(1), 1-8.
15. Kim, A.J., & Ko, E. (2010). Impacts of luxury fashion brand’s social media marketing on customer relationship and purchase intention. Journal of Global Fashion Marketing, 1(3), 164-171.
16. Lin, X., Featherman, M., Brooks, S.L., & Hajli, N. (2019). Exploring gender differences in online consumer purchase decision making: An online product presentation perspective. Information Systems Frontiers, 21(5), 1187-1201.
17. Moghadamzadeh, A., Ebrahimi, P., Radfard, S., Salamzadeh, A., & Khajeheian, D. (2020). investigating the role of customer co?creation behavior on social media platforms in rendering innovative services. Sustainability, 12(17).
18. Opresnik, M.O., Hollensen, S., & Kotler, P. (2020). Social media marketing: A practitioner approach. 4Th Edition. Opresnik Management Consulting.
19. Ringle, C.M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: the importance-performance map analysis. Industrial Management & Data Systems, 116, 1865-1886.
20. Salamzadeh, A., & Markovic, M.R. (2018). Shortening the learning curve of media start-ups in accelerators: Case of a developing country. In Evaluating media richness in organisational learning (pp. 36-48). IGI Global.
21. Salamzadeh, A., Farsi, J.Y., & Salamzadeh, Y. (2013). Entrepreneurial universities in Iran: A system dynamics model. International Journal of Entrepreneurship and Small Business, 20(4), 420-445.
22. Sanchez, G. (2013). PLS Path Modeling with R, Trowchez Editions. Berkeley, 2013.
23. Sreejesh, S., Paul, J., Strong, C., & Pius, J. (2020). Consumer response towards social media advertising: Effect of media interactivity, its conditions and the underlying mechanism. International Journal of Information Management, 54.
24. Statcounter. (2021). Social media stats in Hungary - March 2021. Retrieved from https://gs.statcounter.com/social-media-stats/all/hungary
25. Statista. (2021). Number of Facebook users in Hungary from September 2018 to March 2021, (in millions). Retrieved from https://www.statista.com/statistics/1029770/facebook-users-hungary/
26. Turban, E., King, D., Viehland, D., & Lee, J. (2008). Electronic Commerce; A Managerial perspective, Pearson Prentice Hall, United State Of America.
27. Wang, Y., & Herrando, C. (2019). Does privacy assurance on social commerce sites matter to millennials? International Journal of Information Management, 44, 164-177.
28. Wu, W., Huang, V., Chen, X., Davison, R.M., & Hua, Z. (2018). Social value and online social shopping intention: the moderating role of experience. Information Technology & People, 31(3), 688-711.