Skip to main content
SpringerLink
Log in

The impact of digital marketing strategies on customer’s buying behavior in online shopping using the rough set theory

  • Original article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Digital Marketing strategies are sets of controllable e-marketing variables that organizations combine to achieve marketing goals and to meet customers’ needs. These strategies are the most important factors that electronic-marketing managers pay attention to the best strategy in order to achieve sales and profitability. This study aims to investigate the effect of these strategies on the buying behavior of customers in online shopping stores in Tehran. For this purpose, five best-selling online stores in Tehran are selected and 79 samples are taken from each of them. For data collection, a 2-tuple fuzzy linguistic representation model is used in order to no lose the linguistic information obtained from customers. For data analyzing and extracting proper rules, two approaches of the rough set theory are used. Based on the results provided by Rosetta software, five rules governing customer behavior are identified as the most important factors affecting buying behavior in online shopping. To evaluate the result, a comparison is carried out between the extracted rules using the proposed rough set technique and the tree diagram of the data obtained by Rapidminer software. Almost all provided rules are confirmed through this comparison along with the opinions of experts. Some of key results according to the obtained rules indicate that the most important digital marketing strategy is the search engine optimization. Moreover, the social media marketing and recommender engine play as second important issue of the marketing management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Applebaum W (1951) Studying customer behavior in retail stores. J Mark 16(2):172–178

    Article  MathSciNet  Google Scholar 

  • Bala M, Verma D (2018) A critical review of digital marketing. Int J Manag 8(10):321–339

    Google Scholar 

  • Bawm ZL, Nath RPD (2014). A Conceptual Model for effective email marketing. In 2014 17th International Conference on Computer and Information Technology (ICCIT) (pp. 250–256). IEEE.

  • Behera RK, Gunasekaran A, Gupta S, Kamboj S, Bala PK (2020). Personalized digital marketing recommender engine. Journal of Retailing and Consumer Services, 53, 101799.

  • Bianchi C, Mathews S (2016) Internet marketing and export market growth in Chile. J Bus Res 69(2):426–434

    Article  Google Scholar 

  • Bu Y, Parkinson J, Thaichon P (2020) Digital content marketing as a catalyst for e-WOM in food tourism. Australasian Marketing Journal (AMJ).

  • Carpio K V, Arce S, Enjolras M, Camargo M (2020) How Institutions Promote Digital Marketing in Small and Medium International Companies: a Comparison Between Costa Rica and France. Technology Innovation Management Review, 10(4).

  • Chiaselotti G, Gentile T, Infusino F (2019) Decision systems in rough set theory: a set operatorial perspective. J Alg Appl 18(01):1950004

    Article  MathSciNet  Google Scholar 

  • Çizmeci F, Ercan T (2015) The effect of digital marketing communication tools in the creation brand awareness by housing companies. Megaron, 10(2).

  • Cường BC (2015) Picture fuzzy sets. J Comput Sci Cybernet. https://doi.org/10.15625/1813-9663/30/4/5032

    Article  Google Scholar 

  • Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400–411

    Article  Google Scholar 

  • Desai V (2019) Digital marketing a review. Int J Trend Sci Res Develop. https://doi.org/10.1016/j.jretconser.2019.101909

    Article  Google Scholar 

  • Dwivedi YK, Rana NP, Slade EL, Singh N, Kizgin H (2020). Editorial introduction: Advances in theory and practice of digital marketing.

  • Fakir Y, Azalmad M, Elaychi R (2020) Study of the ID3 and C4. 5 learning algorithms. J Med Inf Dec Mak 1(2), 29.

  • Fitrani AS, Rosid MA, Findawati Y, Rahmawati Y, Anam AK (2019). Implementation of ID3 algorithm classification using web-based weka. In Journal of Physics: Conference Series (Vol. 1381, No. 1, p. 012036). IOP Publishing.

  • Girchenko T, Ovsiannikova Y (2016) Digital marketing and its role in the modern business processes. Eur Cooper 11(18):24–33

    Google Scholar 

  • Han Z, Zhang Q, Wen F (1999) A survey on rough set theory and its application. Kongzhi Lilun Yu Yingyong/control Theory Appl 16(2):323–333. https://doi.org/10.1016/j.trit.2016.11.001

    Article  MathSciNet  Google Scholar 

  • Herrera F, Martínez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6):746–752

    Article  Google Scholar 

  • Hollebeek LD, Macky K (2019) Digital content marketing’s role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. J Interact Mark 45:27–41

    Article  Google Scholar 

  • RapidMiner Inc. (2020). RapidMiner _ Best Data Science & Machine Learning Platform. https://rapidminer.com/

  • Ivkovic M, Milanov D (2010) Affiliate internet marketing: Concept and application analysis. In 2010 International Conference on Education and Management Technology (pp. 319–323). IEEE.

  • Jia X, Rao Y, Shang L, Li T (2020) Similarity-based attribute reduction in rough set theory: a clustering perspective. Int J Mach Learn Cybernet 11:1047–1060. https://doi.org/10.1007/s13042-019-00959-w

    Article  Google Scholar 

  • Kannan PK (2017) Digital marketing: a framework, review and research agenda. Int J Res Mark 34(1):22–45

    Article  Google Scholar 

  • Karimi S, Naghibi HS (2015) Social media marketing (SMM) strategies for small to medium enterprises (SMEs). Int J Inf Bus Manag 7(4):86

    Google Scholar 

  • Kim D, Ammeter AP (2018) Shifts in online consumer behavior: a preliminary investigation of the net generation. J Theor Appl Electron Commer Res 13(1):1–25

    Article  Google Scholar 

  • Kritzinger WT, Weideman M (2013) Search engine optimization and pay-per-click marketing strategies. J Organ Comput Electron Commer 23(3):273–286

    Article  Google Scholar 

  • Kulkarni KK, Kalro AD, Sharma D, Sharma P (2020) A typology of viral ad sharers using sentiment analysis. J Retail Consum Serv. https://doi.org/10.1016/j.jretconser.2019.01.008

    Article  Google Scholar 

  • Malik R (2017) An Emp Study Dig Market Elem 8(3):715–718

    Google Scholar 

  • Martínez L, Rodriguez R M, Herrera F (2015) 2-tuple linguistic model. In The 2-tuple Linguistic Model (pp. 23–42). Springer, Cham.

  • Moheimani A, Sheikh R, Hosseini SMH, Sana SS (2021) Assessing the preparedness of hospitals facing disasters using the rough set theory: guidelines for more preparedness to cope with the COVID-19. Int J Syst Sci Oper Log, 1–16.

  • Neti S (2015) Social media and its role in marketing. Bus Econ J. https://doi.org/10.4172/2151-6219.1000203

    Article  Google Scholar 

  • Nizar NA, Janathanan C (2018). Impact of digital marketing on consumer purchase behaviour. In APIIT Business, Law & Technology Conference.

  • Othman ML, Aris I, Abdullah SM, Bakar MLAMA, Othman MR (2009) Discovering decision algorithm of numerical distance relay using rough-set-theory-based data mining. Eur J Sci Res 33(1):30–56

    Google Scholar 

  • Pawlak Z (1998) Rough set theory and its applications to data analysis. Cybern Syst 29(7):661–688

    Article  Google Scholar 

  • Piñeiro-Otero T, Martínez-Rolán X (2016). Understand Digit Market Bas Actions. https://doi.org/10.1007/978-3-319-28281-7_2

  • Qowidho T, Zarlis M, Nababan E B, Agusnady A, Sembiring BS (2019). Analysis Of c4. 5 and ID3 Methods in Determining Student Graduation. In Journal of Physics: Conference Series (Vol. 1255, No. 1, p. 012025). IOP Publishing.

  • Sajjadian F, Sheikh R, Souri ME, Sana SS (2018) Application of rough set and netnography in tourism marketing analysis. J Modell Manag. https://doi.org/10.1108/JM2-02-2018-0029

    Article  Google Scholar 

  • Sarkar B, Ullah M, Choi SB (2019) Joint inventory and pricing policy for an online to offline closed-loop supply chain model with random defective rate and returnable transport items. Mathematics 7:497–551

    Article  Google Scholar 

  • Saura JR, Palos-Sanchez PR, Correia MB (2019) Digital marketing strategies based on the e-business model: Literature review and future directions. Org Transf Manag Innov Fourth Ind Revol. https://doi.org/10.4018/978-1-5225-7074-5.ch005

    Article  Google Scholar 

  • Sawlani DK, Susilo D (2020) How digital marketing helps higher education institution branding. Int J Multidis Edu Res 5(3):45–55

    Google Scholar 

  • Sett BK, Dey BK, Sarkar B (2020) The effect of O2O retail service quality in supply chain management. Mathematics 8(10):1743

    Article  Google Scholar 

  • Shah J (2018) Impact of digital marketing in business and politics. Int J Sci Eng Res 9(2):10–14299

    Google Scholar 

  • Sharma S, Singh S, Kujur F, Das G (2021) Social media activities and its influence on customer-brand relationship: an empirical study of apparel retailers’ activity in India. J Theor Appl Electron Commer Res 16(4):602–617

    Article  Google Scholar 

  • Shen CW, Luong TH, Ho JT, Djailani I (2020) Social media marketing of IT service companies: analysis using a concept-linking mining approach. Ind Mark Manage 90:593–604

    Article  Google Scholar 

  • Solodka N, Liashenko O (2020) SEO web-resource strategy development. In International scientific and technical conference Information technologies in metallurgy and machine building (pp. 424–427).

  • Solomon, M. R. (2010). Consumer behaviour: A European perspective. Pearson education.

  • Stávková J, Stejskal L, Toufarová Z (2008) Factors influencing consumer behaviour. ZEMEDELSKA EKONOMIKA-PRAHA- 54(6):276

    Google Scholar 

  • Taherdoost H, Jalaliyoon N (2014). Marketing vs E-Marketing. International J Acad Res Manag (IJARM), 3(4), 335–340. http://elvedit.com/journals/IJARM/wp-content/uploads/2015/01/marketing-e-marketing.pdf

  • Terrance AR, Shrivastava S, Kumari A (2018) Importance of search engine marketing in the digital world. In: ICITKM, vol 14, pp 155–158

  • Tiago MTPMB, Veríssimo JMC (2014) Digital marketing and social media: Why bother? Bus Horiz 57(6):703–708

    Article  Google Scholar 

  • Todor RD (2016). Blending traditional and digital marketing. Bulletin of the Transilvania University of Brasov. Economic Sciences. Series V, 9(1), 51.

  • Veleva S S, Tsvetanova AI (2020). Characteristics of the digital marketing advantages and disadvantages. In IOP Conference Series: Materials Science and Engineering (Vol. 940, No. 1, p. 012065). IOP Publishing.

  • Wang P, McCarthy B (2020) What do people “like” on Facebook? Content marketing strategies used by retail bank brands in Australia and Singapore. Australasian Marketing Journal (AMJ).

  • Wei G (2017) Picture 2-tuple linguistic Bonferroni mean operators and their application to multiple attribute decision making. Int J Fuzzy Syst 19(4):997–1010

    Article  MathSciNet  Google Scholar 

  • Wei W, Liang J (2019) Information fusion in rough set theory: An overview. Inform Fusion 48:107–118

    Article  Google Scholar 

  • Xie Z, Du L, Lv X, Wang Q, Huang J, Fu T, Li S (2020) Evaluation and analysis of battery technologies applied to grid-level energy storage systems based on rough set theory. Trans Tianjin Univ. https://doi.org/10.1007/s12209-020-00237-9

    Article  Google Scholar 

  • Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MathSciNet  Google Scholar 

  • Yao Y (2008) Probabilistic rough set approximations. Int J Approximate Reasoning 49(2):255–271

    Article  Google Scholar 

  • Zhang X, Kumar V, Cosguner K (2017) Dynamically managing a profitable email marketing program. J Mark Res 54(6):851–866

    Article  Google Scholar 

  • Zhang S, Gao H, Wei G, Wei Y, Wei C (2019) Evaluation based on distance from average solution method for multiple criteria group decision making under picture 2-tuple linguistic environment. Mathematics 7(3):243

    Article  Google Scholar 

Download references

Funding

The author do not have any financial interests/personal relationships which may be considered as potential competing interests.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran

    Ebrahim Forghani, Reza Sheikh & Seyed Mohammad Hassan Hosseini

  2. Department of Mathematics, Kishore Bharati Bhagini Nivedita College, Ramkrishna Sarani, Behala, Kolkata, 700060, India

    Shib Sankar Sana

Authors
  1. Ebrahim Forghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Sheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Mohammad Hassan Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shib Sankar Sana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shib Sankar Sana.

Ethics declarations

Conflict of interest

The author declared that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Forghani, E., Sheikh, R., Hosseini, S.M.H. et al. The impact of digital marketing strategies on customer’s buying behavior in online shopping using the rough set theory. Int J Syst Assur Eng Manag 13, 625–640 (2022). https://doi.org/10.1007/s13198-021-01315-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-021-01315-4

Keywords

Search

Navigation