Neural Computing and Applications

, Volume 31, Supplement 2, pp 877–890 | Cite as

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

  • Anil KumarEmail author
  • Gaurav Kabra
  • Eswara Krishna Mussada
  • Manoj Kumar Dash
  • Prashant Singh Rana
Original Article


Transactions through the web are now a progressive mechanism to access an ever-increasing range of services over more and more diverse environments. The internet provides many opportunities for companies to provide personalized online services to their customers, but the quality and novelty of some web services may adversely affect the appeal and user gratification. In the future, prediction of the consumer intention needs to be a main focus for selecting the web services for an application. The aim of this study is to predict online consumer repurchase intentions; to accomplish this objective a hybrid approach is chosen with a combination of machine learning techniques and artificial bee colony (ABC) algorithm being used. The study starts with identification of consumer characteristics for repurchase intention, followed by determining the feature selection of consumer characteristics and shopping mall attributes (with >0.1 threshold value) for the prediction model using ABC. Finally, validation using k-fold cross has been employed to measure the best classification model robustness. The classification models, viz. decision trees (C5.0), AdaBoost, random forest, support vector machine and neural network, are utilized to predict consumer purchase intention. Performance evaluation of identified models on training–testing partitions (70–30%) of the data set shows that the AdaBoost method outperforms other classification models, with sensitivity and accuracy of 0.95 and 97.58%, respectively, on testing the data set. Examining the consumer repurchase intentions by considering both shopping mall and consumer characteristics makes this study unique.


Artificial bee colony algorithm Classification Consumer k-Fold cross-validation Prediction Sensitivity 



The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Aghdaie MH, Alimardani M (2015) Target market selection based on market segment evaluation: a multiple attribute decision making approach. Int J Oper Res 24(3):262–278MathSciNetzbMATHGoogle Scholar
  2. 2.
    Aghdaie MH, Zolfani SH, Zavadskas EK (2014) Synergies of data mining and multiple attribute decision making. Procedia Soc Behav Sci 110:767–776Google Scholar
  3. 3.
    Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142Google Scholar
  4. 4.
    Al-dweeri RM, Obeidat ZM, Al-dwiry MA, Alshurideh MT, Alhorani AM (2017) The impact of e-service quality and e-loyalty on online shopping: moderating effect of e-satisfaction and e-trust. Int J Mark Stud 9(2):92–103Google Scholar
  5. 5.
    Anagnostopoulos C, Hand J, Adams NM (2012) Measuring classification performance: the hmeasure package. Accessed 18 Jan 2016
  6. 6.
    Azad N, Kasehchi H, Asgari H, Bagheri H (2014) An exploration study on detecting important factors influencing brand loyalty in retail stores. Decis Sci Lett 3(1):117–120Google Scholar
  7. 7.
    Azizi S, Makkizadeh V (2012) Consumer decision-making style: the case of Iranian young consumers. J Manag Res 4(2):88–102Google Scholar
  8. 8.
    Bilgihan A, Bujisic M (2015) The effect of website features in online relationship marketing: a case of online hotel booking. Electron Commer Res Appl 14(4):222–232Google Scholar
  9. 9.
    Blake BF, Neuendorf KA, Valdiserri CM (2003) Innovativeness and variety of internet shopping. Internet Res 13(3):156–169Google Scholar
  10. 10.
    Bright LF, Daugherty T (2012) Does customization impact advertising effectiveness? An exploratory study of consumer perceptions of advertising in customized online environments. J Mark Commun 18(1):19–37Google Scholar
  11. 11.
    Caballero JCF, Martínez FJ, Hervás C, Gutiérrez PA (2010) Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks. IEEE Trans Neural Networks 21(5):750–770Google Scholar
  12. 12.
    Chan TY, Kadiyali V, Park YH (2007) Willingness to pay and competition in online auctions. J Mark Res 44(2):324–333Google Scholar
  13. 13.
    Chen YC, Shang RA, Kao CY (2009) The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment. Electron Commer Res Appl 8(1):48–58Google Scholar
  14. 14.
    Childers TL, Carr CL, Peck J, Carson S (2002) Hedonic and utilitarian motivations for online retail shopping behavior. J Retail 77(4):511–535Google Scholar
  15. 15.
    Colantone I, Crinò R (2014) New imported inputs, new domestic products. J Int Econ 92(1):147–165Google Scholar
  16. 16.
    Cowart KO, Goldsmith RE (2007) The influence of consumer decision-making styles on online apparel consumption by college students. Int J Consum Stud 31(6):639–647Google Scholar
  17. 17.
    Danaher PJ, Wilson IW, Davis RA (2003) A comparison of online and offline consumer brand loyalty. Mark Sci 22(4):461–476Google Scholar
  18. 18.
    Duch-Brown N, Grzybowski L, Romahn A, Verboven F (2017) The impact of online sales on consumers and firms. Evidence from consumer electronics. Int J Ind Organ 52:30–62Google Scholar
  19. 19.
    Field A (2013) Discovering statistics using IBM SPSS statistics. Sage, Thousand OaksGoogle Scholar
  20. 20.
    Field JM, Heim GR, Sinha KK (2004) Managing quality in the e-service system: development and application of a process model. Prod Oper Manag 13(4):291–306Google Scholar
  21. 21.
    Forbes LP (2013) Does social media influence consumer buying behavior? An investigation of recommendations and purchases. J Bus Econ Res 11(2):107–112Google Scholar
  22. 22.
    Gao J, Zhang C, Wang K, Ba S (2012) Understanding online purchase decision making: the effects of unconscious thought, information quality, and information quantity. Decis Support Syst 53(4):772–781Google Scholar
  23. 23.
    Goldsmith RE, Hofacker CF (1991) Measuring consumer innovativeness. J Acad Mark Sci 19(3):209–221Google Scholar
  24. 24.
    Hair JF, Anderson RE, Babin BJ, Black WC (2010) Multivariate data analysis: a global perspective, vol 7. Pearson, Upper Saddle River, NJGoogle Scholar
  25. 25.
    Hastie T, Tibshirani R, Friedman J, Franklin J (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27(2):83–85Google Scholar
  26. 26.
    He H, Li Y, Harris L (2012) Social identity perspective on brand loyalty. J Bus Res 65(5):648–657Google Scholar
  27. 27.
    Herbes C, Ramme I (2014) Online marketing of green electricity in Germany—a content analysis of providers’ websites. Energy Policy 66:257–266Google Scholar
  28. 28.
    Hsing Wu C, Kao SC, Lin HH (2013) Acceptance of enterprise blog for service industry. Internet Res 23(3):260–297Google Scholar
  29. 29.
    Hsu CL, Chang KC, Chen MC (2012) The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. IseB 10(4):549–570Google Scholar
  30. 30.
    Hung YH, Huang TL, Hsieh JC, Tsuei HJ, Cheng CC, Tzeng GH (2012) Online reputation management for improving marketing by using a hybrid MCDM model. Knowl-Based Syst 35:87–93Google Scholar
  31. 31.
    Jeng SP (2008) Effects of corporate reputations, relationships and competing suppliers’ marketing programmes on customers’ cross-buying intentions. Serv Ind J 28(1):15–26Google Scholar
  32. 32.
    Jeng SP (2011) The effect of corporate reputations on customer perceptions and cross-buying intentions. Serv Ind J 31(6):851–862Google Scholar
  33. 33.
    Jin CH (2013) The effects of individual innovativeness on users’ adoption of Internet content filtering software and attitudes toward children’s Internet use. Comput Hum Behav 29(5):1904–1916Google Scholar
  34. 34.
    Johnson EJ, Moe WW, Fader PS, Bellman S, Lohse GL (2004) On the depth and dynamics of online search behavior. Manage Sci 50(3):299–308Google Scholar
  35. 35.
    Kang YJ, Lee WJ (2015) Self-customization of online service environments by users and its effect on their continuance intention. Serv Bus 9(2):321–342Google Scholar
  36. 36.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  37. 37.
    Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697Google Scholar
  38. 38.
    Keerthi SS, Gilbert EG (2002) Convergence of a generalized SMO algorithm for SVM classifier design. Mach Learn 46(1–3):351–360zbMATHGoogle Scholar
  39. 39.
    Kim C, Galliers RD, Shin N, Ryoo JH, Kim J (2012) Factors influencing Internet shopping value and customer repurchase intention. Electron Commer Res Appl 11(4):374–387Google Scholar
  40. 40.
    Kim HW, Gupta S (2009) A comparison of purchase decision calculus between potential and repeat customers of an online store. Decis Support Syst 47(4):477–487Google Scholar
  41. 41.
    Kim YK, Lee MY, Park SH (2014) Shopping value orientation: conceptualization and measurement. J Bus Res 67(1):2884–2890Google Scholar
  42. 42.
    Kochukalam CA, Peters MJ (2016) Shopping cart experience-building consumer experience at the check-out stage for online buying. Int J Res Soc Sci 6(1):483–490Google Scholar
  43. 43.
    Kumar A, Dash MK (2013) Constructing a measurement in service quality for Indian banks: structural equation modeling approach. J Internet Bank Commer 18(1):1–18MathSciNetGoogle Scholar
  44. 44.
    Kumar A, Dash MK (2014) Factor exploration and multi-criteria assessment method (AHP) of multi-generational consumer in electronic commerce. Int J Bus Excell 7(2):213–236MathSciNetGoogle Scholar
  45. 45.
    Kumar A, Dash MK (2015) E-service quality dimensions’ effect on customers’ willingness to buy: structural equation modelling approach. Int J Serv Oper Manag 22(3):287–303Google Scholar
  46. 46.
    Kumar A, Dash MK (2016) Using DEMATEL to construct influential network relation map of consumer decision-making in e-marketplace. Int J Bus Inf Syst 21(1):48–72Google Scholar
  47. 47.
    Küster I, Vila N, Canales P (2016) How does the online service level influence consumers’ purchase intentions before a transaction? A formative approach. Eur J Manag Bus Econ 25(3):111–120Google Scholar
  48. 48.
    Lai JY (2016) E-SERVCON and e-commerce success: applying the DeLone and McLean model. In: Butrime E, Zuzeviciute V (eds) Web design and development: concepts, methodologies, tools, and applications. IGI Global, Hershey, pp 816–838Google Scholar
  49. 49.
    Lee HH, Chang E (2011) Consumer attitudes toward online mass customization: an application of extended technology acceptance model. J Comput Mediat Commun 16(2):171–200Google Scholar
  50. 50.
    Lee MY, Kim YK, Fairhurst A (2009) Shopping value in online auctions: their antecedents and outcomes. J Retail Consum Serv 16(1):75–82Google Scholar
  51. 51.
    Lee KC, Kwon S (2008) Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: a causal map approach. Expert Syst Appl 35(4):1567–1574Google Scholar
  52. 52.
    Lee EJ, Shin SY (2014) When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo. Comput Hum Behav 31:356–366Google Scholar
  53. 53.
    Li M, Huang L, Tan CH, Wei KK (2013) Helpfulness of online product reviews as seen by consumers: source and content features. Int J Electron Commer 17(4):101–136Google Scholar
  54. 54.
    Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22Google Scholar
  55. 55.
    Ling CS, Suan SCT (2012) Online purchasing behaviour among the younger generation. Int J Undergrad Stud 1(1):1–7Google Scholar
  56. 56.
    Lu ACC, Gursoy D, Lu CYR (2016) Antecedents and outcomes of consumers’ confusion in the online tourism domain. Ann Tour Res 57:76–93Google Scholar
  57. 57.
    Lysonski S, Durvasula S (2013) Consumer decision making styles in retailing: evolution of mindsets and psychological impacts. J Consum Mark 30(1):75–87Google Scholar
  58. 58.
    Mahdjoubi L, Hao Koh J, Moobela C (2014) Effects of interactive real-time simulations and humanoid avatars on consumers’ responses in online house products marketing. Comput Aided Civil Infrastruct Eng 29(1):31–46Google Scholar
  59. 59.
    Maniak R, Midler C, Beaume R, Pechmann F (2014) Featuring capability: how carmakers organize to deploy innovative features across products. J Prod Innov Manag 31(1):114–127Google Scholar
  60. 60.
    McCullough Johnston K (2001) Why e-business must evolve beyond market orientation: applying human interaction models to computer-mediated corporate communications. Internet Res 11(3):213–225Google Scholar
  61. 61.
    Merle A, Chandon JL, Roux E, Alizon F (2010) Perceived value of the mass-customized product and mass customization experience for individual consumers. Prod Oper Manag 19(5):503–514Google Scholar
  62. 62.
    Merlo O, Lukas BA, Whitwell GJ (2012) Marketing’s reputation and influence in the firm. J Bus Res 65(3):446–452Google Scholar
  63. 63.
    Mohanty R, Ravi V, Patra MR (2010) Web-services classification using intelligent techniques. Expert Syst Appl 37(7):5484–5490Google Scholar
  64. 64.
    Nissen ME, Sengupta K (2006) Incorporating software agents into supply chains: experimental investigation with a procurement task. MIS Q 30(1):145–166Google Scholar
  65. 65.
    Nunnally JC (1978) Psychometric theory. McGraw-Hill, New YorkGoogle Scholar
  66. 66.
    Pappas N (2017) Effect of marketing activities, benefits, risks, confusion due to over-choice, price, quality and consumer trust on online tourism purchasing. J Mark Commun 23(2):195–218Google Scholar
  67. 67.
    Park YA, Gretzel U (2010) Influence of consumers’ online decision-making style on comparison shopping proneness and perceived usefulness of comparison shopping tools. J Electron Commer Res 11(4):342–354Google Scholar
  68. 68.
    Park DH, Lee J (2009) eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electron Commer Res Appl 7(4):386–398Google Scholar
  69. 69.
    Pires GD, Stanton J, Rita P (2006) The internet, consumer empowerment and marketing strategies. Eur J Mark 40(9/10):936–949Google Scholar
  70. 70.
    Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  71. 71.
    Rajeev PV, Rekha VS (2016) Opinion mining of user reviews using machine learning techniques and ranking of products based on features. In: Proceedings of the international conference on soft computing systems, Springer India, pp 627–637Google Scholar
  72. 72.
    Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, 1993, IEEE, pp 586–591Google Scholar
  73. 73.
    Rieger MO (2012) Why do investors buy bad financial products? Probability misestimation and preferences in financial investment decision. J Behav Finance 13(2):108–118Google Scholar
  74. 74.
    Riquelme IP, Román S (2014) The influence of consumers’ cognitive and psychographic traits on perceived deception: a comparison between online and offline retailing contexts. J Bus Ethics 119(3):405–422Google Scholar
  75. 75.
    Rizwan M, Sultan H, Parveen S, Nawaz S, Sattar S, Sana M (2013) Determinants of online shopping and moderating role of innovativeness and perceived risk. Asian J Empir Res 3(2):142–159Google Scholar
  76. 76.
    Rygielski C, Wang JC, Yen DC (2002) Data mining techniques for customer relationship management. Technol Soc 24(4):483–502Google Scholar
  77. 77.
    Seng JL, Chen TC (2010) An analytic approach to select data mining for business decision. Expert Syst Appl 37(12):8042–8057Google Scholar
  78. 78.
    Sicilia M, Ruiz S (2010) The effects of the amount of information on cognitive responses in online purchasing tasks. Electron Commer Res Appl 9(2):183–191Google Scholar
  79. 79.
    Soltani Z, Navimipour NJ (2016) Customer relationship management mechanisms: a systematic review of the state of the art literature and recommendations for future research. Comput Hum Behav 61:667–688Google Scholar
  80. 80.
    Statistics and facts about e-commerce in India. Accessed 18 Feb 2017
  81. 81.
    Thirumalai S, Sinha KK (2011) Customization of the online purchase process in electronic retailing and customer satisfaction: an online field study. J Oper Manag 29(5):477–487Google Scholar
  82. 82.
    Van der Heijden H, Verhagen T (2004) Online store image: conceptual foundations and empirical measurement. Inf Manag 41(5):609–617Google Scholar
  83. 83.
    Varma Citrin A, Sprott DE, Silverman SN, Stem DE Jr (2000) Adoption of Internet shopping: the role of consumer innovativeness. Ind Manag Data Syst 100(7):294–300Google Scholar
  84. 84.
    Verhagen T, Van Dolen W (2009) Online purchase intentions: a multi-channel store image perspective. Inf Manag 46(2):77–82Google Scholar
  85. 85.
    Wagner G, Schramm-Klein H, Steinmann S (2017) Consumers’ attitudes and intentions toward Internet-enabled TV shopping. J Retail Consum Serv 34:278–286Google Scholar
  86. 86.
    Wang Y, Tseng MM (2013) Customized products recommendation based on probabilistic relevance model. J Intell Manuf 24(5):951–960Google Scholar
  87. 87.
    Yang MH, Weng SS, Hsiao PI (2014) Measuring blog service innovation in social media services. Internet Res 24(1):110–128Google Scholar
  88. 88.
    Zhou Q, Xia R, Zhang C (2016) Online shopping behavior study based on multi-granularity opinion mining: China versus America. Cognit Comput 8(4):587–602Google Scholar
  89. 89.
    Zolfani SH, Aghdaie MH, Derakhti A, Zavadskas EK, Varzandeh MHM (2013) Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating. Expert Syst Appl 40(17):7111–7121Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Anil Kumar
    • 1
    Email author
  • Gaurav Kabra
    • 2
  • Eswara Krishna Mussada
    • 3
  • Manoj Kumar Dash
    • 4
  • Prashant Singh Rana
    • 5
  1. 1.School of ManagementBML Munjal UniversityGurgaonIndia
  2. 2.Department of Operations ManagementXavier Institute of ManagementBhubaneswarIndia
  3. 3.School of Engineering and TechnologyBML Munjal UniversityGurgaonIndia
  4. 4.Indian Institute of Information Technology and Management, GwaliorGwaliorIndia
  5. 5.Computer Science and Engineering DepartmentThapar University PatialaPunjabIndia

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