Modeling Online Shopping Behaviour During COVID-19 Using the TOE Framework


  • Imran Batada IoBM, Karachi


This Case Study explores the utilization of an online ordering platform run by Muller & Phipps Pakistan (M&P) during the COVID-19 lockdown. The company experienced a massive increase in online orders during the lockdown. The study explores the impact that organizational readiness (ORD), data and payment security (DPS), user satisfaction (USAT), user friendliness (UFR), and competition (COMP), have on the utilization of the online platform (UTIL). As in many other countries, the COVID-19 lockdown brought businesses in Pakistan to a complete halt, thereby putting pressure on people to resort to online purchases. Consequently, the app developed by M&P was widely used during the lockdown. This case study aims to determine the satisfaction levels of people who opted for online ordering. Data were obtained from an online survey delivered to customers who used the online ordering platform of M&P, and was used to determine their satisfaction levels. The study adopted the Technology-Organisation-Environment (TOE) framework to assess the impact of organizational readiness (ORD), data and payment security (DPS), user satisfaction (USAT), user friendliness (UFR), and competition (COMP), on utilization of the online store (UTIL). It was observed that technological factors played the most significant role in the utilization of online shopping. User satisfaction, data and payment security, and user-friendliness were found to be the most important technological factors affecting satisfaction levels.


Acock, A. C. (2013). Discovering structural equation modelling using Stata 13. College Station, TX: Stata Press.

Aljowaidi, M. A., S.Arbia, and S.Arabia . (2015). A Study of e-Commerce Adoption Using the TOE Framework in Saudi Retailers: Firm Motivations, Implementation and Benefits. September, 253.

Arbuckle, J. L., (2016) IBM SPSS Amos 24 User’s Guide. Armonk, NY: IBM.

Awa, H. O., et al. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science and Technology Policy Management, 6, 76-94.

Awa, H. O., Ojiabo, O. U., & Orokor, L. E. (2017). Integrated technology-organization-environment (TOE) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6), 893-921

Awa, H., et al. (2017). Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. The Bottom Line, 30

Bandalos, D. L., & Finney, S. J. (2010). Factor analysis: Exploratory and confirmatory. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 93–114-). New York: Routledge

Bauerová, R., & Klepek, M. (2018). Technology Acceptance as a Determinant of Online Grocery Shopping Adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis66(3), 737-746.

Belhekar, V. M. (2016). Statistics for Psychology Using R. Thousand Oaks, CA: Sage.

Boomsma, A., Hoyle, R. H., & Panter, A. T. (2012). The structural equation modeling research report. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 341–358). New York: Guilford Press.

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York: Guilford Publications.

Buuren, S. (2012). Flexible Imputation of Missing Data (Chapman & Hall/CRC Interdisciplinary Statistics). Chapman and Hall/CRC.

Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Mahwah, NJ: Erlbaum.

Chapman, C., & Feit, E. M. (2015). R for marketing research and analytics. New York, NY: Springer.

Chatterjee, S. (2015). Security and privacy issues in e-commerce: A proposed guidelines to mitigate the risk. Proceedings of 2015 IEEE International Advance Computing Conference (IACC) (pp. 393-396).

Chen, J. K. C., et al. (2013). Exploring e-readiness on e-commerce adoption of SMEs: Case study South-East Asia. Proceedings of 2013 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1382-1386).

Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1-13

Darlington, R. B., & Hayes, A. F. (2017). Regression Analysis and Linear Models: Concepts, Appli¬ca¬tions, and Implementa¬tion. New York: The Guilford Press

Dugard P, Todman J and Staines H (2010) Approaching multivariate analysis. A practical introduc¬tion. Second Edition. Routledge: New York.

Field, A. P. (2018). Discovering statistics using SPSS. London, England: SAGE.

Finch, W. H., & Bolin, J. E. (2017). Multilevel Modeling Using Mplus. Boca Raton, FL: Taylor & Francis Group

Fox, J., Byrnes, J. E., Boker, S., & Neale, M. C. (2012). Structural equation modeling in R with the sem and OpenMx packages. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 325–340). New York: Guilford Press.

Gana, K., Broc, G., (2019) Structural Equation Modeling with lavaan. London: Wiley

Gangwar, H., Date, H., and Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. J. Enterp. Inform. Manage. 28, 107–130

Gangwar, Hemlata & Date, Hema & Raoot, A.D. (2014). Review on IT adoption: Insights from recent technologies. Journal of Enterprise Information Management. 27. 488-502.

Garson, G. D. (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing.

Govindaraju, R., & Chandra, D. R. (2011). E-commerce adoption by Indonesian small, medium, and micro enterprises (SMMEs): Analysis of goals and barriers. Proceedings of 2011 IEEE 3rd International Conference on Communication Software and Networks (pp. 113-117)

Grolemund, G and Wickham, H (2017) R for Data Science O'Reilly Media: Gravenstein Highway North, Sebastopol, Canada.

Hadi Putra, P. O., & Santoso, H. B. (2020). Contextual factors and performance impact of e-business use in Indonesian small and medium enterprises (SMEs). Heliyon, 6(3),

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Los Angeles, CA: Sage

Hancock, G. R., & Mueller, R. O. (Eds.). (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing, Inc

Heck, R. H., & Thomas, S. L. (2015). An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus (3rd ed.). New York: Routledge.

Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. OpenStax. Rice University

Howitt, D., & Cramer, D. (2017). Understanding statistics in psychology with SPSS. Boston. Pearson Higher Education.

Hox, J. J. (2013). Multilevel Regression and Multilevel Structural Equation Modeling. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (pp. 281-294). New York: Oxford University Press

Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New York: Guilford Press.

Jaggia, S. and Kelly, A. (2013) Business Statistics, Communi¬cating with Numbers. McGraw-Hill, Irwin

Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data Analysis: A Model Comparison Approach. Routledge. New York

Kilic, A. (2018). Can Factor Scores be Used Instead of Total Score and Ability Estimation? International Journal of Assessment Tools in Education, 6(1), 25-35.

Kline, R. B., (2016) Principles and Practice of Structural Equation Modeling (4th edition). New York/London: Guilford.

Loehlin, J. C., & Beaujean, A. A. (2017). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. New York, NY: Taylor & Francis.

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81-95

Mohtaramzadeh, M., T.Ramayah, and C.Jun-Hwa . (2018). B2B E-Commerce Adoption in Iranian Manufacturing Companies: Analyzing the Moderating Role of Organizational Culture. International Journal of Human-Computer Interaction 34 (7): 621–639.

Morteza, G., & Sai, H. T. (2013). The role of owner/manager in adoption of electronic commerce in small businesses: The case of developing countries. Journal of Small Business and Enterprise Development, 20, 754-787.

Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén

Pallant, J. (2013). SPSS Survival Manual. A step by step guide to data analysis using SPSS, 4th edition. Allen & Unwin,

Pappas, G., I. O.Kourouthanassis, P. E.Giannakos, and M. N.Lekakos .(2017). The Interplay of Online Shopping Motivations and Experiential Factors on Personalized e-Commerce: A Complexity Theory Approach. Telematics and Informatics 34 (5): 730–742.

Raghunathan, T. (2015). Missing data analysis in practice. CRC Press, Boca Raton, FL

Rodríguez-Ardura I., Meseguer-Artola A. (2010). Toward a longitudinal model of e-commerce: environmental, technological, and organizational drivers of B2C adoption. Inform. Soc. 26 209–227

Rogers E. M. (1995). Diffusion of Innovations, 2nd ed. New York, NY: Free Press

Roy, T.K., R. Acharya and A. Roy. (2016). Statistical survey design and evaluating impact. Cambridge University Press, Delhi.

Schumacker, R. E., & Lomax, R. G. (2016). A Beginner’s Guide to Structural Equation Modeling (4th ed.). New York: Routledge

Scupola, A. (2009). SMEs’ e-commerce Adoption: Perspectives from Denmark and Australia. Journal of Enterprise Information Management 22 (1/2): 152–166.

Sweet, SA and Grace-Martin, K (2012) Data Analysis with SPSS: A First Course in Applied Statistics. Fourth Edition. Pearson: London

Tabachnick, B. G. & Fidell, L. S. (2017). Using multivariate statistics: Chicago: University of Chicago Press.

Thompson, B. (2018). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.

Tornatzky G. & Fleischer M. (1990). The Process of Technology Innovation, Lexington, MA, Lexington book

Tripopsakul, S. (2018). Social media adoption as a business platform: an integrated TAM-TOE framework. Polish Journal of Management Studies 18(2), 350-362.

Turban, J., J. K.Lee, D.King, T. P.Liang, and D.Turban . (2010). Electronic Commerce Bergen: Prentice Hall Press.

Ullman, J. (2013). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (6th ed.). New York: Pearson.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186-204

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3), 425-478.

Wang J. Wang X. (2019) Structural Equation Modeling: Applications Using Mplus. Chichester, West Sussex. John Wiley.

Wywial, J.L. (2015). Sampling designs dependent on sample parameters of auxiliary variables. Springer, New York

Yu, T.-K., M.-L.Lin, and Y.-K.Liao . (2017). Understanding Factors Influencing Information Communication Technology Adoption Behavior: The Moderators of Information Literacy and Digital Skills. Computers in Human Behavior 71: 196–208.