Measurement scale for online flow conditions A new approach based on shopping applications
Main Article Content
Keywords
Online Flow Conditions, Measurement Scale, Shopping Apps, Mobile Commerce
Abstract
The concept of online flow refers to users' immersive and holistic experience while browsing the internet. Although this cognitive state has been widely studied, it depends on specific conditions often analysed from the user's perspective, such as the balance between demands and abilities, the clarity of objectives, and the nature of feedback received. This study aims to explore online flow conditions specifically in shopping applications by proposing a measurement scale, an area that has received limited attention in the literature. To achieve this, an in-depth literature review was conducted, followed by methodological procedures for scale construction outlined by DeVellis (2017). This process involved defining relevant dimensions, formulating specific items, and applying them to a sample of 200 shopping applications from Brazil and Spain, along with rigorous validation and confirmation of the scale's internal consistency. The results reveal the creation of an innovative and statistically validated scale, demonstrating its effectiveness as a tool for measuring online flow conditions in virtual environments. Applying this scale provides valuable insights for developers, enabling the creation of digital environments that foster flow and enhance user engagement.
References
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Herrando, C., Jiménez-Martínez, J., & Martín-De Hoyos, M. J. (2019). Social commerce users’ optimal experience: Stimuli, response and culture. Journal of Electronic Commerce Research, 20(4), 199–218.
Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23–34. https://doi.org/10.1016/j.intmar.2008.10.003
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psy-chometrika, 23(3), 187–200. https://doi.org/10.1007/BF02289233
Kim, H.-C. (2010). Effective organization of design guidelines reflecting designer’s design strategies. International Journal of Industrial Ergonomics, 40(6), 669–688. https://doi.org/10.1016/j.ergon.2010.08.003
Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of web advertising, flow, and personalization. Computers in Human Behavior, 33, 256–269. https://doi.org/10.1016/j.chb.2014.01.015
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Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online con-sumer behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83
Lam, L. W. (2012). Impact of competitiveness on salespeople’s commitment and performance. Journal of Business Research, 65(9), 1328–1334. https://doi.org/10.1016/j.jbusres.2011.10.026
López, M. F. B., Virto, N. R., & San-Martín, S. (2018). Local food shopping: Factors affecting users’ behavioural e-loyalty. Administrative Sciences, 8(3), Article 47. https://doi.org/10.3390/admsci8030047
Maes, A., van Geel, A., & Cozijn, R. (2006). Signposts on the digital highway: The effect of se-mantic and pragmatic hyperlink previews. Interacting With Computers, 18(2), 265–282. https://doi.org/10.1016/j.intcom.2005.06.004
Mahnke, R., Benlian, A., & Hess, T. (2015). A grounded theory of online shopping flow. Inter-national Journal of Electronic Commerce, 19(3), 54–89. https://doi.org/10.1080/10864415.2015.1000222
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers’ m-commerce mobile applications: An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139–157. https://doi.org/10.1016/j.jbusres.2019.08.032
Malhotra, N. K., & Dash, S. B. (2016). Marketing research: An applied orientation (7th ed.). Pearson Education.
Mendes-da-Silva, W. (2019). Contributions and limitations of narrative reviews and systematic reviews in the business area. Journal of Contemporary Administration, 23(2), 149–166. https://doi.org/10.1590/1982-7849rac2019170322
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for sys-tematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), Ar-ticle e1000097. https://doi.org/10.1371/journal.pmed.1000097
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Neil, T. (2014). Mobile design pattern gallery: UI patterns for smartphone apps (2nd ed.). O’Reilly Media.
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42. https://doi.org/10.1287/mksc.19.1.22.15184
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Ozkara, B. Y., Ozmen, M., & Kim, J. W. (2017). Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. Journal of Retailing and Consumer Services, 37, 119–131. https://doi.org/10.1016/j.jretconser.2017.04.001
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Pahlevan Sharif, S., She, L., Yeoh, K. K., & Naghavi, N. (2022). Heavy social networking and online compulsive buying: The mediating role of financial social comparison and mate-rialism. Journal of Marketing Theory and Practice, 30(2), 213–225. https://doi.org/10.1080/10696679.2021.1909757
Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199. https://doi.org/10.1016/j.im.2014.08.008
Peifer, C., & Engeser, S. (Eds.). (2021). Advances in flow research (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-53468-4
Pervan, M., Curak, M., & Pavic Kramaric, T. (2018). The influence of industry characteristics and dynamic capabilities on firms’ profitability. International Journal of Financial Studies, 6(1), Article 4. https://doi.org/10.3390/ijfs6010004
Punchoojit, L., & Hongwarittorrn, N. (2017). Usability studies on mobile user interface design patterns: A systematic literature review. Advances in Human-Computer Interaction, 2017, Article 6787504. https://doi.org/10.1155/2017/6787504
Rettie, R. (2001). An exploration of flow during internet use. Internet Research, 11(2), 103–113. https://doi.org/10.1108/10662240110388294
Revelle, W. (2024). psych: Procedures for psychological, psychometric, and personality re-search (R package version 2.4.3) [Computer software]. Northwestern University. https://cran.r-project.org/web/packages/psych /
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). E-learning continuance: The impact of in-teractivity and the mediating role of imagery, presence, and flow. Information & Man-agement, 53(4), 504–516. https://doi.org/10.1016/j.im.2015.11.005
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). A PLS-neural network analysis of motiva-tional orientations leading to Facebook engagement and the moderating roles of flow and age. Frontiers in Psychology, 11, Article 1869. https://doi.org/10.3389/fpsyg.2020.01869
Saffer, D. (2014). Microinteractions: Designing with details. O’Reilly Media.
Salleh, N. A. M., Ha, N. C., Kitchen, P. J., & Mohd-Any, A. A. (2019). Online purchasing: The role of web experience factors. International Journal of Electronic Marketing and Re-tailing, 10(3), 260–282. https://doi.org/10.1504/IJEMR.2019.100218
Shih, H.-P., & Jin, B. (2011). Driving goal-directed and experiential online shopping. Journal of Organizational Computing and Electronic Commerce, 21(2), 136–157. https://doi.org/10.1080/10919392.2011.568614
Sin, A. S., & Wu, J. (2019). Effects of 3D vs 2D interfaces and product-coordination methods. International Journal of Retail & Distribution Management, 47(8), 855–871. https://doi.org/10.1108/IJRDM-08-2018-0165
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Tezza, R., Bornia, A. C., & Andrade, D. F. (2011). Measuring web usability using item response theory: Principles, features and opportunities. Interacting With Computers, 23(2), 167–175. https://doi.org/10.1016/j.intcom.2011.02.004
Tezza, R., Bornia, A. C., & Spenassato, D. (2016). Empirical comparison of the multidimen-sional models of item response theory in e-commerce. Production, 36(3), 503–532. https://doi.org/10.1590/0103-6513.159013
Wilson, M. (2005). Constructing measures: An item response modeling approach. Lawrence Erlbaum Associates.
Ameen, N., Tarhini, A., Mahmood, S., & Madichie, N. O. (2021). Going with the flow: Smart shopping malls and omnichannel retailing. Journal of Services Marketing, 35(3), 325–348. https://doi.org/10.1108/JSM-02-2020-0062
Ayada, W. M., & Hammad, M. A. E. E. (2023). Design quality criteria for smartphone applica-tions interface and its impact on user experience and usability. International Design Journal, 13(4), 339–354. https://doi.org/10.21608/idj.2023.216159.1100
Babbie, E. (2005). The basics of social research (3rd ed.). Wadsworth Publishing.
Barta, S., Flavián, C., & Gurrea, R. (2021). Managing consumer experience and online flow: Differences in handheld devices vs PCs. Technology in Society, 64, Article 101477. https://doi.org/10.1016/j.techsoc.2020.101477
Barthelmäs, M., & Keller, J. (2021). Antecedents, boundary conditions and consequences of flow. In C. Peifer & S. Engeser (Eds.), Advances in flow research (2nd ed., pp. 71–107). Springer. https://doi.org/10.1007/978-3-030-53468-4_3
Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: An integrated model of trust, user experience and branding. Computers in Human Behavior, 61, 103–113. https://doi.org/10.1016/j.chb.2016.03.014
Bilgihan, A., Okumus, F., Nusair, K., & Bujisic, M. (2014). Online experiences: Flow theory, measuring online customer experience in e-commerce and managerial implications for the lodging industry. Information Technology & Tourism, 14(1), 49–71. https://doi.org/10.1007/s40558-013-0003-4
Bolen, M. C., & Özen, Ü. (2020). Understanding the factors affecting consumers’ continuance intention in mobile shopping: The case of private shopping clubs. International Jour-nal of Mobile Communications, 18(1), 1–24. https://doi.org/10.1504/IJMC.2020.104517
Brunner, M., Nagy, G., & Wilhelm, O. (2012). A tutorial on hierarchically structured constructs. Journal of Personality, 80(4), 796–846. https://doi.org/10.1111/j.1467-6494.2011.00749.x
Chen, T.-Y., Hsu, T.-C., & Lu, Y.-H. (2018). Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41, 281–287. https://doi.org/10.1016/j.jretconser.2017.12.010
Chernyshenko, O. S., Stark, S., Chan, K.-Y., Drasgow, F., & Williams, B. A. (2001). Fitting item response theory models to two personality inventories: Issues and insights. Multivari-ate Behavioral Research, 36(4), 523–562. https://doi.org/10.1207/S15327906MBR3604_03
Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale devel-opment. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
Daud, K. A. M., Khidzir, N. Z., Ismail, A. R., & Abdullah, F. A. (2018). Validity and reliability of instrument to measure social media skills among small and medium entrepreneurs at Pengkalan Datu River. International Journal of Development and Sustainability, 7(3), 1026–1037.
DataReportal. (2024). Digital 2024: Global overview report. https://datareportal.com/reports/digital-2024-global-overview-report
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publica-tions.
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study—A case of Chi-na. Computers in Human Behavior, 53, 249–262. https://doi.org/10.1016/j.chb.2015.07.014
Gil, A. C. (2002). How to develop research projects (4th ed.). Atlas.
Guo, Y. M., & Poole, M. S. (2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 19(4), 369–390. https://doi.org/10.1111/j.1365-2575.2007.00292.x
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Herrando, C., Jiménez-Martínez, J., & Martín-De Hoyos, M. J. (2019). Social commerce users’ optimal experience: Stimuli, response and culture. Journal of Electronic Commerce Research, 20(4), 199–218.
Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23–34. https://doi.org/10.1016/j.intmar.2008.10.003
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psy-chometrika, 23(3), 187–200. https://doi.org/10.1007/BF02289233
Kim, H.-C. (2010). Effective organization of design guidelines reflecting designer’s design strategies. International Journal of Industrial Ergonomics, 40(6), 669–688. https://doi.org/10.1016/j.ergon.2010.08.003
Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of web advertising, flow, and personalization. Computers in Human Behavior, 33, 256–269. https://doi.org/10.1016/j.chb.2014.01.015
King, A. B. (2003). Speed up your site: Web site optimization. New Riders.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online con-sumer behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83
Lam, L. W. (2012). Impact of competitiveness on salespeople’s commitment and performance. Journal of Business Research, 65(9), 1328–1334. https://doi.org/10.1016/j.jbusres.2011.10.026
López, M. F. B., Virto, N. R., & San-Martín, S. (2018). Local food shopping: Factors affecting users’ behavioural e-loyalty. Administrative Sciences, 8(3), Article 47. https://doi.org/10.3390/admsci8030047
Maes, A., van Geel, A., & Cozijn, R. (2006). Signposts on the digital highway: The effect of se-mantic and pragmatic hyperlink previews. Interacting With Computers, 18(2), 265–282. https://doi.org/10.1016/j.intcom.2005.06.004
Mahnke, R., Benlian, A., & Hess, T. (2015). A grounded theory of online shopping flow. Inter-national Journal of Electronic Commerce, 19(3), 54–89. https://doi.org/10.1080/10864415.2015.1000222
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers’ m-commerce mobile applications: An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139–157. https://doi.org/10.1016/j.jbusres.2019.08.032
Malhotra, N. K., & Dash, S. B. (2016). Marketing research: An applied orientation (7th ed.). Pearson Education.
Mendes-da-Silva, W. (2019). Contributions and limitations of narrative reviews and systematic reviews in the business area. Journal of Contemporary Administration, 23(2), 149–166. https://doi.org/10.1590/1982-7849rac2019170322
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for sys-tematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), Ar-ticle e1000097. https://doi.org/10.1371/journal.pmed.1000097
Moreira, J., Jr., Tezza, R., Andrade, D. F., & Bornia, A. C. (2013). Algorithm of a computerized adaptive test based on the theory of response to the item for estimating the usability of e-commerce sites. Production, 23(3), 525–536. https://doi.org/10.1590/S0103-65132012005000073
Nayal, P., & Pandey, N. (2020). Digital coupon redemption: Conceptualization, scale develop-ment and validation. Australasian Journal of Information Systems, 24, 1–22. https://doi.org/10.3127/ajis.v24i0.2249
Neil, T. (2014). Mobile design pattern gallery: UI patterns for smartphone apps (2nd ed.). O’Reilly Media.
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42. https://doi.org/10.1287/mksc.19.1.22.15184
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Ozkara, B. Y., Ozmen, M., & Kim, J. W. (2017). Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. Journal of Retailing and Consumer Services, 37, 119–131. https://doi.org/10.1016/j.jretconser.2017.04.001
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Pahlevan Sharif, S., She, L., Yeoh, K. K., & Naghavi, N. (2022). Heavy social networking and online compulsive buying: The mediating role of financial social comparison and mate-rialism. Journal of Marketing Theory and Practice, 30(2), 213–225. https://doi.org/10.1080/10696679.2021.1909757
Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199. https://doi.org/10.1016/j.im.2014.08.008
Peifer, C., & Engeser, S. (Eds.). (2021). Advances in flow research (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-53468-4
Pervan, M., Curak, M., & Pavic Kramaric, T. (2018). The influence of industry characteristics and dynamic capabilities on firms’ profitability. International Journal of Financial Studies, 6(1), Article 4. https://doi.org/10.3390/ijfs6010004
Punchoojit, L., & Hongwarittorrn, N. (2017). Usability studies on mobile user interface design patterns: A systematic literature review. Advances in Human-Computer Interaction, 2017, Article 6787504. https://doi.org/10.1155/2017/6787504
Rettie, R. (2001). An exploration of flow during internet use. Internet Research, 11(2), 103–113. https://doi.org/10.1108/10662240110388294
Revelle, W. (2024). psych: Procedures for psychological, psychometric, and personality re-search (R package version 2.4.3) [Computer software]. Northwestern University. https://cran.r-project.org/web/packages/psych /
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). E-learning continuance: The impact of in-teractivity and the mediating role of imagery, presence, and flow. Information & Man-agement, 53(4), 504–516. https://doi.org/10.1016/j.im.2015.11.005
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). A PLS-neural network analysis of motiva-tional orientations leading to Facebook engagement and the moderating roles of flow and age. Frontiers in Psychology, 11, Article 1869. https://doi.org/10.3389/fpsyg.2020.01869
Saffer, D. (2014). Microinteractions: Designing with details. O’Reilly Media.
Salleh, N. A. M., Ha, N. C., Kitchen, P. J., & Mohd-Any, A. A. (2019). Online purchasing: The role of web experience factors. International Journal of Electronic Marketing and Re-tailing, 10(3), 260–282. https://doi.org/10.1504/IJEMR.2019.100218
Shih, H.-P., & Jin, B. (2011). Driving goal-directed and experiential online shopping. Journal of Organizational Computing and Electronic Commerce, 21(2), 136–157. https://doi.org/10.1080/10919392.2011.568614
Sin, A. S., & Wu, J. (2019). Effects of 3D vs 2D interfaces and product-coordination methods. International Journal of Retail & Distribution Management, 47(8), 855–871. https://doi.org/10.1108/IJRDM-08-2018-0165
Stepchenkova, S., Tang, L., Jang, S. C., Kirilenko, A. P., & Morrison, A. M. (2010). Benchmark-ing CVB website performance: Spatial and structural patterns. Tourism Management, 31(5), 611–620. https://doi.org/10.1016/j.tourman.2009.06.015
Supriadi, O. A. (2019). User interface design of mobile-based commerce. IOP Conference Se-ries: Materials Science and Engineering, 662(2), Article 022047. https://doi.org/10.1088/1757-899X/662/2/022047
Tezza, R., Bornia, A. C., & Andrade, D. F. (2011). Measuring web usability using item response theory: Principles, features and opportunities. Interacting With Computers, 23(2), 167–175. https://doi.org/10.1016/j.intcom.2011.02.004
Tezza, R., Bornia, A. C., & Spenassato, D. (2016). Empirical comparison of the multidimen-sional models of item response theory in e-commerce. Production, 36(3), 503–532. https://doi.org/10.1590/0103-6513.159013
Wilson, M. (2005). Constructing measures: An item response modeling approach. Lawrence Erlbaum Associates.
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Borba, E. H. de, Tezza, R., & Meseguer-Artola, A. (2025). Measurement scale for online flow conditions: A new approach based on shopping applications. Journal of Telecommunications and the Digital Economy, 13(2), 1-24. https://doi.org/10.18080/jtde.v13n2.1198
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Digital Economy & Society
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How to Cite
Borba, E. H. de, Tezza, R., & Meseguer-Artola, A. (2025). Measurement scale for online flow conditions: A new approach based on shopping applications. Journal of Telecommunications and the Digital Economy, 13(2), 1-24. https://doi.org/10.18080/jtde.v13n2.1198
