Navigating the Learning Landscape: Social Cognition and Task-Technology Fit as Predictors for MOOCs Continuance Intention by Sales Professionals

Aakash Kamble; Nitin Upadhyay; Nayna Abhang

doi:10.19173/irrodl.v25i1.7567

Authors

Aakash Kamble FLAME University, Lavale, Pune, India https://orcid.org/0000-0002-6152-302X
Nitin Upadhyay Indian Institute of Management Jammu, Jammu and Kashmir (U/T), India https://orcid.org/0000-0001-8301-8924
Nayna Abhang Medline Industries India Private Limited, Pune, India https://orcid.org/0009-0005-2034-7794

DOI:

https://doi.org/10.19173/irrodl.v25i1.7567

Keywords:

self-directed learning, MOOC, sales professional, social cognition theory, self-development, social recognition, task-technology fit, continued intentions

Abstract

Massive open online courses (MOOCs) have gained popularity among sales professionals who use them for self-directed learning and upskilling. However, research related to their intentions to continue learning is scarce. Drawing from the social cognition theory, this research aimed to address this gap by investigating the role of task-technology fit, self-development, and social recognition in sales professionals’ continued use of MOOCs. The study hinged on empirical research and used a survey to collect data from 366 sales professionals. The results suggest that task-technology fit, self-development, and social recognition play a significant role in sales professionals’ continued use of MOOCs. The study has practical implications for organizations promoting employee learning and development. The findings provide valuable information for MOOC designers and providers to develop more effective courses that meet the needs of sales professionals.

References

Alyoussef, I. Y. (2021). Massive open online course (MOOCs) acceptance: The role of task-technology fit (TTF) for higher education sustainability. Sustainability, 13(13), Article 7374. https://doi.org/10.3390/su13137374

Artis, A. B., & Harris, E. G. (2007). Self-Directed learning and sales force performance: An integrated framework. Journal of Personal Selling & Sales Management, 27(1), 9–24. https://doi.org/10.2753/PSS0885-3134270101

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175–1184. https://psycnet.apa.org/doi/10.1037/0003-066X.44.9.1175

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351¬–370. https://doi.org/10.2307/3250921

Bussey, K., & Bandura, A. (1999). Social cognitive theory of gender development and differentiation. Psychological Review, 106(4), 676–713. https://psycnet.apa.org/doi/10.1037/0033-295X.106.4.676

Cagiltay, N. E., Cagiltay, K., & Celik, B. (2020). An analysis of course characteristics, learner characteristics, and certification rates in MITx MOOCs. The International Review of Research in Open and Distributed Learning, 21(3), 121–139. https://doi.org/10.19173/irrodl.v21i3.4698

Castaño-Muñoz, J., Kalz, M., Kreijns, K., & Punie, Y. (2018). Who is taking MOOCs for teachers’ professional development on the use of ICT? A cross-sectional study from Spain. Technology, Pedagogy and Education, 27(5), 607–624.

Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216–227. https://doi.org/10.1016/j.compedu.2009.01.014

Conde, R., Prybutok, V., & Sumlin, C. (2021). The utilization of online sales forums by salespeople as a mesosystem for enhancing sales-activity knowledge. Journal of Business & Industrial Marketing, 36(4), 630–640. https://doi.org/10.1108/JBIM-03-2020-0129

Cooper, C. L., & Lu, L. (2016). Presenteeism as a global phenomenon: Unraveling the psychosocial mechanisms from the perspective of social cognitive theory. Cross Cultural & Strategic Management, 23(2). https://doi.org/10.1108/CCSM-09-2015-0106

Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201–214.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Filieri, R., Acikgoz, F., Ndou, V., & Dwivedi, Y. (2021). Is TripAdvisor still relevant? The influence of review credibility, review usefulness, and ease of use on consumers’ continuance intention. International Journal of Contemporary Hospitality Management, 33(1), 199–223. https://doi.org/10.1108/IJCHM-05-2020-0402

Fowler, F. J., Jr. (2002). Survey research methods (3rd ed.). Sage.

Goodhue, D. L., & Thompson, R. L. (1995). Task-Technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689

Greenhow, C., & Lewin, C. (2016). Social media and education: Reconceptualizing the boundaries of formal and informal learning. Learning, Media and Technology, 41(1), 6–30. https://doi.org/10.1080/17439884.2015.1064954

Griffiths, M. A., Goodyear, V. A., & Armour, K. M. (2022). Massive open online courses (MOOCs) for professional development: Meeting the needs and expectations of physical education teachers and youth sport coaches. Physical Education and Sport Pedagogy, 27(3), 276–290. https://doi.org/10.1080/17408989.2021.1874901

Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Helm, R., Möller, M., Mauroner, O., & Conrad, D. (2013). The effects of a lack of social recognition on online communication behavior. Computers in Human Behavior, 29(3), 1065–1077.

Homburg, C., Workman, J. P., Jr., & Jensen, O. (2002). A configurational perspective on key account management. Journal of Marketing, 66(2), 38–60. https://doi.org/10.1509/jmkg.66.2.38.18471

Hosen, M., Ogbeibu, S., Giridharan, B., Cham, T.-H., Lim, W. M., & Paul, J. (2021). Individual motivation and social media influence on student knowledge sharing and learning performance: Evidence from an emerging economy. Computers & Education, 172, Article 104262. https://doi.org/10.1016/j.compedu.2021.104262

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Huang, G., & Ren, Y. (2020). Linking technological functions of fitness mobile apps with continuance usage among Chinese users: Moderating role of exercise self-efficacy. Computers in Human Behavior, 103, 151–160. https://doi.org/10.1016/j.chb.2019.09.013

Hunter, G. K., & Perreault, W. D., Jr. (2006). Sales technology orientation, information effectiveness, and sales performance. Journal of Personal Selling & Sales Management, 26(2), 95–113. https://doi.org/10.2753/PSS0885-3134260201

Itani, O. S., Agnihotri, R., & Dingus, R. (2017). Social media use in B2B sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66, 64–79. https://doi.org/10.1016/j.indmarman.2017.06.012

Khashan, M. A., Elsotouhy, M. M., Alasker, T. H., & Ghonim, M. A. (2023). Investigating retailing customers’ adoption of augmented reality apps: Integrating the unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF). Marketing Intelligence & Planning, 41(5), 613–629.

Kim, D., Jung, E., Yoon, M., Chang, Y., Park, S., Kim, D., & Demir, F. (2021). Exploring the structural relationships between course design factors, learner commitment, self-directed learning, and intentions for further learning in a self-paced MOOC. Computers & Education, 166, Article 104171. https://doi.org/10.1016/j.compedu.2021.104171

Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.

Knowles, M. S., Holton, E. F., III, Swanson, R. A., & Robinson, P. A. (2020). The adult learner: The definitive classic in adult education and human resource development (9th ed.). Routledge. https://doi.org/10.4324/9780429299612

Koukis, N., & Jimoyiannis, A. (2019). MOOCS for teacher professional development: Exploring teachers’ perceptions and achievements. Interactive Technology and Smart Education, 16(1), 74–91. https://doi.org/10.1108/ITSE-10-2018-0081

Kuo, T. M., Tsai, C.-C., & Wang, J.-C. (2021). Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. The Internet and Higher Education, 51, Article 100819. https://doi.org/10.1016/j.iheduc.2021.100819

Lassk, F. G., Ingram, T. N., Kraus, F., & Di Mascio, R. (2012). The future of sales training: Challenges and related research questions. Journal of Personal Selling & Sales Management, 32(1), 141–154. https://doi.org/10.2753/PSS0885-3134320112

Liu, Y., Zhang, M., Qi, D., & Zhang, Y. (2022). Understanding the role of learner engagement in determining MOOCs satisfaction: A self-determination theory perspective. Interactive Learning Environments, 31(9), 6084–6098. https://doi.org/10.1080/10494820.2022.2028853

Martin, C. A., Rivera, D. E., Riley, W. T., Hekler, E. B., Buman, M. P., Adams, M. A., & King, A. C. (2014). A dynamical systems model of social cognitive theory. In D. Tilbury (Chair), 2014 American Control Conference (pp. 2407–2412). IEEE. https://doi.org/10.1109/ACC.2014.6859463

Milligan, C., & Littlejohn, A. (2017). Why study on a MOOC? The motives of students and professionals. The International Review of Research in Open and Distributed Learning, 18(2), 92–102. https://doi.org/10.19173/irrodl.v18i2.3033

Mısır, H., & Işık-Güler, H. (2022). “Be a better version of you!”: A corpus-driven critical discourse analysis of MOOC platforms’ marketing communication. Linguistics and Education, 69, Article 101021. https://doi.org/10.1016/j.linged.2022.101021

Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology, 36(10), 1081–1100. https://doi.org/10.1080/0144929X.2017.1347201

Nov, O., Naaman, M., & Ye, C. (2010). Analysis of participation in an online photo-sharing community: A multidimensional perspective. Journal of the American Society for Information Science and Technology, 61(3), 555–566. https://doi.org/10.1002/asi.21278

Olsson, U. (2016). Open courses and MOOCs as professional development – is the openness a hindrance? Education + Training, 58(2), 229–243. https://doi.org/10.1108/ET-01-2015-0006

Park, S., Jeong, S., & Ju, B. (2018). Employee learning and development in virtual HRD: Focusing on MOOCs in the workplace. Industrial and Commercial Training, 50(5), 261–271. https://doi.org/10.1108/ICT-03-2018-0030

Radford, A. W., Robles, J., Cataylo, S., Horn, L., Thornton, J., & Whitfield, K. E. (2014). The employer potential of MOOCs: A mixed-methods study of human resource professionals’ thinking on MOOCs. The International Review of Research in Open and Distributed Learning, 15(5), 1–25. https://doi.org/10.19173/irrodl.v15i5.1842

Rahi, S., Khan, M. M., & Alghizzawi, M. (2021). Factors influencing the adoption of telemedicine health services during COVID-19 pandemic crisis: An integrative research model. Enterprise Information Systems, 15(6), 769–793.

Rollins, M., Nickell, D., & Wei, J. (2014). Understanding salespeople’s learning experiences through blogging: A social learning approach. Industrial Marketing Management, 43(6), 1063–1069. https://doi.org/10.1016/j.indmarman.2014.05.019

Sablina, S., Kapliy, N., Trusevich, A., & Kostikova, S. (2018). How MOOC-Takers estimate learning success: Retrospective reflection of perceived benefits. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3768

Shao, Z. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOCs. Internet Research, 28(1), 232–250. https://doi.org/10.1108/IntR-11-2016-0335

Shapiro, H. B., Lee, C. H., Roth, N. E. W., Li, K., Çetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers & Education, 110, 35–50.

Shon, M., Lee, D., & Kim, J. H. (2021). Are global over-the-top platforms the destroyers of ecosystems or the catalysts of innovation? Telematics and Informatics, 60, Article 101581. https://doi.org/10.1016/j.tele.2021.101581

Singh, A., & Sharma, A. (2021). Acceptance of MOOCs as an alternative for internship for management students during COVID-19 pandemic: An Indian perspective. International Journal of Educational Management, 35(6), 1231–1244. https://doi.org/10.1108/IJEM-03-2021-0085

Spreng, R. A., Harrell, G. D., & Mackoy, R. D. (1995). Service recovery: Impact on satisfaction and intentions. Journal of Services Marketing, 9(1), 15–23. https://doi.org/10.1108/08876049510079853

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.

Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2022). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650. https://doi.org/10.1080/10494820.2019.1674888

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Wan, L., Xie, S., & Shu, A. (2020). Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model. SAGE Open, 10(3). https://doi.org/10.1177/2158244020941858

Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139, Article 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156

Wang, S.-L., & Wu, P.-Y. (2008). The role of feedback and self-efficacy on web-based learning: The social cognitive perspective. Computers & Education, 51(4), 1589–1598. https://doi.org/10.1016/j.compedu.2008.03.004

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028

Yan, M., Filieri, R., Raguseo, E., & Gorton, M. (2021). Mobile apps for healthy living: Factors influencing continuance intention for health apps. Technological Forecasting and Social Change, 166, Article 120644. https://doi.org/10.1016/j.techfore.2021.120644

Zhang, Y., Tian, Y., Yao, L., Duan, C., Sun, X., & Niu, G. (2022). Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning. Computers & Education, 179, Article 104427. https://doi.org/10.1016/j.compedu.2021.104427

Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92, 194–203. https://doi.org/10.1016/j.compedu.2015.10.012