Journal article · 2017
Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses
Journal of Organizational and End User Computing, 29(3), pp. 49–67 · Published
Summary
Strang and Vajjhala (2017) surveyed a convenience sample of 65 senior supply chain management students at the State University of New York who had to use a new mandatory learning management system, testing which Technology Acceptance Model 3 (TAM3) factors explain resistance and predict actual course grade. Perceived usefulness, enjoyment, voluntariness and results demonstrability predicted behavioral intention. Behavioral intention predicted actual grade performance (β = .052, T = 2.44, p = .02), confirming a key tenet of TAM3.
Practical implication. Decision makers who understand why management students resist new mandatory software could develop strategies to address the critical success factors, in the hope of increasing organizational performance.
- The Problem
- Universities impose mandatory learning management systems on students, yet it is unclear which Technology Acceptance Model 3 factors explain resistance and whether they predict actual performance.
- The Methodology
- Post-positivist within-group survey of 65 senior supply chain management students at SUNY using the TAM3 instrument, with hierarchical, surface response and multiple regression on behavioural intention and actual course grade.
- The Core Finding
- Among 65 senior supply chain students using a new mandatory LMS, perceived usefulness, enjoyment, voluntariness and results demonstrability explained 52.3% of behavioral intention, and behavioral intention predicted actual course grade — confirming a key TAM3 tenet with a performance outcome rather than self-report alone.
- The Citation
- Strang, K. D., & Vajjhala, N. R. (2017). Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses. Journal of Organizational and End User Computing, 29(3), 49–67. https://doi.org/10.4018/JOEUC.2017070103
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The study analysed 65 senior undergraduate supply chain management students at the State University of New York (SUNY) who had to use a new mandatory Moodle learning management system in an online course, using course grade as the measure of actual behavior.
- Finding 2. In the SUNY LMS study, a multiple regression model with four factors—perceived usefulness, enjoyment, voluntariness and results demonstrability—captured 52.3% of the variance in behavioral intention (p < .01).
- Finding 3. The best-fitting model for actual course grade in the SUNY LMS study captured 48.6% of the variance (adjusted r² = 42.3, p < .01) using perceived resources, perceived ease of use, enjoyment, voluntariness and behavioral intention.
- Finding 4. Behavioral intention to use the mandatory LMS predicted students’ actual grade performance (multiple regression β = .052, T = 2.44, p = .02), confirming a key tenet of TAM3.
- Finding 5. Among the 65 supply chain students, perceived enjoyment was the only factor significantly correlated with behavioral intention (R = +0.59, p < .05), and none of the TAM3 factors were significantly correlated with grade.
- Finding 6. Computer self-efficacy, perceptions of external control, computer anxiety, subjective norm, image, job relevance and output quality were not causally related to either behavioral intention or actual performance in the LMS study.
- Finding 7. Including all TAM3 and demographic factors, the regression captured 88.3% of the variance in behavioral intention (adjusted r² = 60.4%) and the surface response regression captured 85.5% of the variance in grade (adjusted r² = 51.2%), but the authors judged these all-factor models theoretically weak.
Source: Strang & Vajjhala (2017), Journal of Organizational and End User Computing, 29(3), pp. 49–67. DOI: 10.4018/JOEUC.2017070103
The study in detail
What question does this paper answer?
Which Technology Acceptance Model 3 (TAM3) factors explain students’ resistance to a new mandatory learning management system (LMS), and do they predict actual performance (course grade) as well as behavioral intention?
What did the study find, in detail?
In a sample of 65 senior supply chain management students at SUNY using a new mandatory Moodle LMS, perceived usefulness, enjoyment, voluntariness and results demonstrability predicted behavioral intention, while perceived resources, perceived ease of use, enjoyment, voluntariness and behavioral intention predicted actual course grade. Behavioral intention predicted actual performance (β = .052, T = 2.44, p = .02); computer self-efficacy, external control, computer anxiety, subjective norm, image, job relevance and output quality were not supported.
Why does it matter?
The authors argue that management students’ technology acceptance motivations will generalize to the future supply chain workforce, so understanding why they resist new mandatory software can help decision makers address critical success factors and improve organizational performance. They also contribute parsimonious models with very few factors that capture 56% of the variance in behavioral intention and 49% in actual performance.
This study is part of the evidence overview What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS
Methods and data
| Research question | Can TAM3 identify predictors of resistance to a new mandatory LMS, measured against both behavioral intention and actual grade? |
|---|---|
| Design | Post-positivist, theory-driven within-group study testing 14 hypotheses from the a priori TAM3 instrument. |
| Sample | 65 senior undergraduate global supply chain management students at SUNY Plattsburgh and Queensbury (43% female, mean age 22.1, 100% domestic US). |
| Methods | Online TAM3 survey (8-point scale), Cronbach’s alpha, correlations, hierarchical and surface response regression, then multiple regression on behavioral intention and course grade. |
| Main result | Behavioral intention predicted actual grade (β = .052, T = 2.44, p = .02); enjoyment and voluntariness were linked to both intention and performance. |
| Implication | Students were ‘hedonistic’: positive enjoyment of the mandatory technology accounted for the absence of anxiety, self-efficacy or control effects. |
| Citation | Strang & Vajjhala (2017) · DOI 10.4018/JOEUC.2017070103 |
Definitions and key terms
- Technology Acceptance Model 3 (TAM3)
- An extension of the Technology Acceptance Model that explains behavioral intention to use a technology through factors such as perceived usefulness, perceived ease of use, enjoyment, subjective norm and computer self-efficacy. — as used in Strang & Vajjhala (2017)
- Behavioral intention (BI)
- A person’s self-reported intention to use a technology, usually treated as the antecedent of actual use. — as used in Strang & Vajjhala (2017)
- Learning management system (LMS)
- Software such as Moodle used to deliver and manage online course content and assessments. — as used in Strang & Vajjhala (2017)
Abstract
The authors explored how a technology model captured the factors that motivated and demotivated students to accept a new learning management system in online supply chain courses at an accredited public American university. Technology resistance is a well-known social science problem that results in reduced performance in business although it has rarely been examined in higher education. A new LMS is mandatory technology that is necessary to use in order to perform well in online global supply chain management courses. The authors drew a sample of graduating supply chain management students to explore this problem because these participants represent the next generation of employees who are likely to work with mandatory technology. The authors argue that it is important to study management students because their technology acceptance motivations will generalize to the future supply chain workforce. If decision makers could understand why management students resist new software, then they could develop strategies to address the critical success factors in hopes that organizational performance might be increased. The design was statistically powerful according to social research standards because the authors examined actual behavior by measuring grade as the dependent variable instead of relying on behavioral intent (BI) which is a subjective perceptional factor because participants are generally asked to self-report this through a survey. In keeping with published empirical literature the authors determined that perceived usefulness predicted BI using multiple regression. In contrary to the existing literature, they did not find that perceived resources (PR) was causally related to actual performance, although they did observe through regression that BI could be forecasted from PR. Their regression model indicated that perceived ease of use (PEOU) was not related to BI but in regression they found PEOU significantly impacted actual performance. These were two contradictory findings that PU impacted BI but not actual performance yet PR and PEOU predicted actual performance but not BI. Another unique departure from the empirical literature was that Computer Self Efficacy (CSE), Perceptions of External Control (PEC), and Computer Anxiety (CANX) were not related to either BI or actual performance. An interesting finding was that perceived enjoyment was strongly related to both BI and actual performance, although the perceptions were opposite between intention versus actual behavior. Multiple regression revealed that lower perceptions of enjoyment was significantly linked to BI while strong perceptions of enjoyment predicted actual performance. Although the authors were certain that peer influence would impact BI and actual performance, in their sample they did not find any support for subjective norm pressure on BI or actual performance. In a similar breakthrough they determined that job relevance and output quality were not causally linked to BI or actual performance. Another statistically significant finding was that positive perceptions of voluntariness were causally related to BI through multiple regression tests and also positively related to actual performance based on hierarchical regression. The authors determined that results demonstrability was causally related to BI from their multiple regression tests but interestingly it was not related to actual performance. The authors extensively discuss the above findings in their conclusions and provide many recommendations for future research. Finally, another valuable contribution the authors made to the scholarly community of practice through this study was to develop large effect-size parsimonious models with very few required factors that captured 56% of the variance on BI and 49% of the variance on actual performance.
Abstract as published in Journal of Organizational and End User Computing.
Keywords: Actual Behavior; Behavioral Intent; Higher Education; Learning Management System; Online Supply Chain Course; Technology Resistance
Limitations and open questions
- The study drew a small convenience sample (N = 65) from undergraduate supply chain management students at a public American university, which reduces the ability of the results to generalize outside this population.
- All participants were American, so culture could not be tested as a predictor; replication with other disciplines, non-American universities, young employees in the workplace and larger samples is recommended.
Open questions
- Does national culture predict technology resistance when tested directly with multicultural samples of students and young employees, for example in ERP internships?
Open questions from the evidence overview Technology adoption and digital consumers.
Practical implications
- Decision makers can use evidence on why management students resist new mandatory software, because the authors argue that these students’ technology acceptance motivations will generalize to the future supply chain workforce.
- For universities that impose a mandatory learning management system, enjoyment and voluntariness were linked to both behavioral intention and actual performance among the 65 students surveyed, although enjoyment perceptions were opposite between intention and actual behavior.
- Universities can note that, among 65 students using a new mandatory learning management system, computer self-efficacy, perceptions of external control and computer anxiety were not related to either behavioral intention or actual performance.
- For decision makers, generalization is limited: the small convenience sample (N = 65) of undergraduate supply chain management students at a public American university reduces the ability of the results to generalize outside this population.
Drawn from Strang & Vajjhala (2017), Journal of Organizational and End User Computing, 29(3), pp. 49–67.
When this research may be relevant
This article may be relevant to literature on technology acceptance and resistance in higher education, mandatory learning management systems such as Moodle, TAM3 validation against actual performance rather than intention alone, and online supply chain management education.
Research topics addressed: technology resistance; technology acceptance; TAM3; learning management system; Moodle; online learning; higher education; supply chain management education; behavioral intention; actual behavior; perceived enjoyment; multiple regression; hierarchical regression; United States
How to cite
- APA 7
- Strang, K. D., & Vajjhala, N. R. (2017). Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses. Journal of Organizational and End User Computing, 29(3), 49–67. https://doi.org/10.4018/JOEUC.2017070103
- MLA 9
- Strang, Kenneth David, and Narasimha Rao Vajjhala. “Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses.” Journal of Organizational and End User Computing, vol. 29, no. 3, 2017, pp. 49–67. https://doi.org/10.4018/JOEUC.2017070103.
- IEEE
- K. D. Strang and N. R. Vajjhala, “Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses,” Journal of Organizational and End User Computing, vol. 29, no. 3, pp. 49–67, Jul. 2017, doi: 10.4018/JOEUC.2017070103.
BibTeX
@article{strang2017student,
title = {Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses},
author = {Strang, Kenneth David and Vajjhala, Narasimha Rao},
journal = {Journal of Organizational and End User Computing},
volume = {29},
number = {3},
pages = {49--67},
year = {2017},
publisher = {IGI Global},
doi = {10.4018/JOEUC.2017070103},
url = {https://doi.org/10.4018/JOEUC.2017070103}
}RIS
TY - JOUR TI - Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses AU - Strang, Kenneth David AU - Vajjhala, Narasimha Rao JO - Journal of Organizational and End User Computing PY - 2017 DA - 2017/07 VL - 29 IS - 3 SP - 49 EP - 67 PB - IGI Global DO - 10.4018/JOEUC.2017070103 UR - https://www.narasimharao.net/research/student-resistance-mandatory-lms-tam3-supply-chain/ LA - en ER -
Links and files
Related work
Related work: the literature this study cites
Strang and Vajjhala cite estimates that 65%-75% of new information technology initiatives are considered failures, mostly for people-related rather than technical reasons[1]. The study builds on the Technology Acceptance Model (TAM), in which perceived usefulness and perceived ease of use were correlated with self-reported system use[2] and the usefulness-intention link held across meta-analyses[3]. TAM3, cited as explaining 70% of the variance in behavioral intention[4], and UTAUT2, which added hedonic motivation, price value and habit[5], supply the constructs tested. The results partly match TAM: usefulness predicted intention, as the TAM literature leads one to expect[2][3]. They depart from it in finding no role for computer self-efficacy, anxiety or perceived control[4], which the authors attribute to ‘hedonistic’ students whose enjoyment of the technology offset those concerns, an interpretation close to UTAUT2’s hedonic motivation[5]. The paper cites evidence that culture affects technology acceptance across countries[6] and that the technology-organization-environment framework has been applied to SMEs[7], and calls for culture to be tested directly.
- [1] Rizzuto, T. E., & Reeves, J. (2007). A multidisciplinary meta-analysis of human barriers to technology implementation. Consulting Psychology Journal: Practice and Research, 59(3), 226–240. https://doi.org/10.1037/1065-9293.59.3.226
- [2] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- [3] King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management Journal, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
- [4] Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences Journal, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
- [5] Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157–178.
- [6] Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management Journal, 33(1), 1–11. https://doi.org/10.1016/S0378-7206(97)00026-8
- [7] Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874. https://doi.org/10.1016/j.ijinfomgt.2013.07.001
From the evidence overview What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Strang & Vajjhala (2017).
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