Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses

Summary

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 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.

Study at a glance

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

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.

Behavioral intention (BI)

A person’s self-reported intention to use a technology, usually treated as the antecedent of actual use.

Learning management system (LMS)

Software such as Moodle used to deliver and manage online course content and assessments.

Limitations

• 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.

How to cite

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

@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}
}