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

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

Kenneth David Strang, Narasimha Rao Vajjhala
Copyright: © 2017 |Pages: 19
DOI: 10.4018/JOEUC.2017070103
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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.
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It is obvious that technology acceptance is increasing with smart phones and other gadgets. However, the motivations are quite different when you require employees to accept and use new ‘mandatory’ technology in the workplace. Technology resistance was found to be the most important predictor for up to 75% of customized software system failures in the United States (Rizzuto & Reeves, 2007). The literature confirms that some employees will resist new mandatory technology and this has now become an expensive problem to organizations in many countries (Al-Qudah & Ahmad, 2014; Baptista & Oliveira, 2015; Gangwar, Date & Raoot, 2014; Gupta, Seetharaman & Raj, 2013; Im, Hong & Kang, 2011; Lian, 2015; Van der Schyff & Krauss, 2014).

In addition to the organizational cost of technology resistance, there is an opportunity lost dimension. Decision makers often resist implementing new technology because they anticipate end user resistance (Luor & Lu, 2012; McDaniel, 2011; Razi & Karim, 2011). The result is a decreasing performance domino effect where managers tend to avoid innovations that would increase revenue or decrease expenses due to anticipated technology resistance accompanied by decreased productivity. New mandatory technology avoidance and software under-utilization have been cited as key reasons for the productivity paradox, which is a contradictory relationship between a high investment in information technology and low organizational performance (Jong-Sung, Sung Hyun & Ho, 2015).

Being forced to utilize technology results in more resistance than voluntary use. Technology resistance is composed of complex motivational and self-efficacy factors that are grounded in psychology and sociology (Abadi, Ranjbarian & Zade, 2012; Zoellner et al., 2012). Several theories have emerged to predict the acceptance of mandatory technology in business. The most popular constructs are the Technology Acceptance Model (Davis, Bagozzi & Warshaw, 1989) and the more recent Unified Theory of Acceptance and Use of Technology Model (Venkatesh & Bala, 2008). The TAM3 has been able to explain 70% of the factor variance in predicting behavioral intention to accept and use technology (Venkatesh & Bala, 2008). Demographic factors such as gender and age have been recognized as additional factors that impact technology acceptance (Oreg & Katz-Gerro, 2006; Stylianou & Jackson, 2007). The key dependent variables in these constructs are behavioral intent and planned behavior which are self-reported perceptions (Matlow, Wray & Richardson, 2012).

However, we found two significant gaps in the current TAM and TAM3 literature. First, although the ability to predict perceived behavioral intention is clear, more research is needed to follow through on examining actual individual behavior in business contexts. Many studies on performance have been at the organizational level of analysis (Gangwar, Date & Ramaswamy, 2015) or on individual performance in the educational psychology discipline (Arpaci, Kilicer & Bardakci, 2015; Kanthawongs, 2011; Van der Schyff & Krauss, 2014). Second, since most studies have identified age as well as gender as significant factors that predict technology resistance, young business entrepreneurs have not been sampled (Jong-Sung et al., 2015; Oliveira, Thomas & Espadanal, 2014).

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