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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).