Research Model and Hypotheses
Drawing on the background literature reviewed, we provide the research model underlying our study in Figure 1.1. The specific hypotheses are discussed later. Adapting ECM into the context of an ERP simulation game, we may observe similar patterns and relationships. As the learners put effort into engaging with the system, they build some levels of expectations about the impact of the simulation game on their skills. In the case of perceived improvement (confirmation) in their knowledge to use ERP systems, they will be satisfied, as ECM posits. In our setting, such satisfaction will be translated into the involvement of learners, which results in continuance learning of the system by the individuals.
Figure 1.1 Proposed model
According to Maclnnis and Jaworski (1989) there are several levels of cognitive efforts in information processing, which demonstrate the degree of cognitive effort on the side of the individual. At higher levels of motivations to process information, users employ more cognitive capacity and try to integrate their own prior knowledge and experience to the message (that is, training in our context); also they add positive or negative attributes to it, which activates a more effortful route of processing the message. We can apply the elaboration likelihood model (ELM; Petty & Cacioppo, 1986) to describe how individuals who spend more cognitive resources and capabilities are likely to experience higher levels of knowledge update. As the ELM posits, the nature of the message determines the strength and persistence of its consequences. In the case of higher individual effort, an individual’s information-processing mechanism activates higher cognitive levels by going beyond simply paying attention or comprehending the argument in the message. Such elaborative processes involve generating updated judgments in response to the information to which the learner is exposed.
In the context of ERPsim training and adapting the ELM, we argue that individuals with motivation and ability put more effort to process the external information. Likewise, they consider and evaluate the details of the arguments presented to them during the learning process, which results in creation of evaluative perceptions on the acquired knowledge through central processing. Whereas, those who engage in the training because of lack of time or resources may use lower levels of their cognitive capacity to treat the information and arguments. In this case, they will rely on their judgments on the peripheral route, which is less stable, less persistent, more prone to counterinfluence, and less predictive of long-term behaviors (Petty & Cacioppo, 1986).
Contextualization of these notions to the setting of our study, we argue that if learners invest the necessary effort to adequately scrutinize and evaluate the provided information, which reflects their level of effort in learning the new skills, they will view the acquired knowledge as being relevant and important to the target behavior and they will more likely to have higher perceived knowledge improvement.
H1: Individual effort will be positively associated with perceived knowledge update.
Past studies found that simulation games improve the learning process of individuals by promoting their psychological involvement (Anderson & Barnett, 2011). Moreover, higher levels of involvement in the learning process positively impact individual’s understanding and promote knowledge transfer (Lave & Wenger, 1991). By applying the ELM and ECM, we attempt to describe the association between the effort invested by a person on acquiring new skills and the psychological involvement in the learning process.
According to the ELM, information recipients can vary widely in their ability and motivation to elaborate on an argument’s central merits, which in turn may constrain how a given influence process impacts their attitude formation or change. Thoughtful evaluation of information activates the central processing route (Petty & Cacioppo, 1986), in which the learner processes the relevant information at higher cognitive levels. Activated central processing requires that a larger portion of the cognitive capacity of an individual needs to be engaged in scrutinizing the arguments and information. This mechanism generally results in more stable and enduring attitudes. Relating this notion to the ERPsim, we need to underscore its design specifications, which are based on the concept of situated cognition. By focusing on realistic and situated context, ERPsim provides a learning process in which individuals can identify more relevancies of communicated information. As they invest more cognitive effort to process the information, the probability that they find more connections between the arguments and prior experience and knowledge is higher, which in turn can increase the participant’s involvement (that is, motivation factor in the ELM) in the process of learning. An active learner is emotionally and cognitively involved, and plays a dynamic and self-motivated role in how and what needs to be learned (Trigwell et al., 2012).
According to the ECM, user expectation is positively related to user satisfaction. The model contends that expectation is another determinant of satisfaction because expectation provides the reference level or baseline for individuals to form evaluative judgments about the focal product or service (Bhattacherjee, 2001). ECM posits that a high baseline level or expectation tends to enhance an individual’s satisfaction whereas low expectation shrinks resulting satisfaction. Similarly, marketing studies found that apart from the association between expectation and perceived performance, which determines confirmation, expectations (that is, individual effort) also affect customer satisfaction (Spreng & Chiou, 2002). Drawing on this argument, a recent study of multimedia Web sites found that extensive effort leads users to involve and interact more with the content of Web sites (Lim, Al-Aali, Heinrichs, & Lim, 2013). Thus, involvement can be seen in relation to the level of motivation of the individual in putting effort into learning a new skill.
H2: Individual effort will be positively associated with involvement with the ERP simulation game.
Research on behavior changes (Bandura, 1997) posits that individuals’ behavior is affected by their judgments of their skills and capabilities to perform a given task. It discusses that psychological procedures alter expectations of personal perceptions of abilities. Moreover, it describes the procedure of determining what actions to take, how long to preserve, and what strategies to apply when individuals attempt to balance their abilities with the challenges of a task. Consistent with Bandura’s self-efficacy theory, when individuals feel a sense of mastery in a domain, they tend to believe that they can achieve a desired performance level. Hence, higher perceptions of knowledge improvement can lead to higher degrees of perceived capability for ERPsim players. This argument has been validated by empirical studies in various contexts including IS and acceptance of technology (Agarwal & Karahanna, 2000). In the context of IS, researchers (Mun & Hwang, 2003) suggest that individuals with higher degrees of self-efficacy usually form more positive perceptions of IT and have higher levels of pleasure, which lead to subjective perceptions of positive affects and satisfaction (Lim, Pan, & Tan, 2005). By finding the difference between self-efficacy levels of learners at two different times, that is, before and after conducting the ERPsim, we created a perceived knowledge improvement construct, which represents the confirmation notion of ECM.
H3: An individual’s perceived knowledge update will be positively associated with their involvement with the ERP simulation game.
The difference stemming from different levels of self-efficacy is all the more apparent in the ERP context because the renowned complexity of an ERP system makes users feel that it is difficult to learn. Whereas, individuals with higher degrees of belief in their abilities tend to both have a higher intention of using an IS and actually use the IS more frequently (Compeau, Higgins, & Huff, 1999). In a learning context, it was found that self-efficacy motivates individuals’ learning intentions through the self-regulatory processes, such as self-evaluation, goal setting, and self-monitoring (Zimmerman, 2000). Similarly, in a recent study of the role of self-efficacy in ERP learning, Chou et al. (2014) found that post-training self-efficacy significantly facilitates ERP learning outcomes; in particular, they identified that higher self-efficacy increased learning willingness and learning capability.
Furthermore, Bandura (1982) indicated that self-efficacy influences individuals’ choice of activities and skill acquisition strategies. Put differently, an individual with high self-efficacy will be more willing to work harder in a committed way to acquire a skill and also will be emotionally more stable in case of any obstacle (Bandura, 1997). Translating those findings into the setting of this study, we claim that those with high perceptions of knowledge improvement and ability (that is, a greater difference between pre- and post-ERPsim self-efficacies) will exhibit more tendencies to learn challenging concepts in a more persistent way. In contrast, individuals with lower perceptions of knowledge improvement will experience more anxiety and frustration, and consequently exhibit less determination in learning a challenging task (that is, ERP system) and will consequently have lower levels of learning intentions.
H4: An individual’s perceived knowledge update will be positively associated with their willingness to learn.
User involvement is found to be a strong predictor of continuance intention (Shiau & Luo, 2010). Empirical evidence in the simulation game field suggests that players who experience higher levels of involvement during a game will have increased learning (Sitzmann, 2011). Likewise, in the ERP setting, involvement is posited to be positively related to usage intention (Amoako-Gyampah, 2007). In the same vein, previous research suggested a direct connection between affective and cognitive dimensions of attitude and intention to use (Lee, Chen, & Ilie, 2012; Van der Heijden, 2004). The theory of reasoned action (Fishbein & Ajzen, 1975) and the TAM (Davis, 1989) also supported these relationships. Moreover, in the IS learning setting, some recent studies found that learning intention is increased by the large amount of time spent navigating in the software as well as by the high level of motivation and involvement in the activity displayed by learners (Wrzesien & Raya, 2010).
H5: An individual’s involvement with the ERP simulation game will be positively associated with their willingness to learn.