Technology characteristics
The integration of technologies into learning environments has been studied for about 30 years. Davis (1986) developed the first version of the technology acceptance model (TAM) to examine antecedents of a technology’s acceptance. He proposed that the capabilities of a technology trigger learners’ motivation to use it, which in turn leads to actual use. More specifically, the features of a technology are assumed to affect perceived ease of use and perceived usefulness, which then affect attitudes toward using that technology and, thus, actual use. Although this model is not explicitly tailored to learning, it has evolved as a basis for educational technology research. Several studies of technology-supported management learning show that perceived ease of use and perceived usefulness affect satisfaction but do not directly predict perceived learning (Arbaugh, 2000a, 2000b; Huang, 2014). Terpend et al. (2014) find that perceived ease of use predicts technology adoption. Selim (2003) also provides evidence that perceived ease of use and usefulness predict technology acceptance, and reveals that ease of use is mostly mediated by usefulness. Sun et al. (2008) conclude that ease of use enables e-learners to focus on the content rather than the technology.
Goodhue and Thompson (1995) introduce task-technology fit (TTF) and argue that “for an information technology to have a positive impact on individual performance, the technology must be utilized and must be a good fit with the tasks it supports.” Related antecedents of technology-supported management learning effectiveness that are frequently analyzed include technology quality and technology reliability. In an early experiment with synchronous technology-supported distance learning based on online lectures and videos, Webster and Hackley (1997) find that both variables influence attitude toward the format and the technology, and that technology quality also influences the relative advantage of the format (i.e., perceived learning). They argue that reliable, efficient, and effective technology interfaces promote learner motivation, while technical complications have the opposite effect. However, they do not find relationships with involvement and participation, cognitive engagement, technology-self-efficacy, or usefulness of the technology. Song et al. (2004) confirm that technical problems are perceived as disadvantages for online learning. Sun et al. (2008) examine technology and internet quality in e-learning but find no effects on the satisfaction of management students. Notably, internet quality may be taken for granted. McGill and Klobas (2009) examine the role of learning management systems and provide empirical evidence that TTF strongly influences perceived learning and weakly affects actual learning. They also show an indirect relationship between TTF and perceived learning through learners’ attitudes toward technology utilization and actual use. Interestingly, they also reveal an effect of TTF on the expected consequences of technology use, although this does not affect actual usage.
Webster and Hackley (1997) note that technology richness has a positive impact on involvement and participation, cognitive engagement, technology self-efficacy, perceived usefulness, attitudes toward technology and format, and perceived learning. They argue that technology richness supports the accessibility of instructors and their feedback, which moderates learner motivation, thereby predicting technology use and perceived learning. Yourstone et al. (2008) state that immediate feedback technologies, such as clickers, can have a positive impact on learning outcomes. Work by Snowball (2014) confirms that passive online activities, such as videos, can be useful for introducing new concepts, while more active components, such as quizzes, are more beneficial for learning. Sloan and Lewis (2014) suggest that lecture-capture videos are related to higher exam scores. Kember et al. (2010) find that technological features that promote constructive dialogue and interactive learning improve understanding. Volery and Lord (2000) and Wu et al. (2010) note that the design and functionality of a learning management system predict perceived learning. Arbaugh and Rau (2007) investigate online learning with different systems and, interestingly, find a negative relationship between technology variety and perceived learning but a positive relationship between technology variety and satisfaction. In addition, Huang (2014) identifies a positive relationship between technology playfulness and satisfaction in a mobile learning environment. He finds that learners’ self-management skills moderate the effects of usefulness and playfulness on satisfaction. These technology-related antecedents of the effectiveness of technology-supported management learning are summarized in Fig. 3.
Format characteristics
While the format of instruction has traditionally been based on the physical classroom, the advent of technologies in management education allows for the emergence of new settings. Higher education research proposes a blended learning environment that is independent from the technology employed. According to Garrison and Kanuka (2004), this format is an “integration of face-to-face and online learning experiences – not a layering of one on top of the other.” López-Pérez et al. (2011) show that blended environments that combine face-to-face classes with online activities (e.g., crosswords, matching, fill in the blank, multiple-choice tests, wikis, forums) reduce dropout rates and improve exam performance. In line with TAM, they show that the perceived utility of online learning is correlated with the motivation generated by the technology, which in turn predicts satisfaction. However, they find that actual learning mainly depends on variables unrelated to blended environments, such as learners’ age, class attendance, or prior experiences—perceived utility and satisfaction do not predict actual learning. Notably, according to Grabe and Christopherson (2008), a lack of class attendance may be offset through online resources. Deschacht and Goeman (2015) find better exam performance for blended environments that integrate self-study, online collaboration, and classroom teaching. However, they also find that these environments are associated with higher dropout rates. They argue that the learning effect may be subject to survivorship bias. McLaren (2004) demonstrates that persistence in online delivery is significantly lower, while learning performance is independent of the format.
Although blended learning environments capture the benefits of technological innovations, such as flexibility in terms of time and place and learner control over pace and content, they also capture the benefits of physical classrooms (i.e., personal interaction through collaboration and community) (Arbaugh, 2014; Concannon et al., 2005). Educational technology research has found that course flexibility leads to e-learning satisfaction (Arbaugh, 2000b; Sun et al., 2008). The rationale is that flexibility allows learners to balance their personal commitments, such as work, family, and other activities, with their studies. Higher education research suggests that learner independence is crucial for building critical thinking skills (Garrison & Kanuka, 2004). Educational psychology research emphasizes that learner control over materials can have a positive impact on cognitive processing due to the possibility of pacing (Mayer et al., 2003; Moreno & Mayer, 2007). Pacing refers to a flexible presentation speed that encompasses pause, rewind, and fast-forward options. While pausing allows learners to restrict cognitive processing at a certain point of time, rewinding can intensify cognitive processing because the learner repeatedly receives the same information. The fast-forward option allows for certain sections to be skipped so that learners end up with shorter sections, which also benefit cognitive processing. The presentation of information in separate parts gives learners the opportunity to gradually build multiple mental representations that can be integrated later (Mayer & Chandler, 2001). Scheiter and Gerjets (2007) note that learner control in multimedia environments stimulates interest and motivation and, thereby, triggers more active and constructive processing. While Arbaugh and Duray (2002) show positive relationships between flexibility and both perceived learning and satisfaction in web-based environments, Arbaugh (2000a) finds no direct relationship between flexibility and perceived learning.
In blended learning environments, the flexibility of online learning is integrated with the preeminent characteristic of classroom teaching: interaction. Alavi (1994) finds that technology-supported learner collaboration and the associated interaction lead to greater satisfaction, self-reported learning, and enhanced exam performance. Collaboration can empower the structuring and sharing of information, leading to exposure to different views and opinions. This requires reiterating prior information when explaining knowledge to others, resolving opposing perspectives through discussions, and internalizing explanations from more knowledgeable peers. Eventually, this leads to more active knowledge processing and construction (Kreijns et al., 2013).
Eid and Al-Jabri (2016) provide evidence that online discussions and chats promote the exchange of knowledge that predicts perceived learning. Furthermore, networking via discussion forums leads to better performance (Walker et al., 2013). Arbaugh (2000a) also finds connections between perceived learning and interaction ease, interaction emphasis, and classroom dynamics. Arbaugh and Benbunan-Fich (2006) investigate online learning among 579 MBA students and find that group learning leads to higher perceived learning and satisfaction than individual learning. While group learning is moderated by an objectivist teaching approach, individual learning is moderated by constructivist instruction. Song et al. (2004) find that a perceived lack of community is detrimental to perceived online learning. In contrast, Eom et al. (2006) state that distance interactions lead to an adaptation of information that assists learners in overcoming feelings of remoteness. They find that interaction predicts satisfaction with online learning, which in turn fosters perceived learning. However, they do not find a direct link between interaction and perceived learning. Concannon et al. (2005) also find that interaction affects the satisfaction of e-learners, while Sun et al. (2008) find no relationship. Eom and Ashill (2018) find direct relationships between both learner-learner and learner-instructor interaction and perceived online learning. They also show that peer interactions in e-learning are beneficial for the self-regulation that predicts perceived learning. Perceived learning, in turn, causes satisfaction (Wu et al., 2010). Hazari et al. (2013) suggest that peer interactions via blogs lead to constructive feedback and self-assessments. On the other hand, Arbaugh and Rau (2007) find that peer interaction in online courses can negatively influence satisfaction, while it can positively affect perceived learning. Wu et al. (2010) reveal that the learning climate in a blended environment mediates the effect of interaction on satisfaction. According to Solimeno et al. (2008), online interaction can be even more beneficial for learning than personal interaction, as the former overcomes much of the interpersonal noise.
A variant of blended environments is flipped learning. According to higher education research, there is no single approach to flipped learning. However, the most important aspects include the provision of content in advance and higher-order learning during face time (O’Flaherty & Phillips, 2015). Therefore, introductions, explanations, and theories are studied individually and asynchronously at each student’s own pace, typically facilitated by a learning management system, while application and transfer problems are handled during class time. Solimeno et al. (2008) emphasize the benefits of asynchronous preparation, including flexibility in consulting materials and reviewing online comments from peers. Such a shift in the individual workload from reworking to preparing fosters ownership before class and enables deeper discussions in class that can be initiated by the learners themselves (O’Flaherty & Phillips, 2015). Flipped learning also supports the pretraining effect proposed in educational psychology research (Moreno & Mayer, 2007). The aim in this regard is to provide learners with relevant prior knowledge or to reactivate it if it is already available. This prepares the human memory with selected knowledge, which can later be integrated with new information. Consequently, pretraining facilitates meaning making and improves cognitive processing (Moreno & Mayer, 2007).
Educational technology research finds that assessment diversity in online environments increases satisfaction, as it enables multiple forms of feedback (Sun et al., 2008). Concannon et al. (2005) suggest that the use of some online tests during a semester reshapes study patterns by triggering continuous review and feedback. These format-related antecedents of the effectiveness of technology-supported management learning are outlined in Fig. 4.
Instructor characteristics
Instructors play a central role in any learning environment (Webster & Hackley, 1997). This role remains important in technology-supported management education, but it is changing (Daspit & D’Souza, 2012; Volery & Lord, 2000). Therefore, examinations of instructor characteristics should consider not only the personalities of instructors but also their roles, particularly with regard to learner-instructor interactions.
Research on instructors’ personality in technology-supported environments mainly focuses on instructors’ attitudes toward and control over the technology. Webster and Hackley (1997) find that the instructor’s attitude toward the technology affects learners’ attitudes toward the format and technology, technology self-efficacy, and perceived learning. In turn, learners’ technology self-efficacy predicts perceived learning (Wu et al., 2010). However, they find no relationship between the instructor’s attitude toward the technology and learners’ involvement and participation, cognitive engagement, or perceived usefulness of the technology. Concannon et al. (2005) find a positive relationship between the instructor’s attitude toward the technology and e-learners’ motivation to use that technology. López-Pérez et al. (2011) show that learner motivation influences actual learning in both the physical and virtual elements of blended environments. In addition, Sun et al. (2008) show a positive effect of the instructor’s attitude on the satisfaction of e-learners. They also emphasize the importance of the instructor’s technical competence.
Webster and Hackley (1997) demonstrate that the instructor’s control over the technology has a positive impact on learners’ attitudes toward a technology, its perceived usefulness, cognitive engagement, and perceived learning. However, they do not find relationships with involvement and participation or technology self-efficacy. Selim (2007) confirms that both attitudes toward and control over the technology affect business students’ e-learning satisfaction.
While the purpose of a traditional lecture is to deliver knowledge, instructors in a technology-supported environment should support active learning as facilitators and mentors (Solimeno et al., 2008). Markel (1999) proposes a change from “a sage on the stage into a guide on the side,” while Volery and Lord (2000) expect the role of the instructor to shift toward being “a learning catalyst and knowledge navigator.” Webster and Hackley (1997) find that such an interactive teaching style has a positive impact on learners’ involvement and participation, cognitive engagement, and attitudes toward format and technology. They find no relationships between an interactive teaching style and the perceived usefulness of the technology, technology self-efficacy, or perceived learning. However, Arbaugh (2000a) shows that efforts to create an interactive online environment predict perceived learning, and that the emphasis on interaction is directly related to satisfaction (Arbaugh, 2000b). Selim (2007) also shows that instructor characteristics, including the teaching style, influence business students’ satisfaction with e-learning.
Interactions between learners and instructors comprise both guidance (i.e., process input) and feedback (i.e., essential input) (Moreno & Mayer, 2007). On the one hand, process-related input promotes learners’ engagement in the right activities, especially the selection, organization, and integration of relevant information that strengthens relevant cognitive processing (Mayer & Moreno, 2003). On the other hand, essential input reduces learners’ extraneous cognitive processing by replacing misconceptions in the human memory (Moreno & Mayer, 2007). Extraneous processing refers to cognitive processes that are irrelevant for making sense of information and, thus, should be minimized. However, feedback must be well designed to avoid additional extraneous processing. For technology-supported environments, Demetriadis et al. (2008) suggest that scaffolding, a technique of appropriate questioning, can trigger learner reflection and deeper processing. They find that scaffolding leads to more knowledge acquisition and knowledge transfer. Moreno and Mayer (2007) confirm that reflection on prior information leads to more active organization and integration of new information. According to Eom et al. (2006), both guidance and feedback increase learner satisfaction, but only feedback improves perceived learning in an online environment. Hwang and Arbaugh (2006) show that feedback does not influence actual learning in blended environments. However, if the search for feedback is triggered by a competitive attitude (i.e., getting ahead of others or preventing others from getting ahead of oneself), it has a positive impact on actual learning. Sun et al. (2008) show that the timeliness of an instructor’s response has no influence on satisfaction with e-learning.
Instructor feedback in technology-supported environments has also been studied in connection with learners’ prior knowledge. Seufert (2003) finds that feedback in a computer-based learning task barely affects learners with a high level of prior knowledge. However, it positively moderates the comprehension of learners with intermediate prior knowledge, presumably due to its summarizing and repetitive nature. At the same time, feedback negatively moderates the recall performance of learners with little prior knowledge. Interestingly, in a computer-based simulation, Nihalani et al. (2011) find that learners with low prior knowledge learn better with the support of the instructor than in cooperation with other beginners and that feedback is disadvantageous for learners with high levels of prior knowledge.
As a variant of feedback, educational psychology scholars study confusion in online environments, which is defined as “the result of contradictions, conflicts, anomalies, erroneous information, and other discrepant events” (Park et al., 2014). They propose that when confusion is “induced, regulated, and resolved appropriately,” it can positively influence learning. D’Mello et al. (2014) find that knowledge and transfer are higher when confusion is deliberately triggered and successfully resolved. Learners’ prior knowledge has small moderation effects. Confusion is assumed to lead to deeper engagement with new information, thereby improving learning (Leutner, 2014).
Although feedback embodies interaction between instructors and learners, the physical presence of the instructor is not essential for improving cognitive processing (Redpath, 2012). Personal interaction can occur through a collaborative online environment or personalized online communication (Arbaugh, 2000c). Mayer (2002) proposes the personalization principle, which posits more effective processing for a conversational communication style in learning materials than for a formal communication style. This increases learners’ attention and encourages them to refer content to themselves (Moreno, 2006). In addition, Beege et al. (2017) find that frontal, as opposed to lateral, instructor orientation in learning videos promotes retention, as para-social interactions can trigger deeper cognitive processing and beneficial affective states. The lack of body language in online settings can be addressed through the use of humor, anecdotes, or emoticons (Whitaker, New, & Ireland, 2016). Guo et al. (2014) find that instructors who speak faster and with more enthusiasm in learning videos increase learner engagement. These instructor-related antecedents of technology-supported management learning effectiveness are illustrated in Fig. 5.
Learner characteristics
The learners themselves play an important role in the effectiveness of technology-supported management learning. Educational technology research initially examined the demographic background and prior experience of learners in technology-supported formats. While it is unclear whether gender predicts perceived learning in an online environment (Arbaugh, 2000a, 2008; Volery & Lord, 2000), both Arbaugh (2000b) and Arbaugh (2008) find that gender does not influence satisfaction. Furthermore, Lancellotti et al. (2016) find no connection between gender and actual learning. Age does not influence perceived e-learning (Arbaugh, 2000a), but it positively predicts actual learning in the physical and virtual settings of a blended environment (López-Pérez et al., 2011).
Prior technological experience also influences actual online learning (López-Pérez et al., 2011), while its relationships with perceived learning and satisfaction are not always significant (Arbaugh, 2000a, 2008; Arbaugh & Rau, 2007; Selim, 2007; Song et al., 2004; Volery & Lord, 2000). Piccoli et al. (2001) examine 146 management students and posit that previous technology experience can be beneficial, while a lack of such experience can promote feelings of anxiety and isolation. Sun et al. (2008) find that computer anxiety has a negative impact on satisfaction with e-learning, as it can hamper a learner’s attitude, which is essential for technology-supported learning (Scheiter & Gerjets, 2007). Solimeno et al. (2008) show that technology promotes perceived and actual learning among learners with low computer anxiety.
In addition to previous technological experience, research has examined the role of prior academic achievements. Nemanich et al. (2009) and Palocsay and Stevens (2008) find that learners’ academic abilities are associated with learning outcomes, particularly in online environments. Scheiter and Gerjets (2007) assume that a high level of prior knowledge moderates learning in multimedia environments. Asarta and Schmidt (2017) show that blended formats have a positive influence on exam performance for learners with high prior performance, while weaker students perform better in traditional formats. Owston et al. (2013) find that high achievers show the highest satisfaction with blended learning environments because they view blended learning as more convenient and engaging, and they feel that they learn key concepts better than in traditional classes.
Educational psychology scholars have considered affective aspects, such as learner motivation and emotions (Park et al., 2014). Motivation is defined as an “internal state that initiates, maintains, and energizes the learner’s effort to engage in learning processes” (Mayer, 2014). The corresponding work is based on the assumption that motivational factors can mediate learning by increasing or decreasing cognitive engagement (Moreno & Mayer, 2007). Selim (2007) shows that motivation affects e-learning acceptance and satisfaction. According to Song et al. (2004), e-learners expect their motivation to be related to learning. López-Pérez et al. (2011) find that motivation predicts actual learning in both the physical and virtual settings of a blended environment. Woo (2014) confirms the correlation between motivation and actual online learning. Eom et al. (2006) also find that motivation in an online environment affects satisfaction, although they do not find a direct link to perceived learning.
Plass et al. (2014) and Um et al. (2012) investigate emotions induced by videos in online learning, and find that positive emotions can promote comprehension and transfer. Their findings suggest that round, face-like shapes and warm colors reinforce the positive emotions that not only reduce the perceived difficulty of the task but also increase motivation and cognitive processing. This effect of emotions on performance can be mediated by motivation and/or moderated by prior knowledge (Leutner, 2014). In contrast, Knoerzer et al. (2016) find that positive emotions induced through music and autobiographic recall reduce actual online learning, possibly because they distract learners from the focal material. However, they find that negative emotions increase learning, possibly due to a perceived need for deeper information processing. They find no connection between emotions and motivation.
Educational psychology research on multimedia learning further posits that “metacognitive factors mediate learning by regulating cognitive processing and affect” (Moreno & Mayer, 2007). Metacognition mainly occurs in the form of self-regulation and reflection during the organization and integration of new information. Moreno and Mayer (2007) find that reflection is beneficial for cognitive processing, which leads to better learning outcomes. Eom and Ashill (2018) show that self-regulation in an e-learning environment mediates the relationship between motivation and perceived learning, which is related to satisfaction. Metacognition seems to be particularly important for non-interactive (i.e., distance) phases in which it is not triggered by interactions. However, metacognition is also important in an interactive setting if “the lesson can be performed in a superficial or automatic fashion” (Moreno & Mayer, 2007).
According to Fryer and Bovee (2016), “although a variety of factors influence learning, few are as important as time on task.” Macfadyen and Dawson (2010) distinguish between online activity and time online, noting that online activity (i.e., written posts, sent messages, completed assessments) indicates learner engagement and predicts actual outcomes, while time online does not. Fritz (2011) also shows that higher activity in the learning management system affects actual learning, while Asarta and Schmidt (2013) as well as Buttner and Black (2014) find no correlation between time online and learning. Based on learning analytics, Zacharis (2015) finds that four online activities predict 52% of the variance in the final grade: number of files viewed, reading and posting messages, content creation contribution, and quiz efforts. These learner-related antecedents of technology-supported management education are illustrated in Fig. 6.
