Startups and corporate ventures with brilliant, innovative products and well-developed technology often fail to maximize their potential or disappear from the market due to inappropriate management strategies. Although the market is rarely predictable, corporations constantly release innovative products to the market, with varying degrees of success or failure. Developing and releasing innovative products and technology successfully are directly related to a corporation’s survival and are necessary for maintaining its compeence. In this study, we examine smart learning, an emerging innovative technology in recent the education market that combines various technologies.

Globally, the number of people using smartphones around the globe has increased over the years. Unlike cellphones intended for simple communication, smart devices with wireless Internet environment, which allows access to various contents, can be used as an educational tool. Due to the development of information technology (IT), smart learning (which depends on wireless Internet technology on mobile devices) has emerged as an alternative to learning in the education market (Yang et al., 2005). According to the National IT Industry Promotion Agency (NIPA), the size of the smart- learning market grew by approximately 7–12% annually from 2.091 trillion won in 2009 to 2.7478 trillion won in 2012; it is estimated to reach 4 trillion won in 2016. Educational facilities, general conglomerates, carriers, and even foreign companies are diving in to this business, and the smart learning market is expected to grow exponentially.

However, according to Hunet’s report, on workers who use smartphones, 56.8% do not use smart learning and would not use smart learning in the future, although they are aware of its usefulness in learning. This shows that smart learning is not actively accepted or used. There are learners who resist this technology, and certain factors influence this resistance. Ram (1987) argued that innovation resistance occurs when faced with new technology, and overcoming the resistance leads to complete acceptance. In other words, negative attitudes and behaviors arise in the face of new technology (smart learning), and users tend to resist smart learning as a learning method. In order for smart learning to proliferate, it is necessary to understand learner resistance and observe the factors influencing such resistance. Carefully monitoring the diffusion process with appropriate intervention and action is important. However, prior studies on smart learning concentrated on acceptance and adoption (Lee, 2012), not resistance. Since learners were expected to accept such technology, previous studies focused on performance such as “services and qualities that would satisfy smart learning users.” However, regardless of how high the quality of the contents or how ideal the system environment is if a fundamental disturbance in participation of learning exists, the intended effectiveness of smart learning will not show. Therefore, there is a need to identify and analyze the factors that lead to learner resistance in accepting smart learning.

Corporations commit a lot of money and resources to R&D. Technological innovation leads to the development of new products and the improvement of existing products, bringing in increased revenue and stable income to businesses. However, the risks associated with technological innovation increase proportionately. Corporations must identify and manage innovation resistance in the process of technology innovation.

The objectives of this study are: First, discovering why user resistance to smart learning exists by focusing on users resistant to smart learning; to identify how learner resistance affects future intention to use smart learning; and to analyze how the influence of each factor varies according to demographic characteristics.

The Ministry of Education and Science Technology (MEST) defines smart learning as “An intelligent custom teacher-learning supportive system to transform required education method, curriculum, evaluation, and general education system in the 21st knowledge and information society.” Smart learning is a human-oriented form of study combined with social and adaptive learning, delivered through the best communication environment.

Learning Smart learning involves accessing learning contents material using mobile devices (smartphones, tablet PCs, e-book devices). Additionally, it can exploit smartphone features such as location-based services or 3D, virtual reality. Smart learning is a many-to-many educational method. That is based on the concept of learning wherever and whenever possible— a ubiquitous environment extended from electronic learning, which was constrained by limitations of time and space. Smart learning enables learning without constraints of time, space, or environmental causes.

Learning has progressed from offline learning, to e-learning, and evolved to smart learning. Offline learning means learning through face-to-face experience with a lecturer in a classroom-like settings, while e-learning means an on-line based experience, through which one is being lectured, using computer and internet. E-learning takes a place in a specific location, such as an office or a home, with a PC that enables an access to learning contents. Smart learning, in a sense that it is based on-line, is similar to e-learning; however, smart learning is distinctive from e-learning since smart learning supports mobility via smart devices.

Smart learning initiatives include such as massive open on-line courses (MOOC) involving participation from Harvard, Stanford, Pennsylvania, Duke, MIT, Princeton, and other universities and the online courses related to computer science, science, mechanical engineering, and applied science provided by EDX, Udacity, and Coursera. These initiatives are intended to eliminate barriers and to provide an open platform for learners (Lim, 2011).

Definition of innovation

Researchers have interpreted innovation in various ways: “Ideas, practices, objects that has appropriate unit of adoption”, (Zaltman and Wallendorf, 1983); “Newly developed ideas, practices, objects in a proper environment perceived as ‘new’ by the initial user” (Biemans, 1992).

Brown (1992) defined innovation using management concepts: “A New technology, a product, or a process with the potential to create new market, alter existing competition pattern, or consumer behavior.” He emphasized that innovation does not have to be a product with technical advancement, but the product must be recognized as “new” by the consumers. Brown’s research was based on Rogers (1983) study. He was the first to define innovation from the perceiver’s point of view: “An idea, practice, or an object perceived as new to an individual or specifically to a receiving unit.”

Diffusion of innovation

Rogers (1983) proposed that the innovation process involving decision making and adoption is a set of mental processes— individuals first face an innovation, then form attitudes toward the innovation, and finally decide whether to adopt it. He defines five intrinsic characteristics of innovations that influence an individual’s decision to adopt or reject an innovation: relative advantage, compatibility, complexity, trial ability, and observability. The speed of innovation adoption and the diffusion process is influenced by the extent of a user’s innovation perception as well as the adopter’s characteristics.

The Technology Acceptance Model (TAM) developed by Davis (1989) is a representative model for explaining the process of receiving new information technologies and is useful for describing user behavior adoption and use of IT. The TAM is a variation (involving IT) of the innovation diffusion model.

According to the TAM, intention to use depends on perceived usefulness and perceived ease of use. The model assumes that these two factors influence users to form a positive attitude toward information system, and devise plans of action, leading to the actual use or adoption of technology (Zhou, 2008). The results of prior studies indicate that smart learning should be viewed as a new innovation (from the perspective of learners).

Innovation resistance

While introducing a new technology or product, promoting its positive aspects and reducing resistance to technology acceptance are crucial in determining its success or failure. Resistance to change is a natural reaction for most consumers due to fear and uncertainty about embracing new technology (Moore, 2002) and the tendency to maintain status quo. An analysis of innovation resistance could provide several implications for the innovation process. Park and Nam (2012) applied the TAM to study the relationship between intention to use and influencing factors in a mobile learning context. However, there are no prior studies on the relationship between smart learning and innovation resistance. Therefore, a study of smart learning based on the model of innovation resistance would benefit the smart learning industry.

Sheth (1981) was the first to use the notion of innovation resistance and proposed lifestyle and perceived risks as factors that influence innovation resistance. According to Ram’s (1987) innovation resistance model, psychological stability is broken when faced with change and consumers opt for resistance to solve this problem. In this model, he identified the factors influencing innovation resistance as user’s perceived innovation characteristic, user characteristics, and diffusion path characteristics. From perceived innovation characteristic, he derived compatibility, perceived risk, relative advantage, and complexity. He distinguished user characteristics into attitude towards existing product and demographic attributes such as user’s gender, age and educational level. Finally, the characteristics of the message and the type of diffusion paths make up the diffusion path characteristics. However, Ram’s (1987) model of innovation resistance had several limitations. Yoo and Lee’s (1994) extended and modified model of innovation resistance showed that diffusion path characteristics are not influencing factors with regard to innovation resistance; rather they serve as a barrier. They modified the model by including detailed macro-environmental factors that influence innovation resistance. Further, they criticized the dynamic results of innovation resistance and rejection and applied the acceptance stage proposed in Rogers (1983), according to which dismissal and acceptance can vary in each decision step; these modifications increased the model’s suitability.

After the publication of Ram’s (1987) initial theories and model of innovation resistance, various studies examined the characteristics of the product, and new technology in many industries. For instance, Yang and Shin (2010) studied touch interface technology in mobile phones; Yoon (2013) performed an empirical study innovation resistance toward social networking service (SNS) among college students in their twenties. Different factors of resistance were obtained in these studies, which is natural given the different consumer characteristics in each industry.

Intention to Use

The concept of intention to use includes customer’s satisfaction with a product or service provided (Zeithaml, Berry and Parasuraman, 1996). It is defined as the thought or intention to use an innovative product in the future. The notion “intention to use” was derived from Fishbein and Ajzen’s (1975) theory of reasoned action. They opposed the theory that attitudes directly influence behavior; they insisted that the intention of the behavior is formed first, before the action. In this study, the intention to use smart learning includes actively including smart learning in the learning process, having an interest in the device(s) and information necessary to perform smart learning, and informing others about the advantages of smart learning.

Moderating Effects of Gender and Age

Ram (1987) listed demographic characteristics of gender, age, educational level, and income as factors of resistance. Oliver and DeSarbo (1988) suggested that in determining customer satisfaction, group’s characteristics have relatively different influencing powers. Ong and Lai (2006) studied cognition of use in adopting electronic learning; they found out that men’s perceived intention to use and perceived ease of use were higher than those of women. Age is an important variable leading to differences in customer behavior.

Choi, Woo, and Jung (2013) studied smart learning in the context of a remote university; they reported that students above 40 expect more customized learning and interactive practical learning than those below 40. Moreover, students aged below 40 prefer smart learning, which can provide real-time information.

The model was based on Ram’s (1987) model of innovation resistance. The independent variable included some of the resisting factors in Ram’s model that were modified according to learner characteristics in a smart learning context. Following Yoo and Lee (1994), the independent variables were separated into two variable groups: perceived innovation characteristics and learner characteristics. Figure 1 presents the research model.

Perceived innovation characteristics and innovation resistance

Relative Advantage:

When users are encountered a new innovation, they perceived a relative advantage compared to existing technology and products, compatibility to themselves, and its complexity. According to Ram (1987), recognizing how good an innovation is compared to existing one in terms of efficacy, convenience of use and price is a relative advantage. Schiffman and Kanuk (1991) argued that consumer resistance occurs when this relative advantage is low. When comparing smart learning with electronic learning or offline lectures, the relative advantage of the former diminishes due to the high price of the device and frequent malfunctions with small screens and sensitive touch pad; therefore, learners will have innovation resistance.

Compatibility:

The degree of conformity perceived in past experience, desire of the consumer, and existing value is called innovation compatibility (Ram, 1987). Schiffman and Kanuk (1991) suggested that low compatibility leads to higher innovation resistance. Consumers might not be comfortable choosing between an innovative product and an existing product that they use, leading to innovation resistance. If users think that smart learning is not as compatible as electronic learning and offline courses or if it does not live up to the expectations of learners, higher innovation resistance will occur.

Complexity:

Complexity causes delay or resistance in accommodating innovation (Rogers, 1983). If consumers find an innovative product or technology difficult to understand, resistance will occur. Ram (1987) and Shiffman and Kanuk (1991) argued that greater complexity leads to greater resistance. Difficult functions and complex interfaces in smart learning devices would lead to resistance.

Learner characteristics and innovation resistance

Attitudes toward existing learning method(s):

If the attitude towards an existing learning method is positive, innovation resistance will appear high. If users feel comfortable and familiar with the actions taken, they would resist any innovation that would alter the existing situation (Harrison, 1968). If a user believes that traditional electronic learning and offline lectures allow learners to focus better and become efficient, the following hypothesis would hold.

Perceived self-efficacy:

Kim and Choi (2009) defined self-efficacy as the degree of subjective evaluation of the efficacy with which one can perform the required tasks and actions. In the context of this study, self-efficacy would be the self-evaluation of one’s capability in using mobile technology and information. Ellen, Bearden, and Sharma (1991) found that perceived self-efficacy and satisfaction with an existing product influence innovation resistance. This study argues that people with higher self-efficacy have less innovation resistance.

Innovation resistance and intention to use

If there were no resistance to innovation, consumers would adopt innovation immediately; the problem is how to reduce resistance in the adoption process (Ram, 1987). Having innovation resistance does not mean the consumers will not adopt the innovation; the range of acceptance can be widened through continuous improvement. Yoo and Lee (1994) suggested that acceptance and resistance can coexist during the innovation process. Ram (1987) used the “resistance threshold” concept to explain the coexistence of acceptance and resistance. Subjective levels of acceptability decide user acceptance and resistance. When smartphones were first released, feature phone users displayed innovation resistance. Today, people use smartphones more than feature phones, well beyond the acceptable levels of innovation acceptance. Thus, higher innovation resistance will lead to lower intention to use.

Moderating effects of gender and age

Ram (1987) identified demographic characteristics such as age, gender, occupation, and income level as factors leading to resistance. Analyzing the factors of learner resistance according to gender and age would help to understand smart learning customers by segmenting them. In this study, the sample was divided based on gender and age. We investigate the moderating variables of gender and age by dividing the sample into users aged over 40 and those under 40 (following Choi, Woo, and Jung 2013); we analyze the structure of the relationship on innovation characteristics and user characteristics affecting innovation resistance and intention to use.

The measurement lists and references used in this study are listed in table 1; the questionnaire was created using survey questions that retained validity and reliability in advanced research contexts. The measurement articles of research variables (except the questions related to demographic characteristics) were measured using a 7-point Likert scale.