Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam

Highlights

• Exploring students' mobile learning to understand the success factors of M-learning.

• How to facilitate the proper development of m-learning in the education environment.

• What factors help increase students' acceptance toward M-learning?

Abstract

The factors that influence students' attitudes towards mobile learning (ML) is an intriguing topic. Though previous studies have examined the determinants influencing the acceptance of ML, few studies have investigated the influences of different cross-cultural factors on ML acceptance in higher education. When students from various countries participate in class, it is very important to address and improve the learning experiences of cross-cultural students. To elucidate this issue, this study proposes a conceptual model and theoretical framework through the theory of planned behavior (TPB) by incorporating diverse constructs to examine the influences of cross-cultural students' attitudes toward ML. Moreover, the following issues must be addressed: (1) examining cross-cultural ML problems within cross-cultural perspectives in a culturally sensitive manner, and (2) identifying the similarities and differences in cross-cultural students' behavioral intentions (BI) when influenced by attitude (ATT), subjective norm (SN), and perceived behavior control (PBC). Data were collected using an online survey from 947 respondents in Taiwan, Vietnam, Indonesia, and China. Our results show that BI towards the adoption of ML was influenced by ATT, SN, and PBC among the Taiwanese, Chinese, Indonesian, and Vietnamese undergraduate students. In addition, PBC was a significant predictor for students in Taiwan and Vietnam but was not a significant predictor for students in China and Indonesia. On the contrary, SN was significant in China and Indonesia but not in Taiwan and Vietnam. These findings showed a weaker relationship with SN in both China and Indonesia. Overall, our proposed model has reached an acceptable level. As a result, 82.3% and 81.2% acceptance levels were found for Taiwan and Vietnam, respectively, indicating that these students exhibit PBC-orientated distinguishing characteristics. This implies that students in Taiwan and Vietnam seem to be have more confidence in their ability to accept and perform a specific task for ML. Meanwhile, it is interesting to note that the 77.3% and 80.2% acceptance levels for China and Indonesia, respectively, indicate that these students also have SN-orientated distinguishing characteristics. The findings imply that students tend to follow other students’ decisions to use or not use ML. These findings are expected to facilitate decision makers and service providers in formulating appropriate strategies to improve the uptake of ML activities. Furthermore, these findings can help us understand the issues facing ML adoption in different cultural settings and contribute to the design and adequate provisions of ML programs.

Introduction

Many scholars have studied mobile learning (ML), which utilizes mobile technologies for educational purposes as a result of its drastic expansion in recent years [1]. Researchers must continue to improve the development of ML by investigating related issues, such as what ML issues have been studied throughout various grade levels [2], student behavioral patterns in ML [3], and factors influencing students' acceptance of ML. Further studies exploring these factors have the potential to increase overall acceptance and willingness to adopt mobile devices in learning environments [[4], [5], [6]]. In support of learners and educators in the ML process, existing reviews of serious ML investigations have examined ways to assist educators and learning institutions to better embrace ML by exploring the subject norms (SN) and perceived behavior control (PBC) that impact students' behavioral intention (BI) to adopt ML approaches in schools where a deep connection between technology and pedagogy exists [7]. Slotta & Linn [8] considered a web-based collaborative probe to promote and maintain positive attitudes toward instruction in science. Additional studies have examined the motivations that influence students’ BI to adopt ML as the factors of evaluation [9].

Despite the abundance of research in the general subject area of ML, few studies have focused on cross-cultural ML problems in Asia. In line with increasing globalization, the field of multinational education development has become an important issue and valuable opportunity for many higher education institutions [10]. As a result of the growing number of international students, educators often incorporate teaching environment features into learning activities by providing ML and knowledge sharing platforms. ML offers educational benefits because its effectiveness is based on mobile technology practices that support pedagogy, which include avoiding inefficient learning activities or unwillingness to adopt ML, maintaining teaching and learning quality, and promoting international academic exchange activities [11]. It is interesting to explore the need to develop country-specific educational strategies and examine cross-cultural differences in the domain of ML. Therefore, examining students from distinct cultural groups with similarities and differences in their BI towards ML is very important for societies and countries. To provide a reference for international education institutions, students from different countries can quickly be integrated into different education systems and environments, thus increasing learning effectiveness and promoting deep cultural and social exchange. These steps will enhance the development of higher education.

Mobile devices have brought about a learning revolution that facilitates changes in the conditions for creative learning. Zahrani and Laxman [12] assert that even if mobile devices provide a wide range of advantages for educators, learners, and institutions, few studies have explored learning readiness and willingness. Some learners were found to regard ML with ambiguity and reluctance in higher education. Common obstacles to ML adoption by students are related to the technologies' perceived ease of use (PEOU) and perceived usefulness (PU) [13]. Determining the factors affecting ML adoption is a crucial part of improving ML penetration rates and combating students’ intangible resistance. While observing different groups, we found that cultural difference is a decisive factor for the acceptance of ML learning. The learning cultures in the East and West are completely different. The East is characterized by cynicism, hierarchy, and collectivism that is founded upon a conservative community. On the contrary, Western culture exhibits optimism, non-hierarchy, individualism, a progressive community, as well as confidence in facing indistinct conditions.

First and foremost, Fig. 1 distinguishes the differences between this study and the traditional Technology Acceptance Model (TAM) while highlighting significant contributions of the study.

The elements of this study are denoted as, “The study = TAM $\cup$ TPB” (Fig. 1). Davis (1989) proposed the TAM [11], which includes the main four facets of PU, PEOU, attitude, and intention. Theory of planning behavior (TPB) was proposed by Ajzen [14]. It is claimed that personal behavior comes from BI, and BI is mainly affected by the three variables of the individual's ATT towards behavior, SN, and PBC. The main factors affecting BI are PU and PEOU in TAM. In contrast, the main factors affecting BI are ATT, SN, and PBC in TPB. However, the PEOU and PU proposed by TAM are similar to the concept of ATT in TPB; it is possible to investigate the impact of PEOU and PU on individuals through the exploration of ATT. This study explores how initial willingness affects BI. However, using only ATT to investigate BI cannot fully express BI because it is difficult to completely comprehend the initial willingness by simply discussing PEOU and PU. Therefore, this study uses SN and PBC from a multi-faceted perspective to investigate the factors that affect initial willingness and its subsequent effect on BI. Although various external variables such as service quality, technical system quality, content quality, and social influence are taken into consideration, there is still room for improvement. Since these factors are specific, simple, and easy to understand, they can be harnessed through the system, design, service, and implementation to increase adoption of new technologies.

Previous studies have focused on issues arising after ML has been adopted (e.g., when students have ML experience), and have rarely paid attention to the issues occurring before the use of ML (e.g., when students do not have prior ML experience).

Of the different ways to improve student learning, technology offers a flexible and impactful solution. However, it is difficult to design appropriate technologies based on optimism and equality. This study first explores the initial thoughts students have towards ML by providing a starting point for students to consider using ML as it is very important to stimulate the initial learning readiness and willingness to use ML in order to improve their knowledge [[15], [16], [17], [18], [19]].

This study's contributions to the education literature are listed as follows: 1. As no empirical ML studies have been conducted for Taiwan (TW), Vietnam (VN), China (CN), and Indonesia (IN) using TPB, this is the first time a study uses TPB to investigate higher education students' ML issues in TW, VN, CN, and IN. 2. This study shows the relative importance of PBC in Taiwan and Vietnam as students in these regions are more confident in themselves and more likely to accept new pedagogical methods. Moreover, the importance of SN is shown in China and Indonesia given that students' decisions are significantly influenced by third parties which include parents, spouses, friends, and teachers. 3. In the field of cross-cultural education, accessible academics can provide more interesting courses for cross-cultural students, thus improving students' initial willingness to approach ML to support educational purposes. For practitioners, this study provides diversified and rich learning functions for DMs to design various training methods for presenting learning diversity for cross-cultural students. From student perspectives, the possibilities of tailoring different approaches and ML plans are provided based on students' needs.

ML relies heavily on digital media that delivers learning materials to students. Therefore, unhandy and substandard designs will lead to learners being unwilling to adopt ML. To adapt to the increasing interests and benefits that gear learners toward ML, we address these issues by assuring an adequate plan and design prior to implementation [14]. Therefore, the following questions are proposed:

RQ1. On different campuses and across-cultures, what factors affect students’ BI towards ML?

RQ2. In higher education, what is the relationship among those factors across campuses in different countries?

RQ3. Before offering an appropriate ML system, can the TPB model be applied for the purpose of ML?

When considering students' perceptions of accepting ML, have any cross-cultural differences been found in the past, and to what extent could such findings be applied to students in different countries? Asian students have unique attributes that differ from students in Western countries. Eastern education has developed a pedagogical method that is centrally organized, teacher-dominated, and group-based [15]. Thus, the adoption rate of ML has not been high in the East. Further, cross-cultural research is required to improve its prospects. In particular, we investigated the factors influencing ML adoption in TW, CN, IN, and VN. From a theoretical perspective, many ML studies have been based on extended technology adoption [16]. So far, no research has been done to investigate constructs such as ATT, SN, PBC, PEOU, PU, instructor's readiness (IR) student's readiness (SR), self-efficacy (SE), and learning autonomy (LA) for cross-cultural students in higher education. The present study aims to determine the accessibility and applicability of TPB in an understudied context. The objective of this study is to investigate the key factors that may hinder or facilitate the adoption of ML through TPB with attitudinal constructs, i.e., ATT, SN, and PBC, and external variables, including PEOU, PU, IR, SR, SE, and LA. We examined four different cultural settings to explore the differences that may exist between the cultures involved.