. International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016270 International Review of Management and Marketing ISSN: 2146-4405 available at http: www.econjournals.com International Review of Management and Marketing, 2016, 6(S8) 270-279. Special Issue for "International Conference on Applied Science and Technology (ICAST) 2016, Malaysia" Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework Safaie Mangir1*, Zakirah Othman2, Zulkifli Mohamed Udin3 1School of Technology Management and Logistics, College of Business, Universiti Utara Malaysia, Malaysia, 2School of Technology Management and Logistics, College of Business, Universiti Utara Malaysia, Malaysia, 3School of Technology Management and Logistics, College of Business, Universiti Utara Malaysia, Malaysia. *Email: mangir@gmail.com ABSTRACT The objective of this paper is to develop a framework on e-learning acceptance among agricultural extension agents in Malaysian agricultural sector. E-learning is viewed as a solution in response to the increasing need for learning and training. This paper will review past literatures for the relevant factors that influence behavioral intention for e-learning acceptance as well as the relevant behavioral theories that provide the foundation for developing research framework to illustrate the relationship between the factors and behavioral intention for e-learning acceptance. The outcome of this research is the proposed framework that demonstrates the relationship between the factors and behavioral intention for e-learning acceptance. Understanding the determinants of e-learning acceptance will lead to the development of more effective and meaningful e-learning services. The research outcome will help to increase the interest to use e-learning, improve the policies and infrastructure, widen the availability and increase user engagement. Investigating the ability of the behavioral theory in predicting e-learning acceptance in agricultural setting will be useful for comparison in the future studies in different settings. Keywords: Acceptance, Agriculture, E-learning, Extension Agent JEL Classifications: Q13, D83 1. INTRODUCTION One of the initiatives in the economic transformation program which will drive Malaysia towards high-income status and global competitiveness to achieving sustainable economic growth is “Establishing e-learning for students and workers” which is projected to achieve gross national income of RM1,487 and 800 job creations by 2020. E-learning which refers to learning process to acquire knowledge using electronic application through the computer and network, is an important approach to enhance and support conventional teaching and provide the ability to render learning to a large population without physical boundaries (Daud et al., 2013). Characteristically easy to deliver, flexible, economical and agnostic to distance and time (Carey and Blatnik, 2005), e-learning has various advantages and is superior over the traditional techniques of learning (Agarwal and Kumar, 2013) and becoming a desirable education, learning and teaching alternative for developing countries (Maldonado et al., 2011) such as Malaysia. E-learning is seen as a solution to meet the growing demand for learning and training that facilitates more users to learn and train at fraction of the cost, offers opportunity for life-long learning, enables self-regulated learning irrespective of time and location and makes education accessible to all, including people living in remote areas or who have work or family constraints. As with the other innovations accessible out there, the rise of e-learning environment should not be overlooked. The use of e-learning which is traditionally known for its utilization in educational institutions has extended to numerous corporate enterprises and organizations hence along these lines, it is always imperative to study the elements that affect and factors that influence the e-learning environment (Hashim and Tasir, 2014). In relation to learning and training, the agricultural extension agents (AEA) are among the most important actors in agriculture Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016 271 industry, as they are one of the important determinants of success for agriculture industry in Malaysia and form a special role as a change agent that is crucial to transform the attitude, competencies and knowledge of the farmers in order to support the achievement of national mission (Shah et al., 2013). The AEAs being the important actor in the agriculture value-chain must first be equipped with all the required knowledge and technology skills to ensure the program planned for the community is effectively and successfully delivered (Abdullah and Samah, 2013). Annor-Frempong and Kwarteng (2006) argue that information and communication technology (ICTs) are more and more seen as practical and cost-effective tools to facilitate knowledge sharing and information delivery stakeholders including extension agents and farmers. According to Hafkin and Odame (2002) and Richardson (2005) the benefits of ICT utilization such as e-learning for purposes of agricultural extension and training are well documented. Public sector programs have attempted to overcome information-related barriers to technological adoption by providing agricultural extension services however such programs have been widely criticized for their limited scale, sustainability and impact (Aker, 2011). Richardson (2005) remarked that the e-learning technologies can be utilized to expand access to training and education and enhance learning in the agricultural sector. The author further stated that the ICT development has popularized e-learning systems in the field of agricultural extension. Understanding that traditional education is no longer effective, the Iranian Ministry of Agriculture has considered the necessity of e-learning in agricultural extension for training extension agents (Ahmadpour and Mirdamadi, 2010). E-learning is seen as a solution to meet the rising demand for training and learning. Through e-learning, there is possible opportunity for self-pace learning and life-long learning anywhere and anytime and more participants can be trained at more affordable cost (Yunus and Salim, 2013). The agriculture sector remains a significant development factor and continues to make important contribution to the national economy in Malaysia despite profound reduction of its contribution to the gross domestic product i.e. from some 30% in the 1970s to only 7% in 2013 (Othman and Jafari, 2014; Shah et al., 2013). The significance of agriculture can be seen in terms of its contribution to the total economy, via its functional role in social and environmental development and linkages within the agriculture ministries and departments and other government agencies such as rural development, natural resources and environment, plantation and commodities (Othman and Jafari, 2014). According to the economic transformation program, agriculture sector in Malaysia is vitally important as it provides rural employment, uplift rural incomes and ensure national food security hence it represents one of the key economic sectors that has become the focus of productivity growth and targeted to contribute to the annual productivity growth of 3.7% by year 2020. In the context of this research, the focus remains on the factors that influence e-learning acceptance by AEAs in Malaysia. 2. PROBLEM STATEMENT Government extension agencies play the central role of intermediaries which are integral to agricultural research and development primarily to disseminate important agricultural information that are all part of an overall agricultural knowledge system linked by ICT to farmers (Abdon and Raab, 2005). In many developing countries, training and education for government extension agencies is clearly top priority however the efforts currently taken are less than desired and there are increasing evidence that indicates the issue with extension services is the result of crisis in agricultural training and education (Gasperini, 2000). Despite various extension strategies such as decentralization, farmer field schools, training and visit and transfer of technology to improve the productivity of farmers and to facilitate the role that extension plays in national development in many developing countries, these extension efforts have not achieved high degree of success and extension systems are left facing many constraints and challenges such high cost of travel to remote areas, delay or loss of information delivery due to insufficient infrastructure and difficult access (Annor-Frempong and Kwarteng, 2006). The typical face-to-face classroom-type methods of training the extension agents are often unsatisfactory due to several reasons such as high number of extension agents scattered throughout the country causing it to be costly and very challenging to implement and manage thus rendering e-learning to be the proper resolution to resolve the issue (Ahmadpour et al., 2010). The agricultural extension service in Malaysia has not sufficiently been effective in developing agriculture since the service is only focusing on the role of technology transfer and efforts on development of human resources have been minimal (Shah et al., 2013). ICT practice in agricultural area in Malaysia is still at the average level (Hassan et al., 2008). Hassan et al. (2009) reported that the level of ICT usage such as internet application among Malaysia agro-based entrepreneurs is at moderate level. Moreover according to Hassan et al. (2009) and Irfan et al. (2006), instead of using ICT in their agro-business, agriculture community prefer to use traditional ways such as asking other entrepreneurs or their neighbor and by relying on traditional mass media such as newspaper, radio and television. Contributing to this problem are factors such as language problem (Deraman and Bahar, 2000), elderly community and low level of education and self-esteem (Tamam et al., 2008). Hassan et al. (2008) further contend that ICT has been used moderately in Malaysian agriculture but it has the potential to help develop and commercialize agriculture sector and make it more competitive among other industries. The issue related to extension service in Malaysia provides an opportunity for AEAs to adopt e-learning to fill the gaps. Despite many organizations in public and private sectors investing considerable amounts of time and money in developing online alternatives to traditional types of education and training systems, the rate of e-learning systems user acceptance is not increasing correspondingly as high as expected (Ahmadpour et al., 2010; Wu and Chen, 2012). Although e-learning increasingly finds its place in a range of educational institutions and organizations, past researches still showed obvious shortcomings in e-learning including the inability to attract the learners’ or trainees’ interest which adversely affected the e-learning implementation. In addition, the learners’ attitude towards e-learning also influences their desire to take part in e-learning courses in agricultural Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016272 extension (Ahmadpour and Soltani, 2012; Yunus and Salim, 2013). There have been some emerging concerns that e-learning has not been able to achieve its full potential, partly due to high attrition and low rate of utilization (Bell et al., 2004; Tyler-Smith, 2006; Wang, 2010) hence the e-learning resource availability does not guarantee its use, nor its effectiveness as a tool to change employee behavior. In spite of the growth of adult learners who participate in online learning, the high dropout rate in online learning has been of concern to many higher education institutions and organizations (Park and Choi, 2009). Yunus and Salim (2013) highlighted that existing studies on e-learning in Malaysia are more focused on certain private organizations and institutions of higher learning. The authors further argued that there was no comprehensive study to evaluate e-learning in the Malaysian public sector. This finding is further confirmed by (Agarwal and Kumar, 2013) and (Ahmadpour et al., 2010) who argue that despite the exciting benefits of e-learning, the adoption of e-learning for agricultural development and related fields particularly in or for developing countries is still in the early phases of adoption and has been slow to take off. Abdon et al. (2008) contend that e-learning is a potentially cost-effective and viable way to facilitate knowledge development among farmers and agricultural professionals but is still not employed widely. The findings of the past research have pointed out that varying factors influence e-learning acceptance therefore, it is necessary to have an in-depth study of those factors that influence e-learning acceptance by AEAs in the Malaysian agricultural industry. The overall objective of this research is to develop a model on e-learning acceptance and investigate the influence of psychological, social, management and training factors on the AEAs’ acceptance of e-learning in agricultural sector in Malaysia. The relationship between the proposed factors and the AEAs’ behavioral intention to use e-learning will be examined. Thus, in the context of e-learning acceptance by AEAs in Malaysian agricultural sector, this study will attempt to achieve the following objectives: 1. To develop a framework on e-learning acceptance. 2. To investigate the impact of attitude beliefs on AEAs’ intention for e-learning acceptance. 3. To investigate the impact of subjective norms on AEAs’ intention for e-learning acceptance. 4. To investigate the impact of perceived behavioral controls on AEAs’ intention for e-learning acceptance. 5. To investigate the impact of management support on AEAs’ intention for e-learning acceptance. 6. To investigate the impact of training on AEAs’ intention for e-learning acceptance. 3. LITERATURE REVIEW E-learning refers to self-study or instructor-led training delivered on a digital device of which the contents and delivery techniques are designed to support individual learning or organizational performance improvement (Clark and Mayer, 2011). Sangrà et al. (2012) further describe e-learning as “an approach to teaching and learning, representing all or part of the educational model applied, that is based on the use of electronic media and devices as tools for improving access to training, communication and interaction and that facilitates the adoption of new ways of understanding and developing learning.” Advances in technology and the interrelation of ICT with teaching and learning settings have accelerated the growth of distance learning and fundamentally changed the way of education and learning. Learning on demand is becoming a type of lifestyle in modern society (McLoughlin and Lee, 2007) and such phenomenon is made possible mostly due to ICT facilitation. Information is constantly sought and shared by learners at home, school or work to solve problem, get help or just to obtain more knowledge to satisfy a curiosity by taking advantage of ICT advancement that besides being consumer of information, learners can also become content producers (McLoughlin and Lee, 2007; Smith and Caruso, 2010; Solomon and Schrum, 2007). The rapid growth of ICT and increasing computer knowledge of the population have led to the usage of many learning and teaching innovative technologies such as mobile-learning and e-learning (Vyas and Nirban, 2014). Technology convergence has enabled employees to demand all types of learning materials via various types of tools such as smart devices besides typical laptops and desktop computer platforms and digital transmission of contents for the purpose of learning and knowledge-seeking known as e-learning is increasingly becoming common workplace learning (Brown and Charlier, 2013). In order to enhance employee and organizational effectiveness, organizational training requires a systematic approach that is geared towards learning and human resource development (Goldstein and Ford, 2001) and organizational training nowadays is supported by e-learning which rides on the power of technology infrastructures and web networking in order to achieve the particular objectives and deliver instruction (Rosenberg, 2001). Workplace learning for continuous improvement is crucial to sustain competitiveness of organizations (Ho et al., 2011). Organizations across the industries time and again have exploited e-learning system to facilitate employee development as e-learning has the capability to deliver knowledge and information to individuals (Yoo and Huang, 2015). Several areas including government and corporate training for employees and distance learning for primary, secondary and tertiary education have found e-learning applications being extended to support their learning and training needs (Pereira et al., 2015). Public organizations have utilized e-learning to facilitate training and employee development and enable training management, skill adequacy planning and training budgeting (Saha et al., 2010) and help to foster organizational learning culture based on knowledge sharing (Chen and Hsiang, 2007). The infrastructure in the country is ready to support e-learning. The Malaysian performance management and delivery unit (PEMANDU) reported that household broadband penetration in Malaysia has reached close to 70% in 2014, and coupled with internet and multimedia technology emergence in recent years, Malaysia is in a good position to harness the power of Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016 273 online learning to lower the delivery cost, enhance the quality of teaching and learning, widen access to good quality content and bring Malaysian expertise to the global community (Malaysian Education Blueprint, 2015-2025). In Malaysian agriculture, the AEAs which is target group for e-learning implementation are potential adopters as more than 50% of them are in the age group of <35 years old (Tiraieyari et al., 2013) and according to Kamarul Zaman et al. (2015), younger generation has greater potential to adopt technology innovation. There will be plenty of opportunity that e-learning may contribute to dissemination and awareness of sustainable development which is part of National Green Technology Policy and sustainable practices being one of the six strategic thrusts of the Eleventh Malaysia Plan 2016-2020. Not implementing e-learning may impair the capability of the agriculture sector to stay on course with the national agenda. 3.1. Theoretical Background Chu and Chen (2015) noted that researches related to behavioral intention for technology acceptance have been developed around the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975), technology acceptance model (TAM) (Davis, 1989) and theory of planned behavior (TPB) (Ajzen, 1991). In TRA, Fishbein and Ajzen (1975) explained that an individual performs a specific behavior based on behavioral intention, which can be determined by subjective norms and the attitude toward the behavior. So TRA is generally comprised of three main constructs i.e. behavioral intention, subjective norms and attitude toward behavior. Thus, a person’s intention toward a specific behavior is affected by the person’s attitude toward that behavioral outcome and the attitude a person perceives other people would have towards the performance of that behavior. A person’s attitude, combined with subjective norms, forms the person’s behavioral intention. TRA however is a relatively poor at predicting those types of behaviors where internal and external factors might control or affect the motivation of the outcome of behavior hence Ajzen (1991) further extended TRA to include perceived behavioral control in addition to subjective norms and attitude as antecedents to behavioral intention. This extension to TRA is called TPB and it extends the incomplete concept of TRA in predicting an actual behavior under the influence of certain motivation that intended behaviors are also controlled by some uncertainty. Hence, a behavior performance depends on intention as well as internal or external factors that may affect the motivational behavior (Shareef et al., 2009). TAM is another widely referenced theoretical model for predicting the intention to use and the acceptance of information system by individuals. It proposes that only perceived ease of use and perceived usefulness determine the attitude toward adoption of ICT and ignores the factors of subjective norms and perceived behavioral control, thus making the model more parsimonious. The attitude, in turn, leads to the intention to use ICT and the eventual acceptance (Bhattacherjee, 2001; Davis et al., 1989). Many studies in the literature have used different technology acceptance and adoption models. The diffusion of innovations (DOI) Theory popularized by Rogers (1983) is one of the well-established frameworks that examine how technological innovations are adopted between individuals within a social system. According to the author, the rate of innovations adoption is determined by five factors: Relative advantage, compatibility, trialability, observability and complexity. Then there is also Taylor and Todd’s (1995) decomposed TPB, which integrates the TPB model and the DOI theory. TAM and TPB are two of the most prevalent theories that are associated with studies on information technology acceptance and both theories explain well the intentions to accept information technologies based on numerous empirical tests in variety of technologies in various settings (Shiue, 2007; Teo, 2012). TPB however, offers more specific information related to subjective norms and the user’s perceived behavioral control which is not considered in TAM, hence the TPB model extends more information about the factors to be considered for the intention to accept the technology (Mathieson, 1991). Mathieson (1991) and Taylor and Todd (1995) argue that TPB has been proven to have similar predictive powers as TAM for technology adoption. TAM puts greater emphasis on technology features rather than social influences for examining technology adoption intention. In this study about e-learning acceptance by AEAs, e-learning unlike an individual application, usually involves interaction with instructors and students. Thus, social influences should not be ignored as e-learning includes interpersonal interactions (Chu and Chen, 2015). The TPB is applied quite comprehensively across several settings that include adoption/acceptance of new technology, technology- based learning such as e-learning and m-learning, organizational improvement and agriculture. Venkatesh and Speier (1999) adapted measures used in the TRA and TPB to investigate how the effects of emotion during new technology training influenced employees’ motivation, intentions and usage of the new technology by employees of an accounting firm. George (2004) utilized the TPB to investigate Internet purchasing by extending the attitude toward behavior and perceived behavioral control components to include Internet trustworthiness beliefs and unauthorized use beliefs to determine online purchasing behavior among college students. Herrero and Rodríguez del Bosque (2008) adapted TPB and add personal innovativeness construct as moderator on the adoption of electronic commerce by web users. Bosnjak et al. (2005) in their study involving college business students adapted the TPB and add moral obligation component to predict and explain the number of participants in web-based panel study to study internalization of moral values. Engle et al. (2010) applied TPB to evaluate entrepreneurial intent by business students across twelve countries. Siragusa and Dixon (2009) adapted TPB to predict higher education students’ behavior and attitudes towards technology-based learning. Chu and Chen (2015) adapted TPB that include social identity and social bonds on e-learning adoption intention among students who enrolled in courses using e-learning technology in Taiwan. Cheon et al. (2012) conducted a study to investigate mobile learning readiness in higher education based on the TBP. Wiethoff (2004) adapted TPB to study effectiveness of training for organizational diversity. Yazdanpanah et al. (2014) adapted TPB and extended additional constructs i.e., perceived risk, self-identity and moral Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016274 norm in their study on water conservation among farmers in Iran. In USA, Lynne et al. (1995) investigated the farmers’ conservation technology adoption decision using TPB. Sparks and Shepherd (1992) extended TPB with self-identity and past consumption constructs to study the antecedents of intention to consume organic products. Colémont and Van den Broucke (2008) used TPB to understand the factors that contribute to unsafe and health damaging behavior among the farmers. Artikov et al. (2006) utilized TPB to study the causes that drive farmer intentions of using climate and weather information and forecasts in farming decisions among farmers in mid-western USA. Beside attitude, subjective norms and perceived behavioral control being the main constructs of TPB, the literatures related to TPB highlighted several other constructs such as emotion, trust, security, innovativeness, moral obligation, social bonds, social identity, training, perceived risk, self-identity and past experience as antecedents to behavioral intention. However there was limited research on management support and training as additional constructs to the TPB. Previous studies indicated that training is a significant factor that influences online learning acceptance and have positive impact on users’ acceptance and their intention to use a particular system (Igbaria et al., 1997; Wolski and Jackson, 1999). Cerveny and Sanders (1986) and Igbaria (1994) contend that management support has been identified as one of the main recurrent factors affecting system success. In view of the above, two additional construct involving management support and training are proposed to be investigated besides the original TPB constructs i.e., behavior intention, attitude, subjective norms and perceived behavioral control. The TPB has been widely used to predict behavioral intention hence the research framework in this study will adapt the TPB and apply behavioral intention, which has been considered as the important basis for examining the behavior of AEAs to accept e-learning system. The key components of TPB are behavioral intention as dependent variable and attitude, subjective norm and perceived behavioral control as independent variables. For the purpose of this study, another two variables are included as independent variable i.e. management support and training which reflects the setting that involves extension agents in Malaysia Department of Agriculture. 3.2. Behavioral Intention Behavioral intention refers to a person’s subjective probability in performing certain behavior (Fishbein and Ajzen, 1975). It decides if the AEAs want to reject or accept the use the technologies in their learning and training duty. Behavioral intention is applied as a dependent variable for three reasons. First, a number of studies have reported a significant and strong relationship between behavioral intention and targeted behavior (Sheppard et al., 1988; Venkatesh and Morris, 2000). Mathieson (1991) highlighted that over time there will be changes either in the environment, user’s expectations or the system itself therefore measuring user acceptance based on the “intention to use” before the system implementation is ready will be required especially when the system does not yet exist. Secondly, according to Agarwal and Prasad (1998), usage intentions are more applicable than actual behavior because they are measured concurrently with beliefs. Thirdly, because e-learning system is not yet implemented, choosing intention to use over actual acceptance is more desirable; thus, examining the intention to accept e-learning facilitates a timely investigation of agricultural AEAs’ acceptance to help prepare the actual implementation. The literature reviews have shown several factors which influence the adoption and acceptance of information technologies such as e-learning. Among the factors that are extensively being studied are attitude towards technology, subjective norms and perceived behavioral control (Karaali et al., 2011; Macharia and Nyakwende, 2010; Park and Cheong, 2007; Park, 2009; Qudais et al., 2010; Siragusa and Dixon, 2009). In relation to these factors, behavioral intention is the ultimate destination that brings about the adoption or acceptance of technology. 3.3. Attitude Attitudes reflect the individual’s positive or negative evaluations of performing a particular target behavior (Fishbein and Ajzen, 1975). According to Rogers (1983) attitude refers to individual’s general feeling or perception of favorableness or un-favorableness towards using innovation or technologies. Ajzen (1991) further expands that the sum of accessible behavioral beliefs determines attitude towards the behavior. The author contends that accessible behavioral beliefs refer to the subjective probability that the behavior will achieve expected outcomes positively or negatively. Attitude is linked to behavioral intention as individual forms psychological intentions to perform behaviors toward which they have positive feeling (Ndubisi, 2004). 3.4. Subjective Norm Also referred as social influence, subjective norm is described as “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1975). Ajzen (1991) further adds that the determinant of subjective norm is the sum of normative beliefs which reflects the perceived behavioral views or expectations of important referents individuals or groups. Subjective norm implies that behavior is initiated by individual’s wish to act according to what the thought or action of the important referent others (Pavlou and Fygenson, 2006). Pantano and Di Pietro (2012) suggest that referent people such as friends and family represent the expectations of other people to perform a particular behavior, thus they can potentially influence the behavior. Subjective norms have been observed to be more important prior to, or in the early stages of implementation when users have limited direct experience from which to develop the attitudes towards the innovation (Hartwick and Barki, 1994; Taylor and Todd, 1995). Borotis and Poulymenakou (2009) contend that subjective norm refers to the individual’s perceptions of broad social pressure to (or not to) perform the expected behavior. The social pressure exerted by the significant “referent” others whose beliefs may be important to the individual whom the individual perceives to support (or not) the behavior, conceives more (or less) likelihood for the individual to perform it. Subjective norm is considered as one of the factors that influences the adoption and the acceptance Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016 275 of technologies and appears in many adoption theories such as TRA and TPB. 3.5. Perceived Behavioral Control To a certain extent the available opportunities and resources to a person determine the chances of achieving the target behavior and of greater psychological interest, is the perception and its impact of behavioral control on intentions and actions (Ajzen, 1991). The author refers perceived behavioral control as “people’s perception of the ease or difficulty of performing the behavior of interest” and it can change across actions and circumstances. The author adds that perceived behavioral control is determined by the sum of accessible control beliefs which refers to the perceived presence of the necessary opportunities and resources to the performance of the intended behavior. According to Tan and Teo (2000) perceived behavioral control refers to the factors that may hinder or encourage the performance of the behavior. Cheng and Huang (2013) identified the determinant factors for perceived behavioral control as ability, resource and opportunity and the authors contend that a person’s behavioral intention will increase when he perceives himself as more in control which is supported by the perception that he has the ability, resources and opportunities to perform a certain behavior. 3.6. Management Support Management support refers to the perceived level of general support offered by the organization’s top management (Igbaria et al., 1997). Management support functions as a change agent to create a more encouraging environment and ensure sufficient resource allocation for the success of IS implementation. Therefore, management support is always related to greater system success and lack of it may render a critical barrier to the effective IT utilization. As with other information systems, management support is critical to e-learning implementation (Morrison, 2003) and top management’s vocal support and consistency are important factors for success (Macpherson et al., 2004). As e-learning requires organization-wide change, the top management support is viewed as important to influence employees of the organization due to its position and ranking hierarchy in the organization. In addition, direct managers may also extend their support and guidance and direction as they are closer and more familiar with employees. They can assist employee in finding the right time to learn and subsequently buying the support from employees for acceptance of the new technology and the process. Management support should be relevant and clear to internal as well as external conditions (Sela and Sivan, 2009). Management support has been identified by the past literatures as one of the main recurrent factors affecting system success (Cerveny and Sanders, 1986; Igbaria, 1994). Sivakumar et al. (2014) conducted research in extension organizations in India and revealed that management support influenced computer utility positively among the extension professionals. 3.7. Training Training in this study is defined as institution’s effort to teach and train their employees to acquire e-learning skills. Previous studies have included training as a significant factor that influence the students’ acceptance of using online learning and concluded that training had a positive impact on users’ acceptance and their intention to use a particular system (Igbaria et al., 1997; Wolski and Jackson, 1999). Thus, the training provided by the institutions will be considered a key factor for the successful implementation of e-learning hence its relationship with the AEA’s intention to use e-learning will be investigated. Raymond (1988) in his study reported that training promotes more diverse and frequent use, favorable attitudes and greater understanding on the application use. Similarly, training was found to have a positive impact on technology acceptance (Igbaria et al., 1995; Nelson and Cheney, 1987). Ali and Magalhaes (2008) discovered that generally, the majority of the organizations were providing training to their employees under different models covering both traditional methods (on-the-job training and instructor-led training) as well as new methods (e-learning) and aware of the importance and effectiveness of training to implementation of e-learning. Research in extension organizations in India discovered that training influenced computer usage positively among extension officials (Sivakumar et al., 2014). The organizational effort of providing training and support for technology (i.e. internet stock trading system) would enhance their capability in utilizing the technology among individual stock traders in Malaysia (Ramayah et al., 2014). Figure 1: Research framework of behavioral intention to e-learning acceptance Mangir, et al.: Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework International Review of Management and Marketing | Vol 6 • Special Issue (S8) • 2016276 In the context of e-learning acceptance by AEAs and adapting the TPB, the research framework as depicted in Figure 1 is proposed to investigate the relationship between behavioral intention for e-learning acceptance as dependent variable and each of the independent variables i.e., attitude, subjective norms, perceived behavioral control, management support and training. The framework is expected to provide better understanding on the factors that contribute to the acceptance of e-learning by the target population i.e., AEAs and can be useful for those who are responsible to implementing ICT initiatives such as e-learning in public or private organization within the agriculture sector. The framework will help in terms of preparing for pre-implementation actions to increase the acceptance level, gain the necessary support and lift the potential barriers of the ICT initiatives. Based on the research framework as illustrated in Figure 1, the hypotheses are postulated as follow: H1: Attitude has a positive relationship with behavioral intention to e-learning acceptance. H2: Subjective norm has a positive relationship with behavioral intention to e-learning acceptance. H3: Perceived behavioral control has a positive relationship with behavioral intention to e-learning acceptance. H4: Management support has a positive relationship with behavioral intention to e-learning acceptance. H5: Training has a positive relationship with behavioral intention to e-learning acceptance. 4. CONCLUSION This study is an attempt to provide a comprehensive review of the relationship between attitude, social influence and perceived behavioral control to the behavioral intention for e-learning acceptance by AEAs in Malaysia. From practical perspective, the study will offer an insight into one of the most important issues in Malaysian agricultural sector, which is learning and training for AEAs. In addition the research findings will help determine the barriers to e-learning, curtail the potential resistance and identify the factors that promote e-learning acceptance in Malaysia agricultural sector. The research findings would be beneficial to the governmental departments to improve the relevant infrastructure to widen the availability of e-learning and increase the engagement among the users. From the theoretical point of view and in relation to the body of knowledge, this research will contribute significantly through the process of investigating the impact of influencing factors on the e-learning acceptance through the framework of the social and behavioral theory. The study also attempts to prove the significant role of the proposed factors (social, psychological, management support and training) and e-learning acceptance and the respective relationships will be tested directly hence the findings will be useful for comparison in the future studies in different country and/or industry. REFERENCES Abdon, B., Raab, R., Ninomiya, S. (2008), e-Learning for international agriculture development: Dealing with challenges. International Journal of Education and Development Using Information and Communication Technology, 4(1), 80-94. Available from: http:// www.search.proquest.com.library.capella.edu/docview/873571330 ?accountid=27965. Abdon, B.R., Raab, R.T. 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