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Factors That Affect The Use Of Technology In Teaching / Learning Environments - Assignment Example

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The use of technology in work is nowadays becoming a common practice for various different organisations and individuals as well. In order to carry out their work smoothly and quickly, use of technology is prime source of concern…
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Factors That Affect The Use Of Technology In Teaching / Learning Environments
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FACTORS THAT AFFECT THE USE OF TECHNOLOGY IN TEACHING LEARNING ENVIRONMENTS The use of technology in work is nowadays becoming a common practice for various different organisations and individuals as well. In order to carry out their work smoothly and quickly, use of technology is prime source of concern (Legris, Ingham, and Collerette, 2003). However there are various different factors that affect the use of technology in teaching and learning environments. Different models have highlighted different factors that directly influence the process of technology acceptance and adaptation. Some of the models and associated factors are as follow: Technology Acceptance Model 1 (TAM 1): TAM 1 has highlighted two factors which are: 1. Perceived Usefulness: Perceived usefulness in general is defined as the probability in terms of future or perspective user’s ability that using a new technological system or application will enhance user’s ability to perform better or giving optimum results (Davis, Bagozzi, and Warshaw, 1989). Moreover users are likely to adopt those technologies that they feel will increase their performances in terms of carrying out work in their walks of life. 2. Perceived Ease of Use: Perceived ease of use is another factor that affects the use of technology in teaching and learning environments (Davis, 1989). It deals with the future user’s perception or an expectation that his desired targeted system is likely to be free from efforts and hassles (King and Re, 2006). Technology Acceptance Model 2 (TAM 2): TAM 2 has highlighted seven additional factors which are: 1. Subjective Norm: It is usually defined as the ability of an individual in order to carry out some work or taking some decision is mainly influenced by others (in general people who are closer to him). Or in other words the action or decision that particular individual opt to take because of his perception of what other people might think of him is called subjective norm (Fishbein and Ajzen 1975, p. 302). 2. Voluntariness (and compliance with social influence): Voluntariness is a specific or will based action of an individual in which he present him to a situation offering his services, expertise, knowledge etc (Venkatesh and Davis, 2000). This intentional action usually comes because of the social influence and is not imposed (mandatory for) on person (Agarwal and Prasad 1997, Hartwick and Barki 1994, Moore and Benbasat 1991). Voluntariness is one of the major factors that affect the use of technology in teaching and learning environments, as it deals with the person’s own willingness of adopting technology and using it rather than imposing on him. 3. Image: Image is another important factor that is likely to affect the use of technology in teaching and learning environments. As usually individuals are very much keen on portraying and managing their strong social image and are very much concerned about what others will think of them in general. Moreover individuals are likely to adopt use of technology provided if its usage will enhance (increase or elevate) their standings socially or in other words if using that particular technology will place them in a particular group. Image is up to somewhat similar to subjective norm as both factors directly or indirectly deals with the perception of what others might think about the individual(s) (Chuttur, 2009). 4. Experience: Another key factor that affects the use of technology in learning and teaching environments is experience. As different individuals tends to learn from experience. In general an individual is relying on other’s opinion or suggestions before using any form or kind of technology but after having experience in terms of using technology he is likely to pass verdicts regarding further using or quitting the use of technology (Hartwick and Barki 1994, pp. 458–459). 5. Job Relevance: The ability to use the technology is dependent upon the job relevance factor. As perspective users are likely to adopt those kinds of technologies which will best suit their needs and which are likely to help or increase their performances in organisational settings. Job relevance is one of the key factors in terms of affecting the use of technology in learning and teaching environments. It deals with the overall understanding user regarding the benefits and relevance of adopting the system (technology). 6. Output Quality: Another important factor that affects the use of technology in different working environments is output quality. As output quality is the primary source of concern for an individual who is deciding to adopt any kind of technology. If the system is giving positive output quality then the user is more likely to engage him more into the use of technology. Similarly if the individual thinks or perceives that using technology will bring ease and productivity in his work he is more likely to adopt the technology (Davis, Bagozzi, and Warshaw, 1992). 7. Results Demonstrability: Technologies can fail provided if proper knowledge and understanding regarding their benefits and usage are not disclosed to users. If the perspective user is not well briefed regarding the role of technology and what benefits he is likely to get upon adopting it, chances are he is not likely to show willingness on using it. However if proper results demonstration is done or taught to him he is likely to use it. Therefore results demonstrability is one of the important factors that affect the use of technology in learning and teaching environments (Moore and Benbasat 1991, p. 203). Technology Acceptance Model 3 (TAM 3): TAM 2 has highlighted six additional factors which are: 1. Computer Self Efficacy: It is one of the important factors that affect the use of technology in different organisational settings. Computer self efficacy in general is individuals own willingness or belief that up to what extent he is capable of doing the desired work using computer. This result usually comes out as a self evaluation of user that what he is likely to do or perform and how much competent he is, in order to carry out his work using computer (Compeau and Higgins, 1995a, 1995b). 2. Perception of External Control: It is usually a self belief or perception of an individual regarding potential of an organisation in providing him with different kind of resources in order to adopt the system. This again plays very important role in terms of affecting the use of technology. As individual perceives that organisation is likely or unlikely will support the opted system (by providing different kind of relevant organisational resources and also support structures) in order to ensure facilitation of the system (Venkatesh, Morris, Davis, and Davis, 2003). 3. Computer Anxiety: Computer anxiety is a psychological factor that also affects the use of technology in learning and teaching environments. Basically it deals with the individual fear and curiosity that what will be the consequences upon having encounter with computer usage (Venkatesh, 2000, p 349). As individuals in these kinds of settings usually have different miss conceptions which leads them towards developing a fear and causes anxiety as a result adoption or use of technology is limited, stopped or slowed by them. Moreover they show hindrances towards computer usage. 4. Computer Playfulness: If we consider computer playfulness in general then it is the intrinsic ability of an individual to adopt new system or technology (Webster and Martocchio, 1992, p. 204). Usually individuals who are more inclined towards adopting new technologies are not reluctant and afraid from having new experiences. Therefore computer playfulness is another key factor that influences the use of technology in teaching and learning environments as those individuals who are confident and willing to experience new things are likely to adopt new technology. 5. Perceived Enjoyment: After using a technology individual is likely to have some frame of mind regarding the usage of that particular technology. The satisfaction and enjoyment the use of technology will create, regardless from any performance consequences that are more likely to occur, are the constituents of perceived enjoyment (Venkatesh, 2000, p. 351). Perceived enjoyment also affects the use of technology up to great extent. As if individual enjoys his work he is likely to give more productivity. 6. Objective Usability: Another factor that influences use of technology in different organisational settings is objective usability. As it deals with the overall comparison between goals achieved and use of system (Venkatesh, 2000, p. 350-51). In other words it gives understanding regarding what impact that particular use of technology has created in terms of getting work done (achieving goals). Unified Theory of Acceptance and Use of Technology (UTAUT): The UTAUT model highlighted six factors which are as follow: 1. Performance Expectancy: Performance expectancy is one of the driving factors that influence use of technology. As usually purpose of adopting new technology is dependent upon the performance expectancy. If the systems is performing above the expectancy there will be more chances of survival of system, however if things are not showing good results as per the expectation, then possible shift of technology or quitting of technology may result as well (Venkatesh, Morris, Davis, and Davis, 2003). 2. Effort Expectancy: Another important factor that affects the use of technology is effort expectancy. In general it deals with the efforts that are required in order to operate or perform function using technology. If individual is satisfied in terms of doing efforts and achieving goals he is likely to use or adopt new technology but if that is not the case then problems may arise (Venkatesh, Morris, Davis, and Davis, 2003). 3. Social Influence: Social influence also plays vital role and affects the use of technology, as there are individuals who are concerned about others beliefs and views. So as a result this belief on others derives them towards adopting new system because these individuals perceives that adopting a system is good for them as prescribed by others to them (Venkatesh, Morris, Davis, and Davis, 2003). 4. Facilitating Conditions: Facilitating condition is another factor that affects the use of technology as it deals with individuals mindset (believes) that an organisation and its part and parcel is there to support the use of technology. Therefore this behaviour of an individual is more likely to influence and affect the use of technology in education and teaching learning environments (Venkatesh, Morris, Davis, and Davis, 2003). 5. Gender: Another important factor that affects the use of technology is gender. As studies have shown that women are more concerned about what others might think about them if they adopt new technology. Similarly Men on the others are not as much concerned about what others think of them in comparison with women (Venkatesh, Morris, Davis, and Davis, 2003). 6. Age: Age is another important factor that affects the use of technology. Generally current situation of the world shows that old people are less likely to adopt changes in terms of technology whereas this ratio is far greater in younger ones (Venkatesh, Morris, Davis, and Davis, 2003). TASK TECHONOLGY FIT (TTF): The TTF model identified two main factors which directly influence the adaptation of technology, which are as follow: 1. Task Characteristics: Task characteristics enable or disable the use of technology. If completion of task requires use of technology then possible adoption in terms of using technology is eminent and if tasks does not necessarily requires use of technology then chances are minimal (Dishaw and Strong, 1999). 2. Technology Characteristics: A characteristic of technology is another important factor that affects the use of technology in learning and teaching environments. As these characteristics one way or other helps in performing work faster and also with ease. Therefore if product have sound technology characteristic and is best fitted as per the job description individuals are likely to gain and use technology in learning and teaching environments (Dishaw and Strong, 1999). EXISTING MODELS USED IN EVALUATING TECHNOLOGY USE Technology Acceptance Model 3 (TAM 3): Venkatesh and Bala (2008) presented a whole new model for explaining the technology acceptance behaviour of the people, names as TAM 3. TAM 3 highlighted and presented three new relations and included the determinants of perceived ease of use into the final model as presented in the figure below: (Venkatesh and Bala, 2008) According to Venkatesh and Bala (2008), there is no crossover in the TAM 3 as the elements associated with the perceived usefulness and perceived ease of use, do not influence each other. The new model claimed three new relationships which are as follow: 1. The moderation of relationships between the perceived usefulness and perceived ease of use by experience. As the experience will directly influence the perceived ease of use and perceived usefulness of the technology. 2. The moderation of computer anxiety and perceived ease of use by experience. The experience will result in directly affecting the relationship between the computer anxiety and perceived ease of use. 3. The moderation of perceived ease of use and behavioural intention by experience. Again the experience will result influence the relationship between the perceived ease of use and behavioural intention, as an increase in the experience will result in reducing the influence of the perceived ease of use on the behavioural intention of the user. Venkatesh and Bala (2008) conducted longitudinal filed studies in order to test the model and for this purposes gathered the data from four different organisations. Despite of its wide use, the main limitation of TAM 3 is that it ignores the social and psychographic elements associated with the use and acceptance of technology (Bagozzi, 2007). Unified Theory of Acceptance and Use of Technology (UTAT): Venkatesh, Morris, Davis, and Davis (2003) came up with the UTAT model in order to explain and analyse the technology acceptance behaviour of the individuals. This model presented the idea that the four elements namely: performance expectancy, effort expectancy, social influence, and facilitating conditions directly influence the technology acceptance and usage behaviour of individuals. The relations of the four main elements are complemented and influenced by other factors like age, gender, experience, and voluntary behaviour as shown in the figure below: (Venkatesh, Morris, Davis, and Davis, 2003) The model was developed by Venkatesh, Morris, Davis, and Davis (2003) while analysing and consolidating the elements of eight different models. Longitudinal study was used in order to test the model. The main limitation of UTAT is the involvement of high numbers of independent and dependent variables. Unified Theory of Acceptance and Use of Technology 2 (UTAT2): Venkatesh, Thong, and Xu (2012) further extended the UTAT Model into UTAT 2 model. This new model resulted in the addition of three new factors in the basic UTAT model which are as follow: 1. Price value 2. Habit 3. Hedonic motivation Venkatesh, Thong, and Xu (2012) presented the idea that these factors directly influence the technology usage and adaption behaviour of the individuals. At the same time Venkatesh, Thong, and Xu (2012) claimed that the factors representing differences in individuals like that of age, experience, and gender influence the relationship between the three main factors and technology acceptance and usage. The model is explained in the figure below: (Venkatesh, Thong, and Xu, 2012) Venkatesh, Thong, and Xu (2012) used the method of partial least squares (PLS) in order to test the model. This model is relatively new and still has to be researched and analysed in detail. Apart from this the generalisation of the research conducted by Venkatesh, Thong, and Xu (2012) is questionable. Task Technology Fit (TTF): This model presents the idea that the technology must be used in such a manner that it totally fits with the task being undertaken. This in turn increases the acceptance level of the technology. This model presented the idea that the technology which has high task fit results in enhancing the overall utilisation and performance. Despite of strong theoretical background and research studies the TTF model have some limitations, for instance it does not incorporate the social and cultural influence into account while explaining the technology acceptance behaviour of the individuals. The TTF models are broadly categorised into two main models namely: utilisation model and the task technology fit model. The first model is dependent on the factors affecting the utilisation like beliefs and attitudes. On the other hand the second model communicated the idea that if the technology is in fit with the task and supports the task then the overall performance is increased. However, the two tasks, if used individually, have their own limitations. For this reason Goodhue and Thompson (1995) combined the both models in order to come up with more comprehensive and effective model named as Technology to performance chain (TPC) as shown in the figure below: (Goodhue and Thompson, 1995) Goodhue and Thompson (1995) tested the model by collecting information from more than 600 individuals and thus showed the impact of the technology task fit on the overall utilisation of the technology and performance. APPLICATION OF EXISTING MODELS IN TODAY’S CONTEXT: The analysts have been trying to change and adapt the technology acceptance models according to the ongoing changes in the business world. With the passage of time the researchers and analysts have incorporated new elements and factors in order to make sure that the models are applicable and useful. All of the models discussed above, need to be modified in order to incorporate the factors and elements characterising the contemporary business environment of today’s world. For instance one important factor which is neglected in all of the models is the ongoing innovation and changes. These innovation and changes are continuously changing the some of the important factors highlighted by these models and thus the overall relationship and connection is changed. At the same time it is important to incorporate the element of the diffusion of innovation and different communication models and strategies into the models describing the behaviour of the individuals regarding the usage and acceptance of technology. Technology has taken an important part in today’s learning and teaching environment and it is important to take into consideration all factors which directly or indirectly influence the usage and acceptance of technology. List of References Agarwal, R., and Prasad, J. (1997). ‘The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies.’ Decision Sci., vol. 28, pp. 557–582. Bagozzi, R. (2007). ‘The legacy of the technology acceptance model and a proposal for a paradigm shift.’ JAIS, vol. 8, no. 7, pp. 244-254. Chuttur, M. (2009). ‘Overview of the Technology Acceptance Model: Origins, Developments and Future Directions’, Indiana University, USA .Sprouts: Working Papers on Information Systems.vol. 9, no. 37, pp. 1- 22. Compeau, D. R., & Higgins, C. A. (1995a). ‘Application of social cognitive theory to training for computer skills.’ Information Systems Research, vol. 6, pp. 118–143. Compeau, D. R., & Higgins, C. A. (1995b). ‘Computer self-efficacy: Development of a measure and initial test.’ MIS Quarterly, vol. 19, pp. 189–211. Davis, F. (1989). ‘Perceived Usefulness, Perceived Ease Of Use, And User Acceptance Of Information Technology’, MIS Quarterly, vol. 13, no. 3, pp. 319- 340. Davis, F., Bagozzi, R., and Warshaw, P. (1989). ‘User acceptance of computer technology: a comparison of two theoretical models.’ Management Science, Vol. 35, no. 8, pp. 982–1003. Davis, F., Bagozzi, R., and Warshaw, P. (1992). ‘Extrinsic and intrinsic motivation to use computers in the workplace.’ J. Appl. Social Psych., Vol. 22, pp. 1111–1132. Dishaw, M., and Strong, D. (1999). ‘Extending the technology acceptance model with task- technology fit constructs’, Information & Management, vol. 36, pp. 9 – 21. Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: A Introduction to Theory and Research. Reading, MA: Addison-Wesley. Goodhue, D., and Thompson, R. (1995). ‘Task-Technology fit and individual performance.’ MIS Quarterly, vol. 19, no. 2, pp. 213-236 Hartwick, J., and Barki, H. (1994). ‘Explaining the role of user participation in information system use.’ Management Sci., vol. 40, pp. 440–465. King, W., and Re, H. (2006). ‘A meta-analysis of the technology acceptance model’, Information & Management, vol. 43, pp. 740–755 Legris, P., Ingham, J., and Collerette, P. (2003). ‘Why do people use information technology? A critical review of the technology acceptance model.’ Information and Management, vol. 40, pp. 191-204 Moore, G. C., and Benbasat, I. (1991). ‘Development of an instrument to measure the perceptions of adopting an information technology innovation.’ Information Systems Res., vol. 2, pp. 192–222. Venkatesh, V. (2000). ‘Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model.’ Information Systems Research, vol. 11, pp. 342–365. Venkatesh, V., and Bala, H. (2008). ‘Technology acceptance model 3 and a research agenda on interventions.’ Decision Sciences, vol. 39, no. 2, pp. 273-315. Venkatesh, V., and Davis, F. (2000). ‘A Theoretical Extension of the Technology Acceptance Model - Four longitudinal Field Studies’, Management Science, vol. 46, no. 2, pp. 186-204. Venkatesh, V., Morris, M., Davis, G., and Davis, F. (2003). ‘User acceptance of information technology: toward a unified view.’ MIS Quarterly, vol. 27, no. 3, pp. 425-478 Venkatesh, V., Thong, J., and Xu, X. (2012). ‘Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology.’ MIS Quarterly, vol. 36, no. 1, pp. 157-178 Webster, J., and Martocchio, J. J. (1992). ’Microcomputer playfulness: Development of a measure with workplace implications.’ MIS Quarterly, vol. 16, pp. 201–226. Read More
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