Technology Adoption in Management Classroom Learning

The paper aims to examine the factors affecting the intention to use technology by the MBA students. The Unified Theory of Acceptance and Use of Technology (UTAUT) is used as a research framework. Survey was conducted in seven metro cities in India viz; Mumbai, Pune, Kolkata, Delhi, Greater Noida, Bangalore and Chennai via questionnaire method. Out of 900 questionnaires distributed 517 students completed the survey questionnaire measuring their responses to five constructs in UTAUT. In addition to determining the factors which affect the usage of technology, the paper also enlists the type of technologies used and their frequency of usage in classroom learning by the students. The students of AICTE approved and University affiliated colleges providing full time Masters degree in Business Administration (MBA) participated in this survey.


INTRODUCTION
Information and communications technology (ICT) has become an important part of most organization and businesses these days (Zhang & Aikman, 2007). Also, Modern technology offers many means of improving teaching and learning in the classroom (Lefebvre, Deaudelin & Losiselle, 2006). Darell et al., predicted that in the right circumstances, new technologies adopted by members of a community will spread by diffusion.

Motivation for research
Understanding individual acceptance and use of information technology is one of the most mature streams of information systems research (Benbasat and Barki 2007;Venkatesh et al. 2007). Wong et al. 2006, pointed out that technology can play a part in supporting face-to-face teaching and learning in the classroom. Many researchers and theorists assert that the use of computers can help students to become knowledgeable, reduce the amount of direct instruction given to them, and give teachers an opportunity to help those students with particular needs (

A note about terminology
Though International Society for Technology and Education (ISTE) and other professional organizations have tried to establish common definitions, there is still no consensus on technology terms. Known variously as educational technology, instructional technology and media, information technology, or information communication technologyterms often reduced to shorthand like EdMedia, IT, and ICTeducational technology is a verbal chameleon, reflecting its surroundings. The word "computer," which before 1945 meant a person (usually a woman) responsible for computations and later referred to machines that occupied several rooms, now is more likely to signify laptops than desktop models in many schools. Lightweight handheld devices, also once thought to be the digital tool of choice for schools of the future (Bull and Garofalo, 2006), are now being replaced by cell phones as ubiquitous computing devices (Margaret S Crocco et al 2008). The term "technology" here encompasses computer hardware (e.g. scanners, cameras, projector) and software applications (e.g. word processing, excel, Internet, PowerPoint) and any technology specific to the students learning area as mentioned in Annexure.

REVIEW OF LITERATURE
In recent years, increases in class size, the diversity of student populations and changes in the expectations of students have all acted as stimuli for an examination of approaches to teaching and learning (Saunders, 2000). Coupled to developments in information and communication technology (ICT), these stimuli have generally led to different and more flexible approaches to learning, often involving the increased use of ICT in the classroom (Collis and Moonen 2001; Hudson et al., 1997;Saunders et al., 1999). For example the use of presentation graphics (e.g., PowerPoint) in the classroom appears to be embraced enthusiastically by faculty and administrators at institutions nationwide. Many classrooms are being equipped with computers and costly projection devices to support presentation graphics as well as other visual presentation media. Faculty members are contributing countless hours in the preparation of slide show presentations to accompany lecture material, necessitating large electronic files that create increasing electronic storage capacity needs. Textbook companies are contracting with individuals to construct textbook-specific slide shows in an effort to increase the marketability of their textbooks. Despite the extensive investments of human and financial resources, few studies exist that clearly delineate the benefits of the use of presentation graphics (Murray, 2001). Specifically, there is limited empirical evidence to date supporting a positive impact on student learning and students_ and professors_ perceptions of the classroom experience (Jennifer M. Apperson et al, 2006). A variety of theoretical perspectives have been advanced to provide an understanding of the determinants of usage. One important line of research has employed intention-based models which use behavioral intention to predict usage and, in turn, focus on the identification of the determinations of intention, such as attitudes, social influences, and facilitating conditions (Davis et al., , 1992Hartwick and Barki 1994;Mathieson 1991). This work is grounded in models from social psychology, such as the Theory of Reasoned Action (TRA) (Ajzen and Fishbein 1980), and the Theory of Planned (TPB) (Ajzen 1985(Ajzen , 1991. From this stream of research, the Technology Acceptance Model (TAM) has emerged as a powerful and parsimonious way to represent the antecedents of system usage through beliefs about two factors: the perceived ease of use and the perceived usefulness of information system (Davis 1989(Davis , 1993Davis et al 1989Davis et al 1992. TAM is an adaptation of the TRA. In TAM, intention is determined by attitude towards usage as well as by the direct and indirect effects of perceived ease of use and perceived usefulness. The practical utility of the model stems from the fact that ease of use and usefulness are factors over which as system designer has some degree of control. To the extent that they are key determinants of usage, they provide direction to designers as to where efforts should be focused (Taylor and Todd, 1995).

Theoretical Framework
The eight original models and theories of individual acceptance that are synthesized by Venkatesh et al. (2003) Table 1.  Rogers (1962) is adapted to information systems innovations by Moore and Benbasat (1991). Five attributes from Rogers' model and two additional constructs are identified.
Relative Advantage* Compatibility* Ease of Use* Visibility* Result Demonstrability* Image Voluntariness of Use * indicates Roger's constructs. Social Cognitive Theory (SCT) by Bandura (1986) is applied to information systems by Compeau and Higgins (1995) to determine the usage. 1. Performance expectancy: It is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance. 2. Effort expectancy: It is defined as the degree of ease associated with the use of the system. 3. Social influence: It is defined as the degree to which an individual perceives that important others believe he or she should use the new system. 4. Facilitating conditions: Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. 5. Behavioural intention: It refers to the individual's decision regarding future system use. 6. Use behaviour: It refers to the actual usage of the system.
The following paragraphs present a brief description of each factors: Performance Expectancy: This construct tries to capture the perceived usefulness of technology considered by the students. The information sought is on-improved job performance, efficiency, higher achievement and usefulness of technology.
Effort Expectancy: This construct includes three factors: Perceived ease of use: The degree to which a person believes that using a particular system would be free of effort. Ease of learning to use the system: The degree to which technology is perceived as being easy to use. Self Efficacy: Self efficacy is the measure of one's own competence to complete tasks and reach goals. Social Influence; It is defined as the extent to which students perceive a social pressure to use technology. It involves two factors.
Subjective Norm: The person's perception that most people who are important to him think he should or should not perform the behavior in question.
Image: The degree to which use of an innovation is perceived to enhance one's image or status in one's social system (Moore and Benbasat 1991, p.195) Facilitating Conditions: It is defined as the extent to which the students perceive institutional support to use technology. ICT Infrastructure: Availability and Reliability of facilities Institutional Policy: Opportunity and Incentives for using technology Training and Technical Support provided: Training to use the system effectively.

RESEARCH METHODOLOGY
Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In this the researcher pursues various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary to know not only the research methods and techniques but also the methodology.

Population/Universe
The Population for this research, the entire group of people that the researcher wishes to investigate (Sekaran 2003), are the students' within the Business Schools in India. The research did not cover the following categories:

Sampling frame and Sample Size
The subject of the study is Full Time students of the AICTE approved and University affiliated MBA Institutes in India.
Around 900 questionnaires were distributed and 517 were returned completed thereby giving a response rate of 57%.

Data Collection
The survey research was conducted in seven cities across four regions in India: North, South, East and West.  North: Delhi-Guru Gobind Singh Indraprastha University (GGSIP), Greater Noida-Mahamaya University.  South: Bangalore-Bangalore University , Visvesvaraya Technological university and Chennai-University of Madras, Anna University.  East: Kolkata-West Bengal University of Technology (WBUT).  West: Mumbai-Mumbai University and Pune-Pune University Participation in this study was voluntary and 517 full time MBA students across various MBA Institute in India completed the survey. Participants were briefed on the purpose of this study through a covering letter and informed that they could decline to participate in the study before or after they had completed the questionnaire. At places were the questionnaire was self administered the participants took approximately 5-7 minutes to complete the questionnaire.

Measures
A survey questionnaire comprising previously validated items was used. Participants were asked to provide their demographic information and respond to 27 statements on the five constructs in this study. They are: Performance Expectancy (PE) (six items), Effort Expectancy (EE) (six items), Social Influence (SI) (six items), Facilitating conditions-FC (Facilitating conditions are divided into two parts. Facilitating conditions-Direct (FCD) and Facilitating conditions -support (FCS) (six items), Behavioral Intention (BI) (three constructs). Also, data about the various software and hardware used in Regular classroom teaching was college to know about the actual use of technology. Each statement was measured on a seven-point Likert scale with 1 = strongly disagree to 7 = strongly agree. Table I shows the items and the sources where the items were adapted. While taking the survey the students were informed that the definition of technology encompasses computer hardware (e.g. scanners, cameras, projector) and software applications (e.g. word processing, excel, Internet, PowerPoint) and any technology specific to their teaching area. Cronbach's coefficient Alpha (Cronbach 1951) is the most popular test of inter-item consistency reliability. This is a test of the consistency of respondents' answers to all the items in a measure. When the items are independent D e c e m b e r , 2 0 1 3 measures of the same concept, they will be correlated with one another (Sekaran 2000). Table 2 above presents the Cronbach's coefficient alpha for n=517. According to Sekaran (2000), reliabilities less than 0.6 are considered to be poor, those in the 0.7 range, acceptable, and those over 0.8 good. The closer the reliability coefficient gets to 1.0, the better. The table below shows the correlation matrix between the various constructs. The Performance Expectancy has a high positive association with Effort expectancy (r = .551). Also the Behavioral Intention and Effort Expectancy had moderate association which each other (r = .450). However, Performance Expectancy (r = .172) and Effort Expectancy (r =.212) were weakly associated with Facilitating Conditions.

Descriptive Analysis
A descriptive statistical analysis is described in this section in order to provide a richer understanding of the students' perceptions. Table 4 below summarizes the frequencies and corresponding percentages for the students' perceptions with respect to Performance Expectancy. As can be seen the students tend to believe that Technology is a useful and productive tool.

44.9%
(232) D e c e m b e r , 2 0 1 3 Table 5 provides the descriptive analysis for students' perception regarding Effort Expectancy. From the table the students quite agree that they find using technology easy to do things they want, it is easy for them to become competent to use technology. However students agree that using technology requires a lot of mental effort.  Table 6 provides statistical analysis regarding the descriptive statistics about the Social Influence to use Technology on the Students. Students are majorly influenced by their friends to use technology. Students agree that the ones who use technology are considered to be smart and enjoy more prestige than others. facilities to use the technology. However, they are neutral when it comes to the necessary infrastructure available in the institute. From table 7b, the students confirm that they are not given any incentives to use the technology. They are neutral in opinion when asked whether training is provided to them for using technology. However, they agree that technical help is available when required. The Behavioral Intention from Table 8 shows that students tend to show a positive approach for using technology in future. The Table 9 below gives us frequency of usage or the actual usage of the various technologies students use in classroom. Students rarely use scanner, discussion boards, PC based statistics software. The frequency of usage of speakers, cameras, Presentation graphics, and spreadsheets are several times a day.  Table 14 below D e c e m b e r , 2 0 1 3