M. Zaman et al. (Eds.): CETS 2010, CCIS 113, pp. 281–291, 2010. © Springer-Verlag Berlin Heidelberg 2010
System by Corporations Using the Technology
Acceptance Model
Hsing-Hui Chu, Ta-Jung Lu, and Jong-Wen Wann Graduate Institute of Technology & Innovation Management
National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan R.O.C.
Abstract. The purpose of this research is to explore enterprises’ acceptance of
Audience Response System (ARS) using Technology Acceptance Model (TAM). The findings show that (1) IT characteristics and facilitating conditions could be external variables of TAM. (2) The degree of E-business has positive significant correlation with behavioral intention of employees. (3) TAM is a good model to predict and explain IT acceptance. (4) Demographic variables, industry and firm characteristics have no significant correlation with ARS acceptance. The results provide useful information to managers and ARS providers that (1) ARS providers should focus more on creating different usages to enhance interactivity and employees’ using intention. (2) Managers should pay attention to build sound internal facilitating conditions for introducing IT. (3) According to the degree of E-business, managers should set up strategic stages of introducing IT. (4) Providers should increase product promotion and also leverage academic and government to promote ARS.
Keywords: Audience Response System (ARS), Technology Acceptance Model,
User Acceptance.
1 Introduction
The rapid change of advancement in information technology (IT), globalization, innovation and knowledge-based economy in recent years has resulted in more harsh and intense challenges to enterprises. . Thus, enterprises have to be more innovative to overcome difficulties for sustainable growth and development. Today enterprises are facing one of the issues that their business thinking is too conservative, together with insufficient IT adoption, these are not conducive to business innovation and business model [1]. The integration of knowledge-based economy and IT has become indispensable condition for enterprises to maintain competitive advantages and also with the competitiveness of countries [2].
In pursuit of better learning outcomes, universities have invested tremendous resources to analyze the function of learning and methods to enhance the learning process. It is beneficial for industry trainers to study trends in higher education and employ strategies that have been proven effective. One method for improving learning outcomes that has been successfully employed in lecture halls is the use of
audience response systems (ARS) [3]. ARS is one of the innovative IT which has been widely used in Europe and the United States by both educational and commercial organizations and has attracted broad academic discussion. Organizations that have adopted ARS technology include Boeing, Academy of the US Federal Bureau of Investigation, IBM, John Deere, McGraw-Hill, National Academy Foundation, Prentice-Hall, Raytheon, Toys ‘R’ Us, United States Army and Navy, Walt Disney World, and YMCA [4].
In Taiwan, ARS is used in more than 300 schools but is adopted by only a few private enterprises. The low usage rate of ARS by Taiwanese enterprises is markedly different from that of major organizations in other countries which prompted us to study this difference in the acceptance of ARS.
2 Objective
This study aims to explore the acceptance of ARS in Taiwanese enterprises. From literature review of ARS, related studies and theories of technology acceptance model (TAM), theoretical basis and factors which may influence user’s perception of usefulness and ease of use to understand the acceptance of ARS were chosen. The following research objectives will be achieved through data collection, questionnaire delivery and data analysis.
(1) What characteristics of ARS and facilitating conditions will affect the "perceived ease of use" and "perceived usefulness", and “user’s intention”? (2) Does the degree of E-business will affect the "perceived ease of use" and
"perceived usefulness", and “user’s intention”?
(3) To interpret Taiwanese enterprises’ acceptance of ARS.
(4) To find out what external factors will affect the acceptance of ARS. (5) To promote and help enterprises to introduce ARS successfully.
3 Literature Review
3.1 Information Technology (IT)
IT is composed of several related parts. One includes techniques for processing large amount of information rapidly, and it is epitomized by the high-speed computer. A second part centers around the application of statistical and mathematical methods to decision-making which is represented by techniques like mathematical programming, and by methodologies like operations research. A third part is in the offing, though its applications have not yet emerged very clearly; it consists of the simulation of higher-order thinking through computer programs [5].
3.2 Audience Response System
ARS is an electronic device designed to allow immediate interaction between an individual presenter and a large audience. An ARS typically consists a remote control that audience members use to respond to questions and an electronic receiver that
records, and optionally, displays individuals’ responses. ARS allows for a large number of individuals to respond simultaneously. Each individual response is recorded by the hub and can be displayed via projector or exported as a data file for use in other software [4]. LaRose [3] divided ARS into four components: a handheld transmitter, a receiver (plugged into the USB port of the presentation computer), the software on the presentation computer and a projection system. For example, in a presentation, presenter poses a question which including four answers (A, B, C, D), audiences could use handheld transmitter to press button to submit individual response, via infrared (IF) or radio frequency (RF) to send individual response to signal receiver (presenter’s computer) [6]. ARS software will count numbers of each answer and show results in formats like pie or bar charts. The name of this system differs in different countries and suppliers. Names other than ARS are shown below:
(1) Classroom Response System (CRS) [7] (2) Classroom Communication System (CCS) [8] (3) Interactive Response System (IRS) [9] (4) Classroom Performance System (CPS) [10] (5) Personal Response System (PRS) [11] (6) Student Response System (SRS) [12] (7) EduClick [13]
(8) Clicker [14]
Table 1. The difference between ARS and traditional teaching method
ARS Traditional teaching method
Interact with large audience, to provide
equal opportunities for interaction Only interact with the minority Understand audience learning performance
immediately during instruction
Understand learning performance after instruction
Audience response can be further analysis in chart
Cannot show response through chart in real time
Built in multiple usage Vary according to instructor Anonymous functions can let audience feel
easy to answer
Respondents may have pressure to answer when system doesn’t have anonymous functions
Responses can save response directly
through system Responses need to be recorded manually
3.3 Theories of Technology Acceptance Model (1) Theory of Reasoned Action (TRA)
TRA was very general model which is used to explain almost any human behavior. TRA proposes that intention solely and directly influences the adoption behavior and that intention is determined by factors of attitudes toward behavior and subjective norms toward behavior [15].
(2) Technology Acceptance Model (TAM)
TAM derived from TRA and developed by Davis. It is used to predict adoption and usage of technology in information systems and
organizational contexts. TAM supposed that perceived ease of use and usefulness are major factors that influence the acceptance of a technology [16].
(3) Theory of Planned Behavior (TPB)
TPB is an extension model of TRA, except original two factors (attitudes toward behavior, subjective norms) originated from TRA, the other is perceived behavioral control. TPB supposed that not all behavior may be under an individual’s volitional control, so behavioral control as an important factor could be influential on behaviors [17].
(4) Technology Acceptance Model 2 (TAM2)
TAM2 is an extension model of TAM, which incorporates two additional theoretical constructs: cognitive instrumental processes and social influence processes. Four cognitive factors including job relevance, output quality, result demonstrability, and perceived ease of use influence perceived usefulness. Three social forces influence perceived usefulness: subjective norm, image, and voluntariness [18].
(5) Unified Theory of Acceptance and Use of Technology Model (UTAUT): Venkatesh et al. combined eight models (Diffusion of Innovation, Theory of Reasoned Action, Theory of Planned Action, Technology Acceptance Model, Combined TAM and TPB, Motivational Model, Social Cognitive Theory, Model of PC Utilization) develop UTAUT which and proposed four factors (performance expectance, effort expectancy, social influence and facilitating conditions) and four moderating variables (gender, age, experience and voluntariness of use) [19].
Table 2. Comparison of TAM models
TRA TAM TPB TAM2 UTAUT
Author Fishbein & Ajzen
Davis Ajzen Davis &
Venkatesh Venkatesh, Morris, Davis & Davis Year 1975 1989 1991 2000 2003 Appropriate situation Volitional action, use to explain human behavior Not all control by volition，use to explain human behavior IT acceptance behaviors IT acceptance behaviors IT acceptance behaviors Factors affect behavioral intention Attitude toward the behavior, Subjective norm Perceived usefulness, Attitude toward the behavior Attitude toward the behavior, Subjective norm, Perceived behavioral control Subjective norm, Perceived usefulness, Perceived ease of use Performance expectancy, Effort expectancy, Social influence
4 Methodology
This study, based on TAM, is focused to explain and forecast IT acceptance behavior, as well as to develop external variables which influence perceived usefulness, perceived ease of use and behavioral intention. Two of the three external variables, characteristics of IT and facilitating conditions, are selected from TAM2 and unified theory of acceptance and use of technology (UTAUT) respectively. The third external variable is the degree of E-business. The research structure is shown below.
Fig. 1. Research structure
The questionnaire was developed from a critical review of literatures. The descriptive statistical analyses of quantitative data are performed using SPSS 15.0 for Windows statistical software. Statistical methods involve descriptive statistics, reliability and correlation analysis.
5 Results
5.1 Descriptive Analysis
214 questionnaires are distributed and collected, there were 26 questionnaires are invalid, and the rate of valid questionnaire is 88%. Top 3 industries of respondents are IT industry (31.9%), manufacturing (18.6%), and financial and insurance industry (10.1%). The numbers of male and female respondents were close, with 70% respondents’ ages under 30 years old. Over 50% of respondents’ working experiences are under 3 years, and most of respondents don’t know about and never use ARS. About 30% of respondents' firm size are under 50 and 50% from firm size over 200. 5.2 Validity of Questionnaire
The initial questionnaire was developed from literature review and related research, and was pretested by 2 experts and 16 employees. Some problems such as the 3 industry categories (manufacturing, service, and financing) were found to be hard to select by respondents. Some question design is not easy to answer and the introduction of ARS is too wordy for them to have a quick understanding of ARS. The questionnaire was revised accordingly.
Table 3. Table 1 Characteristics of respondents
Industries % ARS characteristics %
IT industry 31.9 Interactivity 37.2
Manufacturing 18.6 Demonstrate result
Immediately
34.0 Financial and insurance industry 10.1 Anonymous response 12.8 Business consultancy 9.0 Allow large audience
participation
12.2
Import and export trade 7.4 Auto-save result 2.7
Retail 6.9 Others 1.1
Education and publishing 6.9 Have heard ARS %
Pharmaceutical and biochemical industry
4.3 Yes 5.3
Food, beverage and entertainment 2.7 No 94.7
Media and advertising 2.1 Have used ARS %
Information professional % Yes 14.4
Yes 77.1 No 85.6 No 22.9 Scores of intention % Gender % Minimum 0 Male 46.3 Maximum 10 Female 53.7 Mean 6.46 Age % Education %
Under 30 69.7 Graduate school and above 31.9
31-40 25.0 University and college 63.3
41-50 3.7 High school 4.8
Over 51 1.6 Firm size %
Working experience (years) % Under 50 30.3
Under 1 year 19.1 51-100 5.9 1-3 39.4 101-150 10.1 4-6 17.6 151-200 3.7 7-9 11.2 Over 201 50.0 Over 10 years 12.8 5.3 Reliability Analysis
The internal reliability or consistency of each aspect of TAM model is listed in Table 4. Cronbach's values are 0.905, 0.858, 0.880, 0.870, 0.852 and 0.920 respectively for ARS characteristics, Facilitating conditions, Degree of E-business, Perceived ease of use, Perceived usefulness, and Behavioral intention. A widely accepted rule of thumb
Table 4. Reliability of questionnaire
Factor N of Items Cronbach's Alpha
ARS characteristics Facilitating conditions Degree of E-business Perceived ease of use Perceived usefulness Behavioral intention 8 6 6 4 4 6 0.905 0.858 0.880 0.870 0.852 0.920
of internal consistency for Cronbach's should be at least 0.70 [20]. In this study, all Cronbach's values are higher than 0.85, which shows the questionnaire has high reliability.
5.4 Correlations
The bivariate correlations among 6 factors are listed in Table 5. Most factors are significantly related to each other, only the degree of E-business does not show correlation with other factors.
Table 5. Bivariate correlations between variables
Variables ARS characteristics Facilitating conditions Degree of E-business Perceived ease of use Perceived usefulness Behavioral intention ARS characteristics 1.000 Facilitating conditions 0.717** 1.000 Degree of E-business 0.019 0.123 1.000 Perceived ease of use 0.703** 0.469** 0.089 1.000 Perceived usefulness 0.845** 0.747** 0.076 0.597** 1.000 Behavioral intention 0.631** 0.645** 0.153* 0.444** 0.716** 1.000
** Correlation is significant at the 0.05 level (2-tailed). * Correlation is significant at the 0.1 level (2-tailed).
Fig. 2. Correlation coefficient
5.5 Analysis of Research Hypotheses
The purpose of this study was to understand relationships among external variables (ARS characteristics, facilitating conditions and degree of E-business), people perception and behavioral intention. From the statistic analysis, 9 hypotheses testing results were summarized in table 6. H1, H2, H3, H4, H7, H8, H9 were accepted, that
is ARS characteristics and facilitating conditions have positive correlations with perceived usefulness and perceived ease of use. Between Perceived ease of use and perceived usefulness exist significant correlation and also with behavioral intention. H5 and H6 were rejected because Degree of E-business did not show significant correlation with perceived usefulness and perceived ease of use but with behavioral intention.
Table 6. Research hypotheses and statistical result
Number Hypotheses Result
H1 AC Æ PU Accept H2 AC Æ PE Accept H3 FC Æ PU Accept H4 FC Æ PE Accept H5 DE Æ PU Reject H6 DE Æ PE Reject H7 PE Æ PU Accept H8 PU Æ BI Accept H9 PE Æ BI Accept
AC: ARS characteristics FC: Facilitating conditions DE: Degree of E-business PE: Perceived ease of use PU: Perceived usefulness BI: Behavioral intention
6 Conclusions
6.1 IT Characteristics Could Be External Variables of TAM
Our results show that characteristics of ARS including interactivity, convenience, instantaneity, and anonymous have significant correlation with perceived ease of use and perceived usefulness. The relative importance of characteristics are interactivity (37.2%), instantaneity (34.0%), convenience (14.9%) and anonymous (14.9%). Activities like training and meetings are found to lack of interactivity and instant response in corporations. Compare with western countries, eastern countries are more conservative and introversive. People tend not to express opinion in the public, and therefore ARS can be a good tool for them to enhance interactivity and make activities more interesting.
6.2 Facilitating Conditions Could Be External Variables of TAM
Facilitating conditions are divided into internal (top manager support, education training, and organization arrange specific staff to assist using) and external (ARS providers, government, and schools) conditions. Our results show that the importance of facilitating conditions are top manager support (76.6%), committed helping staff (70.7%), providers’ promotion (61.7%), schools’ (56.9%) and government’s (50.0%) support and endorsement. The importance of all variables was higher than 50% which demonstrated that these are important factors for introducing IT. Internal facilitating conditions are more significant than external ones because they are closer to employees to increase their behavioral intention of IT acceptance and adoption.
6.3 The Degree of E-Business Is Not a Factor of IT Behavioral Intention
The degree of E-business did not have significant correlation with perceived ease of use and perceived usefulness and hence H5 and H6 were rejected. We consider this factor unlike ARS characteristics and facilitating conditions have direct relationship with ARS, but it has significant correlation with behavioral intention. And we depend on result to revise ARS acceptance model to build linkage between degree of E-business and behavioral intention.
6.4 Preference Group of ARS
This research try to explore difference of ARS acceptance in demographic variables (gender, age, education, working experience), industries and firm sizes, but the result show that they didn’t have. Since most of respondents did not know (85.6%) ARS and it’s not popular in enterprises, not preference group can be found.
7 Suggestions
7.1 Creating Multiple Interaction Channels for Using ARS
Providers should emphasize interactivity and instantaneity when promoting or demonstrating ARS, and try to create multi-use or other interesting ways to enhance interactivity, real time response and innovative demonstration to reach the goal of popularizing ARS.
7.2 Improving Internal Facilitating Conditions
Enterprises’ managers and ARS providers should understand that facilitating conditions are important factors for successful introduction of new IT tools. Managers are key persons to lead, require, and provide incentives to encourage employees using new IT. In addition, difficulties in using the system by the staff will reduce their intention of use. Staff assigned specifically for solving IT problems in time can effectively reduce resistance to new IT. Providers should focus on external facilitating conditions to promote ARS. In Taiwan, there are more than 800 teachers have been using ARS in teaching [21] which shows that it is a good venue for providers to promote ARS through schools and government.
7.3 Developing Strategic Stages of Introducing IT
Although the relationship between the degree of E-business and perception was not established, but it has significant correlation with behavioral intention. Hence, the higher degree of E-business the better acceptance of new IT and vice versa. The nature of company should be studied to understand whether it is suitable for introducing new IT or not because if employees are unfamiliar with IT may result in resisting behavior. We suggest low degree of E-business should have strategic stages of introducing IT and build sound internal facilitating conditions to increase employees’ willingness.
7.4 Increasing the Promotion of ARS
The results show that 85% of respondents did not know ARS, 70% respondents are under 30 years old and we distribute and gather questionnaires through internet, the samples’ characteristics of this study should have higher probability of knowing new IT, if samples from internet didn’t know ARS, and we must can infer the population knowing ARS is lower than 15%, so providers should increase the promotion of ARS, and also through academic or government.
8 Future Research
8.1 Comparison between Eastern and Western Countries
The targets of this study were employees of Taiwan enterprises, but it’s common that western people express their personal opinions, so they may not need ARS to enhance interactivity. Future research could have a comparison between eastern and western countries’ enterprise on ARS characteristics and acceptance of ARS.
8.2 Define and Filter Samples
Introducing ARS does not have direct correlation with profit, and the mean of using intention is 6.46, it means respondents still wait to see. Our research did not choose specific groups to explore ARS acceptance, some firms’ size are lower 50 people and some did not place importance on education training. Therefore, we suggest future study can define and filter samples before distribute questionnaire, such as firm size over 50 or 100 people and companies which pay much attention to education training.
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