Student Acceptance of Twitter in Marketing Courses: An Application

of the TAMBen Lowe, University of Kent, UKDes Laffey, University of Kent, UK

Steven D’Alessandro, Macquarie University, AustraliaHume Winzar, Macquarie University, Australia

Web 2.0 and Education in Business Courses

• Proliferation and diffusion of Web 2.0 among student cohort

• Experimentation into new ways of using technology to facilitate learning– Blogs, wikis, Twitter, VLEs, Second Life etc.– … experiential learning and development of soft skills

• Yet still relatively novel and little is known about adoption of Web 2.0 within the classroom– Gap between student take up and academic take up

• Aim: To understand the key drivers of adoption of Web 2.0 technologies by students in marketing classes

Web 2.0 and Education in Business Courses

• The use of technology in class not new!• Reactions have been positive and negative• Typical negative reactions include:– Further burdens on staff– Frustration/anxiety/confusion– Student familiarity with passive, rather than active learning

• Typical benefits include:– Subject matter expertise– New ways of learning– Social equity (Hunt, Eagle and Kitchen 2004)

Using Twitter in Business Courses

• What is Twitter?• Benefits:

– Real-world examples to the class in a timely fashion• eg, “#cb933 An example of the social implications of low prices

http://bit.ly/aNHt2E”

– Enables marketing concepts to be taught by using the technology• eg, Twitter Monitter and EWOM

– Conciseness– Robustness– Convenience– Non-intrusiveness – Can examine user behaviour in Twitter

Using Twitter in Business Courses

• Some Tweets:– Alert students to recent marketing events (e.g., “#cb933 Will a

downward stretch and a lower price point hurt the Jimmy Choo brand? http://bit.ly/6g3LBh”)

– Disseminate further information on contemporary marketing issues (e.g., “#cb933 See what McKinsey & Co have to say about pricing digital media: http://bit.ly/231A2H, this is a classic "reference price" issue”)

– Disseminate timely examples of key concepts discussed in class (e.g., “#cb933Managing WOM when something bad happens. Maclaren and brand equity: http://bit.ly/3VF3OS”)

– Raise issues based on concepts discussed in class to encourage introspection (e.g., “#cb933 Why do retailers use BOGOFs rather than discounts?”).

Technology Acceptance Model

• McCorkle et al (2001): Take up of learning technologies can be viewed in terms of product adoption– Derived from TRA, highly cited, robust, and used in a variety of

different contexts– Augmented TAM models show improvements in nomological and

predictive validity– Affect hypothesised to be more important for consumer models

Augmented TAM

• Developed to understand intentions to use online auctions (eg, eBay) (Stern et al 2008)

• Similar context to this study– Adoption of a new electronic platform in a

“consumer” context• Yang and Yoo (2004) find that affective variables

do not affect intention in an organisational setting– Extension to examine hedonic and utilitarian attitudes

with the HED-UT scale (Voss et al 2003)

Augmented TAM

• Consumers who have more positive feelings towards computers (affinity) are likely to find them easier to use and more useful (Stern et al. 2008)

• Risk tolerance has been identified as an important factor in the adoption of innovations and in particular online technology– Twitter perceived to be risky for some (eg, socially and in terms of

time). – Risk propensity positively associated with PEOU (Stern et al. 2008)– People with higher risk propensity likely to perceive Twitter as

easier to use (eg, perhaps some form of psychological barrier based upon perceived complexity) (Raju 1980)

Augmented TAM

• Impulsiveness – engagement in unplanned behaviour– Twitter noted as having some degree of social and time

risk– Risk propensity associated with exploratory tendencies

(Raju 1980). Impulsiveness has been found to relate to risk propensity (eg, James and Cunningham 1987).

– Therefore, we expect people with a higher risk propensity to be more impulsive

– We also expect impulsiveness to be associated with future intentions

Augmented TAM

Method

• Implemented the use of Twitter in two marketing courses (about 300 students)

• Participation was voluntary• Tweeted about 3-4 times a week on a variety of

contemporary issues• After ten weeks of tweeting students were then surveyed

using measures from the literature• Common method bias• Model estimated using PLS

– Less restrictive assumptions than other techniques (eg, SEM)• Small sample sizes• Distribution free• No collinearity• Independence of observations not required

Results – Coefficients and Diagnostics

αs > .95AVEs >.5Composite reliabilities >.91

Results – t-tests

Findings and Discussion

• The standard TAM holds up well and is robust and parsimonious. – PEOU and PU are statistically significant and contribute to

our explanation of intention• The augmented TAM also adds value to our

underlying explanation of intentions to use Twitter– Affinity is a statistically significant predictor of PU and

PEOU– Risk is a statistically significant predictor of PEOU– Utilitarian attitudes are statistically significant predictors of

intention, but hedonic attitudes are not

Findings and Discussion• Students with a higher propensity for risk are more likely to find Twitter to be

easy to use– Suggests that there is a psychological barrier to the adoption of Twitter/technology– Twitter does have benefits (see Lowe and Laffey 2011)– Need to minimise the factors likely to contribute to risk perceptions

• In class demonstration – Twitter is easy to use! • May become less important as Twitter diffuses

• Affect does not seem to be too important for students– If the learning technology is fun/exciting etc. this is not important in terms of intentions– The most relevant driver of intentions seems to be more functionally related – “what’s in it

for me?” rather than “Wow this is cool!”• Show students what the benefits of using Twitter are

– At least in this case the addition of hedonic attitude does not improve the TAM• Perhaps this is because the learning environment is perceived to be more utilitarian for many students? • Would the results differ if the model was assessing something “cool” like Spotify (in a non-learning

environment)– Is TAM too “utilitarian” and rational?

Questions?

Quantitative Findings

– 78% statistically different from 3 (neutral)– Frequency distributions indicated a bi-modal distribution – students

either liked it or weren’t too interested in it– Main benefits:

• enhanced learning about the subject of marketing• a more enjoyable module• concise and useful communication• Timeliness• greater realism• greater application of marketing theory to real-world examples• career skills in the use of new technology• Twitter not overly burdensome