Psychological Determinants and Consequences of Internet Usage:

An Extension of the Technology Acceptance Model

Yang Lu, Savvas Papagiannidis and Eleftherios Alamanos

Newcastle University Business School, Newcastle University, 5 Barrack Rd, NE1 4SE, Newcastle upon Tyne, U.K.

Keywords: Technology Acceptance Model, Internet, Social Inclusion, Psychological Needs, Well-being, Emotions.

Abstract: The Internet, as a representation of pervasive technological platforms, has brought a series of effects on our

everyday life. An exploration of the determinants and consequences of Internet use from the psychological

and social perspectives can facilitate current information system studies. As such, this paper hypothesised and

examined the effects of a number of psychological factors on technology acceptance. A comprehensive

framework has been put forward and empirically tested with data collected from 615 Internet users. Statistical

results support that the hypothesised antecedents, i.e. social inclusion and psychological needs satisfaction,

have significant effects on users’ beliefs and intentions of Internet usage. Also, the individuals’ continuance

intention of using the Internet significantly affects their emotional reactions, well-being, and perceived value

of the Internet.

1 INTRODUCTION

Over the years, there has been an increasing interest

in exploring the potential emotional influence of

pervasive technologies, such as the Internet. Studies

from a psychological perspective have largely been

focused on the impact of excessive Internet use,

especially its negative causes and effects (e.g.

problematic Internet use, Internet addiction,

compulsive Internet use). However, there has been

little discussion about the wider emotional

consequences that the Internet can bring to the public.

As such this study’s first objective is to make a

contribution by exploring the emotional antecedents

and outcomes of using the Internet. By tackling this

objective this paper aims to make a second significant

contribution related to technology acceptance. Over

the years, the Technology Acceptance Model (TAM)

has facilitated understanding of technology

acceptance and has made possible extensions and

elaborations for the contextualisation of information

technology (IT) studies (Lee et al., 2003). At the same

time, though, excessive focus on replication and the

subtle adaptation of popular models such as TAM

could restrict the progress of information system (IS)

research (Venkatesh et al., 2007; Venkatesh et al.,

2012). Integrating individual characteristics, rather

than over-emphasising system and design

characteristics, may offer a way to enhance IS and IT

studies (Ajzen, 2005; Benbasat and Barki, 2007;

Venkatesh, 2000). To this end, a number of studies

have incorporated psychological factors, such as

cognitive absorption (Mohd Suki et al., 2008), flow

(Hausman and Siekpe, 2009), psychological needs

and self-determination (Partala and Saari, 2015;

Partala, 2011), and emotions (Beaudry and

Pinsonneault, 2010), etc. Still there is more much

scope for considering psychological factors as

antecedents and outcomes of acceptance Given that

psychological states and individual differences are

gaining importance in technology acceptance studies,

this article extends TAM by incorporating emotional

constructs, i.e. social inclusion, basic psychological

needs, well-being, perceived value, and emotions.

2 LITERATURE REVIEW

2.1 Technology Acceptance

TAM has been longitudinally found at the centre of

technology acceptance. Compared with other

technology acceptance theories. The first version of

TAM includes five main constructs, namely,

perceived usefulness (PU), perceived ease of use

(PEOU), attitude toward using (Attitude),

behavioural intention to use (BI), and actual system

use (USE) (Davis et al., 1989). (Davis et al., 1989)

Lu, Y., Papagiannidis, S. and Alamanos, E.

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model.

DOI: 10.5220/0006898400460056

In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST 2018), pages 46-56

ISBN: 978-989-758-324-7

showed that PU and PEOU have direct effects on BI

instead of being mediated by attitude. The authors

suggested omitting attitude to explain intention more

concisely (Davis et al., 1989; Venkatesh et al., 2003).

PU and PEOU are grounded on behavioural

psychology and the observation of technology

adoption (Davis et al., 1989). They are the two most

influential determinants that represent human beliefs

and represent the foundation of technology

acceptance theories (Davis, 1989; Venkatesh and

Davis, 2000; Davis et al., 1989). PEOU is the degree

to which a person believes that using the Internet

would be free of effort (Davis et al., 1989; Davis,

1989). PU refers to the degree to which a person

believes that using the Internet would enhance

performance in completing particular tasks (Davis et

al., 1989; Davis, 1989). The relationships between

PU, PEOU, and Intention have been retained in most

TAM-based empirical studies (Lee et al., 2003).

Additionally, a meta-analysis by (Lee et al., 2003)

showed that the majority of the studies support the

idea that PEOU affects PU, and both PU and PEOU

have influences on Intention or USE. Our study

examines the psychological impact on existing users,

hence we have focused on the intentions to continue

using the internet (continuance intention (CI).

H1: An individual’s (a) perceived ease of use, and

(b) perceived usefulness of using the Internet has a

significant positive influence on the intention to con-

tinue using it, while (c) perceived ease of use positively

affects the perceived usefulness of the Internet.

2.2 Social Inclusion and Information

and Communication Technologies

Various definitions of social inclusion (SI) have been

introduced over the years that offer different vantage

points into this multi-dimensional phenomenon, e.g.

(Secker et al., 2009; Sayce, 2001; Huxley et al., 2012;

The Charity Commission, 2001). For the purpose of

this study, social inclusion is defined as an

individual’s perceived opportunities and rights of

access to the social and economic world and

participation in socially valued activities (Richardson

and Le Grand, 2002; Sayce, 2001). Although social

inclusion is based on the concept of social exclusion,

social inclusion cannot be simply viewed as “non-

exclusion”, but rather as creating opportunities

proactively and having freedom in making choices

(Andrade and Doolin, 2016; Selwyn, 2002; Phipps,

2000). Accordingly, social inclusion relates to the

emotional and health benefits generated by access to

social capital, social acceptance and social activity, as

well as positive actions taken by an individual to

dealing with social exclusion, then enabling people to

fully participate in the society (Andrade and Doolin,

2016; Sayce, 2001; Secker et al., 2009; The Charity

Commission, 2001).

Not surprisingly, social inclusion/exclusion

closely relates to digital inclusion/exclusion, with

high digital inclusion being a catalyst for social

inclusion (Hill et al., 2015; Selwyn, 2002; Tapia et al.,

2011). With the proliferation of ICTs, digital

inclusion has become an increasingly important issue

as it describes how ICTs serve society and promote

social inclusion (Tapia et al., 2011; Hill et al., 2015).

Diffusion of new forms of technological

breakthrough could potentially exacerbate existing

social exclusion or even create new ways through

which digital exclusion can be manifested (Andrade

and Doolin, 2016). On the other hand, it can also

bring many advantages. First of all, social inclusion

motivates people to use connecting technologies such

as mobile phones, social networking sites, and e-

learning systems (Choi and Chung, 2013; Park, 2010;

Park et al., 2013; Smith and Sivo, 2012). Empirical

results suggest that social inclusion has positive

effects on one’s PU, PEOU, and CI of using mobile

phones (Park et al., 2013). Social capital is a key

element of social inclusion, which is generated

through individuals' social activities and interactions,

and offers benefits for their social participation (Choi

and Chung, 2013; Secker et al., 2009). Perceived

social capital positively and significantly correlates to

perceived usefulness and ease-of-use on SNS among

graduate students (Choi and Chung, 2013). Social

presence and sociability facilitate users' degree of

social inclusion as well, which has been found to

positively correlate with PU, PEOU, and CI on using

e-learning systems (Smith and Sivo, 2012).

Moreover, the beneficial impact of social inclusion is

also reflected in enhancing the well-being of citizens

(especially those ICT-engaged individuals) via

technology use. For instance, socially excluded people

tend to shop online via computer or cell phone rather

than in-store (Dennis et al., 2016). Such preferences

can potentially mitigate the negative effects of social

exclusion on well-being and the happiness of

individuals with mobility difficulties (Dennis et al.,

2016). It is worth noting that ICTs do not increase

social inclusion automatically. They promote

participation in social activities and communities, and

in turn can help transform social inclusion into well-

being (Andrade and Doolin, 2016).

H2: Social inclusion positively influences the

users’ (a) perceived ease of use of, (b) perceived

usefulness of, and (c) continuance intention of using

the Internet.

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model

2.3 Self-determination Theory, Basic

Psychological Needs, and

Technology Acceptance

According to the Self-Determination Theory (SDT),

when faced with new skills and ideas, people have

innate needs to feel effective, agentic and being

connected, which derive from the three basic

psychological needs for competence (NC), autonomy

(NA), and relatedness (NR) (Ryan and Deci, 2000c;

Ryan and Deci, 2000b). These three psychological

needs are the basis of maintaining an individual’s

intrinsic motivation and self-determining extrinsic

motivation (Ryan and Deci, 2000b). Specifically,

interpersonal activities can catalyse people’s need for

competence and fulfilling this need enhances their

intrinsic motivation (Gagné and Deci, 2005; Ryan

and Deci, 2000b). Intrinsic motivations could be

diminished by external factors such as rewards,

threats, deadlines, and competition pressure, which

hinder the individuals’ experienced autonomy

(Gagné and Deci, 2005; Ryan and Deci, 2000b). The

environmental and social contextual conditions that

support or control the needs for autonomy and

competence could facilitate or undermine intrinsic

motivation and social functioning (Ryan and Deci,

2000c; Ryan and Deci, 2000b). Satisfying the need

for relatedness is the main motivation driving people

to perform activities which, per se, are less enjoyable

or not of interest, but valued by people connected to

them (Roca and Gagné, 2008).

Studies based on SDT have reported close

relationships between Internet use, needs satisfaction,

and psychological states. Need fulfilment can

indirectly lead to excessive Internet use, which is

fully mediated by psychological distress (Wong et al.,

2014). Psychological distress, such as social anxiety,

has direct influences on excessive Internet use as well

(Casale and Fioravanti, 2015). For males, this

influence can be partially mediated by the satisfaction

of the need for self-presentation, which can be met

through social networking service use (Casale and

Fioravanti, 2015). In addition, the basic psychological

need satisfaction perceived online and in daily life

both significantly predicts Internet use behaviour and

the emotional effect among elementary school

children (Shen et al., 2013). Participants who fulfilled

their psychological needs online tend to spend more

time on and more frequently use the Internet, and they

will also experience more positive outcomes (Shen et

al., 2013). In the context of e-learning system use,

users can be intrinsically motivated by fulfilling the

three psychological needs, which in turn affects their

well-being and emotional responses (Gagné and Deci,

2005; Roca and Gagné, 2008; Ryan and Deci, 2000b;

Ryan and Deci, 2000a). The need for autonomy is one

of the salient needs that could be satisfied to a

significantly larger extent by technology use,

especially in successful cases of technology adoption

(Partala, 2011; Partala and Saari, 2015). Previous

work which incorporated the SDT with technology

acceptance theories supported a number of significant

relationships between the three psychological needs

and technology acceptance constructs. More

specifically, although PEOU has been found to be

positively affected by the three psychological needs,

their influences on PU and intentions are relatively

ambiguous. The majority of the empirical studies

suggest that the three needs have significant positive

influences on PU and BI. Still, the needs for

competence and autonomy were not found to

significantly relate to PU in three of the studies

(Nikou and Economides, 2017; Sørebø et al., 2009;

Roca and Gagné, 2008). Notably, these studies were

conducted in different contexts, with models

featuring additional determinants, such as intrinsic

motivation (Sørebø et al., 2009), perceived enjoyment

(Lee et al., 2015), perceived playfulness (Roca and

Gagné, 2008), etc.

H3: The users’ need for competence positively

affects their (a) perceived ease of use of, (b) perceived

usefulness of, and (c) continuance intention of using

the Internet.

H4: The users’ need for autonomy positively

affects their (a) perceived ease of use of, (b) perceived

usefulness of, and (c) continuance intention of using

the Internet.

H5: The users’ need for relatedness positively

influences their (a) perceived ease of use of, (b)

perceived usefulness of, and (c) continuance intention

of using the Internet.

2.4 Well-being, Perceived Value, and

Social Inclusion

An individual’s degree of well-being can be affected

by social inclusion and the satisfaction of basic

psychological needs (Andrade and Doolin, 2016;

Dennis et al., 2016; Broadbent and Papadopoulos,

2013; Ryan and Deci, 2000c; Deci and Ryan, 2000;

Tay and Diener, 2011). The positive influences of

social inclusion and need fulfilment on well-being

can be enhanced by technology use (Roca and Gagné,

2008; Gagné and Deci, 2005; Andrade and Doolin,

2016). Accordingly, this study defines well-being as

the degree of need satisfaction and life quality

enhancement by using the Internet. Empirical studies

have explored the role of well-being in technology

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

acceptance. For instance, in studying the mobile

money service agents’ technology readiness and

acceptance, subjective well-being has been found to

be a positive outcome of mobile money service use,

which was directly affected by PU and PEOU

(Rahman et al., 2017). Well-being can act as both a

driver and an outcome of social networking service

(SNS) use (Munzel et al., 2017). Subjective well-

being can only increase the highly extraverted

individuals' time spent on SNS when they are

unhappy, which consequently improves their general

well-being (Munzel et al., 2017). In addition, well-

being can also be measured from the perspective of

psychological flourishing (psychological wealth,

positive emotions, and life satisfaction) and mental

health (“the lack of depressive symptoms”) (Partala

and Saari, 2015). Regarding the users’ most

influential experiences of successful and

unsuccessful technology adoptions, psychological

flourishing well-being has been found to be largely

dependent on the fulfilment of needs and concordance

of value (Partala and Saari, 2015).

H6: Users’ continuance intention to use the

Internet has a positive impact on their well-being.

Researchers have developed a number of

constructs to represent different values affecting

technology acceptance and use, such as

performance/utilitarian value (e.g. PU and PEOU),

hedonic value (e.g. cognitive absorption, perceived

enjoyment, and playfulness), social value (e.g.

subjective norm and social influence), and monetary

value (e.g. price value) (Lowry et al., 2013; Agarwal

and Karahanna, 2000; Venkatesh et al., 2012; Davis

et al., 1989). Turel et al. (2007) decomposed users’

overall perceived value to a multi-dimensional

determinant of short messaging service acceptance.

Their study demonstrated that the hedonic and

monetary values significantly influence behavioural

intention, that performance value was a potential

moderator on use intentions and that the social value

did not show a significant impact on use intentions

(Turel et al., 2007). On the other hand, perceived

performance value, which describes the perceived

benefits and profits offered by the IS/IT, has been

found to be an antecedent of acceptance of hotel front

office systems (Kim et al., 2008). (Wang, 2014)

investigated utilitarian and monetary aspects of

perceived value, which illustrated the user’s “overall

assessment of the utility” regarding the mobile

government system. Results indicated that mobility,

security, and PU were antecedents of the overall

perceived value, while technology satisfaction, trust

in technology, trust in the agent, and trust in

government were the consequences (Wang, 2014).

Users’ perceived benefits, i.e. perceived usefulness,

perceived enjoyment, and social image, and

perceived sacrifice, i.e. perceived risk, were all found

to have a positive effect on their overall assessment

of the perceived value of media tablet adoption (Yu

et al., 2015). Taking into account that this study aims

to examine the emotional and psychological factors

related to the adoption of a pervasive technological

paradigm, i.e. the Internet, the users’ perceived value

is investigated from a comprehensive perspective. As

such, perceived value is defined as the justification of

the experience of using the Internet in individuals’

daily life, regardless of whether this is for work or for

personal purposes (Okada, 2005).

H7: Users’ continuance intention to use the

Internet has a positive impact on their perceived

value.

2.5 Emotional Responses to Internet

Use

An emotional response is defined as a set of

emotional reactions elicited during IT/IS use or by

use experiences, such as happiness, anger, anxiety,

and excitement (Westbrook and Oliver, 1991;

Beaudry and Pinsonneault, 2010). Prior studies

provide evidence that users’ emotions critically affect

beliefs, intentions, and behaviours in technology

acceptance and adoption contexts (Beaudry and

Pinsonneault, 2010; Kim and Lennon, 2013; Chang et

al., 2014). For instance, positive emotions such as

happiness and excitement were found to positively

relate to information technology use, either directly

or indirectly (Beaudry and Pinsonneault, 2010). How-

ever, negative emotions, e.g. anger and anxiety, also

have an indirect positive influence on technology use.

This article adopts (Beaudry and Pinsonneault,

2010) classification of the emotional responses,

specifically toward information technologies. Their

framework has been developed by combining two

appraisals of technology assessment which determine

users’ emotional reactions toward a new IT (Beaudry

and Pinsonneault, 2005; Beaudry and Pinsonneault,

2010). The primary appraisal is whether a user

perceives a new technology as constituting an

opportunity or a threat, which is in line with the

individual’s goal achievement (Bagozzi, 1992;

Beaudry and Pinsonneault, 2010). Fundamentally, the

goal or outcome of an individual can be either

achieved or not, which in turn triggers pleasant or

unpleasant feelings toward events in both planned and

unplanned cases (Bagozzi, 1992). This primary

appraisal determines the users’ emotional reactions as

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model

positive (they perceive the technology as an

opportunity, they achieve the goal) or negative (they

perceive the technology as a threat, and do not

achieve the goal). Notably, individuals can

experience both positive and negative emotions,

triggered by the same external stimulation, thus the

levels of these two dimensions of emotions can be

measured separately (Chang et al., 2014; Partala and

Kujala, 2015; Russell and Carroll, 1999). The

emotions aroused by the adoption of a given IT may

vary among individuals depending on their unique

psychological evaluations (Beaudry and

Pinsonneault, 2010).

The second appraisal refers to the degree of users’

perceived control over the achievement of the

expected outcome of accepting a technology

(Beaudry and Pinsonneault, 2010; Lazarus and

Folkman, 1984). This dimension further classified the

emotions triggered by an IT event into four

categories, i.e. achievement, challenge, loss, and

deterrence emotions. The achievement and challenge

emotions are experienced when the users perceived

an IT as an opportunity that would generate positive

outcomes, such as happiness and excitement

(Beaudry and Pinsonneault, 2010). The achievement

emotions refer to the users' pleasant feeling when they

are able to achieve their goal by using the IT with very

little effort (Lee et al., 2012; Beaudry and

Pinsonneault, 2010). Challenge emotions could

enhance users' positive attitudes toward the

technology and help them achieve their goals (Lee et

al., 2012; Beaudry and Pinsonneault, 2005). A new IT

which is perceived as a threat would be likely to

trigger loss or deterrence emotions (Beaudry and

Pinsonneault, 2010). When individuals lack control

over their expected outcomes of the new technology,

they are likely to experience loss emotions such as

anger, disappointment and frustration (Beaudry and

Pinsonneault, 2010). Finally, when users have some

control over their expected outcomes, their emotional

reactions fall into the deterrence aspect, represented

by anxiety, fear, worry, distress, etc. (Beaudry and

Pinsonneault, 2010).

H8: An individuals’ continuance intention to use

the Internet has a positive impact on their (a)

achievement and (b) challenge emotions, and has a

negative impact on their (c) loss and (d) deterrence

emotions.

Based on the above hypotheses, Figure 1 presents

the emotional-TAM model (E-TAM) which depicts

the main effects.

3 METHODOLOGY

3.1 Data Collection and Sampling

A quantitative approach was adopted. The

questionnaire was made available online and data

collected using a consumer panel. The sample

respondents were Internet users in the United States.

670 full questionnaires were initially received. Prior

to the main survey, a pilot study was carried out with

10 participants. Based on evaluation of this pilot

study and the average completion time of the main

study, collected questionnaires that had been

completed in less than five minutes were excluded

Figure 1: Research framework: E-TAM.

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

Table 1: Demographic profile of respondents

Demographic characteristic Frequency

(n=615)

Percentage

(%)

Gender

Male 266 43.3%

Female 349 56.7%

Age

20-29 69 11.2%

30-39 127 20.7%

40-49 114 18.5%

50-59 139 22.6%

60 or over 166 27.0%

Current employment status

Full-time employed 258 42.0%

Part-time employed 64 10.4%

Out of work (looking for work) 26 4.2%

Out of work (not looking for

work)

6 1.0%

Homemaker 77 12.5%

Student 16 2.6%

Retired 125 20.3%

Unable to work 43 7.0%

Ethnicity

African American 65 10.6%

Native American 6 1.0%

USA White 452 73.5%

Asian American 28 4.6%

Hispanic American 37 6.0%

Multiracial 8 1.3%

Other White Background 15 2.4%

Other 4 0.7%

Education attainment

Some high school or less 12 2.0%

High school graduate or

equivalent

118 19.2%

Vocational/technical school 54 8.8%

Some college, but no degree 157 25.5%

College graduate 156 25.4%

Some graduate school 22 3.6%

Graduate degree 78 12.7%

Professional degree 18 2.9%

Residence area

Urbanized area 256 41.6%

Urban cluster 231 37.6%

Rural area 128 20.8%

Household income

$0- $24,999 114 18.5%

$25,000-$49,999 161 26.2%

$50,000-$74,999 138 22.4%

$75,000-$99,999 95 15.4%

More than $100,000 107 17.4%

from the dataset. Additionally, this study removed

questionnaires completed by selecting the same

answer for most of the scaled measurement items,

including the 11 reversed ones. By applying the

above-stated criteria in the data screening process,

615 completed questionnaires were entered into the

analysis. Table 1 illustrates the participants’ profile.

3.2 Measurement Items

Table 2 presents the constructs’ factor loadings and

reliability, which describes the variability of

independent variables explained by the measurement

model. Items for the TAM variables, i.e. PEOU, PU,

and CI, were adapted from (Venkatesh, 2000;

Fishbein and Ajzen, 1975; Davis, 1989). Social

inclusion items were adopted from (Richardson and

Le Grand, 2002), while items for the psychological

needs for competence, autonomy, and relatedness

were adapted from the Work-related Basic Need

Satisfaction scale (Van den Broeck et al., 2010).

Items measuring the well-being and perceived value

were adapted for the Internet users in the post-

adoption context from (El Hedhli et al., 2013) and

(Okada, 2005) respectively. Lastly, we included ten

potential emotional responses to using the Internet

(Beaudry and Pinsonneault, 2010). The majority of

the items were measured using a 7-point Likert scale

(“Strongly disagree” to “Strongly agree”).

3.3 Data Analysis Strategy

SPSS v.23 and SPSS Amos v.24 were used for the

statistical analysis of the main hypotheses. A

confirmatory factor analysis was undertaken to

ensure construct reliability and validity. The

composite reliability, Cronbach’s α, and model fit

indices were satisfactory (Table 2). There was no

convergent validity issue with the model (Table 3). A

structural model has been established to test

hypotheses H1-H8 (Table 4).

4 RESULTS

The E-TAM framework satisfied the model fit criteria

(Table 4), with the majority of hypotheses being

accepted except for H2b, H3c, and H4b. More

specifically, all TAM relationships (H1) were

statistically supported. PEOU showed significant and

strong influence on CI (H1a) and PU (H1c). The path

Perceived Usefulness → Continuance Intention

(H1b) was the weakest among the three hypothesised

effects. The proposed antecedents, i.e. social

inclusion and the three basic psychological needs,

were found to significantly affect users’ perceptions

of and continuance intention of using the Internet

(H2-H5 partially supported). When comparing

standardised coefficients, the four antecedents

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model

showed overall stronger effects on PU and PEOU

than on CI. Among the four antecedents, social

inclusion was the most significant factor influencing

PEOU, and the users’ need for competence most

strongly affects PU. When it came to CI, the effect of

the need for competence was not significant. Lastly,

the statistical analysis supported the significance of

the six-proposed psychological and emotional

outcomes of using the Internet (H6-H8 all significant

at <0.01 level). The users’ continuance intention of

Internet use strongly and positively affected their

well-being, perceived value, achievement emotions,

and challenge emotions. Negative emotions, i.e. loss

emotions and deterrence emotions, were negatively

affected by CI, with path estimates much smaller than

positive outcomes.

Table 2: Item loading and reliability.

Variable Loading Reliability References

Perceived Ease of Use

(PEOU)

0.821 Cronbach’s α = 0.925

C.R. = 0.927

(Venkatesh, 2000)

0.840

0.932

0.892

Perceived Usefulness (PU)

0.880 Cronbach’s α = 0.936

C.R. = 0.938

0.935

0.924

Continuance Intention (CI) 0.877 Cronbach’s α = 0.868

C.R. = 0.868

0.875

Social Inclusion (SI) 0.807 Cronbach’s α = 0.898

C.R. = 0.884

(Richardson and Le

Grand, 2002)

0.867

0.660

0.705

0.836

Need for Competence (NC) 0.866 Cronbach’s α = 0.913

C.R. = 0.915

(Van den Broeck et al.,

2010)

0.917

0.870

Need for Autonomy (NA) 0.869 Cronbach’s α = 0.889

C.R. = 0.890

0.921

Need for Relatedness (NR)

0.876 Cronbach’s α = 0.921

C.R. = 0.927

0.936

0.885

Well-being (WB)

0.875 Cronbach’s α = 0.783

C.R. = 0.857

(El Hedhli et al., 2013)

0.856

Perceived Value (PV) 0.906 Cronbach’s α = 0.806

C.R. = 0.829

(Okada, 2005)

0.749

0.694

Achievement Emotions

(AE)

0.895 Cronbach’s α = 0.899

C.R. = 0.899

(Beaudry and

Pinsonneault, 2010)

0.847

0.851

Challenge Emotions (CE) 0.778 Cronbach’s α = 0.761

C.R. = 0.765

0.796

Loss Emotions (LE) 0.922 Cronbach’s α = 0.890

C.R. = 0.892

0.872

Deterrence Emotions (DE) 0.895 Cronbach’s α = 0.940

C.R. = 0.941

0.941

0.916

Notes: Items measured by 7-point Likert scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor

disagree; Somewhat agree; Agree; Strongly agree.

Reliability test: C.R. = composite reliability (>0.7); Cronbach’s α ((>0.7).

Method: M.L.; Model fit: χ2 (551) = 1200.367, CMIN/DF = 2.179, GFI = 0.904, CFI= 0.967, RMSEA= 0.044.

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

Table 3: Convergent validity test.

AVE SI NC NA NR PEOU PU CI WB PV AE CE LE DE

0.607 0.779

0.783 0.284 0.885

0.802 0.291 0.852 0.895

0.809 0.199 0.693 0.623 0.899

PEOU

0.761 0.343 0.570 0.568 0.461 0.872

0.834 0.295 0.674 0.589 0.606 0.672 0.913

0.767 0.334 0.527 0.537 0.394 0.762 0.590 0.876

0.749 0.199 0.667 0.592 0.814 0.508 0.645 0.411 0.866

0.621 0.324 0.661 0.655 0.580 0.688 0.664 0.734 0.672 0.788

0.748 0.414 0.662 0.639 0.552 0.699 0.591 0.688 0.602 0.756 0.865

0.619 0.230 0.658 0.661 0.683 0.461 0.572 0.339 0.703 0.568 0.633 0.787

0.805 -0.221 -0.158 -0.142 -0.062 -0.348 -0.156 -0.347 -0.093 -0.276 -0.336 0.064 0.897

0.842 -0.203 -0.076 -0.077 0.037 -0.262 -0.086 -0.328 0.012 -0.212 -0.293 0.126 0.840 0.918

Note: AVE = average variance extracted (>0.5), abbreviations of the constructs were present in Table 2.

Table 4: Statistical results of hypotheses test: structural equation model (H1-H8).

Hypotheses Path Coef. (t-test)

H1a Perceived Ease of Use → Continuance Intention 0.480 (12.052***)

H1b Perceived Usefulness → Continuance Intention 0.114 (2.836**)

H1c Perceived Ease of Use → Perceived Usefulness 0.406 (10.376***)

H2a Social Inclusion → Perceived Ease of Use 0.183 (4.842***)

H2b Social Inclusion → Perceived Usefulness 0.038 (1.166ns)

H2c Social Inclusion → Continuance Intention 0.095 (3.607***)

H3a Need for Competence → Perceived Ease of Use 0.228 (2.543*)

H3b Need for Competence → Perceived Usefulness 0.358 (4.720***)

H3c Need for Competence → Continuance Intention 0.121 (1.914ns)

H4a Need for Autonomy → Perceived Ease of Use 0.251 (3.026**)

H4b Need for Autonomy → Perceived Usefulness -0.096 (-1.374ns)

H4c Need for Autonomy → Continuance Intention 0.157 (2.764**)

H5a Need for Relatedness → Perceived Ease of Use 0.111 (2.167*)

H5b Need for Relatedness → Perceived Usefulness 0.223 (5.132***)

H5c Need for Relatedness → Continuance Intention 0.171 (4.704***)

H6 Continuance Intention → Well-being 0.711 (14.730***)

H7 Continuance Intention → Perceived Value 0.875 (15.278***)

H8a Continuance Intention → Achievement Emotions 0.859 (19.242***)

H8b Continuance Intention → Challenge Emotions 0.653 (11.286***)

H8c Continuance Intention → Loss Emotions -0.333 (-7.101***)

H8d Continuance Intention → Deterrence Emotions -0.258 (-5.979***)

Method: M.L.; Model fit: χ2 (602) = 2530.516, CMIN/DF = 4.204, CFI= 0.901, RMSEA= 0.072.

Significant at p: ns = > .05; * = < .05; ** = < .01; *** = < .001

5 DISCUSSION

5.1 Technology Acceptance

This study has extended TAM using a number of

psychological antecedents and outcomes. As the

majority of the hypotheses (H1-H8) were accepted,

this study further corroborated the robustness,

flexibility for extension, and explanatory power of

TAM (Mathieson, 1991; Venkatesh et al., 2003;

Davis, 1989). Path analysis results suggested that

PEOU had a stronger influence than PU on CI. This

research did not support previous literature which

suggested that PEOU is less influential than PU when

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model

it comes to affecting technology acceptance, e.g.

(Chau, 1996; Chau and Hu, 2001; Davis et al., 1989).

One possible interpretation may be that the users’

increasing familiarity with the Internet may alter their

expectations on new ICTs (Mathieson, 1991).

5.2 Social Inclusion and Satisfaction of

Needs

This paper has provided evidence for the relationships

between social inclusion and technology acceptance

(i.e. PEOU and CI), which is broadly consistent with

previous findings (Choi and Chung, 2013; Park,

2010; Park et al., 2013; Smith and Sivo, 2012). Two

of the main effects between psychological need

satisfaction and TAM were not supported, namely

Need for Competence → Continuance Intention and

Need for Autonomy → Perceived Usefulness. The

overall effects of the need for competence on TAM

were in line with previous results (Huang et al., 2016;

Lee et al., 2015; Roca and Gagné, 2008). The

influence of the need for autonomy on PEOU and CI

partially supported the studies of (Hew and Kadir,

2016; Huang et al., 2016; Nikou and Economides,

2017; Roca and Gagné, 2008). Statistical results

reported significant relationships between the need

for relatedness and TAM, which were broadly

consistent with (Huang et al., 2016; Lee et al., 2015;

Nikou and Economides, 2017). The effects of the

needs for autonomy and relatedness on ones’

continuance intention were significant, which

partially corroborated the viewpoint that the

psychological need fulfilment perceived online

enhances Internet use (Shen et al., 2013).

5.3 Psychological Outcomes

This paper has investigated six psychological

outcomes of using the Internet. Path coefficients

indicated that the intention to continue using the

Internet positively affected the positive outcomes, i.e.

well-being, perceived value, and positive emotions.

The negative coefficients between intention and

negative emotions offered additional evidence that

the outcome of using the Internet is, overall,

beneficial. These findings also agreed with previous

studies suggesting that users could experience both

positive and negative emotions triggered by the same

technology (Beaudry and Pinsonneault, 2010; Chang

et al., 2014; Partala and Saari, 2015; Partala and

Kujala, 2015). The results presented a strong

relationship between continuance intention and well-

being, which is consistent with (Rahman et al., 2017;

Munzel et al., 2017; Partala and Saari, 2015). The

correlation between the continuance intention of

Internet use and perceived value was significant and

strong, which confirmed the finding of (Kim et al.,

2008; Partala and Saari, 2015).

6 LIMITATIONS AND FUTURE

RESEARCH AVENUES

This article is not without limitations. First of all,

longitudinal studies are required with the aim of

elaborating how the big changes in technological

paradigms transform individuals’ emotional states.

Secondly, this paper posited direct effects between

psychological factors and TAM-based constructs.

Further tests and validations such as the interactions

and crossover effects between these emotional

variables are required. Lastly, the data was collected

from consumers in the U.S. to elaborate the

influential emotional states and consequences of

using the Internet. The compatibility of the E-TAM

framework should be examined in other contexts,

such as users in societies with different cultural

backgrounds.

REFERENCES

Agarwal, R. and Karahanna, E. 2000. Time flies when you're

having fun: Cognitive absorption and beliefs about

information technology usage. MIS Quarterly, 665-694.

Ajzen, I. 2005. Attitudes, personality, and behavior,

McGraw-Hill Education (UK).

Andrade, A. D. and Doolin, B. 2016. Information and

communication technology and the social inclusion of

refugees. Mis Quarterly, 40, 405-416.

Bagozzi, R. P. 1992. The self-regulation of attitudes,

intentions, and behavior. Social Psychology Quarterly,

178-204.

Beaudry, A. and Pinsonneault, A. 2005. Understanding user

responses to information technology: A coping model of

user adaptation. MIS Quarterly, 493-524.

Beaudry, A. and Pinsonneault, A. 2010. The other side of

acceptance: Studying the direct and indirect effects of

emotions on information technology use. MIS Quarterly,

34, 689-710.

Benbasat, I. and Barki, H. 2007. Quo vadis TAM? Journal of

the Association for Information Systems, 8, 7.

Broadbent, R. and Papadopoulos, T. 2013. Impact and

benefits of digital inclusion for social housing residents.

Community Development, 44, 55-67.

Casale, S. and Fioravanti, G. 2015. Satisfying needs through

Social Networking Sites: A pathway towards

problematic Internet use for socially anxious people?

Addictive Behaviors Reports, 1, 34-39.

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

Chang, Y., Dong, X. and Sun, W. 2014. Influence of

characteristics of the Internet of Things on consumer

purchase intention. Social Behavior and Personality, 42,

321-330.

Chau, P. Y. K. 1996. An empirical assessment of a modified

Technology Acceptance Model. Journal of Management

Information Systems, 13, 185-204.

Chau, P. Y. K. and Hu, P. J. H. 2001. Information technology

acceptance by individual professionals: A model

comparison approach. Decision Sciences, 32, 699-718.

Choi, G. and Chung, H. 2013. Applying the Technology

Acceptance Model to Social Networking Sites (SNS):

Impact of subjective norm and social capital on the

acceptance of SNS. International Journal of Human-

Computer Interaction, 29, 619-628.

Davis, F. D. 1989. Perceived usefulness, perceived ease of

use, and user acceptance of information technology. MIS

Quarterly, 319-340.

Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. 1989. User

acceptance of computer technology: A comparison of

two theoretical models. Management Science, 35, 982-

1003.

Deci, E. L. and Ryan, R. M. 2000. The" what" and" why" of

goal pursuits: Human needs and the self-determination of

behavior. Psychological Inquiry, 11, 227-268.

Dennis, C., Alamanos, E., Papagiannidis, S. and Bourlakis,

M. 2016. Does social exclusion influence multiple

channel use? The interconnections with community,

happiness, and well-being. Journal of Business Research,

69, 1061-1070.

El Hedhli, K., Chebat, J.-C. and Sirgy, M. J. 2013. Shopping

well-being at the mall: Construct, antecedents, and

consequences. Journal of Business Research, 66, 856-

863.

Fishbein, M. and Ajzen, I. 1975. Belief, attitude, intention

and behavior: An introduction to theory and research,

Reading, Mass, Addison-Wesley Pub. Co. .

Gagné, M. and Deci, E. L. 2005. Self-determination theory

and work motivation. Journal of Organizational

Behavior, 26, 331-362.

Hausman, A. V. and Siekpe, J. S. 2009. The effect of web

interface features on consumer online purchase

intentions. Journal of Business Research, 62, 5-13.

Hew, T.-S. and Kadir, S. L. S. A. 2016. Predicting the

acceptance of cloud-based virtual learning environment:

The roles of Self Determination and Channel Expansion

Theory. Telematics and Informatics, 33, 990-1013.

Hill, R., Betts, L. R. and Gardner, S. E. 2015. Older adults’

experiences and perceptions of digital technology:

(Dis)empowerment, wellbeing, and inclusion.

Computers in Human Behavior, 48, 415-423.

Huang, Y. C., Backman, K. F., Backman, S. J. and Chang, L.

L. 2016. Exploring the implications of virtual reality

technology in tourism marketing: An integrated research

framework. International Journal of Tourism Research,

18, 116-128.

Huxley, P., Evans, S., Madge, S., Webber, M., Burchardt, T.,

Mcdaid, D. and Knapp, M. 2012. Development of a

social inclusion index to capture subjective and objective

life domains (phase II): Psychometric development

study. Health Technology Assessment, 16, 1-248.

Kim, J. and Lennon, S. J. 2013. Effects of reputation and

website quality on online consumers' emotion, perceived

risk and purchase intention: Based on the Stimulus-

Organism-Response Model. Journal of Research in

Interactive, 7, 33-56.

Kim, T. G., Lee, J. H. and Law, R. 2008. An empirical

examination of the acceptance behaviour of hotel front

office systems: An extended Technology Acceptance

Model. Tourism Management, 29, 500-513.

Lazarus, R. S. and Folkman, S. 1984. Coping and adaptation.

The Handbook of Behavioral Medicine, 282-325.

Lee, W., Xiong, L. and Hu, C. 2012. The effect of Facebook

users’ arousal and valence on intention to go to the

festival: Applying an extension of the technology

acceptance model. International Journal of Hospitality

Management, 31, 819-827.

Lee, Y., Kozar, K. A. and Larsen, K. R. 2003. The technology

acceptance model: Past, present, and future.

Communications of the Association for information

systems, 12, 50.

Lee, Y., Lee, J. and Hwang, Y. 2015. Relating motivation to

information and communication technology acceptance:

Self-determination theory perspective. Computers in

Human Behavior, 51, 418-428.

Lowry, P. B., Gaskin, J., Twyman, N., Hammer, B. and

Roberts, T. 2013. Taking ‘fun and games’ seriously:

Proposing the Hedonic-Motivation System Adoption

Model (HMSAM). Journal of the Association for

Information Systems, 14, 617-671.

Mathieson, K. 1991. Predicting user intentions: Comparing

the technology acceptance model with the Theory of

Planned Behavior. Information Systems Research, 2,

173-191.

Mohd Suki, N., Ramayah, T. and Mohd Suki, N. 2008.

Internet shopping acceptance: Examining the influence

of intrinsic versus extrinsic motivations. Direct

Marketing: An International Journal, 2, 97-110.

Munzel, A., Meyer-Waarden, L. and Galan, J.-P. 2017. The

social side of sustainability: Well-being as a driver and

an outcome of social relationships and interactions on

social networking sites. Technological Forecasting and

Social Change.

Nikou, S. A. and Economides, A. A. 2017. Mobile-Based

Assessment: Integrating acceptance and motivational

factors into a combined model of Self-Determination

Theory and Technology Acceptance. Computers in

Human Behavior, 68, 83-95.

Okada, E. M. 2005. Justification effects on consumer choice

of hedonic and utilitarian goods. Journal of Marketing

Research, 42, 43-53.

Park, N. 2010. Adoption and use of computerbased voice

over Internet protocol phone service: Toward an

integrated model. Journal of Communication, 60, 40-72.

Park, N., Kim, Y.-C., Shon, H. Y. and Shim, H. 2013. Factors

influencing smartphone use and dependency in South

Korea. Computers in Human Behavior, 29, 1763-1770.

Psychological Determinants and Consequences of Internet Usage: An Extension of the Technology Acceptance Model

Partala, T. 2011. Psychological needs and virtual worlds:

Case Second Life. International Journal of Human-

Computer Studies, 69, 787-800.

Partala, T. and Kujala, S. 2015. Exploring the role of ten

universal values in using products and services.

Interacting with Computers, 28, 311-331.

Partala, T. and Saari, T. 2015. Understanding the most

influential user experiences in successful and

unsuccessful technology adoptions. Computers in

Human Behavior, 53, 381-395.

Phipps, L. 2000. New communications technologies: A

conduit for social inclusion. Information,

Communication and Society, 3, 39-68.

Rahman, S. A., Taghizadeh, S. K., Ramayah, T. and Alam,

M. M. D. 2017. Technology acceptance among micro-

entrepreneurs in marginalized social strata: The case of

social innovation in Bangladesh. Technological

Forecasting and Social Change, 118, 236-245.

Richardson, L. and Le Grand, J. 2002. Outsider and insider

expertise: The response of residents of deprived

neighbourhoods to an academic definition of social

exclusion. Social Policy and Administration, 36, 496-

515.

Roca, J. C. and Gagné, M. 2008. Understanding e-learning

continuance intention in the workplace: A self-

determination theory perspective. Computers in Human

Behavior, 24, 1585-1604.

Russell, J. A. and Carroll, J. M. 1999. On the bipolarity of

positive and negative affect. Psychological Bulletin, 125,

Ryan, R. M. and Deci, E. L. 2000a. The darker and brighter

sides of human existence: Basic psychological needs as a

unifying concept. Psychological Inquiry, 11, 319-338.

Ryan, R. M. and Deci, E. L. 2000b. Intrinsic and extrinsic

motivations: Classic definitions and new directions.

Contemporary Educational Psychology, 25, 54-67.

Ryan, R. M. and Deci, E. L. 2000c. Self-determination theory

and the facilitation of intrinsic motivation, social

development, and well-being. American Psychologist,

55, 68-78.

Sayce, L. 2001. Social inclusion and mental health.

Psychiatric Bulletin, 25, 121-123.

Secker, J., Hacking, S., Kent, L., Shenton, J. and Spandler, H.

2009. Development of a measure of social inclusion for

arts and mental health project participants. Journal of

Mental Health, 18, 65-72.

Selwyn, N. 2002. ‘E-stablishing’an inclusive society?

Technology, social exclusion and UK government policy

making. Journal of Social Policy, 31, 1-20.

Shen, C.-X., Liu, R.-D. and Wang, D. 2013. Why are children

attracted to the Internet? The role of need satisfaction

perceived online and perceived in daily real life.

Computers in Human Behavior, 29, 185-192.

Smith, J. A. and Sivo, S. A. 2012. Predicting continued use

of online teacher professional development and the

influence of social presence and sociability. British

Journal of Educational Technology, 43, 871-882.

Sørebø, Ø., Halvari, H., Gulli, V. F. and Kristiansen, R. 2009.

The role of self-determination theory in explaining

teachers’ motivation to continue to use e-learning

technology. Computers and Education, 53, 1177-1187.

Tapia, A. H., Kvasny, L. and Ortiz, J. A. 2011. A critical

discourse analysis of three US municipal wireless

network initiatives for enhancing social inclusion.

Telematics and Informatics, 28, 215-226.

Tay, L. and Diener, E. 2011. Needs and subjective well-being

around the world. Journal of Personality and Social

Psychology, 101, 354.

The Charity Commission 2001. The promotion of social

inclusion. In: COMMISSION, T. C. (ed.). UK: Crown.

Turel, O., Serenko, A. and Bontis, N. 2007. User acceptance

of wireless short messaging services: Deconstructing

perceived value. Information and Management, 44, 63-

73.

Van Den Broeck, A., Vansteenkiste, M., De Witte, H.,

Soenens, B. and Lens, W. 2010. Capturing autonomy,

competence, and relatedness at work: Construction and

initial validation of the Workrelated Basic Need

Satisfaction scale. Journal of Occupational and

Organizational Psychology, 83, 981-1002.

Venkatesh, V. 2000. Determinants of perceived ease of use:

Integrating control, intrinsic motivation, and emotion

into the Technology Acceptance Model. Information

Systems Research, 11, 342-367.

Venkatesh, V. and Davis, F. D. 2000. A theoretical extension

of the Technology Acceptance Model: Four longitudinal

field studies. Management Science, 46, 186-204.

Venkatesh, V., Davis, F. D. and Morris, M. G. 2007. Dead or

alive? The development, trajectory and future of

technology adoption research. Journal of the Association

for Information Systems, 8, 267.

Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D.

2003. User acceptance of information technology:

Toward a unified view.

MIS Quarterly, 27, 425-478.

Venkatesh, V., Thong, J. Y. L. and Xu, X. 2012. Consumer

acceptance and use of information technology:

Extending the unified theory of acceptance and use of

technology. MIS Quarterly, 36, 157-178.

Wang, C. 2014. Antecedents and consequences of perceived

value in Mobile Government continuance use: An

empirical research in China. Computers in Human

Behavior, 34, 140-147.

Westbrook, R. A. and Oliver, R. L. 1991. The dimensionality

of consumption emotion patterns and consumer

satisfaction. Journal of Consumer Research, 18, 84-91.

Wong, T. Y., Yuen, K. S. and Li, W. O. 2014. A basic need

theory approach to problematic Internet use and the

mediating effect of psychological distress. Frontiers in

Psychology, 5.

Yu, J., Lee, H., Ha, I. and Zo, H. 2015. User acceptance of

media tablets: An empirical examination of perceived

value. Telematics and Informatics.

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies