Understanding the Impact of Social Media and Socio-demographic
Variables on the Family Income in a Developing Society
Aurelius Ratu, Edy Subali, Marsudi, Banu Prasetyo, Arfan Fahmi, Siti Zahrok, Enie Hendrajati,
Niken Prasetyawati, Dyah Satya, Ratna Rintaningrum
Sepuluh Nopember Institute of Technology, Faculty of Business and Technology Management, Keputih, Surabaya,
Indonesia
Keywords: TAM Model, Family Income, Education, Business Duration, Social-Media Usage.
Abstract: Technological developments in the form of social media presence should have an impact on the economy of
a society. A large number of studies show the influence of the use of social media to increase individual and
group‘s income. However, research on 101 householders around the Sepuluh Nopember Institute of
Technology shows different results. This study aims to develop the settlement around ITS by prioritizing
technology as an image of residential development. Using the TAM model for predicting the impact on family
income and ordinal logistic regression analysis for examining sociodemographic variables, the increase in
family income is significantly influenced by the level of education followed by the duration of the undertaken
business (as the confounder). The result also shows that the influence of existing technology is only limited
to general knowledge about technological progress itself. Both Perceived Ease of Use and Perceived
Usefulness have no significance (p-value 0.05) to increase family income. More approaches are needed to
enable people to use and to adopt social media technology as a tool for increasing family income.
1 INTRODUCTION
Technological progress provides the foundation for
economic prosperity. The acceptance and use of
technology as economic empowerment both
individuals and groups (organizations) are increasing
in the last decade. This increase does not
automatically increase income itself, but at least
awareness of the benefits of information technology
in everyday life has emerged and developed. Several
models used to observe the use of information
technology were developed from TAM, TAM 2,
UTAUT up to Theory of Diffusion of Innovation. A
number of variables like personality trait (Ha & Stoel,
2009; Irani, Dwivedi, & Williams, 2009), social-
demographic (Hardill & Olphert, 2012; Kalimullah &
Sushmitha, 2017; Nayak, Priest, & White, 2010),
cultural aspect (Chen & Chan, 2014; Dasgupta &
Gupta, 2019a; Fernández Robin, McCoy, Yáñez
Sandivari, & Yáñez Martínez, 2014) even the
variables in the Education process are taken up as a
consideration in measuring the use of information
technology (Ifinedo, 2017; Khee, Wei, & Jamaluddin,
2014; Todaro et al., 2018).
In developing countries like Indonesia, the use of
information technology as a business tool is still
relatively small. Only companies or big business
organizations can own and manage web-based
technology as part of their business. On the other
hand, coping with such challenges, the community
seems to find its way of doing business through the
use of social media. This effort has grown
tremendously in recent years. Social media is a useful
and low-cost tool. As stated (Comin & Mestieri,
2014), any prior knowledge that reduces the
magnitude of these costs should foster technology
adoption. In this respect, we comprehend ‘prior
knowledge’ as depending on usefulness and ease to
use in adopting the technology. These days, the usage
of information technologies has inevitably become a
trend and also a necessary tool for today’s life.
Various social media applications that are present
in mobile devices do not necessarily show the
capabilities of their use for developing the prosperity
of individual and group economic. Some research that
explores the community as consumers shows that this
is due to the problem of presenting the quality and
scope of information (Stal & Paliwoda-Pękosz,
2019), enjoyment and trust in purchase (Ha & Stoel,
Ratu, A., Subali, E., Marsudi, ., Prasetyo, B., Fahmi, A., Zahrok, S., Hendrajati, E., Prasetyawati, N., Satya, D. and Rintaningrum, R.
Understanding the Impact of Social Media and Socio-demographic Variables on the Family Income in a Developing Society.
DOI: 10.5220/0009960406510658
In Proceedings of the International Conference of Business, Economy, Entrepreneurship and Management (ICBEEM 2019), pages 651-658
ISBN: 978-989-758-471-8
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
651

2009), on good health (Nayak et al., 2010), the
influence of the media itself for consumers
(Voorveld, Bronner, Neijens, & Smit, 2015). While
in terms of the people who use it as a business facility
(seen as a manufacturer offering a product), the
difficulty lies more in the usability of smartphone
user interface (De Barros, Leitão, & Ribeiro, 2013),
social bonds and structure (Hossain & de Silva,
2009), and competitive advantages (Dirsehan, 2015).
In this paper, we focus our research on the family as
producers who use social media as a business tool by
adding several socio-demographic variables as
antecedents for the use of social media.
2 BACKGROUND
2.1 Technology Acceptance Model
(TAM)
We adapted and used TAM as an analysis tool. The
Technology Acceptance Model (TAM) is gaining
popularity for understanding the relationship between
humans and technology. This model was developed
as a measure to understand the motivation behind the
use of technology for human needs (Davis, 1985).
Based on the theory of reasoned action (Fishbein &
Ajzen, 1975) and other related studies, Davis
suggested that three factors can explain user
motivation: Perceived Ease of Use, Perceived
Usefulness, and Attitude Toward Using the system
(Davis, 1989). In explaining perceived usefulness and
usage intentions in terms of social influence processes
(subjective norm, voluntariness, and image) and
cognitive instrumental processes (job relevance,
output quality, result demonstrability, and perceived
ease of use), this model was developed and extended
into TAM 2 (Venkatesh & Davis, 2000) and later into
Unified Theory of Acceptance and Use of
Technology, UTAUT (Venkatesh, Thong, Statistics,
Xu, & Acceptance, 2016).
The two primary constructs of TAM consist of
perceived usefulness and perceived ease of use.
Perceived usefulness is defined as the extent to which
a person believes that using technology will enhance
her/his productivity, and perceived ease of use is the
extent to which a person believes that using
technology will be free of effort. Through an
extension of the technology acceptance model
(TAM), some studies examined the individual
acceptance and usage of a website adding perceived
entertainment value and perceived presentation
attractiveness (Heijden, 2000), the usage of the
information system in e-shopping adding web-design,
customer service, privacy (Ha & Stoel, 2009), the
usage of ride-sharing service adding personal
innovativeness, environmental awareness, and
perceived risk (Wang, Wang, Wang, Wei, & Wang,
2018), and the usage of mobile technology adding
Access to information, information quality, and
information navigation (Stal & Paliwoda-Pękosz,
2019).
Based on this model, we used TAM to research
families in utilizing social media (mobile technology)
as a means to increase income. In a society with an
emerging economy, this model becomes a useful tool
for accessing the likelihood of acceptance of
information technology and helps in understanding
factors that drive acceptance of the technology, so
that appropriate features can be designed to facilitate
the acceptance by users.
2.2 Economic Prosperity
Technology has an impact on social change. Humans
cannot avoid the technological system that continues
to develop and surround it, including economic
problems. The contribution of new technology to
economic growth can only be realized when new
technology is widely diffused and used. Diffusion
itself results from a series of individual decisions to
begin using the technology, decisions which are often
the result of a comparison of the uncertain benefits of
the new invention with the uncertain costs of adopting
it.
The use of mobile applications such as WhatsApp,
Facebook, Instagram as a means of doing business
indeed appears as a continuous and rather slow
process. The diffusion ultimately determines the pace
of economic growth and the rate of change in
productivity. Besides, so far, research often places the
community/people who use mobile applications as
consumers. This kind of research is unavoidable,
considering the community is in stages of the
consumer society as a result of the separation of roles.
However, today, there is a shift in meaning.
The internet, social media, and digital devices that
follow us everywhere have changed the economic
nature of consumer activism: citizen-consumer began
to be perceived as a rational chooser, the citizen-
consumer became more of a co-producer in
production and innovation processes (Lammi &
Pantzar, 2019). Some of this can be observed from
studies that comprehend the benefit of technology
innovation. Some studies considered that technology
innovation could significantly have an impact on
socio-economic issues. To name a few, it can be
mentioned, i.e.; unemployment and skills
ICBEEM 2019 - International Conference on Business, Economy, Entrepreneurship and Management
652

development (Van Rensburg, Telukdarie, & Dhamija,
2019), espoused cultural traits influence users’
acceptance and use of the Internet technology in a
government agency in an emerging economy, India
(Dasgupta & Gupta, 2019a), grassroots innovation
based on local community units can relate to
appropriate technology activities for sustainable
development (Shin, Hwang, & Kim, 2019), and in
macro-level of society, technology increases national
confidence in financial markets through creation of
increased transparency (Salehan, Kim, & Lee, 2018).
3 METHOD
3.1 Measures
First, we adapted and translated TAM into the
Indonesian language consisting of 26 statements. The
thirteen statements are relating to Perceived Ease of
Use (PE), and The rest is relating to Perceived
Usefulness (PU). These statements were assessed by
a 5-point scale where each head of the family (or his
wife) was asked to give their approval from strongly
disagree (1) to strongly agree (5). Each construct was
acceptable at 0,76 (PE) and 0.91 (PU) Cronbach's
alpha.
3.2 Participants
One hundred and one people from the residence
around ITS were asked to fill in a self-report
questionnaire. It was conducted for two months.
Table 1 shows the data concerning socio-
demographic variables
Table 1: Contingency table showing demographic variables (N = 101)*
Total Average Income**
0 1 2 3
N N N N
Age Under 25 years 4 5 1 0
25 - 35 years 8 14 5 2
36 – 45 years 10 17 7 4
More than 45 years 7 11 4 2
Gender Male 9 22 5 7
Female 20 25 12 1
Marital Status Married 23 40 16 8
Single 4 6 0 0
Widow or divorcee 2 1 1 0
Marriage Age 1 – 5 years 4 7 5 1
6 - 10 years 3 10 5 1
more than ten years 16 24 6 6
Widow or divorcee 6 6 1 0
Children have no children 4 8 1 0
One child 7 16 6 2
2 - 3 children 11 20 10 5
more than three children 7 3 0 1
Last Education Primary School 9 14 1 0
Yunior High School 7 8 2 0
Senior High School 12 19 13 3
Bachelor Degree 1 6 1 5
Magister 0 0 0 0
Business product Culinary 21 40 13 7
Services (online app) 2 4 3 0
Boarding provider 0 0 0 1
The other 6 3 1 0
Understanding the Impact of Social Media and Socio-demographic Variables on the Family Income in a Developing Society
653

Business Duration less than one year 11 10 3 1
1 - 3 years 5 11 4 1
more than three years 13 26 10 6
Have a Side Job Yes 8 6 3 5
No 21 41 14 3
Partner (wife or husband)
works as
Government Employees 0 0 1 0
Entrepreneur (own
business)
11 11 5 2
Private (following other
people)
5 18 8 1
Does not work 9 10 2 5
The other 4 8 1 0
Children covered One child 7 18 7 3
Two children 10 14 7 1
Three children 3 3 1 3
more than three children 5 3 0 1
No child covered 4 9 2 0
Have social media Yes 16 37 16 8
No 13 10 1 0
Use social media for
business
Yes 10 17 8 6
No 19 30 9 2
Note:
* Number and percentages based on cases with valid responses
** 0 = under 3 million rupiah per month
1 = 3 – 5 million rupiah per month
2 = 6 – 10 million rupiah per month
3 = more than 10 million rupiah per month
3.3 Data Analysis
We categorized the average total income per month
from each family in quartile. The cross-tabulation
then was performed to avoid multicollinearity in
independent variables. After removing highly
correlated variables, any remaining variables were
included in multivariate analysis. Proportional odds
ordinal logistic regression was used to examine the
relationship between total average family’s income
and socio-demographic variables. Here, We treated
the TAM model as a categorical variable and included
it in the analysis.
All predictor whose p-value < 0,2 were included
and selected as a candidate for the multivariate
analysis. The significance then was evaluated in 0,05
alpha level. Any variables which were more
prominent than 0,05 alpha level was removed from
the model. With the remaining variables, we then
conducted a margin analysis to see the likely impact
of each remaining variable on increasing family
income. Finally, proportional odds assumptions were
tested. All data were analyzed using STATA 14.
4 RESULTS
After all, data were collected, and the scale
measurement was conducted, we proceeded with
bivariate correlate analysis. Last education and
having social media variables had a robust correlation
with the total average family’s income (p-value
<0,01). So, we decided to exclude the having social
media variable from the model. By analyzing p-
values 0.2, 0.1, and 0.05 consecutively, only two
independent variables remained as shown in Table 2.
ICBEEM 2019 - International Conference on Business, Economy, Entrepreneurship and Management
654

Table 2: Ordinal Logistic Regression
Total_Average_Income Odds Ratio. St.Err. t-value p-
value
[95% Conf Interval] Sig
0b. Last Education 1. . . . . .
1.Junior High 1.147 0.695 0.23 0.820 0.350 3.760
2.Senior High 3.636 1.808 2.60 0.009 1.372 9.634 ***
3.Bachelor 12.632 9.126 3.51 0.000 3.065 52.054 ***
0b. Business Duration 1. . . . . .
1.One – Three Years 2.643 1.531 1.68 0.094 0.849 8.229 *
2.More than 3 years 3.501 1.726 2.54 0.011 1.332 9.202 **
Mean dependent var 1.040 SD dependent var 0.882
Pseudo r-squared 0.086 Number of obs 101
Chi-square 21.191 Prob > chi2 0.001
Akaike crit. (AIC) 240.246 Bayesian crit. (BIC) 261.167
*** p<0.01, ** p<0.05, * p<0.1
The bivariate result showed that the odds of
increasing family income is more significant in
families with high school and bachelor graduates.
Meanwhile, for the business duration, the families
who have been in business for more than three years
are more significant than the families under three
years. To see the effect of the interaction of two
variables between Last Education and Business
duration on increasing family income, we used
margins command to predict probability.
For income under 3 million, the higher level of
education, the fewer odds of increasing family
income, and it was also influenced by the variable of
the business duration, as shown in Figure 1. For
income between 3-5 million, the higher level of
education was more significant on families that have
been in business for less than one year, while families
who have been in business for more than one year
were lower than as shown in Figure 2.
Figure 1.
Figure 2.
For income between 6-10 million and above 10
million per month, the higher the level of education
and the longer the business is undertaken, gave
impact on the family income, as shown in Figure 3
and Figure 4. All test was done with p-value 0.05.
In addition to the results above, we also did a
separate analysis for the TAM model that we
collected from the questionnaire. For the results of
testing Perceived Ease of Use (PE) and Perceived
Usefulness (PU), and adding General Knowledge
(GK) about information technology on total family
income, we found that PE and PU did not have a
significant effect on total average income as shown in
Table 3
Understanding the Impact of Social Media and Socio-demographic Variables on the Family Income in a Developing Society
655

Table 3. Pairwise Correlation
Total Average Income
1.000
Perceived of Usefulness 0.117 1.000
p-value 0.244
Perceived Ease of Use 0.176 0.755* 1.000
p-value 0.078 0.000
General Knowledge of
Information Technology
0.287* 0.591* 0.802* 1.000
p-value 0.004 0.000 0.000
* shows significance at the .05 level
5 DISCUSSION
The study aimed to predict the effect of using social
media along with other socio-demographic variables
on the family income. The results on the effect of
using social media on family income did not fulfill
our expectations. The result indicated that the
development of information technology and social
media applications, which should support the spread
of information, had not had an impact on the economy
of the community. It did not mean that families do not
know anything about technological developments.
The analysis of Perceived Ease of Use, Perceived
Usefulness, and General Knowledge about
information technology on total average family
income showed that only the General Knowledge
variable was more significant (p-value 0.05). We
interpreted that families realize the development of
social media. However, how to use social media in
increasing family income was the toughest challenge.
The result of the use of social media is similar to
the research conducted by (Mack, Marie-Pierre, &
Redican, 2017). Their findings showed that
differences in cognitive frameworks between novice
and experienced entrepreneurs, which impacts their
ability to recognize opportunities and respond to
technological change. In our study, we recognized the
cognitive frameworks formed by the duration of
business that was undertaken. As a result, the
business duration affects the family's incomes, except
there was no effect of using social media. These
findings also confirm what has been analyzed by
(Hossain & de Silva, 2009) that social ties or social
capital have more influence on the use of technology.
Relating to socio-demographic variables, we
found that the level of education was more significant
in increasing the family incomes. The higher the level
of education, the abler the families are to increase
their income. In a developing country, education term
seems not to be detached from every solving
problems (Glaeser & Henderson, 2017; Kamolsook,
Badir, & Frank, 2019; Kananukul,
Watchravesringkan, & Hodges, 2015; Sharif, 1994;
Tarhini, Hone, Liu, & Tarhini, 2017). Families who
have taken a higher education level have more
favorable economic opportunities than families with
a lower education level. Considering the use and
adopting of technology for economic development is
linear, families with low levels of education need
more attention (Dasgupta & Gupta, 2019b; Griffy-
brown, 2012; Sesto, 1983; Sharif, 1994). Some
studies have tried to approach this issue by promoting
‘augmented reality’ learning in which powerful
knowledge is the cornerstone of education in/for the
Good Society (Lynch, Kamovich, Longva, &
Steinert, 2019; Thomas, 2018). There are no further
examinations whether in the process of education
with an understanding of augmented reality or
entrepreneurial design could respond to changes and
technological developments in real society
Furthermore, the impact of increasing family
income by level education was also affected by the
business duration. The result of our study shows that
the most significant percentage of income
improvement is in families with senior high school
and bachelor levels as confounded by business that
has been undertaken. This result correlates with
findings that explain competitive advantage as the
improvement efforts of cost, product, service, and
communication (Dirsehan, 2015; Jafari Navimipour
& Soltani, 2016). We argue that the ability to see
opportunities and challenges in a business
management process is affected and shaped first by
education and the business processes undertaken. As
stated by Bögel & Upham, sustainability transitions,
both individually and the existence of individuals in
social groups, affect a rational choice for
consumption and technology acceptance (Bögel &
Upham, 2018). These results give a new perspective
for the understanding of family income in developing
society both economically and in the use of
information technology. In future work,
improvements on how to use media social as
information technology can be designed to increase
ICBEEM 2019 - International Conference on Business, Economy, Entrepreneurship and Management
656

family income by promoting social media advantages
in increasing income.
6 CONCLUSIONS
The level of education and the duration of the
business undertaken have a significant effect on the
family's economic income. We could not verify that
social media has an impact on family income. This
finding indicates that with the presence of
technological advances, the families have not been
able to accept it as a potential aspect for increasing
income. Our study was conducted in Indonesia, a
developing country with an emerging technological
infrastructure where people typically are slower to
adopt new technologies for promoting small and
medium scale business. This study result should help
the government in making policies, although it must
be recognized that the initiation of using social media
as business media arises from the needs of the society
itself.
ACKNOWLEDGMENTS
We thank LPPM Sepuluh Nopember Institute of
Technology for funding our research. The views
conveyed in this publication do not necessarily
represent the views of the supporting institution.
REFERENCES
Bögel, P. M., & Upham, P. (2018). Role of psychology in
sociotechnical transitions studies: Review in relation to
consumption and technology acceptance.
Environmental Innovation and Societal Transitions,
28(January), 122–136.
https://doi.org/10.1016/j.eist.2018.01.002
Chen, K., & Chan, A. H. S. (2014). Predictors of
gerontechnology acceptance by older Hong Kong
Chinese. Technovation, 34(2), 126–135.
https://doi.org/10.1016/j.technovation.2013.09.010
Comin, D., & Mestieri, M. (2014). Technology Diffusion:
Measurement, Causes, and Consequences. Handbook
of Economic Growth, 2, 565–622.
https://doi.org/10.1016/B978-0-444-53540-5.00002-1
Dasgupta, S., & Gupta, B. (2019a). Espoused cultural
values as antecedents of internet technology adoption in
an emerging economy. Information and Management,
(January), 0–1.
https://doi.org/10.1016/j.im.2019.01.004
Dasgupta, S., & Gupta, B. (2019b). Information &
Management Espoused cultural values as antecedents
of internet technology adoption in an emerging
economy. Information & Management, (February
2017), 0–1. https://doi.org/10.1016/j.im.2019.01.004
Davis, F. D. (1985). A technology acceptance model for
empirically testing new end-user information systems:
theory and results. Ph.D. Dissertation, (May), 291.
https://doi.org/oclc/56932490
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease
of Use, and User Acceptance of Information
Technology. MIS Quarterly, 13(3), 319–340.
https://doi.org/10.2307/249008
De Barros, A. C., Leitão, R., & Ribeiro, J. (2013). Design
and evaluation of a mobile user interface for older
adults: Navigation, interaction and visual design
recommendations. Procedia Computer Science,
27(Dsai 2013), 369–378.
https://doi.org/10.1016/j.procs.2014.02.041
Dirsehan, T. (2015). Building Innovative Competitive
Advantage in the Minds of Customers. In A. Brem & É.
Viardot (Eds.), Adoption of Innovation (pp. 75–93).
Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-319-14523-5_6
Fernández Robin, C., McCoy, S., Yáñez Sandivari, L., &
Yáñez Martínez, D. (2014). Technology Acceptance
Model: Worried about the Cultural Influence? BT -
HCI in Business. In F. F.-H. Nah (Ed.) (pp. 609–619).
Cham: Springer International Publishing.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention
and Behavior: An Introduction to Theory and Research.
Contemporary Sociology (Vol. 6).
https://doi.org/10.2307/2065853
Glaeser, E., & Henderson, J. V. (2017). Urban economics
for the developing World: An introduction. Journal of
Urban Economics, 98, 1–5.
https://doi.org/10.1016/j.jue.2017.01.003
Griffy-brown, C. (2012). Technology in Society.
Technology in Society, 34(2), 107–108.
https://doi.org/10.1016/j.techsoc.2012.04.001
Ha, S., & Stoel, L. (2009). Consumer e-shopping
acceptance: Antecedents in a technology acceptance
model. Journal of Business Research, 62(5), 565–571.
https://doi.org/10.1016/j.jbusres.2008.06.016
Hardill, I., & Olphert, C. W. (2012). Staying connected:
Exploring mobile phone use amongst older adults in the
UK. Geoforum, 43(6), 1306–1312.
https://doi.org/10.1016/j.geoforum.2012.03.016
Heijden, H. van der. (2000). Using the Technology
Acceptance Model to Predict Website Usage:
Extensions and Empirical Test. Serie Research
Memoranda, (july). Retrieved from
https://ideas.repec.org/p/vua/wpaper/2000-25.html
Hossain, L., & de Silva, A. (2009). Exploring user
acceptance of technology using social networks.
Journal of High Technology Management Research,
20(1), 1–18.
https://doi.org/10.1016/j.hitech.2009.02.005
Ifinedo, P. (2017). Examining students’ intention to
continue using blogs for learning: Perspectives from
technology acceptance, motivational, and social-
Understanding the Impact of Social Media and Socio-demographic Variables on the Family Income in a Developing Society
657

cognitive frameworks. Computers in Human Behavior,
72, 189–199. https://doi.org/10.1016/j.chb.2016.12.049
Irani, Z., Dwivedi, Y. K., & Williams, M. D. (2009).
Understanding consumer adoption of broadband: an
extension of the technology acceptance model. Journal
of the Operational Research Society, 60(10), 1322–
1334. https://doi.org/10.1057/jors.2008.100
Jafari Navimipour, N., & Soltani, Z. (2016). The impact of
cost, technology acceptance and employees’
satisfaction on the effectiveness of the electronic
customer relationship management systems.
Computers in Human Behavior, 55, 1052–1066.
https://doi.org/10.1016/j.chb.2015.10.036
Kalimullah, K., & Sushmitha, D. (2017). Influence of
Design Elements in Mobile Applications on User
Experience of Elderly People. Procedia Computer
Science, 113, 352–359.
https://doi.org/10.1016/j.procs.2017.08.344
Kamolsook, A., Badir, Y. F., & Frank, B. (2019).
Consumers’ switching to disruptive technology
products: The roles of comparative economic value and
technology type. Technological Forecasting and Social
Change, 140(December 2018), 328–340.
https://doi.org/10.1016/j.techfore.2018.12.023
Kananukul, C., Watchravesringkan, K., & Hodges, N.
(2015). Exploring the Impact of Consumers’ Second-
Hand Clothing Motivations on Shopping Outcomes: An
Investigation of Weekend Market Patronage in
Thailand BT - Marketing Dynamism & Sustainability:
Things Change, Things Stay the Same…. In J.
Robinson Leroy (Ed.) (pp. 242–245). Cham: Springer
International Publishing.
Khee, C. M., Wei, G. W., & Jamaluddin, S. A. (2014).
Students’ Perception towards Lecture Capture based on
the Technology Acceptance Model. Procedia - Social
and Behavioral Sciences, 123, 461–469.
https://doi.org/10.1016/j.sbspro.2014.01.1445
Lammi, M., & Pantzar, M. (2019). The data economy: How
technological change has altered the role of the citizen-
consumer. Technology in Society, 59(March), 101157.
https://doi.org/10.1016/j.techsoc.2019.101157
Lynch, M., Kamovich, U., Longva, K. K., & Steinert, M.
(2019). Combining technology and entrepreneurial
education through design thinking: Students’
reflections on the learning process. Technological
Forecasting and Social Change, (January 2018),
119689. https://doi.org/10.1016/j.techfore.2019.06.015
Mack, E. A., Marie-Pierre, L., & Redican, K. (2017).
Entrepreneurs’ use of internet and social media
applications. Telecommunications Policy, 41(2), 120–
139.
https://doi.org/https://doi.org/10.1016/j.telpol.2016.12.
001
Nayak, L. U. S., Priest, L., & White, A. P. (2010). An
application of the technology acceptance model to the
level of Internet usage by older adults. Universal
Access in the Information Society, 9(4), 367–374.
https://doi.org/10.1007/s10209-009-0178-8
Salehan, M., Kim, D. J., & Lee, J. N. (2018). Are there any
relationships between technology and cultural values?
A country-level trend study of the association between
information communication technology and cultural
values. Information and Management, 55(6), 725–745.
https://doi.org/10.1016/j.im.2018.03.003
Sesto, S. L. D. E. L. (1983). Technology and Social Change
William Fielding Ogbum Revisited, 183–196.
Sharif, N. (1994). Technology Change Management :
Imperatives for Developing Economies.
Shin, H., Hwang, J., & Kim, H. (2019). Appropriate
technology for grassroots innovation in developing
countries for sustainable development: The case of
Laos. Journal of Cleaner Production, 232, 1167–1175.
https://doi.org/10.1016/j.jclepro.2019.05.336
Stal, J., & Paliwoda-Pękosz, G. (2019). Mobile Technology
Acceptance Model: An Empirical Study on Users’
Acceptance and Usage of Mobile Technology for
Knowledge Providing BT - Information Systems. In M.
Themistocleous & P. Rupino da Cunha (Eds.) (pp. 547–
559). Cham: Springer International Publishing.
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017).
Examining the moderating effect of individual-level
cultural values on users’ acceptance of E-learning in
developing countries: a structural equation modeling of
an extended technology acceptance model. Interactive
Learning Environments, 25(3), 306–328.
https://doi.org/10.1080/10494820.2015.1122635
Thomas, H. (2018). Powerful knowledge, technology and
education in the future-focused good society.
Technology in Society, 52, 54–59.
https://doi.org/10.1016/j.techsoc.2017.09.005
Todaro, E., Silvaggi, M., Aversa, F., Rossi, V., Nimbi, F.
M., Rossi, R., & Simonelli, C. (2018). Are Social Media
a problem or a tool? New strategies for sexual
education. Sexologies, 27(3), e67–e70.
https://doi.org/10.1016/j.sexol.2018.05.006
Van Rensburg, N. J., Telukdarie, A., & Dhamija, P. (2019).
Society 4.0 applied in Africa: Advancing the social
impact of technology. Technology in Society, (March),
1–12. https://doi.org/10.1016/j.techsoc.2019.04.001
Venkatesh, V., & Davis, F. D. (2000). A Theoretical
Extension of the Technology Acceptance Model: Four
Longitudinal Field Studies. Management Science,
46(2), 186–204.
https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Thong, J. Y. L., Statistics, B., Xu, X., &
Acceptance, T. (2016). Unified Theory of Acceptance
and Use of Technology: A Synthesis and the Road
Ahead, 17(5), 328–376.
Voorveld, H. A. M., Bronner, F. E., Neijens, P. C., & Smit,
E. G. (2015). Media Guiding Consumers Across
Different Stages of the Purchase Process BT -
Marketing Dynamism & Sustainability: Things
Change, Things Stay the Same…. In J. Robinson Leroy
(Ed.) (p. 90). Cham: Springer International Publishing.
Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2018).
An empirical study of consumers’ intention to use ride-
sharing services: using an extended technology
acceptance model. Transportation.
https://doi.org/10.1007/s11116-018-9893-4
ICBEEM 2019 - International Conference on Business, Economy, Entrepreneurship and Management
658