ADVANCES IN BUSINESS-RELATED
SCIENTIFIC RESEARCH JOURNAL (ABSRJ)

Volume 13, Number 2 (2022)
ISSN 1855-931X

Review of research in the field of financial and non-financial
reporting and its connection to sustainable development
Ivana Martinčević, Do Vesna Sesar, Krešimir Buntak
Longitudinal study of small and medium enterprises and family
businesses in an emerging market
Márton Gosztonyi
Extending tam for information systems to acceptance
research/model of consumer goods (CGAM): a theoretical
approach
Katrin Förster, Wolfgang H. Schulz
Retirement financial planning: gender, age-related,
expenditure differences among indonesian households
Faizah Syihab, Muhammad Lugman Nurhakim

and

Assessing the role of perceived value on purchase intention on
livestream platforms
Minh Anh Tran

ABSRJ, Volume 13, Number 2

REVIEW OF RESEARCH IN THE FIELD OF FINANCIAL
AND NON-FINANCIAL REPORTING AND ITS
CONNECTION
TO SUSTAINABLE DEVELOPMENT
Ivana Martinčević*
University North,
Croatia
ivana.martincevic@unin.hr

Vesna Sesar
University North,
Croatia
vesna.sesar@unin.hr

Krešimir Buntak
University North,
Croatia
kresimir.buntak@unin.hr

Abstract
The aim of this paper is: (1) to explore the theoretical background on financial
and non–financial reporting, and sustainable development; (2) to research
and review novelties that academic researchers have done in the field of
financial and non-financial reporting; (3) to research and review novelties
that academic researchers have done in the field of financial and nonfinancial reporting and their links to sustainable development; and (4) to
provide a review of the research literature in these areas. In our research,
we have used the detailed analysis of literature review, using the Scopus
database, to summarize past findings in a research field. By analysing the
relevant scientific sources and based on the results of the research, it can
be concluded that there is lack of research in the area that connects financial
and non-financial reporting to sustainable development and it needs further
efforts and research. The intention and goal of this research was to make
not only academic researchers but also managers in practice aware of the
importance of financial and non-financial reporting in the function of
sustainable development, as well as to make researchers and practitioners
aware that there is insufficient research in this area. The obtained results
can be used to expand the current scientific knowledge about the financial
and non-financial reporting and its connection to sustainable development.

Key Words
Sustainable development; financial reporting; non-financial reporting;
sustainable management; reporting regulations.
*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

INTRODUCTION
Globalization and internationalization of the market put great pressure on the
business of the company, which is reflected in the economic development of
the company and society as a whole. Corporate reporting, which includes
financial and non-financial reporting, today is one of the prerequisites and
foundations of effective business decision-making. Having accurate and
quality information from mandatory and voluntary reporting is key to making
business decisions and managing an organization. Access to information
and data is greatly facilitated through corporate reporting (financial and nonfinancial) in the function of sustainable growth and development because it
enables timely decision-making, managing, and positioning of companies in
the market. The basic premise of the functioning of the market and all its
stakeholders is the availability of accurate and timely information, but there
is still unequal access to information between its participants (both internal
and external). Due to that, the problem of information asymmetry arises with
the appearance of the problem of unfavourable choice and moral hazards,
and privileged information. Quality and transparent corporate reporting
(financial and non-financial) is a prerequisite for the efficient functioning of
the capital market, which today ultimately leads to the cohesion of the
elements of sustainable development. This paper aims to explore and show
the importance of financial and non-financial reporting, and its connection
with the sustainable growth and development of companies.
For the purpose of researching and reviewing novelties in the field of
financial and non-financial reporting, numerous world literature has been
researched. In this research, papers within the Scopus database were used
to gather information on the research topic, thus showing that the field of
financial and non-financial reporting has been researched by a large number
of authors but is still insufficient in the field of financial and non-financial
reporting in the function of sustainable development. To demonstrate the
results of research (and the scarcity of this research topic regarding the
connection between financial and non-financial reporting, and sustainable
development), the results of of a search of the Scopus database are
presented, having led to 882 scientific papers dealing with financial and nonfinancial reporting, and 285 papers dealing with financial and non-financial
reporting and its connection to sustainable development. The number of
papers investigating financial and non-financial reporting and its connection
to sustainable development has increased substantially from 2018 to 2022,
showing an understanding of the importance of the observed topic and the
necessity of implementation and application in business. Most of the
research papers were from Italy, the United States, and Spain, and most of
them belong to the subject area of Business, Management and Accounting.
THEORETICAL FRAMEWORK
Financial and non-financial reporting

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Financial and non-financial reporting is an important element of good and
quality management of the organization. Responsibility for the quality
management and leadership of the organization lies with the management
in charge of performing basic managerial functions: planning, organizing,
leading, and controlling. Inefficient organization and its inefficient
management are limiting factors in the overall development of an
organization, both nationally and internationally. On the other hand, the
successful execution of the basic management functions by quality
management achieves a high level of efficiency, which leads to attaining a
competitive position in the market. Successful management implies efficient
and effective execution of business operations and focused and transparent
management of the organization. Being successful in the market means
achieving a positive business result, generating a profit, and thus
maintaining and improving the company's position in the market. The
information needed for business decision-making is obtained from various
sources, but we can say with certainty that the key, if not the most important,
source of information for business decision-making is financial statements
(financial reporting) as holders of accounting information. When it comes to
company management, it is impossible to ignore accounting as a
fundamental source of information (Žager et al., 2007).
Financial reporting has existed for many years and over time has changed
and evolved according to the needs of the market and its users, as it must
always be in accordance with adopted accounting standards. Modern
financial reporting is based on the so-called conceptual frameworks, the
most famous of which are the FASB and the IASB. Unlike financial reporting
non-financial reporting currently belongs to additional voluntary reporting
(disclosure), which today is a good business practice regarding the
publication of financial and non-financial information that is not covered by
mandatory (legal) reporting, increasing business transparency and
strengthening integrity, position, and reputation of companies in the market.
Non-financial information reporting refers to the disclosure of all other
information except financial information, and its development has advanced
in recent years (Gulin et al., 2016). Non-financial reporting is a process of
preparing and publishing non-financial information on the efficient operation
of companies in terms of social responsibility and the environment. Nonfinancial reporting is becoming the interest of global and domestic
researchers and includes business ethics, financial accounting, and strategic
management (Turzo et al., 2022).
The link between sustainability, and financial and non-financial reporting,
with an emphasis on sustainability reporting, is provided by the EstebanArrea and Garcia-Torea study (2022), which examines the current level and
regulation of sustainability reporting by examining possible increases in
sustainable transparency for all the stakeholders. European Union countries
have adapted their legislation to include the EU Non-Financial Reporting
Directives 2014/95 EU, i.e. the "Non-Financial Reporting Directive" (NFRD).
The Non-Financial Reporting Directive prescribes only the minimum
requirements for the publication of the non–financial reporting (NFI) and
does not contribute to increasing the comparability of the non–financial

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reports (Aureli et al., 2019; Borga et al., 2009; Pizzi et al., 2021), which
suggests to public interest entities to publish non-financial reports on social
and environmental aspects (Garcia-Torea et al., 2020). Research conducted
by Saverio et al. (2015) investigate whether the corporate social
responsibility (CSR) orientation of a firm affects its reporting incentives.
Cosma et al. (2022) deal with non-financial reporting regulated by the EU
Directive 2014/95, i.e. they want to examine and present the limitations of
the Non-Financial Reporting Directive (NFRD) in achieving its essential
purpose that refers to achieving sustainability and contribution (goals of the
United Nations Agenda (UN) 2030). The first study, showing the effects of
the NFR Directive’s reporting across multiple countries, was conducted by
Ottenstein et al. (2022). They wanted to examine the effects of the EU
Directive 2014/95 of Non-Financial Reporting on corporate sustainability
reporting practices (with an emphasis on the availability and quality of
information, and its comparability and credibility). Through regression
analyses they show that the EU Directive 2014/95 affects the quantity and
quality of sustainability reporting (Ottenstein et al., 2022). From 2018, EU
Directive 2014/95 requires large companies (listed companies, banks,
insurance companies, i.e. companies of public interest) to submit nonfinancial reports, while small and medium-sized companies are not subject
to this regulation.
So far, non-financial reporting requirements have been regulated through
a standard of voluntary reporting (e.g. the Global Reporting Initiative), which
has shown in practice the incompatibility of data (Christensen et al., 2021).
The causes of incompatibility may be related to the lack of standardization
and the multitude of guidelines governing NFIs and the use of different NFIrelated standards (Van Wensen et al., 2011; Boiral, 2017). Zarzycka and
Krasodomska (2022) in their research did regression analysis to examine
different Key Performance Indicators (KPIs) and the way companies publish
them in their non-financial reports. Results indicate that their comparison
between different industries was difficult. It is through this research that the
authors contribute to the understanding of the differences in the quality of
the presentation of KPIs and the selection of published KPIs (Zarzycka and
Krasodomska, 2022). To achieve comparability of information, it is crucial to
achieve and implement reporting based on standards (for non–financial
reporting), which would mean that standards require a mandatory
requirement for all reporting participants and not only those of public interest
(EFRAG, 2021). Financial indicators are not enough to develop and maintain
a competitive position where non-financial indicators create great
importance and a role. Hategan et al. (2021) through their research confirm
that there is a positive correlation between non-financial reporting and
financial performance, thus confirming that non-financial reporting affects
financial performance. The challenge of non-financial reporting is also
addressed by Beleneși et al. (2021), who confirm the existence of a
statistically significant positive correlation of medium intensity between the
index of publishing non-financial information and publishing a non-financial
report. The combination of financial (legally regulated and mandatory for all
market participants) and non-financial reporting and its application is visible

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through practice and gives clear results which the management of the
organization is based on.
Sustainable development
The concept of sustainable development originated and began to be
mentioned in the 80s of the 20th century, and the focus is on the connection
between the development of society and environmental protection. Today,
companies need to have an effective and, above all, clear framework on how
to face the challenges that the market poses to them, i.e. how to move
towards a sustainable society and achieve sustainable development.
Orientation of sustainable and quality management ensures process
optimization, minimizing resource consumption, and maximizing other
benefits associated with the business processes of the organization. There
is a wide range of tools, frameworks, principles, strategies, and processes
on the market that create and guide the relationship and action towards the
sustainability framework. The concept of sustainable management is based
on the quality management of the organization and is aimed at preserving
the sustainability of managed organizations (resources, processes, people).
It is necessary to strengthen the comprehensiveness and credibility of
sustainability reports through the involvement of stakeholders in the
sustainability reporting process. The same can be achieved by strengthening
internal and external management mechanisms, which is the responsibility
of corporate managers (Habiba et al., 2022). Ensuring sustainability through
all business processes and activities of the company leads to long-term
success and competitiveness, and contributes to the image and reputation
of the company.
The concept of sustainable development was first mentioned in 1987.
where sustainable development is defined through three key factors: 1.
economic (emphasis on ensuring one's economic development); 2.
environmental (sustainability of future development and reduction of
environmental pollution); and 3. social (ensuring proper cohesion in society).
All three dimensions overlap and complement each other, and their joint
action and respect ensure and achieve social cohesion and sustainability of
the economic system. Respecting the concept of sustainable development
and doing business sustainably requires companies to define strategies and
create an economy that can regenerate and be sustainable. To ensure
sustainable development in the company's business, numerous business
transformations are necessary, from various digital to technological
transformations, while respecting economic concepts, which are potential
and possible solutions for sustainable development. Several opportunities or
strategies contribute to sustainable development, namely green growth, the
circular economy, and the bio-economy. All of the above are aimed at
focusing on resource efficiency and encouraging low-pollution industries, as
well as encouraging recycling and the circular flow of materials and energy
(Beg et al., 2018; Beg, 2018).
At the global level, it is necessary to awaken and develop an awareness
of the need to accept the concept of sustainable development. The

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application of the concept of sustainable development is crucial in achieving
a balance between economic, social, and environmental requirements of a
globalized market that is faced daily with unemployment, migration, climate
change, poverty, etc. Research done by Elif (2019) proves that social
responsibility (connected to sustainable development) has a positive
relationship with financial performance. Habiba and Mahbub (2019) believe
that companies need to monitor sustainable behaviour to improve efficiency
and reputation, in addition to increasing chances of market survival. The
Environmental, Social and Governance (ESG) Guidelines are part of nonfinancial reporting and help companies in the transparency of published
data. A study conducted in Japan by Darnall et al. (2022) shows that
companies that use ESG guidelines present 39% more sustainability data
compared to companies that publish sustainability reports but do not follow
the guidelines for ESG reporting (Darnall et al., 2022). Salehi and Arianpoor
(2021) identify essential indicators necessary for business sustainability and
reporting, and provide guidance for future performance evaluation and
reporting decisions. Financial and non-financial reporting is one of the key
factors contributing to sustainable development. It is necessary to fully
implement the regulation (from the aspect of non-financial reporting) to
achieve complete sustainability from the aspect of reporting in the function
of sustainable development.
METHODOLOGY
For the purpose of researching and reviewing the field of financial and nonfinancial reporting, numerous world literature has been researched. Papers
within the Scopus, Web of Science, and Google Scholar databases were
used to gather information on the research topic and its connection to
sustainable development, which shows insufficient research in this area. In
all three databases, very few papers were found to have investigated the
financial and non-financial reporting in the function of sustainable
development. Furthermore, papers within the Scopus database were used
to gather information on the research area of financial and non-financial
reporting in the function of sustainable development, and are presented in
this paper. Many international researchers have dealt with and studied
financial and non-financial reporting, but a small number connect it with
sustainable development. Today, financial and non-financial reporting is the
subject of numerous research and topics by numerous authors from various
fields. Many studies dealing with this topic have shown that financial and
non-financial reporting is in the function of transparent business and visibility
of the organization, contributing to sustainable business and creating loyal
and long-term relationships with business partners and the market.
However, there is still lack of research that deals with financial and nonfinancial reporting, especially research that connects financial and nonfinancial reporting with sustainable business and development, which is
evident from the review of the researched literature. To show research
papers on this topic, we present here the results of Scopus database

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searches that include the keywords “Financial reporting + Non–financial
reporting + Sustainable development” searched in “Title, Abstract, and
Keywords” of documents belonging to Scopus databases. A search of the
Scopus database using the following approach (TITLE-ABS ((financial AND
reporting)) AND ((non-financial AND reporting)) AND (sustainable AND
development) AND (LIMIT-TO (LANGUAGE, "English") resulted in 285
scientific papers.
FINDINGS
As mentioned, we searched the Scopus database to find data and studies
that deal with financial and non-financial reporting in the function of
sustainable development. The first research results show results on financial
and non-financial reporting, while the second step of the research presents
results on financial and non-financial reporting in the function of sustainable
development. The first results are the results following the research
approach (TITLE-ABS- financial AND reporting)) AND ((non-financial AND
reporting), while the second step of the research presents results following
the research approach (TITLE-ABS- financial AND reporting)) AND ((nonfinancial AND reporting)) AND (sustainable AND development). The
presented researched results are grouped into several areas according to:
(1) documents by type; (2) documents by subject area; (3) documents by
year; (4) documents by country/territory; (5) documents per year by source;
and (6) documents by author.
The first research results present an investigation of financial and nonfinancial reporting. This step resulted in 882 papers (articles (807), reviews
(47), and Conference papers (28) (Figure 1).
Figure 1. Documents by type investigating financial and non-financial
reporting in the Scopus database (1993-April 2022)

Source: Own survey.

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The research area of the research topic presents the period from 1993 to
April 2022, and includes various subject areas (Figure 2). Figure 2 presents
the documents by subject area exploring the financial and non-financial
reporting in the period between 1993 and April 2022 in Scopus. Most of the
papers belong to the subject area of Business, Management and
Accounting, Economics, Econometrics and Finance, Social Sciences,
Environmental Science, Energy, Decision Sciences, Engineering, Medicine,
Arts and Humanities, and Computer Science. Other areas have fewer than
10 papers (only 2%) regarding financial and non–financial reporting.
Figure 2. Documents by subject area investigating financial and nonfinancial reporting in the Scopus database (1993-April 2022)

Source: Own survey.

The number of papers that investigate financial and non-financial
reporting has increased substantially in 2021 (143 papers). A growing trend
is noticeable from 2015 (46 papers), 2016 (38 papers), 2017 (61 papers),
2018 (80 papers), 2019 (77 papers), 2020 (131 papers), 2021 (143 papers),
and April 2022 (72 papers) (Figure 3).
Figure 3. Number of papers investigating financial and non-financial
reporting in the Scopus database (1993-April 2022)

Source: Own survey.

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Most of the research papers were from the United States (211 papers per
country), Italy (103 papers per country), the United Kingdom (88 papers per
country), Australia (82 papers per country), Germany (52 papers per
country), Canada (48 papers per country), Spain (46 papers per country),
China (41 papers per country), France (34 papers per country), and Malaysia
(23 papers per country). Other countries that are exploring research areas
have fewer than 20 papers per county (Figure 4).
Figure 4. Country of origin of the authors investigating financial and nonfinancial reporting in the Scopus database (1993-April 2022)

Source: Own survey.

Figure 5 presents the journals that published the papers investigating
financial and non-financial reporting in the period between 1993 and April
2022 in Scopus. As seen in Figure 5, the growing trend with a larger number
of papers started in 2008 and has had an increasing trend since 2018. The
largest number of papers were published in Sustainability Switzerland (48
papers), Journal of Applied Accounting Research (25 papers), Business
Strategy and the Environment (21 papers), Meditari Accountancy Research
(19), and Sustainability Accounting Management and Policy Journal (18
papers). Other journals have published fifteen or fewer papers regarding the
research topic.
Figure 5. The journals that published the papers investigating financial and
non-financial reporting in the Scopus database (1993–April 2022)

Source: Own survey.

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Regarding authors who deal the most with the research topic are Cormier,
D. (11 papers), Raimo, N. (8 papers), Vitolla, F. (8 papers), Aki, E.A. (7
papers), Tsang, A. (7 papers), García-Sánchez, I.M., Guyatt, G., Hakoum,
M.B., Magnan, M., and Velte, P. (each author has 6 papers). Other authors
have fewer than 5 papers that explore financial and non-financial reporting
(Figure 6).
Figure 6. Documents by the author investigating financial and non-financial
reporting in the Scopus database (1993–April 2022)

Source: Own survey.

The second step of the research presents an investigation of financial and
non-financial reporting and its connection to sustainable development. This
step resulted in 285 papers (articles (258), reviews (16), and Conference
papers (11) (Figure 7). Through this research, the lack of investigation
regarding the connection between financial and non-financial reporting and
sustainable development is evident. The first paper that puts reporting
(financial and non–financial) in relation to sustainable development was
published in 1998, so the period of this research is from 1998 to April 2022.
As in the first step of the research, most papers are articles (regarding the
first step it was 882 articles, in the second step of research: 285 articles).
Figure 7. Documents by type investigating financial and non-financial
reporting and its connection to sustainable development in the Scopus
database (1998-April 2022)

Source: Own survey.

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The research area of the research that explores financial and nonfinancial reporting and its connection to sustainable development regards
the period from 1989 to April 2022, and includes various subject areas
(Figure 8). Figure 8 presents the documents by subject area exploring the
financial and non-financial reporting and its connection to sustainable
development in the period from 1998 to April 2022 in the Scopus database.
Most of the papers belong to the subject area of Business, Management and
Accounting, Social Sciences, Environmental Science, Economics,
Econometrics and Finance, Energy, Engineering, Decision Sciences,
Computer Science, and Arts and Humanities. Other areas have fewer than
10 papers regarding financial and non-financial reporting and its connection
to sustainable development. Linked to the data of the first step of research,
and the area that deals with and explores financial and non-financial
reporting and its connection to sustainable development is yet again the area
Business, Management, and Accounting, just as in the first step of the
research results.
Figure 8. Documents by subject area exploring the financial and nonfinancial reporting and its connection to sustainable development in the
Scopus database (1998-April 2022)

Source: Own survey.

The number of papers that investigate financial and non-financial
reporting and its connection to sustainable development has again increased
substantially in 2021 (63 papers). A growing trend is noticeable in 2018 (30
papers), 2019 (26 papers), 2020 (62 papers), 2021 (63 papers), and April
2022 (40 papers) (Figure 9).

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Figure 9. Number of papers investigating financial and non-financial
reporting and its connection to sustainable development in the Scopus
database (1998-April 2022)

Source: Own survey.

Most of the research papers were from Italy (59), the United States (31
papers per country), Spain (30 papers per country), the United Kingdom (28
papers per country), Germany (26 papers per country), Australia (17 papers
per country), Romania (15 papers per county), China (12 papers per
country), Poland (11 papers per country), and France (10 papers per
country). Other countries that explore research areas have fewer than 10
papers per country (Figure 10).
Figure 10. Country of origin of the authors investigating financial and nonfinancial reporting and its connection to sustainable development in the
Scopus database (1998-April 2022)

Source: Own survey.

Figure 11 presents the journals that published the papers investigating
financial and non-financial reporting and its connection to sustainable

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development in the period from 1998 to April 2022 in Scopus. As seen in
Figure 11, the growing trend of papers started in 2008, having increased
since 2020, when 14 documents regarding financial and non-financial
reporting and its connection to sustainable development were published in
2020 in the journal Sustainability Switzerland. The largest number of papers
were published in Sustainability Switzerland (36 papers), Business Strategy
and the Environment (16 papers), Corporate Social Responsibility and
Environmental Management (12), Journal of Applied Accounting Research
(12 papers), and Sustainability Accounting Management and Policy Journal
(11 papers). Other journals have published ten or fewer papers on the
research topic. Linked to the data of the first step of research, the journal
that deals with and explores financial and non-financial reporting and its
connection to sustainable development, is yet again Sustainability
Switzerland, just as in the first step of the research results.
Figure 11. The journals that published the papers investigating financial and
non-financial reporting and its connection to sustainable development in the
Scopus database (1998–April 2022)

Source: Own survey.

Regarding authors who deal the most with the research topic and explore
financial and non-financial reporting and its connection to sustainable
development are García-Sánchez, I.M (6 papers), Caputo, F., Hahn, R.,
Leopizzi, R., Pizzi, S., Reimsbach, D., Velte, P., and Venturelli, A. (each
author has 5 papers). In addition, Akisik, O., and Gal, G. eachhave 4 papers.
Other authors have fewer than 4 papers that explore financial and nonfinancial reporting and its connection to sustainable development (Figure
12). The author who researches financial and non-financial reporting, also
connecting it with sustainable development is García-Sánchez, I.M, who also
appears in the first step of the research.

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Figure 12. Documents by the authors investigating financial and nonfinancial reporting and its connection to sustainable development in the
Scopus database (1998–April 2022)

Source: Own survey.

The presented research results are divided into two parts. The first part
of the research presents data related to financial and non-financial research,
and the interest of researchers in this field in the period between 1993 and
April 2022, when the first research covering this topic appeared. The second
part of the research presents data that combines financial and non-financial
reporting with sustainable development in the period from 1998 to April 2022,
when the first document linked to this topic appeared. It is evident that a large
number of researchers are engaged in financial and non-financial research,
but not enough researchers recognize the importance of financial and nonfinancial reporting for the sustainable development of the organization, as
shown by insufficient data and an insufficient focus in this direction.
DISCUSSION AND IMPLICATIONS
This paper presents the theoretical concepts and importance of financial
reporting, non-financial reporting, and sustainable development. We can say
with certainty that there is a strong connection between the process of
financial and non-financial reporting and sustainable development, i.e.
quality and transparent reporting contributes to the sustainable development
of the organization, although the area is still insufficiently explored. The
paper presents an analysis of data from the Scopus database and shows
the trend from 1993 to April 2022 related to financial and non-financial
reporting, and the trend from 1998 to April 2022 related to the relationship of
financial and non-financial reporting with sustainable development (the
analysed years were taken into account as the beginning of the research in
the mentioned area). The presented data show that the intensive growth and
interest in research on this topic began in 2018, both for research on the
process of financial and non-financial research and the research that links it
with sustainable development. From the aspect of financial and non-financial

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reporting research, the top three countries that explore the research topic
the most are (1) the United States, (2) Italy, and (3) the United Kingdom. The
top three journals that publish the most on research topics are (1)
Sustainability Switzerland, (2) Journal of Applied Accounting Research, and
(3) Business Strategy and the Environment, while the top three authors are
(1) Cormier, D., (2) Raimo, N., and (3) Vitolla, F.
The top three countries that in their research link financial and nonfinancial reporting to sustainable development are (1) Italy, (2) the United
States, and (3) Spain. The top three journals that have published the link
between financial and non-financial reporting with sustainable development
are (1) Sustainability Switzerland, (2) Business Strategy and the
Environment, and (3) Corporate Social Responsibility, while the top three
authors are (3) García-Sánchez, I.M., (2) Caputo, F., and (3) Hahn, R. What
we can conclude from the review of the presented research data is that the
same countries (the United States and Italy) and the same journals
(Sustainability Switzerland, and Business Strategy and the Environment)
appear in both areas of the presented research data, while the authors
studying this topic do not overlap in the first and second step of research.
One group of authors studies financial and non–financial reporting, and the
other group of authors studies financial and non–financial reporting and its
connection to sustainable development. The only author who studies both
areas but is not among the top three authors in the area of financial and non–
financial reporting is García-Sánchez, I.M.
Based on the data presented in both areas of our research, the Republic
of Croatia and its neighboring countries (Slovenia, Bosnia and Herzegovina,
Serbia, and Montenegro) do not appear as countries that study and explore
this topic. The topic of our research through this paper and the review of the
analyzed data is the basis for expanding the same research and an even
deeper analysis and the link between financial and non-financial reporting
with sustainable development. Databases other than Scopus were not
compared in this paper, which is a recommendation for further research in
this direction and consolidation of data from other databases, their analysis
and connection. This area of research needs more empirical evidence,
theoretical and quantitative evidence, and is an area for much further
research.
CONCLUSION
This study was designed to survey articles on financial and non-financial
reporting and its connection to sustainable development in peer-reviewed
journals listed in the Scopus database. The research in question comprised
of a range of a total of 1.167 papers (882 of them regarding financial and
non–financial reporting, and 285 of them regarding financial and non–
financial reporting and its connection to sustainable development) published
over a period of 29 years (from 1993 to 2022). This mapping considered a
literature overview and had scope to review the current state and efforts of
researchers in this area. The findings of this research indicate that the impact

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

of financal and non-financial reporting in the function of sustainable
developmnet is on the verge of disrupting current corporative reporting and
will have an effect on corporate reporting. This is the research area that
needs more empirical evidence, theoretical and quantitative evidence, but
also presents baseline for future research. This research contributes to
strengthening the awareness among researchers and practitioners about
review of important areas of research topics (documents by type, documents
by subject area, documents by year, documents by country/territory,
documents per year by source, documents by author) regarding financial
and non–financial reporting and its connection to sustainable development.
Most of the papers analysed gave a theoretical point of view on the analysed
problem, which points to the need for even greater efforts and research in
this area. The main question for researchers and managers remains how to
integrate, compare and manage both financial and non–financial reporting in
the function of sustainable development. In such a context, organizations will
have to adapt non–financial reporting with current financial reporting as an
obligation in order to gain competitive advantage on the market, and to
ensure transparent, comparable, accurate and reliable information
necessary for company management. This can achieve the maximum effect
based on their interaction and can lead to company-sustainable
development. Non-financial reporting has developed into reporting that
presents a challenge to any company, primarily due to differences in
managerial views on the usefulness of conducting such business practices.
The goal in the future is to achieve harmonization at the level of non-financial
reporting (as is the case with financial reporting) in order for non-financial
reporting to fulfil its purpose.
Moreover, even though one of the most reputable scientific databases
(Scopus) was consulted, there may have been relevant studies and papers
that were not indexed in the abovementioned database, which would have
otherwise enriched the study. This research gives a brief overview of a
researched field that is actually insufficiently explored through the scientific
literature, and presents a wide area for further researchers.
In conclusion, economic development and growth, as well as the
globalization and internationalization of society and markets, cannot be
stopped, however it is necessary to think in the direction of ensuring a
healthy society and a quality economy that will have less harmful effects on
the environment. Therefore, sustainable development is undoubtedly one of
the key elements in formulating and implementing development policies in
the world. A quality and transparent reporting process (financial and nonfinancial) contributes to sustainable development. The business policy of the
company today should be directed towards the system of sustainable
development and balance of company management through the cooperation
of a dynamic business and economic activities of the system, and in
cooperation with the essential information and data provided by the reporting
process (financial and non-financial).

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REFERENCES
Aureli, S., Magnaghi, E., Salvatori, F. (2019). The Role of Existing Regulation and Discretion
in Harmonising Non-Financial Disclosure, Accounting in Europe, 16(3), 290–312.
Beg, M. (2018). Održivi ekonomski razvoj – prepreke i rješenja, Zbornik radova znanstvenog
skupa: Modeli razvoja hrvatskog gospodarstva, Ekonomski fakultet Zagreb, 1(1), 371–394.
Beg, M., Sekur, T., Smolić, Š. (2018). Industrial policies of today: The green industry concept,
Economic and Social Development (Book of Proceedings), 27th International Scientific
Conference on Economic and Social Development, 717–727.
Beleneși, M., Bogdan, V., Popa, D.N. (2021). Disclosure dynamics and non-financial reporting
analysis. The case of Romanian listed companies, Sustainability (Switzerland), 13(9), 47–
32.
Boiral, O., Henri, J.F. (2017). Is Sustainability Performance Comparable? A Study of GRI
Reports of Mining Organizations, Business & Society, 56(2), 283–317.
Borga, F., Citterio, A., Noci, G., Pizzurno, E. (2009). A sustainability report in small
enterprises: Case studies in Italian furniture companies, Business Strategy and the
Environment, 18(3), 162–176.
Bozzolan, S., Fabrizi, M., Mallin, C.A., Michelon, G. (2015). Corporate Social Responsibility
and Earnings Quality: International Evidence, International Journal of Accounting, 50(4),
361–396.
Christensen, H.B., Hail, L., Leuz, C. (2021). Mandatory CSR and sustainability reporting,
Economic analysis and literature review, Review of Accounting Studies, 26(3), 1176–1248.
Cosma S., Leopizzi R., Nobile L., Schwizer, P. (2022) Revising the Non-Financial Reporting
Directive and the role of board of directors: a lost opportunity?,Journal of Applied Accounting
Research, 23(1), 207–226.
Darnall, N., Ji, H., Iwata, K., Arimura, T.H.(2022).
Do ESG reporting guidelines and
verifications enhance firms' information disclosure? Corporate Social Responsibility and
Environmental Management, Article in Press.
EFRAG (2021). Proposals for a Relevant and Dynamic EU Sustainability Reporting StandardSetting; European Reporting Lab, Brussels, Belgium, p. 228
Elif, A.S. (2019) Corporate social responsibility and financial performance: The moderating
role of ownership concentration in Turkey, Sustainability (Switzerland) 11(13), 36–43.
https://doi.org/10.3390/su11133643
Esteban-Arrea, R., Garcia-Torea, N. (2022). Strategic responses to sustainability reporting
regulation and multiple stakeholder demands: an analysis of the Spanish EU non-financial
reporting directive transposition, Sustainability Accounting, Management and Policy
Journal, 13(3), 600–625.
Garcia-Torea, N., Larrinaga, C. and Luque-Vílchez, M. (2020). Academic engagement in
policy-making and social and environmental reporting, Sustainability Accounting,
Management and Policy Journal, 11(2), 281–290.
Gulin D., Grbavac J., Hladika M. (2016). Kvaliteta korporativnog izvještavanja kao
pretpostavka transparentnosti poslovnih subjekata, Zbornik radova Ekonomskog fakulteta
Sveučilišta u Mostaru, Special Issue, 154–180. https://hrcak.srce.hr/203923
Habibaa, A.S., Mahbub, Z. (2019). CEO Compensation and Sustainability Reporting
Assurance: Evidence from the UK, Journal of Business Ethics, 158(1), 233–252.
Habibaa, A.S., Khaldoon, A., Khaled, H. (2022). Creating sustainability reports that matter:
an investigation of factors behind the narratives, Journal of Applied Accounting Research,
23(3), 738–763.
Hategan, C.D., Pitorac, R.I., Milu, N.D. (2021). Assessment of the mandatory non-financial
reporting of Romanian companies in the circular economy context, International Journal of
Environmental Research and Public Health, 18(24), 128–199.
Ottenstein, P., Erben, S., Jost, S., Weuster, C.W., Zülch, H. (2022). From voluntarism to
regulation: effects of Directive 2014/95/EU on sustainability reporting in the EU, Journal of
Applied Accounting Research, 23(1), 55–98.
Pizzi, S., Venturelli, A., Caputo, F. (2021). The ‘comply-or-explain’ principle in directive
95/2014/EU. A Theoretical analysis of Italian PIEs, Sustainability Accounting, Management
and Policy Journal, 12(1), 30–50.

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Salehi, M., Arianpoor, A. (2021). The relationship among financial and non-financial aspects
of business sustainability performance: evidence from Iranian panel data, TQM Journal,
33(6), 1447–1468.
Turzo, T., Marzi, G., Favino, C., Terzani, S. (2022). Non-financial reporting research and
practice: Lessons from the last decade, Journal of Cleaner Production, 345 (15 April 2022).
https://doi.org/10.1016/j.jclepro.2022.131154
Van Wensen, K., Broer, W., Klein, J., Knopf, J. (2011). The State of Play in Sustainability
Reporting in the European Union; The European Union: Maastricht, The Netherlands.
Zarzycka, E., Krasodomska, J. (2022). Non-financial key performance indicators: what
determines the differences in the quality and quantity of the disclosures?, Journal of Applied
Accounting Research, 23(1), 139–162.
Žager K., Tušek B., Vašiček V., Žager L. (2007). Osnove Računovodstva, Računovodstvo za
neračunovođe. Zagreb: Hrvatska zajednica računovođa i financijskih djelatnika.

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LONGITUDINAL STUDY OF SMALL AND MEDIUM
ENTERPRISES AND FAMILY BUSINESSES IN AN
EMERGING MARKET
Márton Gosztonyi
University of Malaya,
Malaysia
gosztonyi.marton@gmail.com

Abstract
In our paper, we analysed a longitudinal survey of Hungarian small and
medium-sized enterprises (SMEs) and family businesses (FBs). We
included a nationally representative, repeated cross-sectional (RCS)
sample. We sought to analyse and answer the question of which trends can
be detected in four segments (ownership structure, revenue, problem
perception, and succession), and whether these trends are similar for SMEs
and FBs. We used Grow Curve Modelling and Hierarchical Linear Models
(GCM-HLM) to analyse the data. Our results shows that the ownership
structure describes a different trend in the case of SMEs and FBs: the former
shows a negative trend line, while the latter shows a positive trend line.
Although, for sales revenue and sales, in the case of detection of problems,
the SMEs and the FBs both can be characterised by an increasing trend line,
and no change can be detected in the preparation for succession for either
SMEs or FBs.

Key Words
Longitudinal study; SMEs; family businesses; emerging market; Grow Curve
Modelling; Hierarchical Linear Models.

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

INTRODUCTION
Small and medium enterprises (SME) and family businesses (FB) represent
the majority of companies and are an important source for the generation of
jobs in most countries (Cadbury, 2000; Fattoum & Fayolle, 2009; Hacker &
Dowling, 2012; Hoy & Sharma, 2010; Kellermanns, Eddleston, Barnett, &
Pearson, 2008, Kuratko & Hodgetts, 2004; Mazzarol, 2006; Ramadani,
Fayolle, Gerguri, & Aliu, 2013). The importance of these businesses to a
country’s economy is substantial.
Family businesses differ from SMEs in many ways (Dunn, 1995; Hoy &
Sharma, 2010; Jorissen, Laveren, Martens, & Reheul, 2005; Mandl, 2008).
The centre of the firm in family businesses is family, which formally or
informally, directly or indirectly influence the firm; their main objectives are
both economic and non-economic, respectively sustainability/ long-term
family income (stability) as well as family satisfaction; their business
orientation is satisfaction of internal and external stakeholders (mainly
family, clients, employees, local community); the style of management is
value-driven, emotional and goal alignment, they compete on quality,
reputation, long-term relationships. Carlock and Ward (2001) described a
family business as a scale which should be balanced between the
requirements and business opportunities and the needs and desires of the
family. Based on this research a very important issue raised recently is
whether the family business should be “family business” during the whole its
life cycle or not. Mandl (2008) noted that the status of being a family business
must not be considered “fixed”. According to her, there are several
businesses that are family businesses over their whole life cycle. On the
other hand, there are businesses which could be ‘transferred’ over their life
cycle from family business to non-family business and vice-versa.
In our study, we investigate this possible transformation between family
and SME businesses. We consider it important to examine how can we
distinguish between family businesses and SMEs in Hungary regarding the
three components (ownership structure, volume of sales and problems or
challenges). Although the family business shares values and characteristics
with other business entities, like SMEs it confronts unique challenges. The
parent-founder faces numerous challenges, including balancing equity with
efficiency, succession with merit, and paternalism with agency. Even if there
is a far-reaching overlap between the two categories, the part of the literature
that specifically focuses on the research of family businesses is expanding.
More and more attention is paid to family business research and we would
like to contribute to this literature with our paper.
To capture these possible differentiations, we conducted a longitudinal
study. Although, a relatively small number of longitudinal analyses have
been performed among small and medium-sized enterprises (SMEs) and
family businesses (FBs). In our analysis, we primarily aim to address this
gap. Aken et al. (2017) conducted one of the most prominent international
longitudinal analyses in recent years and found that different governance
mechanisms may be interchangeable for SME firms. Alshibani and Volery’s
(2021) study, Wu’s (1996) longitudinal study on the Chinese market, and

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Kok’s (2014) European longitudinal study have shown that SMEs present a
growing role in job creation in the economy in the long run.
Although there is little longitudinal research on SMEs, the research about
family businesses is even meagre. Two-thirds of the world’s private
companies are family-owned, and their scientific research is on the rise
(Heras-Rosas & Herrera, 2020). Nonetheless, there has been little
longitudinal analysis on family businesses. Numerous research has
measured on comparative data, either qualitatively or quantitatively (HisrichFülöp, 1997), and found that FBs play a prominent role in the SME sector
both in terms of volume and impact, these results were not examined on time
series data. For example, within the Hungarian SME sector, family
businesses account for 60-70% (Hisrich-Fülöp, 1997) of the total number of
SMEs. The business segments of FBs are significantly intertwined, like the
ownership and management (Hisrich-Fülöp, 1997), which in the case of
SMEs, is separated, thus the business life cycle of FBs is different from
SMEs (Gersick et al., 1997).
Gersick et al. (1997) define family business as the one where at least 51
per cent of the business is owned by a family and the family is involved in
the management of the business or the transfer of ownership takes place in
part or in full within the family. However, researchers often use a second
definition for FBs, which applies to all the above-mentioned criteria except
for the subjective self-classification, i.e., the enterprise does not have to
consider itself as a family business (Gersick et al., 1997). In our analysis, we
examined both definitions and found that the classification according to the
first definition fits better to our data.
In our paper, the comparison of SMEs and FBS is made along the
dimensions that show the largest differences. Thus, we analysed the
ownership structure, revenue streams, and problem perception, and
succession issues. The aim of this research is to examine which changes
took place between 2017 and 2020 in different economic dimension of
Hungarian SME sector, and whether these changes describe different trend
lines for SMEs and FBs.
METHODOLOGY AND DATA
Statistics have been dealing with longitudinal analyses for more than 150
years (Lebo & Weber, 2015). During the long history of the method, the
boundary conditions necessary for longitudinal analysis of a data set have
been developed. Three such basic conditions (meta-conditions) must be met
(DeBoef & Lin, 2004): (a) multiple measurements must be made at intervals
that can be clearly separated from each other, as classical panel tests are
reliable from 40 (t-time) measurements, in which case quadratic, i.e. nonlinear correlation patterns can be detected; b) the data recorded at different
times must answer the same question(s); and c) the sample size must be
the same, according to a measurement aspect.
In our research, we gathered data in two waves (2017 and 2020), and
hence, our data do not meet the criterion of a minimum of 40 measurement

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dates. Therefore, it is important to note that the level of validity of our analysis
does not reach the level of validity of classical panel studies. Thus, our data
is inadequate for quadratic correlation analysis. In addition, the second
meta-condition was also violated, as the two surveyed questionnaires
differed at several points. Some of the questions asked remained the same,
while additional questions were added to the questionnaire in the second
wave. This caused a high rate of data loss during data matching. Finally, the
third meta-condition of a classical panel analysis became impossible due to
the GDPR regulations; therefore, we could not question the same
respondents in both waves. Consequently, our longitudinal sample can be
methodologically classified into repeated cross-sectional (RCS) sample
selection.
The structure and analysis of repeated cross-sectional studies (RCS) vary
from panel studies, i.e., aggregated cross-sectional time series studies
(PCSTS), as cross-sectional units appear only once in the data. Today,
however, a number of large-sample analyses are being conducted with an
RCS sample, such as the U.S. NAES surveys, CBS/NYT polls, full ICPSR
survey, Gallup surveys, and Michigan consumer surveys (Clarke et al., 2005;
Hopkins, 2012; Lebo et al., 2007; Lebo & Weber, 2015; Segal & Spaeth,
2002; Wood, 2009).
For RCS analyses, similar to PCSTS analyses, autocorrelation is one of
the biggest statistical challenges. However, general solutions, such as
differentiation or the use of the “lagging” dependent variable method cannot
be applied to RCS since the delay performed in ti results in a logical pitfall.
Nonetheless, a multilevel modelling framework has also been developed for
RCS samples (Lebo & Weber, 2015). For multilevel models, RCS data can
be examined at the aggregate and individual levels. In the aggregate case,
most research simply collects observations from all time and aggregates
data by subperiods (e.g., Blaydes & Chaney, 2013; Jerit & Barabas, 2012;
Moy et al., 2006; Romer, 2006; Stroud, 2008), in which case the
observations are treated as if they had been taken in a single cross-section.
However, this method has its limitations. It is evident that individual-level
analyses can be omitted as an alternative, as seen in the research of BoxSteffensmeier (2009) and DeBoef and Lin (2004); however, significant
interpretation error can be observed with this method.
Hence, we used two commonly used analysis methodology for RCS
samples, i.e., the Grow Curve Modeling (GCM) and the Hierarchical Linear
Models (HLM). Our results are, therefore, based on a GCM-HLM modelling,
to which test statistics were provided by repeated measures of ANOVA tests.
A multilevel model of change, also known as growth curve modelling
(GCM), is a flexible and efficient method for analysing longitudinal time
series data. There are a number of important and comprehensive literature
that describe the method in detail (Bryk & Raudenbush, 1987, 1992, 2002;
Lindenberger & Ghisletta, 2004; Rogosa & Saner, 1995; Singer & Willett,
2003; Snijders & Bosker, 1999); hence, we only highlight the most important
features of the method. The GCM contains four types of variables: the
measure of the outcome variable, the measure of time, the predictor of
variable changing in time, and one or more time-invariance predictors. The

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outcome variable is usually a dependent variable of the variables changing
in time, the value of which can be measured at each time point (Singer &
Willett, 2003). Time-varying predictors are also included in the model as
independent variables. The Level 1 model for GMC provides the estimated
value of the outcome variable in the population, which is assumed to be the
aggregate result of the effect of predictors on changes over time within
population units. Following the notation of Singer and Willett (2003), the
equation of this model with two variable predictors is as follows:
𝑌𝑖𝑗 = 𝜋0𝑖 + 𝜋1𝑖 TIME𝑖𝑗 + 𝜋2𝑖 𝑋2𝑖𝑗 + 𝜋3𝑖 𝑋3𝑖𝑗 + 𝜀𝑖𝑗
Where Yij is the estimated result for person i at time j, TIMEij is the value of
time for person i at time j, X2 and X3 are the two time-varying predictors
within the predictive person, π0i (initial state) is Y when time is zero, and
both time variable predictors are zero, π1i (rate of change) is the slope of
the linear line of person i, π2i is the unique effect of X2 on Y, π3i is the
unique effect of X3 on Y, and finally, εij is the error expression within-person.
The standard deviation of this error theorem is estimated in the model.
For the second level of the GCM model, the estimated parameters are
the outcome variables of the new equations, in which the time-invariant
variables are the predictors. For example, one possible system of equations
for the Level 2 model based on Singer and Willett (2003) is as follows:
𝜋0𝑖 = 𝛾00 + 𝛾01 𝐹𝑖 + 𝜁0𝑖
𝜋1𝑖 = 𝛾10 + 𝛾11 𝐹𝑖 + 𝜁1𝑖
𝜋2𝑖 = 𝛾20 + 𝛾21 𝐹𝑖 + 𝜁2𝑖
𝜋3𝑖 = 𝛾30 + 𝛾31 𝐹𝑖 + 𝜁3𝑖
In this model, F (Female) is a dummy variable (code, female: F = 1 male:
F = 0). Level 2 γ00, γ10, γ20, and γ30 are the estimated values of the four
Level 1 predictors, i.e., π0i, π1i, π2i, and π3i, when all time-invariant
predictors are zero. In our example, only one time-invariant predictor was
used, and the value of F was 0, therefore γ00, γ10, γ20 and γ30 represent
the effect of gender. The model can be extended with additional
interpersonal variables, and with their estimated effects, they will provide a
similar interpretation. The error terms ζ0i, ζ1i, ζ2i, and ζ3i represent
individual differences in Level 1 parameters that are not explained by Level
2 predictors. The model also estimates the standard deviations and
covariances of these error terms.
In summary, the goal of GCM statistical modelling is to find the model that
best fits the data. Typically, we test a series of models to test whether there
is sufficient variability in the data over time. If variability is met for time as a
prerequisite, further model building may take place, involving additional
predictor variables (Cillessen & Borch, 2006). GCM offers several benefits.
First, it allows researchers to simultaneously examine the way measured
data relate and change at the aggregate and individual levels. Second, GCM
techniques estimate the time velocity of the average change in the sample
as well as the variability of the change within the sample. Third, GCM can
specifically assess and model the standard deviation of measurement errors
at a given time point (Preacher et al., 2008), and finally, GCM can reduce

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the biases caused by the dropout rate known in longitudinal studies (Curran
et al., 2010).
As our data were recorded at two time points, they are clearly only
capable of detecting linear change. Thus, we have incorporated Hierarchical
Linear Modeling (HLM) into our model, allowing us to use a better method of
analysis to fit our data structure. HLM complements GCM analysis by
supplementing multilevel conceptual models with linear analysis of nested
data (Bryk & Raudenbush, 1992, 2002; Klein et al., 1994; Ozkaya et al.,
2013; Arregle et al., 2006; De Leeuw, cited in Raudenbush & Bryk, 2002).
HLM allows us to capture the effect of higher-level constructs on lower-level
constructs, highlighting the complex relationships between them (Hofmann,
1997; Bryk & Raudenbush, 1992). HLM can also effectively manage data
aggregation and separation (Magnusson et al., 2011), which has been a
critical consideration for our data structure. Thus, the HLM technique also
provides a solution for how to involve individuals who did not participate in
one of the data collection (a fundamental issue for RCS samples) or, for
example, how to combine data from individuals tested at different ages in
different samples (Woltman et al., 2012; Helson et al., 2002; Alder & Scher,
1994). HLM in longitudinal studies flexibly handles data collection
irregularities, which is beneficial for our study (Osgood & Smith, 1995).
During the matching process of our longitudinal database, we merged our
databases along three fixed variables: the family business variable, the
region variable and the economic sector variable. Coupling occurred only
when all three fixed variables depicted the same values in both waves.
As a result of the sequential matching process, our longitudinal database
has a total of 526 items, which means that we have managed to match 263
businesses from the first wave to the data recorded in the second wave. This
implies that in the first wave, the drop-out rate was 31%, while in the second
wave, the drop-out rate was 47%. In the case of the questioner questions,
we managed to pair a total of 37 questions from both questionnaires, which
were divide into 12 topic blocks, resulting in a total of 81 fitted variables. The
distribution of these variables is summarized in Table 1.
Table 1: Statistics on the distributions of the variables in our longitudinal
database
Total number of questions
Number of blocks
Basic variables
Ownership structure
Business management
Employees
Family business
Succession
Trainings
Innovation
Social sustainability
Finance
Media Orientation
Problems

2017
37
12
4
13
6
13
2
8
2
5
11
2
5
10

2020
37
12
4
13
6
13
2
8
2
5
11
2
5
10

Longitudinal
37
12
8
26
12
26
4
16
4
10
22
4
10
20

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TOTAL
Source: Own survey.

81

81

162

In order to compare FBs and SMEs, we developed a subsample of family
businesses that includes businesses that see themselves as a family
business and at least 51 percent of the company is owned by a family, the
family participates in the family business in the management of the business,
the members of the family participate in the operation of the business as
employees, or the transfer of management and ownership takes place, in
whole or in part, within the family. Based on this definition, the subsample of
our family business contained 205 items.
HYPOTHESES
In our research, we tested eight hypotheses related to SMEs and FBs to
examine how can we distinguish between family businesses and SMEs in
an emerging market. Based on the literature we focused on those three
dimensions that show the differences between the two market entities. We
examined the ownership structure as one of the core determinants of the
success of family businesses (Miller, Le Breton-Miller, & Lester, 2011).
Research has shown ownership to be of importance for the strategic
development and long-term survival of family businesses (Zahra, 2003;
Anderson & Reeb, 2003; Miller & Le Breton-Miller, 2005), and it is a complex
phenomenon that constitutes dimensions beyond the juridical and financial
aspects of ownership. Ownership is considered by the literature to be one of
the basic characteristics that differentiates FBs and SMEs. Therefore we
examined to what extent and in what direction did the ownership structure of
Hungarian SMEs and FBs change between 2017 and 2020? Moreover, what
factors will influence the ownership structure of Hungarian SMEs and FBs in
2017 and 2020? Based on these research questions, our hypotheses
became the following:
H1: In the case of Hungarian SMEs and FBs, no change in the ownership
structure can be detected between 2017 and 2020.
H2: Ownership structure of SMEs and FBs can be explained by the same
variables in the two waves mainly the business subjective classification and
the succession strategy.
Apart from ownership and succession we examined two additional
dimensions (volume of sales, and problems / challenges). We include the
analysis of volume of sales in our research because in an emerging market
this factor has a far-reaching effect on the economic perspective of the
companies. For this reason, it can be identified as an important factor when
comparing companies (Arnold & Quelch 1998; Dawar & Chattopadhyay
2002). Our research questions aimed at finding out to what extent and in
what direction did the volume of sales revenue of Hungarian SMEs and FBs
change between 2017 and 2020? Moreover, what factors influence the

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development of the sales volume of Hungarian SMEs and FBs in 2017 and
2020? Based on these research questions, our hypotheses became the
following:
H3: In the case of Hungarian SMEs and FBs, no change in the volume of
sales revenue can be detected between 2017 and 2020.
H4: Sales of SMEs and FBs can be explained by the same factors in the two
waves, namely the financial situation of the company, the number of
employees of the firms, the training and development opportunities for the
employees and the value of export.
In our paper, we thought it important to address the question of how FBs
and SMEs evaluate and see the socio-economic context. What are the areas
that are perceived as problems or challenges, and how much have these
areas changed over the years. We also cover what factors are responsible
for these perceptions. Our hypotheses were as follows:
H5: In the case of SMEs and FBs in Hungary, no change can be detected in
the case of mentioning problems / challenges between 2017 and 2020.
H6: Problem perception of SMEs and FBs can be explained by the same
factors in the two waves.
Approximately one-third of the family business literature is devoted to
succession issues (Sharma, Chua, & Chrisman, 2000, p. 234). Furthermore,
according to several authors and consultants, one of the main reasons (if not
the single most important reason) for the high failure rate among first- and
second-generation family businesses is their inability to manage the complex
and highly emotional process of ownership and management succession
from one generation to the next (Magretta, 1998; Matthews et al., 1999). As
the succession strategy is also a key factor to distinguish between FB and
SMEs. Based on that our research questions aimed to examine to what
extent and in what direction did the issue of succession for Hungarian SMEs
and FBs change between 2017 and 2020? Moreover, what factors influence
the development of succession in the case of Hungarian SMEs and FBs in
2017 and 2020? Based on these research questions, our hypotheses
became the following:
H7: In the case of SMEs and FBs in Hungary, no change can be detected in
the case of the issue of succession between 2017 and 2020.
H8: The succession of SMEs and FBs can be explained by the same factors
(orientation, and ownership structure) in the two waves.
COMPARATIVE TREND ANALYSIS OF SMES AND FBS
We performed a comparative study of SMEs and FBs on longitudinal data
by employing the ownership structure, sales revenue, perception of
problems, and the issue of succession.

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

To examine the change in ownership structure, we analysed the change
in private ownership in the total sample and the change in family ownership
in the family business subsample. Repeated measure ANOVA testing was
used as a prerequisite for the study to test whether the data structure in both
the SME and Family Business sample was appropriate for subsequent
GCM-HLM modelling (Appendix Tables 1 and 2). The values of the different
multivariate tests are significant for both samples, and hence, the means in
the two waves are significantly different. The epsilon value for the test, which
examines the null hypothesis that the error covariance matrix of
orthonormalized transformed dependent variables is proportional to an
identity matrix, is also significant in both cases (.000, .000) and shows a
value close to 0.1 (.0997, .0987). In addition, the ANOVA analysis shows a
significant value that a linear trend can be detected between the data points
(.000, .000), as well as the value of the partial Eta1 belonging to the linear
trend, which shows a sufficiently low value (.114, .112). This depicts that
there is a significant difference and a linear trend in the variable measuring
ownership structure in the SME and family business sectors.
In the GCM-HLM analysis, we used a scaled identity matrix to capture the
variances using the variables measuring the share of ownership (private
ownership in the case of SMEs and family ownership in the case of family
businesses) as the dependent variable. In our GCM-HLM analysis, we used
the time variable as a predictor variable (fixed effect) and allowed the
comparison of the averages along the waves, as well as the intercept value,
which helped us to transform the initial values of the cases to X = 0.
Moreover, we worked with a random effect based on the number of cases,
i.e., we allowed our HLM model to calculate variances at the individual level,
for which we used unstructured covariance and calculated with Restricted
Maximum Likelihood (REML) in the HLM methodology.
It can be seen from the table of fixed effects (Table 2) that the average of
the variable for SMEs in 2017 was 91.802 (+ - 1.524), which was associated
with a statistically significant (.000) linear value of -14.508 (+ - 2.480) in 2020.
This means that the average of the variable shows a decrease of -14.508
between 2017 and 2020. In the case of family businesses, however, the
average of the variable in 2017 was 86.04 (+ - 1,662), accompanied by a
statistically significant (.000) linear increase of 8,502 (+ - 1,949) by 2020.
Based on the differences at the individual level, which can be read from
the table of covariance parameters, the values measured at level 1 (602.23
+ - 69,958, 309.38 + - 32,498) are also significant for SMEs and family
businesses (.000). However, the measured value for level-2 is not always
significant. For the value, the variance of the intercept (UN 1,1), the variance
of the line (UN 2,2), and the covariance between the two (UN 2,1) were
1

Multivariate tests can be used to test the significance of the deviation of the means. A Wilks'
Lambda p-values are significant (.000), Hotelling's Trace test value (.000) as well as Roy's
Largest Root (.000) and Pillai's Trace value (.000). Thus, based on multivariate tests, the
means of the variable show a significant difference. In addition, for the Sphericity Assumed
test, which shows covariance differences between different time points, a significant value
(.000) is also obtained, which also confirms that significant differences can be detected
between the averages measured in the two waves of the variable.

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

calculated. The value of the intercept for SMEs is 8.775, which is
insignificant, the value of the straight line is 413.736, which is also not
significant, and finally, the value of the covariance between the two is 60.204, which is also insignificant. In the case of family businesses, the value
of the intercept is significant at 257.450, the value of the straight is
insignificant at 150.655, and finally, the value of the covariance between the
two is -196.79, which is significant.
This implies that at the individual level, enterprises differ from the average
trend in the average for SMEs; however, they do not differ for FBs.
Table 2: GCM-HLM results for ownership structure of SMEs and Family
Businesses
Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate
Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 91.802281 1.524212 255.406 60.229 .000 88.800657
94.803906
time
-14.508667 2.480494 267.518 -5.849 .000 -19.392440
-9.624893
Estimates of Covariance Parametersa

Parameter
Repeate Varianc
d
e
Measure
s
Intercept UN (1,1)
+
time
[subject UN (2,1)
= id]
UN (2,2)

Estimate
602.231697

8.775914
60.204463
413.736600
b

95% Confidence Interval
Wald
Lower
Std. Error Z
Sig. Bound
Upper Bound
69.95853 8.60
5
8

.00
0

479.60463
756.212491
6

75.04932
.117
7
56.81327 1.06
3
0

.90
7
.28
9

4.613592E 166934279.51915
-7
8
171.556432
51.147505

.000000

.

.

.

.

Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate
Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 86.048780 1.662843 249.223 51.748 .000 82.773764
89.323797
time
8.502009 1.949351 282.588 4.361 .000 4.664918
12.339101
Estimates of Covariance Parametersa

Parameter
Repeated
Variance
Measures
Intercept + UN (1,1)
time
UN (2,1)
[subject =
id]
UN (2,2)
Source: Own survey.

Wald
Z

95% Confidence Interval
Lower
Upper
Sig. Bound
Bound

Estimate

Std. Error

309.384085

32.498577 9.520 .000 251.817181 380.111126

257.450627

368.183104
133.340966
.

46.992488 5.479 .000 180.021366
-196.790673 32.372894
.000
6.079
260.240379
150.655293b .000000
.
.
.

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Thus, in the case of enterprises, the share of private ownership in the
ownership structure in 2017 and 2020 follows a declining trend, but
enterprises may deviate from this trend at the individual level. In contrast, in
the case of family businesses, we can show an increasing trend in the case
of family property, from which FBs do not differ significantly at the individual
level.
We do not have enough information with respect to 2017 data to explain
the change for SMEs and FBs, as no correlation can be detected with other
variables. However, in the 2020 data, the ownership structure for SMEs
correlated with three variables: family ownership ratio (.257**), family
business subjective judgment (-.227*), and succession strategy (-.175*). In
the FB sample, the variable correlated with two variables: the private
ownership ratio (.237**) and the succession strategy (-.145*). However, in
both cases, the variables were not fit into an OLS model. Hence, a stochastic
explanation of the probable causes of the changes in the statistical sense
cannot be given based on the present database.
In the following, we move on to the analysis of the data from the point of
view of sales revenue. With all this, we analyse the change in the financial
performance of SMEs and FBs on time series data. For our analysis, we
repeatedly used ANOVA as a test statistic, based on the results of which it
can be concluded that for both SMEs and FBs, a significant difference and
a linear trend can be detected in the two waves in the variable measuring
annual sales volume (Tables 3 and 4 in Appendix).
In the GCM-HLM analysis, we used the settings already described for the
ownership structure to build our model. As a dependent variable, we used a
five-value variable measuring annual sales revenue (1 = 50-100 mHUF, 2 =
100-300 mHUF, 3 = 300-500 mHUF, 4 = 500 mHUF - 1 billion HUF, 5 = more
than 1 billion HUF). In the case of the GCM-HLM results, the table of fixed
effects shows that in 2017, the average of the variable for SMEs was 3.07
(+ - .077), for which .057 (+ - .088) was a statistically significant (0.000) linear
value associated with 2020. For FBs, there was also a significant increase
of 1.02 among the variables on average.
Individual-level differences at level-1 are significant, with values of .822
(+ - .078) for SMEs and 1.22 (+ - .15) for FBs. At the individual level, neither
SMEs nor FBs deviate significantly from the average trend of the average.
Table 3: GCM-HLM results of sales revenue of SMEs and Family
Businesses
Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 3.076046 .077021 309.517 39.938 .000 2.924494
3.227597
time
.057034 .088616 346.547 .644
.000 -.117258
.231327
Estimates of Covariance Parametersa
Parameter

Std.
Estimate Error

95%
Wald Z Sig. Interval

Confidence

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Lower
Bound
Repeated
Variance
Measures
Intercept + time UN (1,1)
[subject = id]
UN (2,1)
UN (2,2)

.822689

.078448

.737497 .116405
-.556385 .095617
.419899b .000000

Upper
Bound

10.487 .000 .682447

.991751

6.336 .000 .541263
-5.819 .000 -.743791
.
.
.

1.004874
-.368979
.

Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 2.170732 .086239 204.000 25.171 .000 2.000697
2.340766
time
1.029268 .122330 204
8.414 .000 .788074
1.270462
Estimates of Covariance Parametersa

Std.
Parameter
Estimate Error
Repeated
Variance
1.221343 .151877
Measures
Intercept + time UN (1,1) .303287 .138193
[subject = id]
UN (2,1)
-.430738 .098413
UN (2,2)

.625081b

.000000

Confidence

Wald
Z

95%
Interval
Lower
Sig. Bound

8.042

.000 .957169

1.558428

2.195
4.377
.

.028 .124166

.740806

.000 -.623624

-.237851

.

.

.

Upper
Bound

Source: Own survey.

Thus, it can be stated that in the case of SMEs and FBs, the volume of
annual turnover shows an increasing trend between 2017 and 2020, and
enterprises did not deviate from this trend at the individual level.
For both SMEs and FBs, we further analysed by conducting OLS
regression on the possible explanations for interpreting the trend line. We
built a total of four models; two for 2017 data while the other two for 2020
data. During the development of each model, 14 explanatory variables and
sales revenue were included in the model as dependent variables. The
stepwise method was used in the model construction.
For SMEs, the explanatory power of the final model in 2017 was 58% (R2
.337), while for FBs, it was 62.2% (R2 .387). The value of the unbiased
estimate of the explanatory power of the 2020 models (R2) is .385 for SMEs
and .400 for FBs. The value of the F-test for the ANOVA test of the models
was significant in all cases (.000), i.e., our models represent a significant part
of the total heterogeneity.
In 2017, for SMEs, the final model retained four variables showing
significant levels for the t-test (.001, .000, .018, .000). The model retained
the variable measuring production for export (Bs value -.169), the negative
value of which shows that the more a given enterprise produces for the
domestic market, the higher its turnover. The number of employees (Bs.
.436) showed that more the people an SME employs, the higher its turnover.
The variable of training provided to employees (Bs -.124) shows that the less

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

an SME provides training to its employees, the higher its sales. Finally, the
variable measuring the number of news sources (Bs .204) showed that more
the information sources an SME is able to realise, the higher its sales
revenue.
For FBs, the final model also retained four variables, the export variable
(Bs -.102), the variable measuring the number of employees (Bs. 490), the
variable measuring the number of trainings (Bs -.155) and the variable
measuring the number of media sources (Bs. 212).
Table 4: OLS regression and t-values
Coefficientsa

Model
1 (Constant)
Export
Number of employees
Training for employees
Orientation, how
media sources

many

Unstandardized
Coefficients
B
Std. Error
1.922
.412

Standardized
Coefficients
Beta

-.444

.135

-.169

.496

.060

.436

-.348

.147

-.124

.163

.041

.204

t
4.666
3.294
8.321
2.374

Sig.
.000
.001
.000
.018

3.984 .000

Coefficientsa

Model
1 (Constant)
Export
Number of employees
Training for employees
Orientation, how
media ources
Source: Own survey.

many

Unstandardized
Coefficients
B
Std. Error
1.520
.426

Standardized
Coefficients
Beta

-.265

.146

-.102

.563

.066

.490

-.419

.154

-.155

.161

.043

.212

t
3.571
1.821
8.562
2.728

Sig.
.000
.040
.000
.007

3.766 .000

In our 2020 model, we used the same model build as the 2017 model
design. For SMEs, the regression algorithm retained three variables in the
final model: the market position (Bs .133), which shows that the better the
market the higher the turnover of the company; the export variable (Bs .169), on the basis of which the same conclusion can be drawn as in 2017,
and finally, the variable measuring the number of employees (Bs .552), on
the basis of which it can be seen that the more employees a company
employs, the higher its turnover. In the case of FBs, three variables had a
significant effect on the dependent variable: the assessment of the
subjective market position (Bs. 132), the export variable (Bs -.131), and the
number of employees (Bs. 576).
Table 5: OLS regression t-values of for SMEs and FBs (2020)

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Coefficientsa
Unstandardized
Coefficients
B
Std. Error
2.226
.451

Model
1 (Constant)
Financial situation of the
.196
company
Export
-.589
Number of employees
.596

Standardized
Coefficients
Beta

t
4.932

Sig.
.000
.007

.072

.133

2.708

.173
.054

-.169
.552

-3.410 .001
11.114 .000

Coefficientsa
Unstandardized
Coefficients
B
Std. Error
1.923
.482

Model
1 (Constant)
Financial situation of the
.188
company
Export
-.446
Number of employees
.614
Source: Own survey.

Standardized
Coefficients
Beta

t
3.993

Sig.
.000
.018

.078

.132

2.396

.189
.059

-.131
.576

-2.367 .019
10.345 .000

Thus, it can be stated that the sales volumes in 2017 and 2020 can be
explained by similar variables for both SMEs and FBs based on our data.
Furthermore, in 2017, the turnover of both SMEs and FBs can be explained
by the same variables for both SMEs and FBs (exports, number of
employees, training for employees, information from several news sources).
In 2020, the export variable and the number of employees also demonstrated
an important explanatory power for both SMEs and FBs; however, the
situation in 2020 is not substantially explained by the number of information
channels or training. In contrast, subjective market judgment played a
greater role in the explanation. In other words, the annual turnover of SMEs
and FBs, which mainly produce for the domestic market and have many
employees, has increased in the last four years.
After analysing sales revenue, the types and problems or challenges
perceived by businesses and whether there has been a shift in the two
waves were assessed. The qualitative responses in the questionnaire were
quantified and a distribution table of the variable was developed using the
multiply response set method (Table 6).
Table 6: What is the TOP3 thing you would change in your environment?

Labor force
Suppliers
Technology
Investment
Regulation
Grants
Profitability

2017
SME (%)

2017
FB (%)

2020
SME (%)

2020
FB (%)

6,3
6,7
0,7
7,2
62,5
4,7
4,1

6,0
6,8
0,9
7,7
64,5
3,4
3,0

20,4
5,2
8,1
7,3
14,3
12,4
11,8

20,8
5,8
7,2
6,2
15,3
12,6
12,4

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

CEO
Competitive situation
Total
Source: Own survey.

2,1
5,7
100,0

1,3
6,4
100,0

2
18,5
100,0

1,0
18,8
100,0

Based on the distributions, it can be clearly seen that in 2017, the
overwhelming majority (64.5%) of the responses were among SMEs and
FBs that would have changed the regulatory environment (reduction of
taxes, reduction of bureaucracy). By 2020, however, one of the most
important challenges has been to find and employ the right workforce
(20.8%), while the fact that over-bureaucratised and high-tax regulations
(15.3%) continue to be a major problem for SMEs and FBs in Hungary.
In our analysis, we performed a repeated measure ANOVA for the
variable measuring the number of nine-valued problems as a pre-statistic for
our GCM-HLM model. The results of the ANOVA test showed a significant
difference in the case of SMEs and FBs (Tables 5 and 6 in Appendix).
In the case of both SMEs and the FB sample, the developed GCM-HLM
model was built according to the system already presented (Table 7). The
table of fixed effects for SMEs shows that the average of the variable in 2017
is 0.88 (+ - .045), and 0.89 (+ - .053) for FB, for which 1.319 (+ - .072) was
associated with a statistically significant (0.000) linear value of 1.258 (+ .081) for FBs by 2020. This implies that the averages of the variables
increased for both SMEs and FBs. For individual level differences, the value
measured at level-1 is significant for SMEs (.536 + - .061), and for FBs (.509
+ - .067) the values are significant in both cases (.000). In the case of level2 measured values, neither the values of the intersection points nor the
values of the straight lines are significant. This means that businesses may
deviate from the average trend at the individual average at the individual
level, both for SMEs and FBs.
Table 7: GCM-HLM results of SMEs and FBs for problems / challenges
Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept .889734 .045683 262.000 19.476 .000 .799781
.979686
time
1.319392 .072997 262.000 18.075 .000 1.175656
1.463127
Estimates of Covariance Parametersa
Std.
Estimate Error

Parameter
Repeated
Variance
.536459
Measures
Intercept + time UN (1,1)
.012405
[subject = id]
UN (2,1) .026010
UN (2,2) .328499b
Estimates of Fixed Effectsa
Parameter Estimate Std. Error df

Wald
Z

95% Confidence Interval
Lower
Upper
Sig. Bound
Bound

.061221 8.763 .000 .428940
.063708 .195
.047092 .552
.000000 .

t

.670928

5.273791E291.789357
7
.581 -.066289
.118308
.
.
.
.846

Sig. 95% Confidence Interval

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Intercept
time

.892683 .053038
1.258537 .081429

Lower Bound Upper Bound
204
16.831 .000 .788111
.997255
204.000 15.456 .000 1.097986
1.419088

Estimates of Covariance Parametersa

Parameter
Repeated
Measures
Intercept +
[subject = id]

Variance

Std.
Estimate Error

Wald
Z

95%
Interval
Lower
Sig. Bound

.509743

7.575

.000 .393529

.660276

.909
.329
.

.363 .007749
.742 -.089396
.
.

.577897
.125429
.

.067295

time UN (1,1) .066919 .073609
UN (2,1) .018016 .054803
UN (2,2) .339816b .000000
Source: Own survey.

Confidence
Upper
Bound

In the case of both SMEs and family businesses, there is a growing trend
in the perception of challenges/problems by businesses between 2017 and
2020, and businesses have not deviated from this trend on an individual
level. This implies that in four years, the spectrum of problems faced by FBs
and SMEs has expanded, albeit slightly.
We do not have enough information to explain the causes of the change
either in the case of SMEs or FBs, as we could not fit the variables into the
OLS model in either case. Hence, a stochastic explanation of the probable
causes of the changes in the statistical sense cannot be given based on the
present database. Although, we consider it very important to point out that
there have been radical changes in perceptions over the past years. The
labor force can be seen as a growing problem. It is likely that this is closely
related to the labor shortage that developed as a result of high emigration.
An interesting development is the reduction of the problem of regulation. We
do not have a clear answer to the reasons for this, but between 2017 and
2020, businesses were not affected by a radical regulatory reorganization,
so it can be assumed that the reduction of the problem can be attributed to
the lack of this. The increase in grants as a problem is probably due to the
decrease in available grant resources. In Hungary, the policy of state
centralism can also be observed in the economic sector. All this means that
access to grants can be strongly linked to the closeness of relations with the
state. The growth of the problem can probably be traced back to the
appearance of this problem. The growth of profitability as a problem can also
be assumed to be linked to the central role of the state in the market. In
Hungary, in the period between 2017 and 2020, the state became an
unavoidable economic actor on the market. Orders for the state can be
characterized as having a strong market-distorting effect and have a strong
influence on which companies receive highly profitable orders and which are
forced out of this circle. Presumably as a result of this factor, businesses
assessed profitability as a growing problem.
Switching to the analysis of succession time series data, we also used
ANOVA as a test statistic, the results of which show that no significant
difference and linear trend can be detected in the two waves in the
succession variable for both SMEs and FBs (Tables 7 and 8 in the

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Appendix). Hence, the degree of presence of the succession strategy
between SMEs and FBs did not change between the two waves. This is also
supported by GCM-HLM analyses (Appendix Table 9)2.
For both SMEs and FBs, we analysed the possible explanations for the
interpretation of the results using OLS regression analysis. We built a total
of four models; two for 2017 data while the other two for 2020 data. For
SMEs, the explanatory power of the final model in 2017 was 41.1% (R2
.169), while for FBs, it was 43.4% (R2 .188). However, no statistically
significant model can be built on the 2020 data for either SMEs or FBs.
For SMEs, the final model for 2017 retained three variables that
demonstrate that the significance levels for the t-test are appropriate (.006,
.000, .000): the variable from information sources (Bs is .171), which shows
that more the news sources that the company is informed about, the more it
can be characterised by having a succession strategy; the variable of the
spouse as owner (Bs. 229), which shows that the higher the share of the
spouse in the company, the more it can be characterized by the SME
ownership strategy, and finally the variable of children as owners (Bs .261),
based on which it can be seen that the more owners of children in an SME,
the more it can be characterised by the succession strategy of the enterprise.
In the case of FBs, the final model retained the same three variables that
measured the orientation from the news source (Bs. 190), ownership of the
spouse (Bs. 248), and the number of children (Bs. 275).
Table 8: 2017 OLS regression t-values for SMEs and FBs
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
2.863
.070

Model
1 (Constant)
Orientation, how many
-.063
media sources
Owner's spouse as owner -.279
Owner's children as owner -.371

Standardized
Coefficients
Beta

t
Sig.
40.638 .000

.023

.171

-2.777 .005

.075
.088

.229
.261

-3.729 .000
-4.233 .000

Coefficientsa
Unstandardized
Coefficients
B
Std. Error
2.885
.074

Model
1 (Constant)
Orientation, how many
-.069
media sources
Owner's spouse as owner -.301
Owner's children as owner -.385

Standardized
Coefficients
Beta

t
Sig.
38.850 .000

.023

.190

-2.969 .003

.078
.090

.248
.275

-3.883 .000
-4.298 .000

2

The mean of the variable included in the GCM-HLM analysis for SMEs was 2.53 (+ - .039),
with an increase of .113 (+ - .055), but the value was not statistically significant (0.092). In the
case of FBs, an increase of .065 can be detected among the averages of the variable,
however, the change wasn’t significant (.303).

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Thus, it can be stated that the succession in 2017 can be explained by
similar variables for both SMEs and FBs. However, no significant model can
be established for 2020, and hence, our hypothesis cannot be proved.
As a summary of our results, we present the verification of our hypotheses
in Table 8.
Table 8: Summary of hypotheses
Hypothesis

Result

Verification method

H1

Not verified

RM ANOVA test and GCMHLM

H2

It cannot be
verified

OLS Regression

H3

Not verified

RM ANOVA test and GCMHLM

H4

Verified

OLS Regression

H5

Not verified

RM ANOVA test and GCMHLM

H6

It cannot be
verified

OLS Regression

H7

Verified

RM ANOVA test and GCMHLM

H8

It cannot be
verified

OLS Regression

Basis of verification
Pillai's Trace, p < .001
Wilks' Lambda, p < .001
Hotelling's Trace, p < .001
Roy's Largest Root, p < .001
Sphericity Assumed, p < .001
Greenhouse-Geisser, p < .00
Huynh-Feldt, p < .001
Sphericity Assumed, p < .001
F-érték, p < .001
t-érték, p < .005, p < .001
t-érték, p < .005, p < .001
Pillai's Trace, p < .001
Wilks' Lambda, p < .001
Hotelling's Trace, p < .001
Roy's Largest Root, p < .001
Sphericity Assumed, p < .001
Greenhouse-Geisser, p < .00
Huynh-Feldt, p < .001
Sphericity Assumed, p < .001
F-érték, p < .001
t-érték, p < .005, p < .001
t-érték, p < .005, p < .001
Pillai's Trace, p < .001
Wilks' Lambda, p < .001
Hotelling's Trace, p < .001
Roy's Largest Root, p < .001
Sphericity Assumed, p < .001
Greenhouse-Geisser, p < .00
Huynh-Feldt, p < .001
Sphericity Assumed, p < .001
F-érték, p < .001
t-érték, p < .005, p < .001
t-érték, p < .005, p < .001
Pillai's Trace, p < .001
Wilks' Lambda, p < .001
Hotelling's Trace, p < .001
Roy's Largest Root, p < .001
Sphericity Assumed, p < .001
Greenhouse-Geisser, p < .00
Huynh-Feldt, p < .001
Sphericity Assumed, p < .001
F-érték, p < .001
t-érték, p < .005, p < .001
t-érték, p < .005, p < .001

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Source: Own survey.

CONCLUSION
In this paper, we conducted a longitudinal survey of small and medium-sized
enterprises and family businesses in Hungary on a national, representative,
cross-sectional sample. The businesses were surveyed in two waves, in
2017 and 2020. For our analysis, we used GCM-HLM modelling, for which
we ran a repeated measures analysis of variance, as well as analysing the
data with the help of OLS regression.
Based on the literature, on the one hand, we examined what changes can
be measured between FBs and SMEs in the case of succession and
ownership and we supplemented the analysis with value of sales and
problem perception, which are important factors of an emerging market
according to the literature. Therefore, we sought to answer the question of
the extent to which enterprises and family businesses operating in the
Hungarian SME sector have changed over the past four years and whether
it is possible to find a difference between FBs and SMEs along these
dimensions.
The summary table of our trend results is summarised in Table 9, where
the increase of the trend is indicated with “+” signal, the decrease of the trend
is shown with a “-” signal, while the “0” represents the constant state of the
trend.
Table 9: Summary of trend movements
Segment
Subsegment
Ownership structure Privately or Family owned
Succession
Finance and market Sales revenue
Problems / Challenges
Source: Own survey.

SMEs
0
+
+

FBs
+
0
+
+

Our results demonstrate that Hungarian SMEs and family businesses
showed similar movements between 2017 and 2020 in several aspects.
However, an important difference in the ownership structure is that while the
decreasing trend of private ownership has taken place in the case of SMEs,
there has been an increasing takeover of family ownership in the case of
family businesses in the last four years. However, we do not have enough
data to explain either the 2017 or the 2020 results to give a statistically
reliable statement regarding the cause of this trend. Therefore, our results,
in line with the literature, support the fact that the ownership structure is an
important factor in distinguishing between FBs and SMEs.
The presence of the succession strategy has neither strengthened nor
decreased among either SMEs or FBs but has remained constant. We can
explain the succession strategy with the same variables for both SMEs and
FBs; however, we were unable to establish a statistically significant model
for the 2020 data. This allows us to conclude that in the case of the

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

succession strategy, contrary to the literature, we do not find a sharp dividing
line in the case of FBs and SMEs in the emerging markets. This result
deserves further research in which it would be worthwhile to include the
influence of norms and culture, which we were unable to address during this
paper.
In our research, we focused on sales value and the subjective perception
of problems. We did all this because both dimensions can be defined as
important factors in an emerging market. In the case of both Hungarian
SMEs and FBs, the share of annual sales increased between 2017 and
2020. Based on our data, the trend can be explained with the same drivers
for SMEs and FBs; however, sales in 2017 and 2020 will be determined by
other factors. In 2017, four variables had an impact on sales revenue,
exports (negative), number of employees, employee training, and
information from multiple news sources. In 2020, the fact that the company
and the high number of employees did not produce for export also had an
important explanatory power; however, the number of information channels
and training did not significantly explain the situation in 2020, whereas
subjective market perception played a significant role in the explanation. In
other words, the annual turnover of SMEs with a large number of employees
producing/providing services to the domestic market and a high number of
employees has increased in the last four years, which was not dependent on
the training of employees. By 2020, the use of various, diversified news
sources was not considered important.
Based on our results, it can also be seen that both in the case of
Hungarian SMEs and in the case of FBs, the number of problems and
challenges that affected their operation increased between 2017 and 2020.
However, we do not have enough data to give a statistically reliable
statement to explain the causes of this trend lines.
In the case of both factors, a smaller difference can be seen between FBs
and SMEs. However, no significant difference can be measured between the
two market entities.
This implies that family businesses and small and medium sized
businesses change in a very similar fashion in Hungary. Based on our data
the processes determining the SME sector, apply to the FB sector as well.
However, a difference can be measured in the ownership structure, which
leads to the conclusion that even in the case of the emerging market, it is
worth treating family businesses separately from the small and mediumsized business sector. In summary, it is worthwhile to carry out further
research in order to better understand and explore the characteristics of the
two sectors as well as their changes over time in the case of the emerging
market as well.
REFERENCES
Alder, A. G., Scher, S. J. (1994). Using growth curve analyses to assess personality change
and stability in adulthood. In T. F. Heatherton & J. L. Weinberger (Eds.), Can personality
change? (pp. 149–173). Washington, DC: American Psychological Association.

38

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Alshibani, S. M., Volery, T. (2021). Social support and life satisfaction among entrepreneurs:
a latent growth curve modelling approach. International Journal of Manpower.30(4), 44–56.
Anderson, R. C., Reeb, D. M. (2003). Founding-family ownership and firm performance:
Evidence from the S&P 500. Journal of Finance, 58(3), 1301–1327.
Blaydes, L, Chaney, E. (2013). The Feudal Revolution and Europe’s Rise: Political
Divergence of the Christian West and the Muslim World before 1500 CE. American Political
Science Review, 107(1), 16–34.
Box-Steffensmeier, J. M., Darmofal, D., Farrell, C. A. (2009). “The Aggregate Dynamics of
Campaigns.” Journal of Politics, 71(1), 309–23.
Bryk, A. S., Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing
change. Psychological Bulletin, 101, 147–158.
Bryk, A. S., Raudenbush, S. W. (1992). Hierarchical linear models in social and behavioral
research: Applications and data analysis methods (1st ed.). Newbury Park, CA: Sage
Publications.
Cadbury, A. (2000). Family firms and their governance: Creating tomorrow’s company from
today’s. London: Egon Zehnder.
Carlock, R. S., Ward, J. L. (2001). Strategic planning for the family business—Parallel
planning to unify the family and business. New York: Palgrave.
Clarke, H., Stewart, M., Ault, M., Elliott, E. (2005). Men, Women and the Political Economy of
Presidential Approval. British Journal of Political Science, 35(1), 31–51.
Cillessen, A. H., Borch, C. (2006). Developmental trajectories of adolescent popularity: A
growth curve modelling analysis. Journal of adolescence, 29(6), 935–959.
Dawar, N. D. N., Chattopadhyay, A. (2002). Rethinking marketing programs for emerging
markets. Long Range Planning, 35(5), 457–474.
Dunn, B. (1995). Success themes in Scottish family enterprises: Philosophies and practices
through the generations. Family Business Review, 8(1), 17–28.
Fattoum, S., Fayolle, A. (2009). Generational succession: Examples from Tunisian family
firms. Journal of Enterprising Culture, 17(2), 127–145.
Hacker, J., Dowling, M. (2012). Succession in family firms: How to improve family satisfaction
and family harmony. International Journal of Entrepreneurship and Small Business, 15(1),
76–99.
Helson, R., Jones, C., Kwan, V. S. (2002). Personality change over 40 years of adulthood:
Hierarchical linear modeling analyses of two longitudinal samples. Journal of personality
and social psychology, 83(3), 752.
Heras-Rosas, C., Herrera, J. (2020). Family Firms and Sustainability. A Longitudinal
Analysis. Sustainability, 12(13), 5477.
Hisrich, R. D., Fülöp, G. (1997). Women entrepreneurs in family business: The Hungarian
case. Family Business Review, 10(3), 281–302.
Hofmann, D. A. (1997). An overview of the logic & rationale of hierarchical linear models.
Journal of Management, 23(6), 723.
Hopkins, D. J. (2012). Whose Economy? Perceptions of National Economic Performance
during Unequal Growth. Public Opinion Quarterly, 76(1), 50–71.
Hoy, F., Sharma, P. (2010). Entrepreneurial family firms. Upper Saddle River, NJ: Pearson
Prentice Hall.
Jerit, J., Barabas, J. (2012). Partisan Perceptual Bias and the Information Environment.
Journal of Politics, 74(3), 672–84.
Jorissen, A., Laveren, E., Martens, R., & Reheul, A.-M. (2005). Real versus sample-based
differences in comparative family business research. Family Business Review, 18(3), 229–
245.
Kellermanns, W. F., Eddleston, K. A., Barnett, T., Pearson, A. (2008). An exploratory study
of family member characteristics and involvement: Effects on entrepreneurial behavior in
the family firm. Family Business Review, 21(1), 1–14.
Kuratko, D. K., Hodgetts, R. M. (2004). Entrepreneurship: Theory, process & practice (6th
ed.). Mason, OH: Thomson South-Western.
Lebo, M. J., McGlynn, A. J., Koger, G. (2007). Strategic Party Government:Party Influence in
Congress, 1789–2000. American Journal of Political Science, 51(3), 464–81.
Lebo, M. J., Weber, C. (2015). An effective approach to the repeated cross‐sectional
design. American Journal of Political Science, 59(1), 242–258.

39

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Lindenberger, U., Ghisletta, P. (2004). Modeling longitudinal changes in old age: From covariance structures to dynamic systems. In R. Dixon, L. Backman, & L.-G. Nilsson (Eds.),
New frontiers in cognitive aging (pp. 199–216). Oxford, UK: Oxford University Press.
Magnusson, P., Westjohn, S. A., Zdravkovic, S. (2011). What? I thought Samsung was
Japanese: Accurate or not, perceived country of origin matters. International Marketing
Review, 28(5), 454–472.
Magretta, J. (1998). Governing the family-owned enterprise: An interview with Finland’s
Krister Ahlström. Harvard Business Review, January/February, 113–123.
Mandl, I. (2008). Overview of family business relevant issues. Vienna: Austrian Institute for
SME Research.
Matthews, C. H., Moore, T. W., Fialko, A. S. (1999). Succession in the family firm: A cognitive
categorization perspective. Family Business Review, 12(2), 159–169.
Mazzarol, T. (2006). Small business management: An applied approach. Prahran, VIC: Tilde
University Press.
Miller, D., Le Breton-Miller, I. (2005). Managing for the long run: Lessons in competitive
advantage from great family businesses. Boston, MA: Harvard Business School Press.
Miller, D., Le Breton-Miller, I., Lester, R. H. (2010). Family ownership and acquisition behavior
in publicly-traded companies. Strategic Management Journal, 31(2), 201–223.
Moy, P., Xenos, M., Hess, V. K. (2006). Priming Effects of Late-Night Comedy. International
Journal of Public Opinion Research, 18(2), 198–210.
Osgood, D. W., Smith, G. L. (1995). Applying hierarchical linear modeling to extended
longitudinal evaluations: The Boys Town follow-up study. Evaluation Review, 19, 3–38.
Ozkaya, H. E., Dabas, C., Kolev, K., Hult, G. T. M., Dahlquist, S. H., Manjeshwar, S. A. (2013).
An assessment of hierarchical linear modeling in international business, management, and
marketing. International Business Review, 22(4), 663–677.
Preacher, K., Wichman, A. L., MacCallum, R. C. Briggs, N. E. (2008). Latent Growth Curve
Modeling. Sage Publications, California.
Ramadani, V., Fayolle, A., Gerguri, S., Aliu, E. (2013). The succession issues in family firms:
Evidence from Macedonia. In 5th E-LAB international symposium of entrepreneurship on
family entrepreneurship: A new field of research, EM Lyon Business School, Lyon, France.
Raudenbush, S. W., Bryk, A. S. (2002). Hierarchical linear models: Applications and data
analysis methods (2nd ed.). Sage Publications, California.
Rogosa, D., Saner, H. (1995). Longitudinal data analysis examples with random coefficient
models. Journal of Educational and Behavioral Statistics, 20, 149–170.
Romer, D. (2006). Capturing Campaign Dynamics, 2000 and 2004: The National Annenberg
Election Survey. Philadelphia: University of Pennsylvania Press.
Segal, J. A., Spaeth, H. J. (2002). The Supreme Court and the Attitudinal Model Revisited.
New York: Cambridge University Press.
Sharma, P., Chua, J. H., Chrisman, J. J. (2000). Perceptions about the extent of succession
planning in Canadian family firms. Canadian Journal of Administrative Sciences, 17(3),
233–244.
Singer, J. D., Willett, J. B. (2003). Applied longitudinal data analysis. Oxford, UK: Oxford
University Press.
Snijders, T. A. B., Bosker, R. (1999). Multilevel analysis. London: Sage.
Stroud, N. (2008). Media Use and Political Predispositions: Revisiting the Concept of
Selective Exposure. Political Behavior, 30(3), 341–66.
Wood, B. D. (2009). The Myth of Presidential Representation. New York: Cambridge
University Press.
Wu, Y. W. B. (1996). An application of hierarchical linear methods to longitudinal
studies. Research in nursing & health, 19(1), 75-82.
Zahra, S. A. (2003). International expansion of U.S. manufacturing family businesses: The
effect of ownership and involvement. Journal of Business Venturing, 18(4), 495–512.

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APPENDIX
Table 1: ANOVA test of SME ownership structure
Descriptive Statistics
Mean Std. Deviation N
Magántuljadon_arány_2017 91.80 24.566
263
Magántuljadon_arány_2020 77.29 33.772
263
Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.114
.886
.129
.129

Partial
Eta
Hypothesi
Square
F
s df
Error df Sig. d
33.855
262.00 .00
1.000
.114
b
0
0
33.855
262.00 .00
1.000
.114
b
0
0
33.855
262.00 .00
1.000
.114
b
0
0
33.855
b

262.00
0

1.000

.00
0

.114

Noncent.
Paramete Observe
r
d Powerc
33.855

1.000

33.855

1.000

33.855

1.000

33.855

1.000

Tests of Within-Subjects Effects

Source
time

Error(ti
me)

Sphericity
Assumed
Greenhou
seGeisser
HuynhFeldt
Lowerbound
Sphericity
Assumed
Greenhou
seGeisser
HuynhFeldt
Lowerbound

Type III
Sum of
Squares df
27680.93
1
5

Mean
Square F
27680.9 33.8
35
55

Partial
Eta
Sig Squar
.
ed
.00
.114
0

27680.93
1.000
5

27680.9 33.8
35
55

.00
.114
0

27680.93
1.000
5
27680.93
1.000
5
214217.0
262
79

27680.9
35
27680.9
35

.00
.114
0
.00
.114
0

214217.0 262.0
79
00
214217.0
79
214217.0
79

262.0
00
262.0
00

33.8
55
33.8
55

Noncent
.
Observ
Parame ed
ter
Powera
33.855

1.000

33.855

1.000

33.855

1.000

33.855

1.000

817.622
817.622
817.622
817.622

Tests of Within-Subjects Contrasts

Source

time

Type
III
Sum
of
Squares
df

Mean
Square

F

Partial
Eta
Noncent. Observe
Square Paramet d
Sig. d
er
Powera

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

time
Error(tim
e)

Linea
27680.935 1
r
Linea 214217.07 26
r
9
2

27680.93 33.85 .00
5
5
0

.114

33.855

1.000

817.622

Table 2: ANOVA test of FB ownership structure
Descriptive Statistics
Mean Std. Deviation N
Family ownership rate 2017 85.88 25.874
199
Family ownership rate 2020 94.53 15.491
199

Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.073
.927
.079
.079

Partial
Eta
Hypothesi
Square
F
s df
Error df Sig. d
15.702
198.00 .00
1.000
.073
b
0
0
15.702
198.00 .00
1.000
.073
b
0
0
15.702
198.00 .00
1.000
.073
b
0
0
15.702
b

1.000

198.00
0

.00
0

.073

Noncent.
Paramete Observe
r
d Powerc
15.702

.976

15.702

.976

15.702

.976

15.702

.976

Tests of Within-Subjects Effects

Type III
Sum of
Source
Squares
time
Sphericity 7450.46
Assumed 2
Greenhous 7450.46
e-Geisser 2
Huynh7450.46
Feldt
2
Lower7450.46
bound
2
Error(tim Sphericity 93949.5
e)
Assumed 38
Greenhou
93949.5
se38
Geisser
Huynh93949.5
Feldt
38
Lower93949.5
bound
38

df
1
1.000
1.000
1.000
198

Mean
Square
7450.4
62
7450.4
62
7450.4
62
7450.4
62
474.49
3

198.0
00

474.49
3

198.0
00
198.0
00

474.49
3
474.49
3

F
15.7
02
15.7
02
15.7
02
15.7
02

Partial
Eta
Sig Squar
.
ed
.00
.073
0
.00
.073
0
.00
.073
0
.00
.073
0

Noncent
.
Observ
Paramet ed
er
Powera
15.702

.976

15.702

.976

15.702

.976

15.702

.976

Tests of Within-Subjects Contrasts

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Source
time

Type III
Sum
of
Squares df

time
Linea
7450.462 1
r
Error(time Linea 93949.53 19
)
r
8
8

Mean
Square F
7450.46 15.70
2
2

Partial
Eta
Square
Sig. d
.00
.073
0

Noncent.
Paramete Observe
r
d Powera
15.702

.976

474.493

Table 3: SME Sales ANOVA Test
Descriptive Statistics
Mean Std. Deviation N
Sales revenue 2017 2.24 1.251
263
Sales revenue 2020 3.259 1.1858
263

Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.257
.743
.346
.346

Partial
Eta
Hypothesi
Square
F
s df
Error df Sig. d
90.696
262.00 .00
1.000
.257
b
0
0
90.696
262.00 .00
1.000
.257
b
0
0
90.696
262.00 .00
1.000
.257
b
0
0
90.696
b

1.000

262.00
0

.00
0

.257

Noncent.
Paramete Observe
r
d Powerc
90.696

1.000

90.696

1.000

90.696

1.000

90.696

1.000

Tests of Within-Subjects Effects
Type
III Sum
of
Squar
Source
es
time
Sphericity 136.54
Assumed
8
Greenhous 136.54
e-Geisser 8
Huynh136.54
Feldt
8
Lower136.54
bound
8
Error(tim Sphericity 394.45
e)
Assumed
2
Greenhous 394.45
e-Geisser 2
Huynh394.45
Feldt
2
Lower394.45
bound
2

df
1
1.000
1.000
1.000
262

Mean
Squar
e
136.54
8
136.54
8
136.54
8
136.54
8

F
90.69
6
90.69
6
90.69
6
90.69
6

Partial
Eta
Sig Squar
.
ed
.00
.257
0
.00
.257
0
.00
.257
0
.00
.257
0

Noncent
.
Observ
Paramet ed
er
Powera
90.696

1.000

90.696

1.000

90.696

1.000

90.696

1.000

1.506

262.00
1.506
0
262.00
1.506
0
262.00
1.506
0

Tests of Within-Subjects Contrasts

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Source
time

Type III
Sum of
Square
s
df

Mean
Square
136.54
8

time
Linea
136.548 1
r
Error(time Linea
26
394.452
)
r
2

F
90.69
6

Partial
Eta
Square
Sig. d
.00
.257
0

Noncent.
Paramete Observe
r
d Powera
90.696

1.000

1.506

Table 4: FB Revenue ANOVA Test
Descriptive Statistics
Mean Std. Deviation N
Sales revenue 2017 2.17 1.235
205
Sales revenue 2020 3.200 1.1350
205
Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.258
.742
.347
.347

Partial
Eta
Hypothesi
Square
F
s df
Error df Sig. d
70.793
204.00 .00
1.000
.258
b
0
0
70.793
204.00 .00
1.000
.258
b
0
0
70.793
204.00 .00
1.000
.258
b
0
0
70.793
b

1.000

Tests of Within-Subjects Effects
Type
III Sum
of
Squar
Source
es
df
time
Sphericity 108.58
1
Assumed
8
Greenhous 108.58
1.000
e-Geisser 8
Huynh108.58
1.000
Feldt
8
Lower108.58
1.000
bound
8
Error(tim Sphericity 312.91
204
e)
Assumed
2
Greenhous 312.91 204.00
e-Geisser 2
0
Huynh312.91 204.00
Feldt
2
0
Lower312.91 204.00
bound
2
0

204.00
0

Mean
Squar
e
108.58
8
108.58
8
108.58
8
108.58
8

.00
0

F
70.79
3
70.79
3
70.79
3
70.79
3

.258

Partial
Eta
Sig Squar
.
ed
.00
.258
0
.00
.258
0
.00
.258
0
.00
.258
0

Noncent.
Paramete Observe
r
d Powerc
70.793

1.000

70.793

1.000

70.793

1.000

70.793

1.000

Noncent
.
Observ
Paramet ed
er
Powera
70.793

1.000

70.793

1.000

70.793

1.000

70.793

1.000

1.534
1.534
1.534
1.534

Tests of Within-Subjects Contrasts

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Source
time

Type III
Sum of
Square
s
df

time
Linea
108.588 1
r
Error(time Linea
20
312.912
)
r
4

Mean
Square
108.58
8

F
70.79
3

Partial
Eta
Square
Sig. d
.00
.258
0

Noncent.
Paramete Observe
r
d Powera
70.793

1.000

1.534

Table 5: ANOVA test of SME problems / challenges
Descriptive Statistics
Mean Std. Deviation N
How many problems 2017 .89
.741
263
How many problems 2020 2.21 .964
263

Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.555
.445
1.24
7
1.24
7

Hypothesi
F
s df
Error df
326.690
262.00
1.000
b
0
326.690
262.00
1.000
b
0
326.690
262.00
1.000
b
0
326.690
b

262.00 .00
0
0

1.000

Tests of Within-Subjects Effects
Type
III Sum
of
Squar
Source
es
df
time
Sphericity 228.91
1
Assumed 4
Greenhou 228.91
1.000
se-Geisser 4
Huynh228.91
1.000
Feldt
4
Lower228.91
1.000
bound
4
Error(tim Sphericity 183.58
262
e)
Assumed 6
Greenhou 183.58 262.0
se-Geisser 6
00
Huynh183.58 262.0
Feldt
6
00
Lower183.58 262.0
bound
6
00

Partial
Eta
Noncent.
Square Paramet
Sig. d
er
.00
.555
326.690
0
.00
.555
326.690
0
.00
.555
326.690
0

Mean
Squar
e
228.9
14
228.9
14
228.9
14
228.9
14

F
326.6
90
326.6
90
326.6
90
326.6
90

.555

Partial
Eta
Sig Squar
.
ed
.00
.555
0
.00
.555
0
.00
.555
0
.00
.555
0

326.690

Observe
d Powerc
1.000
1.000
1.000
1.000

Noncent
.
Observ
Paramet ed
er
Powera
326.690

1.000

326.690

1.000

326.690

1.000

326.690

1.000

.701
.701
.701
.701

Tests of Within-Subjects Contrasts

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Type III
Sum of
Square
Source
time s
time
Linea 228.91
r
4
Error(time Linea 183.58
)
r
6

df
1
26
2

Mean
Square F
228.91 326.69
4
0

Partial
Eta
Square
Sig. d
.00
.555
0

Noncent.
Paramete Observe
r
d Powera
326.690

1.000

.701

Table 6. ANOVA test of FB problems / challenges
Descriptive Statistics
Mean Std. Deviation N
How many problems 2017 .89
.759
205
How many problems 2020 2.15 .976
205
Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.539
.461
1.17
1
1.17
1

Hypothesi
F
s df
Error df
238.874
204.00
1.000
b
0
238.874
204.00
1.000
b
0
238.874
204.00
1.000
b
0
238.874
b

204.00 .00
0
0

1.000

Tests of Within-Subjects Effects
Type
III Sum
of
Squar
Source
es
df
time
Sphericity 162.35
1
Assumed 1
Greenhou 162.35
1.000
se-Geisser 1
Huynh162.35
1.000
Feldt
1
Lower162.35
1.000
bound
1
Error(tim Sphericity 138.64
204
e)
Assumed 9
Greenhou 138.64 204.0
se-Geisser 9
00
Huynh138.64 204.0
Feldt
9
00
Lower138.64 204.0
bound
9
00

Partial
Eta
Noncent.
Square Paramet
Sig. d
er
.00
.539
238.874
0
.00
.539
238.874
0
.00
.539
238.874
0

Mean
Squar
e
162.3
51
162.3
51
162.3
51
162.3
51

F
238.8
74
238.8
74
238.8
74
238.8
74

.539

Partial
Eta
Sig Squar
.
ed
.00
.539
0
.00
.539
0
.00
.539
0
.00
.539
0

238.874

Observe
d Powerc
1.000
1.000
1.000
1.000

Noncent
.
Observ
Paramet ed
er
Powera
238.874

1.000

238.874

1.000

238.874

1.000

238.874

1.000

.680
.680
.680
.680

Tests of Within-Subjects Contrasts

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Type III
Sum of
Square
Source
time s
time
Linea 162.35
r
1
Error(time Linea 138.64
)
r
9

Mean
Square F
162.35 238.87
1
4

df
1
20
4

Partial
Eta
Square
Sig. d
.00
.539
0

Noncent.
Paramete Observe
r
d Powera
238.874

1.000

.680

Table 7: ANOVA test for the succession variable for SMEs
Descriptive Statistics
Mean Std. Deviation N
Your company has a succession strategy 2017 2.53 .591
224
Your company has a succession strategy 2020 2.585 .6907
224
Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.004
.996
.004
.004

F
.933
b

.933
b

.933
b

.933
b

Hypothesi
s df
1.000
1.000
1.000
1.000

Tests of Within-Subjects Effects
Type III
Sum of
Square
Source
s
df
time
Sphericity
.377
1
Assumed
Greenhous
.377
1.000
e-Geisser
Huynh.377
1.000
Feldt
Lower.377
1.000
bound
Error(tim Sphericity
90.123 223
e)
Assumed
Greenhous
223.00
90.123
e-Geisser
0
Huynh223.00
90.123
Feldt
0
Lower223.00
90.123
bound
0

Partial
Eta
Square
Error df Sig. d
223.00 .33
.004
0
5
223.00 .33
.004
0
5
223.00 .33
.004
0
5
223.00
0

.33
5

Mean
Squar
e
F
.93
.377
3
.93
.377
3
.93
.377
3
.93
.377
3

.004

Noncent.
Paramete Observe
r
d Powerc
.933

.161

.933

.161

.933

.161

.933

.161

Partial
Eta
Noncent. Observe
Sig Square Paramet d
.
d
er
Powera
.33
.004
.933
.161
5
.33
.004
.933
.161
5
.33
.004
.933
.161
5
.33
.004
.933
.161
5

.404
.404
.404
.404

Tests of Within-Subjects Contrasts
Type III
Sum of
Mean
Source
time
Squares df
Square F

Partial
Eta
Noncent.
Observed
Sig. Squared Parameter Powera

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

time
Linear .377
Error(time) Linear 90.123

1
.377
223 .404

.933 .335 .004

.933

.161

Table 8. ANOVA test for the succession variable of FBs
Descriptive Statistics
Mean Std. Deviation N
Your company has a succession strategy 2017 2.51 .592
202
Your company has a succession strategy 2020 2.569 .6966
202

Multivariate Testsa

Effect
tim Pillai's
e
Trace
Wilks'
Lambda
Hotelling'
s Trace
Roy's
Largest
Root

Valu
e
.004
.996
.004
.004

F
.724
b

.724
b

.724
b

.724
b

Hypothesi
s df
1.000
1.000
1.000
1.000

Tests of Within-Subjects Effects
Type III
Sum of
Square
Source
s
df
time
Sphericity
.300
1
Assumed
Greenhous
.300
1.000
e-Geisser
Huynh.300
1.000
Feldt
Lower.300
1.000
bound
Error(tim Sphericity
83.200 201
e)
Assumed
Greenhous
201.00
83.200
e-Geisser
0
Huynh201.00
83.200
Feldt
0
Lower201.00
83.200
bound
0

Partial
Eta
Square
Error df Sig. d
201.00 .39
.004
0
6
201.00 .39
.004
0
6
201.00 .39
.004
0
6
201.00
0

.39
6

Mean
Squar
e
F
.72
.300
4
.72
.300
4
.72
.300
4
.72
.300
4

.004

Noncent.
Paramete Observe
r
d Powerc
.724

.135

.724

.135

.724

.135

.724

.135

Partial
Eta
Noncent. Observe
Sig Square Paramet d
.
d
er
Powera
.39
.004
.724
.135
6
.39
.004
.724
.135
6
.39
.004
.724
.135
6
.39
.004
.724
.135
6

.414
.414
.414
.414

Tests of Within-Subjects Contrasts
Type III
Partial
Sum of
Mean
Eta
Noncent.
Observed
Source
time
Squares df
Square F
Sig. Squared Parameter Powera
time
Linear .300
1
.300
.724 .396 .004
.724
.135
Error(time) Linear 83.200
201 .414

Table 9: GCM-HLM results for SMEs and FBs for succession

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 2.532448 .039231 225.100 64.553 .000 2.455142
2.609755
time
.113940 .055882 264.128 2.039 .092 .003908
.223971
Estimates of Covariance Parametersa

Parameter
Repeated
Measures
Intercept +
[subject = id]

Variance

Std.
Estimate Error

Wald
Z

95%
Interval
Lower
Sig. Bound

.296575

8.627

.078 .236301

.034378

time UN (1,1) .051307 .035426
UN (2,1) -.037980 .025358
UN (2,2) .171262b .000000

1.448 .148 .013257
-1.498 .134 -.087681
.
.
.

Confidence

.372224

Upper
Bound

.198564
.011721
.

Estimates of Fixed Effectsa
95% Confidence Interval
Parameter Estimate Std. Error df
t
Sig. Lower Bound Upper Bound
Intercept 2.519651 .041383 203.002 60.886 .000 2.438055
2.601247
time
.065715 .063576 204.138 1.034 .303 -.059636
.191065
Estimates of Covariance Parametersa

Parameter
Repeated
Measures
Intercept +
[subject = id]

Std.
Estimate Error

Wald
Z

95%
Interval
Lower
Sig. Bound

.319285

.040972

7.793

.000 .248284

.410591

time UN (1,1) .030081 .041840
UN (2,1) -.019442 .030164
UN (2,2) .188322b .000000

.719
-.645
.

.472 .001970
.519 -.078564
.
.

.459449
.039679
.

Variance

Confidence
Upper
Bound

49

EXTENDING TAM FOR INFORMATION SYSTEMS TO
ACCEPTANCE RESEARCH/MODEL OF CONSUMER
GOODS (CGAM): A THEORETICAL APPROACH
Katrin Förster*
Zeppelin University,
Germany
k.foerster@zeppelin-university.net

Prof. Dr. habil. W.H. Schulz
Zeppelin University,
Germany
wolfgang.schulz@zu.de

Abstract
The aim is to broaden the Technology Acceptance Model (TAM) application
to consumer goods and to conceptualise the Consumer Good Acceptance
Model (CGAM) based on TAM while keeping its core elements and
relationships as they were initially presented. Therefore, structural elements
of the TAM were reviewed, critical statements of the usage of TAM and how
it was constructed were considered to extend the TAM while eliminating
flaws and creating value for manufacturers. The conceptualised CGAM is
theoretically well supported. The framework upon which the TAM is created
allows to adjust it to any acceptance process no matter which good it is. On
condition that the definitions of the core elements of the TAM are adjusted,
and relevant external factors for the consumer good are introduced. This
study provides a new approach to widen the application area of the TAM by
extending it to the acceptance of consumer goods.

Key Words
Technology Acceptance Model; consumer economics; consumer behaviour;
consumer acceptance; behavioural intention.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

INTRODUCTION
The TAM is a widely applied model for technology-related acceptance
research. It is an essential tool for researchers since acceptance can be
seen as an influential variable for the successful introduction of new
technology or products, as well as for its intended use (van der Laan, Heino
& De Waard, 1997). Considering that people are “fundamentally decision
makers” (Saaty, 2008, p.83) signifies that every action is eventually the result
of a decision, consciously or unconsciously made (Nelson, 1970; Saaty,
2008). Consequently, every consumption decision is linked to an evaluative
process that finally leads to a decision and thus to accepting and using
chosen services or products (Adell, 2009; Bettmann, Johnson & Payne,
1991). This implies that acceptance is not only relevant in the context of
technological products or IS but also for any consumer good designed to be
bought by people.
The phenomenon of acceptance has therefore not only been an important
topic in the field of IS and technology research (Benbasat & Barki, 2007) but
also for consumer research and marketing studies (Gefen, Karahanna &
Straub, 2003; Kollat, Engel & Blackwell, 1970; Pikkarainen et al., 2004; Siró
et al., 2008). Contemplating the nature of decision making and the part this
process plays in any behaviour, it is clear that the decision to use or buy
something can ultimately lead to not only accepting a technology, but also a
product or service. Therefore, relating it to the TAM and the fact that TAM
has been used to study consumer acceptance in various settings (McCoy,
Galletta & King, 2007; Roy, 2017; Childers, Carr, Peck & Carson, 2001) and
is well supported and accepted, it is logical to presume its successful
extension to non-technological consumer goods. Thus adjusting the TAM
would allow to forecast the acceptance of goods while assessing the
influence of culture, social influence and product characteristics.
This presumption proved to be accurate, based on the TAM's theoretical
conceptualisation, showing a modified TAM, now called CGAM, is applicable
for consumer goods. Consequently, the attempt to adapt the TAM for
consumer goods is not only reasonable but also achievable and creates
value for manufacturers.
WHAT IS ACCEPTANCE?
A common element in acceptance research is that there is no coherent and
generally accepted definition of the term or how to measure acceptance
(Lucke, 1995; Quiring, 2006). However, a consensus exists about the
common and scientific use of the term, where its synonyms are “approval”,
“diffusion”, and especially “adoption” (Lucke, 1995; Williams et al., 2009). A
distinction between “acceptance” and “adoption” was made by Kollmann
(2004) and by Renaud and Van Biljon (2008), describing “adoption” more
like a process, and “acceptance” more like an attitude towards a technology
playing an important role after the actual purchase. Considering the work of
Adell (2009) and Kollmann (1998), acceptance is logically seen as a process,

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

and “adoption” and “acceptance” are used interchangeably. Especially when
focusing on the acceptance of a consumer good, Adell’s (2009, p. 27) fourth
criterion for acceptance appears to fit well applying it to consumer goods,
namely: “This definition of acceptance aims for a behavioral change and may
be seen as being based on the earlier categories, in that the will to use a
system is based on drivers’ assessment of the usefulness of the system (as
in category 2) as well as all other attitudes to the system and its effects (as
in category 3).“ This acknowledges that a product cannot be accepted or
declined without the consumer being aware of it, which presupposes that the
decision to purchase, or at least sample, the product has already been made.
This conception is coherent with the work of Adell (2009) and Kollmann
(1998, 2004).
Additionally, researchers agree on one common factor in the acceptance
process: that an individual’s judgment affects acceptance and acceptability.
Regan et al. (2002, p. 10) states that “While everyone seems to know what
acceptability is, and all agree that acceptability is important, there is no
consistency across studies as to what ’acceptability’ is and how to measure
it.”
Considering the statement by Regan et al. (2002), it becomes apparent
that increasing knowledge about how and by what mechanism the
acceptance of consumer goods is influenced, will not only help to improve
acceptance but also provide valuable information for manufacturers as to
how a product should be modified, and positioned in the market. Additionally,
more profound knowledge may be gained of the nature of “acceptability” and
how it could best be measured.
TAM: WHY IT SHOULD BE USED TO PREDICT CONSUMER
ACCEPTANCE OF NON-TECHNOLOGICAL CONSUMER GOODS
The TAM developed by Venkatesh and Bala (2008), henceforth named TAM
III, is an extension of the TAM originally devised by Davis (1989), and further
developed as a TAM progression by Venkatesh and Davis (2000), herein
referred to as TAM II. The original TAM was not only extended over the years
through progressive research but also adapted to incorporate critical
comment by Adams, Nelson and Todd (1992) and Davis and Venkatesh
(1996).
Davis (1989) designed the original TAM to explain and predict the
behaviour of technology users. Further, he aimed to detect factors that
influenced the acceptance or rejection process. In order to do so, Davis used
the Theory of Reasoned Action (TRA) to connect the five factors: system
design features, perceived usefulness (PU), perceived ease of use (PEOU),
attitude toward using and the actual system use (Davis, 1989; Davis,
Bagozzi & Warshaw, 1989). However, the model’s core elements PU and
PEOU are influenced by external variables (Arnold & Klee, 2016). These two
factors are relevant in order to control the beliefs users have about a system.
The model can also be used to foreshadow the behavioural intentions of
prospective users and their actual system use (Davis, 1989; Davis, Bagozzi

52

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

& Warshaw, 1989). TAM was criticised as a subjective norm was not
included as an influencing factor (Arnold & Klee, 2016). This deficiency was
eliminated by Venkatesh and Davis in 2000 when they aimed for
explanations of PU and usage intentions based on social influence and
cognitive instrumental processes. Subjective norm, voluntariness and
image, define the social influence processes, implying “the ways other
people affect one’s beliefs, feelings, and behaviour” (Mason, Conrey &
Smith, 2007, p. 279; Venkatesh & Davis, 2000, p. 187). Cognitive
instrumental processes are defined by job relevance, output quality,
demonstrable results and perceived ease of use. By specifying the external
variables influencing PU and behavioural intentions, Venkatesh and Davis
(2000) created the TAM II.
Further development of TAM II towards the formulation of TAM III by
Venkatesh and Bala (2008) is based on the Venkatesh (2000) model of
determinants affecting PEOU. Venkatesh subsequently addressed the
criticism that there was a lack of understanding about the determinants
influencing one of the key drivers of acceptance (Taylor & Todd, 1995). The
determinants can be divided into two categories: anchor and adjustment
variables. The anchor variables, namely computer self-efficacy, perceptions
of external control, computer anxiety and computer playfulness, are based
on initial judgment and will be adjusted over time with the accumulation of
experience. The evolution of the original TAM towards a more
comprehensive and precise TAM III reflects the four components PU, PEOU,
behavioural intention and use behaviour (from TAM I), and the moderating
variable, voluntariness, and the variable, experience influenced by time
(introduced in TAM II), all being considered core elements of the model.
Additionally, the determinants of PU and PEOU are invariably adjusted
whenever the product of interest changes (Pikkarainen et al., 2004;
Venkatesh & Bala, 2008).
Limitations as Advocates
That aside, the focus of this conceptual research approach is on TAM III, as
the most advanced version of the TAM. Besides the already discussed
limitations, which Venkatesh, Davis and Bala have eliminated, current
research lists the following aspects as critical.
Despite being a vastly accepted and widely used source for researchers
TAM has been subject to criticism for leading research in new directions
without fully understanding its antecedents (Benbasat & Barki, 2007).
Despite this discussion being of great relevance in general, it is believed
subordinate to the interests of this particular research at the present time.
This belief arises because it is not the structure that is considered weak, but
the restricted scope of research about its elements. Goodhue (2007) argues
that TAM only reveals the factors influencing technology usage without
capturing how technology affects user performance. Aiming to extend TAM
to consumer goods, the effects on user performance are somewhat
irrelevant, as the purpose of consumer goods is to satisfy needs, considering
the four categories of consumer goods classification (Bucklin, 1963;

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Holbrook & Howard, 1977; Holton, 1958; Luck, 1959), and focuses less on
enhancing performances.
Price and cost have not been addressed in TAM or its extension, which
is not necessarily critical as the TAM was mainly applied in workplace
settings, where price and cost are more relevant for the firm. However, in the
market place for consumer goods, price and costs are of great relevance as
they can have an immense impact on the decision to accept and consume a
good (Brown & Venkatesh, 2005; Coulter & Coulter, 2007; Lunceford, 2009).
This suggests the relative unimportance of time and effort as sole drivers for
the acceptance process (Venkatesh, Thong and Xu, 2012).
Additionally, voluntariness as a moderating variable is a theoretically wellsupported TAM element, including the individual utilization of innovation. A
voluntary usage setting of Information Technology (IT) usage in a working
environment is an arguable inclusion as such decisions are mostly based on
top-down directives. Again, this “weakness” in TAM can be utilized to support
its adaptation for consumer goods because in that context, consumers
usually do have a choice whether or not to purchase and use a product.
Consequently, this reservation does not apply to TAM in a consumer good
setting, presuming that usage or purchase behaviour is voluntary. In contrast
to voluntariness, social influencing factors that might not have a significant
impact in the working environment, are of greater relevance for consumer
good acceptance (Ang, Ramayah & Amin, 2015; Malatji, van Eck & Zuva,
2020; Shan & King, 2015).
Theoretical Backup
So far, considering why TAM should be used to predict consumer
acceptance of non-technological consumer goods, has been partially
answered by using criticism of the model as advocacy for its extension
towards consumer goods.
Regarding the theoretical background of TAM there are other aspects
supporting the extension.
The initial TAM by Davis (1989) was built upon two theories that originated
in the field of social psychology, the TRA by Ajzen and Fishbein (1980) and
the Theory of Planned Behaviour (TPB) by Ajzen (1991). TRA is based on
the precept that humans normally behave in a reasonable way, implying that
information, both explicit and implicit, are considered. Similar to the process
of acceptance, the theory is based on a causal chain; thus, different factors
lead to specific behaviour. The enhancement by perceived behavioural
control and consequently the development of the TPB allows, in contrast to
the TRA, the inclusion of all behaviour – under full volitional control or not.
TPB is the more suitable model to understand and predict human behaviour
as the performance of a behaviour is more likely when its evaluation is
positive, social pressure to perform the behaviour occurs, and people think
they can do so or have the opportunity to do so (Ajzen, 2005). Given the
origin of TAM, a modification of the TAM is a reasonable objective when
creating an applicable model for the consumer goods market. This can be
based on the relation of those theories with acceptance and consumer

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Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

behaviour. Accordingly, it can be assumed that the TAM would be applicable
in testing the acceptance of any consumer good.
Literature research showed various applications and enhancements of
TAM, focusing on different IS and applying new external variables (Lee,
Kozar & Larsen, 2003). Additionally, research has taken TAM out of the
working environment context to successfully apply it to non-organizational
settings (Agarwal & Karahanna, 2000; Davis, Bagozzi & Warshaw, 1989,
1992; Mathieson, 1991; Szajna, 1994), supporting the research attempt to
open up TAM for consumer goods.
Considering that new processes and constructs were included in closing
knowledge gaps regarding human decision-making processes and
behaviour, the TAM has a profound theoretical foundation. This makes it
transferable to most consumer behaviour-related research, as the model
well covers human behaviour and decision-making processes. Thus,
changing its application area from technology applications to consumer
goods, in general, is theoretically well supported. Moreover, as discussed
above, several points of criticism are advantageous for the extension of TAM
into consumer goods. This research hypothesises that an extension of TAM
for consumer goods is possible while assuming all effects are unaltered.
Despite having argued that an extension of TAM for consumer goods is
possible, the question of need and contribution remains. Several models are
well-established in the field of consumer behaviour and marketing research.
Table 1 shows some of these models, including their deficiencies and why
the conceptualized CGAM is the better alternative. Additionally, it can be
pointed out that some of these models are considered ‘grand models’ due to
their large scope (Kassarjian 1982). Considerable complexity alone supports
the case of transforming TAM into CGAM, as the aim is not only to extend
TAM but to offer based on a well-tested model, a new model that allows easy
adaptation even for manufacturers. Focusing the extension of the TAM on
the external factors that influence PU and PEOU enables any company or
product development department to adjust the model to their product of
interest at a low expense rate.
Table 1: Benefits of CGAM over existing models
Model
EngelBlackwellKollatModel
(1979)

Focus
“1. To highlight more
clearly the
interrelationships
between stages in the
decision process and
the various endogenous
and exogenous
variables.
2. To clarify the
relationship between
attitude and behavior to
reflect the contribution
of Fishbein extended
model. Beliefs and

Deficiencies
Information and
experience are
characterised as an
important phase upon
which several of the
decision process stages
of problem recognition
are based. Additionally,
the scope of application
is unclear. (Rau &
Samiee, 1981)
The linear structure does
not represent the buyer
decision-making, as

Benefits of CGAM
Experience is not a
primary
requirement; this is
particularly
valuable
concerning
innovative
products. Thus, the
CGAM enables
acceptance to be
tested even before
the market launch,
without the
consumer having

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intentions are
introduced as explicit
variables for the first
time as in normative
compliance.
3. To define variables
with greater precision
and to specify functional
relationships to permit
empirical testing"
(Engel, Blackwell, Kollat
1979).
Explains consumer’s
brand choice behaviour
over a period of time
based on stimulusresponse (Howard &
Sheth, 1969).

those elements might not
occur in that specific
order or even
concuurently (Bringer
&Lutz, 1986; Phillips &
Bradshaw, 1992).

any experience
with the product.

Complex, especially for
routine purchases.
Additionally, consumer’s
do not follow the
complete path of the
model (Olshavsky &
Granbois, 1979).

Nicosia
Model
(1966)

Explains consumer
behaviour by creating a
link between the
organization and the
consumer (Nicosia,
1966).

The theory
of planned
behavior
(TPB)
(Ajzen,
1991)

“The theory of planned
behavior (TPB) has
been used successfully
to explain and predict
behavior in a multitude
of behavioral domains.”
(Ajzen, 2020, p. 341)

Assumes the presence of
predispositions
influenced by firm or
brand. Additionally, it is
rather difficult to find a
distinct focus, as
Nicosia’s attempt to point
out a focus is allencompassing.
Additionally, it is from a
marketer’s perspective
and not a consumer’s.
Validity of the suggested
relationships is not
empirically supported.
(Nicosia, 1966; Rau &
Samiee, 1981; Tuck
1976)
Non in this context. Was
used as foundation for
TAM.

CGAM does not
specially require
the consumer to
follow a specific
buying or decision
process behaviour.
Further, it is simple
enough to be
applied for all kind
of goods, with high
or low engagement
rate.
CGAM allows to
test consumer
acceptance for
products that are
new to the market
or not specifically
connected to a
specific firm or
brand.

HowardSheth
Model
(1969)

As Ajzen stated
1991 the TPB is
open for further
adjustment and
introduction of new
predictors, thus
considering how
Davis (1989) did
this a further
adjustment while
considering the
current variables is
in principle
possible (Ajzen,
1991).

Source: Own survey.

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Summarising, the benefit of CGAM is that it allows an early-stage
acceptance determination while also generating knowledge about the factors
influencing the level of acceptance. Additionally, CGAM does not focus on
brands or firms or implies knowledge about a firm or brand, nor does it
presume experience. Therefore, CGAM is a more practice- and
manufacturer-oriented model.
CONCEPTUALISATION OF CGAM
As stated above, Pikkarainen et al. (2004) and Venkatesh and Bala (2008)
pointed out that the external variables determining PU and PEOU will need
adjustment whenever the product of interest changes. These determinants
correspondingly form the starting point for the modification process. They
can be divided into four categories: social influence processes, cognitive
instrumental processes (Venkatesh & Davis, 2000), as well as anchor, and
adjustment, variables (Venkatesh & Bala, 2008).
In contrast to other approaches extending the TAM beyond IS, where
variables were incorporated depending on the specifics of the context within
which the study’s focus was situated (Hsu & Lu, 2004), extending TAM for
consumer goods calls for a more radical approach. As it is not sufficient to
just add more variables or exchange a few, the complete set of variables
determining PU and PEOU will need to be adjusted so that the
characteristics of the good of interest can be met, consequently requiring a
departure from the main body of TAM III as Venkatesh and Bala (2008)
conceived it. This approach was also suggested by Benbasat and Barki
(2007). In order to extend TAM, the core elements will remain as they were,
although the definitions of PU and PEOU need also to be adjusted. Some of
the variables influencing PU and PEOU will be adopted from TAM III, with
consequent changes in the definitions required. All newly integrated
variables are, on the one hand, defined based on the literature, while
incorporating the finding from Förster (2017), or the definitions are, on the
other hand, based on what the construct aims to address in the case of
consumer good acceptance.
Admittedly, this approach is rather general, as no classification for what
kind of consumer good this model will be conceptualised is given. There are
two types of consumer goods, which are further segmented into three
groups. The first type concerns frequency and duration, differentiating
between durable, semidurable, and non-durable goods (Graber-Kräuter,
2018). The buying decision process characterises the second type, which
includes convenience, shopping, and speciality goods. For more detailed
definitions, see Holton (1958). Since this paper is based on Förster (2017),
the focus is on convenience goods. However, the goal is to develop a
generally applicable CGAM.
PU was defined as “the degree to which a person believes that using a
particular system would enhance his or her job performance” (Davis, 1989,
p. 320). Since systems are no longer the focus, the perception of what
constitutes PU, and its addresses, must be modified. A more social

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influence-based definition was proposed by Förster (2017, p. 35): “PU is the
degree to which a person believes that consuming a particular product would
increase their social status or contribute to the image they are trying to
communicate to the outside world” consistent with their lifestyle and needs
(as subsequently qualified). In order to adjust the definition for PEOU to be
more product-oriented, Förster (2017, p. 35) defined PEOU as follows:
“PEOU is the degree to which a person believes that consuming or buying a
product would be free of effort” while also considering the investment-tobenefit-ratio (as subsequently qualified). Both definitions are addressing the
shift towards products while failing to include all categories of determinants.
Before introducing new definitions for PU and PEOU, it is indispensable to
consider the definitions of the four variable categories influencing PU and
PEOU.
The concepts influencing PU are social influence processes, which imply
“the ways other people affect one’s beliefs, feelings, and behavior” (Mason,
Conrey & Smith, 2007, p. 279; Venkatesh & Davis, 2000, p. 187) and
cognitive instrumental processes. The latter focuses on the linkage of higherlevel goals to specific actions which are essential for goal achievement
(Venkatesh, 2000). Förster (2017, p. 39) introduced a new description of
cognitive instrument processes: “consumers form judgments about PU in
parts based on cognitively comparing what a product is capable of in terms
of representing what they want to communicate to the outside world”. In
addition, the variables influencing PEOU are anchor and adjustment
variables. Anchors are general information upon which individuals can rely
on in the absence of specific knowledge. Besides, anchoring information is
often unconsciously included in the decision-making process. Adjustment
variables relate to beliefs based on actual interaction with a system or
product (Venkatesh, 2000).
The two moderating variables, experience and voluntariness, are also
seen as core elements of TAM. Venkatesh and Davis (2000) implied that
experience diminishes the effect social influence has on PU and usage
intention over time. Voluntariness is defined as “the extent to which potential
adopters perceive the adoption decision to be nonmandated” (Agarwal &
Prasad, 1997, p. 564). This would differentiate the mandatory and voluntary
usage settings, as the contingency of mandatory usage setting impacts the
compliance effect of subjective norms on the intention to use. Additionally,
the effect of a subjective norm on intention to use is strengthened by the
compliance effect, increasing as an individual’s need to fulfil a social actor’s
expectations. The social actor is considered as an executor of rewards and
punishment dependent on the implementation and non-implementation of
the relevant behaviour (French & Raven, 1959; Venkatesh & Davis, 2000).
Figure 1 presents the framework upon which the CGAM will be
conceptualised while considering all TAM III’s underlying assumptions as
correct.

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Figure

1:

TAM

III

as

framework

Experience

for

conceptualisation

Voluntariness

Constructs social
influence processes
Constructs cognitive
instrumental processes

Perceived Usefulness

Behavioural
Intention

Anchor variables

Adjustment variables

Use Behaviour

Perceived Ease of
Use
Technology Acceptance Model

Source: Own illustration based on Venkatesh & Bala (2008).

Considering the previously discussed definitions of the variable
categories influencing PU and PEOU, determinates can be selected based
on consumer good characteristics and their general validity. General validity
is important here in so far as the determinants should be applicable for
consumer goods in general.
Variables of the construct of social influence processes
Starting with variables of the construct of social influence processes,
subjective norm and image are two determinates which are adapted from
TAM. Subjective norm was defined as a “person’s perception that most
people who are important to him think he should or should not perform the
behaviour in question” (Fishbein & Ajzen, 1975, p. 302) and has direct
influence on PU and behavioural intention, as well as image. Research by
Stafford (1966) supports the integration of the variable subjective norm, as
his research showed that adaptation of brand choices is often influenced by
the leader of one social group since he or she influences its group members.
Image also has a direct effect on PU and is a significant factor when it
comes to communicating one’s own image to the outside world. The
transferred image can be linked to the self-concept of a person, which is
described as “beliefs a person holds about their attributes, and how they
evaluate these qualities” (Solomon et al., 2010, p. 144). In the context of
social interaction, the image that is communicated to others evokes a higher
concern “about the social appropriateness of products and consumption
activities” (Solomon et al., 2010, p. 149). In the context of consumer goods,
there are two relevant definitions regarding image. First, image as "the
degree to which use of an innovation is perceived to enhance one’s image
or status in one’s social system" (Moore & Benbasat, 1991, p. 195); this is
consistent with TAM II. Second, Keller defined image in the context of brands
as “perceptions about a brand as reflected by the brand associations held in
memory” (Keller, 1993, p. 2). Taking into account that TAM originated from

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theories in the field of social psychology, it is of great relevance that a
person’s image can be related to a product image, including variables
addressing these topics.
A newly integrated variable in the context of social influence processes is
social conformity; addressing a person’s desire to fit in, while avoiding
negative attention (Jahoda, 1959). Cialdini and Goldstein (2004, p. 606)
referred to conformity as “the act of changing one’s behavior to match the
responses of others”. Considering social conformity, especially in the context
of consumer goods, is important. As was pointed out by McCracken (1986),
consumer goods are used to extract cultural meaning, since cultural
meaning is projected from a culturally constituted world onto consumer
goods. Furthermore, through goods, cultural meaning is made visible for the
individual as well as conveying an inherent concreteness that individuals
would not otherwise have (McCracken, 1986). Attitudes and expectations
subconsciously received from others also influence one’s own behaviour.
Thus, considering social influence for the consumer goods' acceptance
process is a necessity (Venkatesh & Morris, 2000).
Considering all proposed variables for the construct of social influence
processes, their relevance is supported by a statement made by McCracken
(1986, p. 73): “goods allow individuals to discriminate visually among
culturally specified categories by encoding these categories in the form of a
set of material distinctions”.
All three variables directly affect PU, while the subjective norm is also
moderated by experience and affects behavioural intention. The latter
relation is not only moderated by experience but also by voluntariness.
Variables of the construct of cognitive instrumental processes
Moving on to the construct of cognitive instrumental processes, two variables
can be used irrespective of the consumer good of interest. The first variable
is lifestyle and the second is perceived quality/brand status. “Lifestyle is
described as a certain type of behavior, or preference for a certain type of
behavior, in which consumption plays an important role” (Sijtsema et al.,
2002, p. 572). Perceived quality, as well as brand status, can influence the
market success of a product (Richardson, Dick & Jain, 1994). Kolter (2000,
p. 3) described quality as “the totality of features and characteristics of a
product or service that bear on its ability to satisfy stated or implied needs”.
Additionally, O’Cass & Choy (2008, p. 342) hold that “Brand status refers to
consumer’s perceptions of quality, prestige, price of a brand and its capability
to act as a status or success symbol”. Combining these two aspects enables
a consideration of the quality-related characteristics of the consumer goods
and how these are perceived by the consumer, as well as delving more
deeply into interactions between the two variables i.e., how the perception
of brand status influences perceived quality, and vice versa.
A third variable for measuring the construct of cognitive instrumental
processes could be the trend factor, although it might not have significant
relevance for some consumer goods, as for example, convenience goods as
described by Holton (1958, p. 53): “convenience goods are those goods for

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which the probable gain from making price and quality comparison among
alternative sellers is thought to be small relative to consumer’s appraisal of
searching costs in terms of time, money and effort”. It can also be assumed
that for those goods, the benefit of considering the tendency to change would
also be small compared to the effort needed to assess the trend factor of
that good.
These three variables directly affect PU without being moderated by
experience or voluntariness.
Anchor variables
Moving on to the determinants of PEOU, there are two variables that can be
proposed as generalised anchors while recognising that the selection is
based on constructs addressing purchase-situation related issues. First is
availability, relating to how easily a product can be accessed, taking into
account its present for the consumer and the frequency, intensity and
visibility of product offerings. Second is price, which “refers to the cost or
sacrifice exchanged for the promised benefits” (Grier & Bryant, 2005, p. 323),
and influences every consumption decision. However, the importance of
these varies depending on the consumer good in question, as research has
shown price sensitivity varies among products and consumption settings
(Wakefield & Inman, 2003).
A third option, which might not be as generalisable as the previous two is
handling. In this context, issues regarding the general handling of a product
can be included, e.g., the effort needed to consume a product or prepare it
for consumption. Depending on the product in question, other aspects such
as the way the product is offered, the temperature of consumption and
offerings, can also be of relevance. Additionally, aspects concerning
changes in those characterises can determine whether change can improve
acceptance by simplifying handling. The variables effecting PEOU are all
moderated by experience.
Adjustment variables
The adjustment variables presented by Venkatesh and Bala (2008) need to
be replaced, as previously explained, by adjustment variables that can be
used to address more specific consumer good characteristics. Therefore,
additional value, sensory experience and indulgence are proposed.
Additional value refers to “the tangible and concrete attributes that a
consumer may directly experience when using or consuming the product”
(Lai, 1995, p. 383), hedonic benefits that are “acquired from a product's
capacity to meet a need of enjoyment, fun, pleasure, or distraction from work
or anxiety” (Lai, 1995, p. 384), as well as holistic benefits, which are
“perceptual benefit(s) acquired from the complementarity, coherence,
compatibility, and consistency in a product constellation as a whole” (Lai,
1995, p. 384).

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Considering that consumption is not only influenced by external factors
such as availability, price and handling, there are more complex enjoymentfocused adjustment variables such as sensory experience, that needs to be
considered. Sensory experience includes the perception of a product with all
your senses, including visualising, smelling, hearing, touching and tasting
(Smith, 2013). Certainly, sensory characteristics concerning taste, texture
and appearance can influence the consumption decision (Clark, 1998).
Therefore, sensory-experience is an adjustment variable, just as hands-on
experience is necessary to evaluate these aspects of a product. With regard
to criticism (Venkatesh, 2000) observes that TAM does not give valuable
information to manufacturers on how to guide development, including the
construct that sensory experience helps overcoming this deficiency.
The third adjustment variable is indulgence which can be referred to as
enjoying life and having fun (Hofstede, 2011). However, indulgence is also
often associated with adverse outcomes or negative feelings, especially
when they result from impulsive behaviour (Ramanathan & Williams, 2007).
All three variables are moderated by experience while affecting PEOU.
Figure 2: Consumer Goods Acceptance Model
Experience

Voluntariness

Subjective Norm
Social Conformity
Image & Self-concept
Lifestyle

Perceived Usefulness

Perceived Quality /
Brand Status
Behavioural
Intention

Anchor

Use Behaviour

Availability
Price
Handling

Perceived Ease of
Use
Technology Acceptance Model

constructs social influence processes
Adjustment

Additional Value
Sensory Experience

constructs cognitive instrumental processes
relations adapted from TAM III

Indulgence

Source: Own illustration based on Venkatesh & Bala (2008).

DISCUSSION & CONCLUSION
This paper presents a conceptualised model, based on TAM, which should
help to measure acceptance of a product and by what process the
acceptance of the product is effected. Additionally, it is hoped to encourage

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fellow researchers to consider this approach and take the idea of TAM to the
next level by using the proposed CGAM.
The revision of the theoretical concepts of the TAM showed that the newly
conceptualised CGAM has great potential, proving to be a valid extension of
the TAM to consumer goods in practice. First, acceptance is a core element
of the TAM and a significant variable of every human decision-making
process (McCracken, 1986). Behaviour can be influenced, as well as led by
acceptability, as the theoretical work by Adell (2009) and Kollmann (1998;
2004) showed. Understanding the concept of acceptance and how reaching
the state of accepting something is a crucial process when extending the
TAM towards consumer goods. Second, human behavioural patterns and
decision-making processes are essential for the TAM, given that its structure
originates from the TRA and TPB (Ajzen, 1991; Ajzen & Fishbein, 1980).
Those topics are also well covered in the CGAM. Third, TAM’s development
process towards TAM III reflects considered research and ensures that
known shortcomings and flaws have been dealt with (Venkatesh, 2000;
Venkatesh & Bala, 2008), giving rise to a well-structured and supported
model. Using the TAMs theoretical background and its structure to widen its
application area to consumer goods in general, is also well supported, as the
foundation is well-based on social psychological constructs, which are
crucial to understanding and predicting human behaviour (McCracken,
1986).
The presented model is accompanied by some blanks for which variables
are proposed in this paper. However, whether these blanks for variables can
be generalised or better left as blank spaces to fit the characteristics of the
product of interest, still needs to be discussed. The presented model is a
solid framework that can be used to study consumer acceptance for
consumer goods while allowing researchers to readily adjust the model to
their needs. Considering how often TAM has been modified to be applicable
for different technology use settings (Lee, Kozar & Larsen, 2003;
Taherdoost, 2018), leaving the blank spaces as presented in Figure 2 seems
appropriate and more flexible, considering the vast diversity of consumer
goods (Bucklin, 1963; Grabner-Kräuter, 2018). In Figure 2 the specifically
listed variables are based on the outcomes of the literature research, and
highly recommended for the acceptance assessment of consumer goods.
The variables suggested for the blank spaces, such as trend factor or
handling, need to be reassessed when applying the model to a specific type
of consumer good.
Since this study is based on Förster’s (2017) approach to extend the TAM
by using tea-to-go as a proxy for consumer goods, more specifically
convenience goods, this issue has not been further addressed. However, as
mentioned above, there are several types of consumer goods. Hence future
research should test the CGAM as it is presented while comparing its
applicability, validity and reliability using different kinds of consumer goods.
In addition to that, it would be possible to assess whether the presented
CGAM is simple and thus generalisable or too specific and therefore not
applicable for any type of consumer good.

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In the CGAM there are several variables focused on the factors
influencing human behaviour, but also several variables which allow the
representation of characteristics of consumer goods. While leaving the
model with blank spaces might be seen as an easy way out, they strengthen
the CGAM insofar as they allow specific inputs to correspond to the product
being considered, thus allowing greater specificity and flexibility in use.
Furthermore, these blanks create flexibility and easy application, which is
crucial for manufacturers, who need to test possible acceptance of newly
developed products in a quick and easy manner, that still generates valuable
and reliable insights.
In order to test the validity of the presented model it needs to be applied
in a specific use situation. In a following paper the conceptualised CGAM will
be applied using “tea-to-go” as an example for consumer goods. As
discussed, applying the model to a specific consumer good might require the
addition of some variables that relate to the specific characteristics of the
consumer good. Additionally, some of the embedded variables may need to
be reconsidered and tested for their applicability in the context of the “tea-togo” case.
Despite having conceptualized a consumer good acceptance model
without having specified the type of consumer good, the contribution is not
totally limited. As there is still need for theoretical adjustment and further
exploration, the presented CGAM has great value for the German teaindustry, as they gave insights about challenging issues and key factors.
Those have been greatly considered while developing the new variables for
CGAM (Förster, 2017) and thus CGAM can be used to test the acceptance
and it’s driver for tea-to-go.

REFERENCES
Adams, D.A., Nelson, R.R., Todd, P.A. (1992). Perceived usefulness, ease of use, and usage
of information technology: a replication. MIS Quarterly, 16(2), 227–247.
Adell, E, (2009). Driver experience and acceptance of driver support systems – a case of
speed adaptation. Institutionen för Teknik och samhälle, Trafik och väg, 2009. Bulletin –
Lunds Universitet, Tekniska högskolan i Lund, Institutionen för teknik och samhälle, 251.
Agarwal, R., Karahanna, E. (2000). Time flies when you’re having fun: cognitive absorption
and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694.
Agarwal, R., Prasad, J. (1997). The role of innovation characteristics and perceived
voluntariness in the acceptance of information technologies. Decision Sciences, 28(3). 557–
582.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50, 179–211.
Ajzen, I. (2005). Attitudes, personality and behavior. Maidenhead: Open University Press.
Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human
Behavior and Emerging Technologies, 2(4), 314-324.
Ajzen, I., Fishbein, M. (1980). Understanding attitudes and predicting social behavior.
Englewood Cliffs, NJ: Prentice-Hall.
Ang, M.C.H., Ramayah, T., Amin, H. (2015). A theory of planned behavior perspective on
hiring Malaysians with disabilities. Equality, Diversity and Inclusion, 34(3), 186–200.
Arnold, C., Klee, C. (2016). Akzeptanz von Produktinnovationen – Eine Einführung.
Wiesbaden: Springer Gabler.

64

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Benbasat, I., Barki, H. (2007). Quo vadis, TAM?. Journal of the Association for Information
Systems, 8(4), 211–218.
Bettmann, J.R., Johnson, E.J., Payne, J.W. (1991). Consumer decision making. In Robertson,
T.S., Kassarjian, H.H. (eds.). Handbook of consumer behaviour, Englewood Cliffs, NJ:
Prentice-Hall, 50–84.
Brinberg, D., Lutz, R.J. (1986). Perspectives on methodology in consumer research. New
York: Springer-Verlag.
Brown, S.A., Venkatesh, V. (2005). Model of adoption of technology in households: a baseline
model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426.
Bucklin, L.P. (1963). Retail strategy and the classification of consumer goods. Journal of
Marketing, 27(1), 50–55.
Childers, T.L., Carr, C.L., Peck, J. Carson, S. (2001). Hedonic and utilitarian motivations for
online retail shopping behavior. Journal of Retailing, 77, 511–535.
Cialdini, R.B., Goldstein, N.J. (2004). Social influence: compliance and conformity. Annual
Review of Psychology, 55, 591–621.
Clark, J.E. (1998). Taste and flavour: their importance in food choice and acceptance.
Proceedings of the Nutrition Society, 57, 639–643.
Coulter, K.S., Coulter, R.A. (2007). Distortion of price discount perceptions: the right digit
effect. Journal of Consumer Research, 34(2), 162–173.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319–339.
Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1989). User acceptance of computer technology:
a comparison of two theoretical models. Management Science, 35(8), 982–1003.
Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use
computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Davis, F.D., Venkatesh, V, (1996). A critical assessment of potential measurement biases in
the technology acceptance model: three experiments. International Journal of Human
Computer Studies, 45, 19–45.
Engel, J., Blackwell, R.D., Kollat, D. (1979). Consumer Behavior, 3rd edition, New York: Holt,
Rinehart, and Winston.
Fishbein, M., Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory
and research. Reading, MA: Addison-Wesley.
Förster, K. (2017). Extension of the technology acceptance model by consumer goods
acceptance using the example of tea-to-go. Master’s thesis, Zeppelin University,
Friedrichshafen.
French, J.R., Raven, B. (1959). The bases of social power. In Cartwright, D.P. (ed.), Studies
in social power, Research Center for Group Dynamics, Institute for Social Research,
University of Michigan, Ann Arbor, 50–167.
Gefen, D., Karahanna, E., Straub, D.W. (2003) Trust and TAM in online shopping: an
integrated model. MIS Quarterly, 27(1), 51–90.
Goodhue, D.L. (2007). Comment on Benbasat and Barki’s “Quo Vadis TAM” article. Journal
of the Association for Information Systems, 8(4), 219–222.
Grabner-Kräuter, S. (2018). Consumer goods. In Kolb, R.W. (ed.), The Sage encyclopedia of
business ethics and society. Thousand Oaks, CA: Sage Publications, 641–642.
Grier, S., Bryant, C.A., (2005). Social marketing in public health. Annual Review of Public
Health, 26, 319–339.
Hofstede, G. (2011). Dimensionalizing cultures: the Hofstede model in context. Online
Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1014.
Holbrook, M.B., Howard, J.A. (1977). Frequently purchased nondurable goods and services.
in Ferber R. (ed.), Selected aspects of consumer behaviour; a summary from the
perspective of different disciplines. Washington, DC: US Government Printing Office, 189–
222.
Holton, R.H. (1958). The distinction between convenience goods, shopping goods, and
specialty goods. Journal of Marketing, 23(1), 53–56.
Howard, J.A., Sheth, J.N. (1969). The theory of buyer behavior. New York: Wiley.
Hsu, C.L., Lu, H.P. (2004). Why do people play on-line games? An extended TAM with social
influences and flow experience. Information and Management, 41, 853–868.

65

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Jahoda, M. (1959). Conformity and independence – a psychological analysis. Human
Relations, 12(2), 99–120.
Kassarjian, H.H. (1982). The development of consumer behavior theory. Advances in
Consumer Reserach IX.
Keller, K.L. (1993). Conceptualizing, measuring, and managing customer-based brand equity.
Journal of Marketing, 57(1), 1–22.
Kollat, D.T., Engel, J.F., Blackwell, R.D. (1970). Current problems in consumer behavior
research. Journal of Marketing Research, 7(3), 327–332.
Kollmann, T. (1998). Akzeptanz innovativer Nutzungsgüter und – systeme: Konsequenzen
für die Einführung von Telekommunikations – und Multimediasystemen. Wiesbaden:
Springer Fachmedien.
Kollmann, T. (2004). Attitude, adoption or acceptance? Measuring the market success of
telecommunication and multimedia technology. Journal of Business Performance
Management, 6(2), 133–152.
Kolter, P. (2000). Marketing management: analysis, planning, implementation, and control.
Upper Saddle River, NJ: Prentice-Hall.
van der Laan, J.D., Heino, A., De Waard, D. (1997). A simple procedure for the assessment
of acceptance of advanced transport telematics. Transportation Research Part C: Emerging
Technologies, 5(1), 1–10.
Lai, A.W. (1995). Consumer values, product benefits and customer value: a consumption
behavior approach. Advances in Consumer Research, 22, 381–388.
Lee, Y., Kozar, K.A., Larsen, K.R.T. (2003). The technology acceptance model: past, present,
and future. Communications of the Association for Information Systems, 12, art. 50, 752–
780.
Luck, D.J. (1959) On the nature of specialty goods. Journal of Marketing, 24(1), 61–64.
Lucke, D. (1995). Akzeptanz – Legitimität in der ‘Abstimmungsgessellschaft’. Wiesbaden:
Springer Fachmedien.
Lunceford, B. (2009). Reconsidering technology adoption and resistance observations of a
semi-Luddite. Explorations in Media Ecology, 8(1), 29–48.
Malatji, W.R., van Eck, R., Zuva, T. (2020). Understanding the usage, modifications,
limitations and criticisms of technology acceptance model (TAM). Advances in Science,
Technology and Engineering Systems Journal, 5(6), 113–117.
Mason, W.A., Conrey, F.R., Smith, E.R. (2007). Situating social influence processes:
dynamic, multidirectional flows of influence within social networks. Personality and Social
Psychology Review, 11(3), 279–300.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model
with the theory of planned behavior. Information Systems Research, 2(3), 173–191.
McCoy, S., Galletta, D.F., King, W.R. (2007). Applying TAM across cultures: the need for
caution. European Journal of Information Systems, 16(1), 81-90.
McCracken, G. (1986). Culture and consumption: a theoretical account of the structure and
movement of the cultural meaning of consumer goods. Journal of Consumer Research, 13,
71–84.
Moore, G.C., Benbasat, I. (1991). Development of an instrument to measure the perceptions
of adopting an IT innovation. Information Systems Research, 2(3), 192–222.
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2),
311–329.
Nicosia, F.M. (1966). Consumer Decision Processes: Marketing and Advertising Implications.
Englewood Cliffs, NJ: Prentice-Hall.
O’Cass, A., Choy, E. (2008). Studying Chinese generation Y consumers’ involvement in
fashion clothing and perceived brand status. Journal of Product and Brand Management,
17(5), 341–352.
Olshavsky, R.W., Granbois, D.H. (1979) Consumer Decision Making- Fact or Fiction?.
Journal of Consumer Research, 6(2), 93-100.
Phillips, H., Bradshaw, R. (1993) How Customers Actually Shop: Customer Interaction with
the Point of Sale. Journal of the Market Research Society, 35(1), 51-62.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., Pahnila, S. (2004). Consumer acceptance of
online banking: an extension of the technology acceptance model. Internet Research, 14(3),
224–235.

66

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Quiring, O. (2006). Methodische Aspekte der Akzeptanzforschung bei interaktiven
Medientechnologien.
Elektronische
Publikationen
der
Universität
München.
Kommunikations
–
und
Medienforschung.
Münchner
Beiträge
zur
Kommunikationswissenschaft, 6(6), 1–29.
Ramanathan, S., Williams, P. (2007). Immediate and delayed emotional consequences of
indulgence: the moderating influence of personality type on mixed emotions. Journal of
Consumer Research, 34(2), 212–223.
Rau, P., Samiee, S. (1981). Models of consumer behavior: The state of the art. Journal of the
Academy of Marketing Science, 9(3), 300-316.
Regan, M.A., Mitsopoulos, E., Haworth, N., Young, K. (2002). Acceptability of in-vehicle
intelligent transport systems to Victorian car drivers, Royal Automobile Club Victoria,
Clayton.
Renaud, K., Van Biljon, J. (2008). Predicting technology acceptance and adoption by the
elderly: a qualitative study. Proceedings of the 2008 annual research conference of the
South African Institute of Computer Scientists and Information Technologists on IT research
in developing countries: riding the wave of technology, Wilderness, South Africa, 210–219,
doi:10.1145/1456659.1456684.
Richardson, P.S., Dick, A.S., Jain, A.K. (1994). Extrinsic and intrinsic cue effects on
perceptions of store brand quality. Journal of Marketing, 58(4), 28–36.
Roy, S. (2017). App adoption and switching behavior: applying the extended tam in
smartphone app usage. JISTEM-Journal of Information Systems and Technology
Management, 14, 239-261.
Saaty, T.L. (2008). Decision making with the analytic hierarchy process. International Journal
of Service Sciences, 1(1), 83–98.
Shan, Y., King, K.W. (2015). The effects of interpersonal tie strength and subjective norms
on consumers’ brand-related eWOM referral intentions. Journal of Interactive Advertising,
15(1), 16–27.
Sijtsema, S., Linnemann, A., van Gaasbeek, T. Van, Dagevos, H. (2008). Variables
influencing food perception reviewed for consumer-oriented product development. Critical
Reviews in Food Science and Nutrition, 42(6), 565–581.
Siró, I., Kápolna, E., Kápolna, B. Lugasi, A. (2008). Functional food. Product development,
marketing and consumer acceptance – a review. Appetite, 51, 456–467.
Smith, B.C. (2013). The nature of sensory experience: a case of taste and tasting.
Phenomenology and Mind, (4), 212–227.
Solomon, M.R., Bamossy, G.J., Askegaard, S.T., Hogg, M.K. (2010). Consumer behaviour: a
European perspective, 4th edn. Harlow: Pearson Education.
Stafford, J.E. (1966). Effects of group influences on consumer brand preferences. Journal of
Marketing Research, 3(1), 68–75.
Szajna, B. (1994). Software evaluation and choice: predictive validation of the technology
acceptance instrument. MIS Quarterly, 18(3), 319–324.
Taherdoost, H. (2019). A review of technology acceptance and adoption models and theories.
Procedia Manufacturing, 22, 960–967.
Taylor, S., Todd, P.A. (1995) Understanding information technology usage: a test of
competing models. Information Systems Research, 6(2), 144–176.
Tuck, M. (1976). How do we choose? London: Methuen & Co Ltd.
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic
motivation, and emotion into the technology acceptance model. Information Systems
Research, 11(4), 342–365.
Venkatesh, V., Bala, H. (2008). Technology acceptance model 3 and a research agenda on
interventions. Decision Sciences, 39(2), 273–315.
Venkatesh, V., Davis, F.D. (2000). A theoretical extension of the technology acceptance
model: four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M.G. (2000). Why don’t men ever stop to ask for directions? Gender,
social influence, and their role in technology acceptance and usage behavior. MIS
Quarterly: Management Information Systems, 24(1), 115–136.
Venkatesh, V., Thong, J.Y.L., Xu, X. (2012). Consumer acceptance and use of information
technology: extending the unified theory of acceptance and use of technology. MIS
Quarterly, 36(1), 157–178.

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Wakefield, K.L., Inman, J.J. (2003). Situational price sensitivity: the role of consumption
occasion, social context and income. Journal of Retailing, 79, 199–212.
Williams, M.D., Dwivedi, Y.K., Lal, B., Schwarz, A. (2009). Contemporary trends and issues
in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10.

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RETIREMENT FINANCIAL PLANNING: GENDER, AGERELATED, AND EXPENDITURE DIFFERENCES
AMONG INDONESIAN HOUSEHOLDS
Faizah Syihab*
University of Trilogi,
Indonesia
faizah@trilogi.ac.is

Muhammad Lugman Nurhakim
University of Indonesia,
Indonesia
lugman11795@gmail.com

Abstract
This study aims to determine which factors influence household perceptions
of retirement planning decisions. Age, marital status, salary, level of
education, loan payment, and savings habit will be identified as significant
variables in the prediction. This study employed the logistic regression (logit)
model, with the sample divided into those who have enrolled in a pension
plan and those who have not begun to plan for a pension fund. The sample
age range was 26 to 58 years old, with a total of 2,808 respondents. This
study discovered that for the age range 26-35 years old, obtaining more
education, being younger, earning higher salaries, having less loan
obligation, and saving less, the household were more likely to have proper
retirement planning. However, between the ages of 36 and 58, planning for
retirement became more likely as they get older, gain more education, enter
marriage life, and have more loan obligations.

Key Words
Retirement planning; pension fund; household; logit.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

INTRODUCTION
Longevity sounds promising and prosperous. According to the International
Database on Longevity (2015), in developing countries, life expectancy has
increased by more than 20 years over the last century. Globally, Indonesia
had one of the top ten world's largest elderly population (Statistics
Indonesia).
Life expectancy has risen dramatically while birth rates have fallen.
Hence, Indonesia will be predicted to become an aging society over the next
few decades. This prediction is a true assumption since it had been proven
by the increase of life expectancy in Indonesia from 72.02 years in 2011 to
73.06 years in 2017, based on the report of Indonesia's Central Agency of
Statistics (2017). This increase was due to several reasons, one of which
was advances in medical science, such as the availability of antibiotic
treatment, vaccines, and health care. Besides its causes, the increase of life
expectancy results in the people who finally retire from their work. The
retirement or pension makes the people have to pay their life. Pension funds
can be a viable alternative to meeting needs and living the good life in
retirement and offer an income assurance for people who are no longer
working, with the amount determined by the number of contributions paid.
This demonstrates how important pension funds can be in overcoming or
mitigating the risk of dying prematurely or in old age.
The majority of people make three major faults when it comes to
retirement planning. To begin with, most people start too late retirement
planning in their late 30s or early 40s. Second, they spend less money since
they do not believe that it is necessary to contribute significantly to pensions.
Other financial pressures, such as real estate purchases, university tuition,
and current life choices, can cause all errors at this time. Both faults result in
lower expected retirement benefits. The last, most people make
conservative investments in pension funds. Investing with a low expected
level means eradicating high risk. As a matter of fact, the pension benefits
obtained were meaningless when it came to meet every day needs
throughout retirement period, and as a consequence, the pension payments
gained cannot be considered to be sufficient to cover all expenses.
Financial planning, according to Gitman et al. (2011), is a dynamic
process, which is defined as the processes of life changes. The life changes
are the phases in life; for example, your financial needs at the age of 30 will
just be different than it was when you turn 40, 50, or even 60. Financial
planning needs to be dynamically adjusted to a phase which are going to be
followed to avoid "financial shock”. Gitman et al. (2011) define the dynamic
process of financial planning as a financial planning life cycle demonstrating
that financial planning begins at the stage of family formation. Financial
planning is not necessary in the early stages of life, such as childhood, high
school, and college, because a person is still financially dependent on
parents and does not generate any revenue. Only within the family formation
phases would anyone normally start working, earning a stable income, and
beginning to plan their finances. When a person reaches retirement age, his
or her source of revenue begins to diminish, and financial planning begins to

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deteriorate, increase the risk of getting less income, and a less comfortable
lifestyle. The figure below shows that financial planning of someone’s cash
inflow decreases during the pension period.
Figure 1: Life Cycle of Financial Planning

Source: Gitman, Joehnk and Billingsley, Personal Financial Planning.

According to Kimiyaghalam, et al. (2017), retirement is the moment when
an employee does not work after getting the standard age of retirement set
by a country's laws or rules. According to Gitman et al. (2011), retirement
must be planned for a long time before one approaches this stage. At this
stage, when revenue is decreasing, financial targets such as maintaining a
living standard, vacation, basic needs, and a variety of other expenses must
be met. Carrying out proper financial planning during retirement is critical
because it is a long term plan and a critical stage in one's life. According to
Gitman et al. (2011), the most important objective participating in financial
planning is good retirement living standards. They say retirement planning
is central to financial planning when compared to another types of financial
planning, and that pensions can be an important factor in achieving a
comfortable life in the future.
According to Lawson & Klontz (2017), the Certified Financial Planner
(CFP) Board's Financial Planning Practice Standards include six steps. The
standards demonstrated the importance of evaluating personal situations
such as retirement age, life expectancy, income, and financial health in terms
of financial satisfaction, debt level, and saving plan. These standards assist
financial planners in providing better recommendations to their clients. Thus,
the aforementioned factors may have influenced people to plan their
retirement.

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LITERATURE REVIEW
According to Gracia Mata (2021), financial literacy had an effect on
retirement planning among young adults in Mexico with gender acting as a
moderator variable. The actual or intended implementation of several
retirement strategies, including private pension funds, asset investing,
government subsidies, and family assistance, is referred to as planning.
Many young Mexican women still considered family and friends to be their
primary source of financial support as they get older, especially as the
number of economic dependents grows. Similarly, Zurlo (2021) discovered
that unmarried women faced more financial challenges in retirement than
married women.
Vivel-Búa et al. (2019) identified the factors influencing retirement
planning in Spain. The study conducted 165,791 observations from 2008 to
2015 and used a probit tobit model. This study and discovered that while
gender was not a significant determinant of the decision to save money for
retirement in a private retirement plan, it had a negative and significant
impact on the amount of money saved. Spanish women invested less in
private pension schemes than men, which may be explained by their lower
earnings.
In line with this, Mahdzan et al., (2017) investigated the factors influencing
retirement planning and retirement portfolio choices, an area that has
received little attention. Income, age, and future expectations were found to
be strongly associated to the likelihood of retire behavior in Kuala Lumpur,
Malaysia. Retirement planning was more likely to be implemented if people
have faith in the country's financial system. A sample of 270 workforces in
Malaysia was studied using logistic and ordered probit regression. While
according to Onduko et al., (2015), financial literacy, earnings, and level of
education of respondents were key variables of retirement planning in
Nairobi, Kenya. Age and years of marriage, in contrast, had no significant
influence on retirement planning. The most important factor that affected
retirement planning decisions was financial literacy because it influenced
both saving and portfolio selection. The study employed multiple regression
on a sample of 158 people working in the formal sector who had saved for
retirement.
Mansor et al. (2015) investigated the factors influencing retirement
planning among Malaysian employees working in the health sector. The
study revealed that age, education level, and income level were statistically
significant and related to retirement planning, whereas gender had no
significant impact on it. To investigate the relationship, they used
demographic factors as determinants and cross tabulation analysis. The
findings revealed that age increased retirement confidence, education
allowed people to learn more about retirement planning, and a sufficient
income was required to plan for retirement.
The same study was conducted by Rey-Ares et al. (2015) in Portugal and
Spain. Using probit, they discovered that education, employment situation,
living area, house ownership, and saving practices were all positively
related, while financial risk aversion and right-wing political orientation were

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negatively related. Ares et al. (2015) argued that after mortgages were paid
off, households were more likely to save. The study included 1,808
individuals with an age range of 55.47. Besides that, Moorthy et al. (2012)
demonstrated that the earlier generation (26-35 years) had a more positive
attitude toward retirement planning and was not concerned about retirement.
According to the findings, the factors influencing retirement planning
behavior were age, educational level, level of income, goal clarity, viewpoint
to retirement, and possible conflict in retirement. The study concluded that
the ideal period to start doing retirement was range from the ages of 26 to
35. The study employed multiple regression on a sample of 300 working
Malaysians ranging in age from 26 to 55 years.
Fontes (2011) used logistic regression to investigate factors influencing
retirement planning in United States. This study discovered a positive and
negative relation to retirement planning. The former consisted of age,
education, male gender, housing, urban residence, married status, and
employment, while the latter consisted of the number of children and
immigrants. Hira et al. (2009) also used logistic regression to discover a
positive relation, such as age, financial information, and becoming an early
investor.
Devaney and Chiremba (2005) conducted research on retirement
planning in the United States. The tobit model of logistic regression was used
to analyze the relationship. This study discovered a positive relation among
retirement planning and variables, such as age, education, risk tolerance,
saver, planning horizon, married status, and white race, and a negative
relation between retirement planning and independent variables, such as
spending and self-employment. According to the study, obtaining additional
education, willing to take risks, and saving more were all associated with
having more money set aside for retirement. This study used spending
versus income as study factors and discovered a negative relationship
between retirement planning and spending. The more respondents spent
than they earned, the less likely they were to make retirement plans.
Furthermore, Hurd (2003) explained that those who had low income
throughout their working life who did not save would experience even lower
levels of consumption in the years ahead and that they could raise long-term
utility by reallocating spending from before to after pension. The data was
collected from the Health and Retirement Study in 1992, and the study used
the tobit model with a total of 12,652 respondents. According to Lum &
Lightfoot (2003), respondents and spouse health were positively related to
retirement planning. The logistic regression study also discovered that age,
male gender, white and black race, education, and revenue were related to
retirement planning among older workers.
As a result of the preceding literature on retirement planning behavior,
researchers would want to conduct additional research into identifying
factors influencing pension fund decisions, specifically in Indonesian
households using IFLS5 from Rand Corporation Database by categorizing
the sample age within the age ranges of 26-35 and 36-58. It is hoped that
this study can discover the determinants of retirement planning behavior in
Indonesian households since retirement planning is important for household

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wealth.
DATA AND METHODOLOGY
This study used cross-section data from the Rand Corporation database.
The information could be obtained from the IFLS5 questionnaires, which can
be found at www.rand.org. The method used logistic regression, specifically
the logit model. Purposive sampling was used to collect the data from
respondents in Indonesia by using STATA. The sample was drawn by using
the respective criteria: first, containing no missing values; second, excluding
respondents with ambiguous answers; third, dismissing respondents under
the age of 26; and finally omitting participants who did not reply to the
question. By applying these criteria, the data that was originally collected
26,508 became 2,808 respondents.
According to Moorthy et al. (2012), the earlier generation age range of 2635 years had a more positive attitude toward retirement planning and was
not concerned about retirement; additionally, the study concluded that the
ideal age range to begin doing retirement was between the ages of 26 and
35. Furthermore, according to the life cycle theory, financial stability after the
age of 35 in the financial stage differed. As a result, the sample group in this
study was divided into two age groups: 26-35 and 36-58 to determine the
retirement financial planning among Indonesian households.
This study consisted of 1,335 male respondents (47.54%) and 1,473
female respondents (52.46%). Based on the total number of respondents,
95.51% of them were married and the remaining 4.49% were unmarried. The
age distribution of the respondents was that 1,374 or 48.93% of respondents
represented the ages between 26 and 35, while 1,434 or 51.07% of them
represented the ages between 36 and 58. Based on the educational
qualification level analysis, those who had elementary school holders were
603 (21.47%), junior high school holders were 381 (13.57%), senior high
school holders were 982 (34.97%), and college/university holders were 842
(29.99%) respondents.
In terms of loan payments, 1,419 respondents (50.53%) had loan
obligations, while 1,389 respondents (49.47%) did not have any loan
payments. In terms of saving habits, 2,735 respondents (97.40%) had a
saving habit, while the remaining (2.6%) did have any. Most workers were
private workers represented by 1,858 respondents (66.17%) and the
remaining consisted of government workers represented by 659
respondents (23.47%), casual workers in agriculture represented by 237
respondents (8.44%), and casual workers not in agriculture represented by
54 respondents (1.92%).
Retirement planning is the dependent variable. The respondent was
asked, "What type of pension plan are you enrolled in?" This study would
investigate whether respondents can improve their retirement planning by
improving their level of education, budgeting, subjective perception, and
anticipating the necessity for financial education. The relevant factors will
be explained further.

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Respondents’ criteria included those who received pension funds as an
employee and had enrolled in a pension plan. The responses were divided
into two categories, namely 1 indicated that the respondent had started to
make retirement plans and 0 indicated that the respondent had not begun to
make retirement plans. This binary value was used to determine why an
individual decided a retirement planning.
This study used the independent variables. The first independent variable
was age (x1_age) for the question “how old are you?”. Age, as a
demographic factor, is the most used variable in retirement planning studies.
Based on the life cycle of financial planning, the age range was divided into
two categories, namely 26-35 and 36-58 years old, to investigate the
differences in behavior between them. The second variable was gender
(x2_gender) as another demographic factor for asking whether the
respondents were male or female, which was coded 1 for male and 0 for
female. The third variable was Marital Status (x3_marstat) by classifying the
study with 1 for "married" status and 0 for other than "married" status. Salary
(x4_salaries) was the fourth independent variable showing monthly earnings
by asking the following question: "Approximately what was your salary/wage
during the last month (including the value of all benefits)?". The fifth
independent variable was education level (x5_edulvl) by classifying the
respondents into six categories: elementary school (12 years), junior high
(15 years), senior high (18 years), college education (22 years), and
university education (24 - 29 year). The sixth independent variable was Loan
Payment (x6_loan), which represented respondents' debt repayment
spending by asking, "What is the total loan that has been paid in the last 12
months?". Finally, Saving (x7_saving) was the seventh independent variable.
The following statements become the research hypothesis.
H1: Respondent age had a significant impact on the likelihood of retirement
planning.
H2: Male respondents were more likely than female respondents to have
retirement plans.
H3: Married respondents were more likely to make retirement plans than
unmarried respondents.
H4: Salary had a significant influence on the likelihood of retirement planning.
H5: Respondents with more years of education were more likely to plan for
pension funds than those with fewer years of education.
H6: Loan payments had a significant impact on the likelihood of retirement
planning.
H7: Saving habits had a significant impact on the likelihood of retirement
planning.
EMPIRICAL FINDINGS AND DISCUSSION
When a predictor variable is a linear function of another independent variable
in the same model, this is referred to as multicollinearity. The correlation
between all independent variables were tested to see whether there was any

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multicollinearity in the model. The model has no multicollinearity, as
evidenced by this result.
Table 1: Correlation Matrix
x1_age
x2_gender
x3_marstat
x4_salaries
x5_edulvl
x6_loan
x7_saving

x1_age
1.0000
0.0956
- 0.0180
0.0374
0.0254
0.0158
0.0308

x2_gender

x3_marstat

x4_salaries

x5_edulvl

x6_loan

x7_saving

1.0000
0.0134
0.0018
0.0667
0.0000
-0.0850

1.0000
0.0099
0.0144
0.0142
0.0010

1.0000
0.0597
0.0031
0.0170

1.0000
0.1243
0.0330

1.0000
0.0173

1.0000

Source: Rand Corporation, IFLS5.

Based on the results of table 1, the correlation coefficient among
independent variables is 0.80 or less, meaning that multicollinearities did not
occur in the data in this study. This indicates that in the model, there was no
independent variable that was a linear function from another independent
variable.
The Goodness of Fit test measures how accurately a model explains the
correlation among the dependent and independent variables. Pseudo R2 is
one of the parameters used for the Goodness -of -fit test in logistic
regression.
Table 2: Summary of Goodness of Fit based on Age Range
Number of Observation
Pseudo R2
Source: Rand Corporation, IFLS5

Age 26-35
1374
0.2023

Age 36-58
1434
0.1659

According to table 2, the Pseudo R2 value in the age group 26-35 years
(20.23%) is greater than in the age range 36-58 years (16.59%), indicating
that the model with all predictor factors could better explain Indonesian
households' decision to have retirement planning in the age range 26-35
years than in the age range 36-58 years.
The Likelihood Ratio was employed to assess whether all the predictor
variables have an effect on the dependent variable, which is represented by
a probability value greater than chi-square (Prob>chi2). Following is the
hypothesis:
H0: All independent variables had no effect on the dependent variable under
consideration.
H1: All independent variables had an effect on the dependent variable being
tested at the same time.
Table 3: Likelihood Ratio
Wald chi2
Prob > chi2
Source: Rand Corporation, IFLS5.

Age 26-35
216.28
0.0000

Age 36-58
196.32
0.0000

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Table 3 shows that H0 is rejected with a 95% confidence level (Likelihood
Ratio statistic is 0.0000), meaning that all seven predictor variables (age,
gender, marital status, salaries, educational levels, loan payment, and
saving habit) were indeed influencing household decisions to have
retirement planning at the same time. The likelihood ratio value (Prob > chi2)
of 0.000 described how predictor variables in the model could
simultaneously explain the retirement planning decisions of Indonesian
households.
By examining Prob > chi2 from each predictor variables, the partial test
determines whether each predictor variable influences the dependent
variable. The relations among independent and dependent variables are as
follows:
Table 4: Partial Test
Age 26-35
Variables

Age 36-58

Coefficient

Age 26-35

Age 36-58

P>z

Age 26-35

Age 36-58

Odd Ratio

x1_age

-0.0573

0.0582

0.0230

0.0000

0.9443*

1.0601*

x2_gender

0.0865

-0.1159

0.5420

0.3570

1.0903

0.8906
3.1534*

x3_marstat

-0.5046

1.1485

0.1110

0.0010

0.6037

x4_salaries

3.11E-07

-1.50E-09

0.0000

0.1020

1*

1

0.2389

0.2199

0.0000

0.0000

1.2698*

1.2459*

x6_loan

-2.07E-09

3.17E-08

0.0420

0.0000

1*

1*

x7_saving

-4.64E-09

2.08E-09

0.0410

0.3860

1*

1

x5_edulvl

*Significance at Confidence level = 95%
Source: Rand Corporation, IFLS5.

Age, salary, education level, loan payment, and saving habit influenced
retirement planning behavior among people aged 26 to 35. For the age range
36-58 years old, retirement planning behavior was influenced by age, marital
status, educational levels, and loan payment.
The following is the Equation:
Pension Fund (age 26-35) = - 0.0573Age + 3.11x10-7Salaries +
0.2389EduLvl - 2.07x10-9Loan 4.64x10-9Saving ……………. (1)
Pension Fund (age 36-58) = 0.0582Age + 1.1485MarStat +
0.2199EduLvl + 3.17X10-8Loan
……………………......... (2)
The logit model's resulting coefficients cannot be directly interpreted. The
coefficient's value is meaningless because it only indicates the direction of
the predictor variable's influence on dependent variable. To interpret the
coefficient values, the coefficients of the estimated logit findings must be
changed into natural antilogarithm to acquire the odds ratio. Odd Ratios
result is shown in table 4.
The odds ratio describes the ratio of two possibilities, that is the possibility
of success and the possibility of failure. The slope sign above indicates the
following statements:

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1. The tendency of people in the age range 26-35 to have pension funds
decreased by 0.9443 times for every year of age increase. In the age
range 36-58, an increase in age developed the likelihood of having
pension funds by 1.0601 times.
2. Married people between the age of 36 and 58 had a 3.1534 times
greater chance of having pension funds. There was no effect of marital
status on individual behavior in having pension funds for those in the
ages of 26-35.
3. In the age range of 26-35, an increase in salary increased the
likelihood of having pension funds by one.
4. Educational levels had a positive effect on retirement planning in both
age groups. The odds ratio values showed that the higher the
education level, the greater the likelihood of having pension funds in
the age range of 26-35 and 36-58, respectively.
5. Among people aged 26 to 35, the likelihood of having a retirement plan
decreased each time the loan payment increased by one. On the other
hand, as loan payments rose by one percentage point, the number of
people who plan for retirement rose.
6. The likelihood of people having retirement plans between the ages of
26 and 35 would decrease each time the respondent's savings
increased by one.
This finding suggests that the relationship between all predictor factors
and retirement planning variable could explain the majority of the theory.
Age, marital status, salary, educational levels, loan, and savings were
important factors to consider when planning a retirement in Indonesia. This
study concluded that the appropriate age to begin implementing retirement
planning varied from the age of 26 and 35.
Age, as a demographic factor, can explain the life cycle of financial
planning and the theory of planned behavior. Age indicates which stage of
the life cycle the respondent is currently in and can be used as a subjective
norm that directs behavior. Respondents aged 26 to 35 had different results
on their choice to save for retirement. Salary is a consideration for younger
people in the transition phase of the financial planning life cycle because
they do not have many financial responsibilities in their daily lives, so the
younger they are, the more likely they are to invest in retirement funds. This
is consistent with the discoveries of Huberman et al. (2007), which
discovered a diminishing relationship of age and the decision to have
retirement funds. Even though there were differences, the findings in this
study were supported by previous studies. Age was not always a detriment
to retirement planning; in the older group, age had a positive influence on
retirement planning. Mansor et al. (2015) discovered a positive relation
between age and retirement planning behavior among respondents aged 3658 years, as did Moorthy et al. (2012), Fontes (2011), Hira et al. (2009), and
Devaney and Chiremba (2005). This behavior was in accordance with the
life-cycle theory. According to the life-cycle economic approach, people tend
to invest in pension funds as they get older. Individuals are more likely to be
motivated to take action for retirement as they get older, so age can help
pre-retirees plan their retirement.

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Marital status is regarded as a subjective norm that influences one's
behavior when he or she married. According to this study, marital status was
not a statistically significant predictor of retirement planning in the younger
group, but married respondents were more likely to plan for retirement than
unmarried ones in the older group. Married respondents had a greater
tendency to be concerned regarding their family's financial stability, causing
them to be more likely to invest in retirement planning. In other words, the
decision to plan for pension was made together within the marriage. Both
outcomes were consistent with the life cycle of financial planning, as
awareness of retirement planning begins in the phase of career development
that is over 35 years old.
One of the demographic factors that influences both younger and older
people's retirement planning behavior is educational level. This is in line with
previous research findings (DeVaney & Chiremba, 2005; Fontes, 2011; Hira
et al., 2009; Moorthy et al., 2012; Mansor et al., 2015; Onduko et al., 2015).
Education level can be considered as attitude toward retirement planning
behavior because education level can help to understand retirement
behavior. Individuals with a higher education level are more likely to have
pension funds because education allows individuals to explore more
information about retirement planning, and the sources of information
influence their decision.
Another financial variable is loan and saving habits, both of which have a
negative impact on younger people's retirement planning behavior. As
previously stated, income becomes a consideration for younger groups in
the transition phase of the financial planning life cycle when they have many
financial responsibilities in their daily needs (loan and saving), resulting in
insufficient funds. As a result, the likelihood of them using retirement funds
is low or difficult to predict. Ares et al. (2015) and DeVaney & Chiremba
(2005) used saving habits as a factor to determine retirement planning and
discovered that those who save had a higher likelihood of planning for
retirement.
Loan variables affect retirement planning behavior in the older and
younger group, but in the opposite direction. The outcome was in
accordance with the previous studies which used categorical spending
variables rather than numerical spending variables like the one used in this
study (DeVaney & Chiremba, 2005; Fontes, 2011; Rey-Ares et al., 2015).
However, the results showed the same effect on retirement planning in the
younger group but having a different positive relationship in the older group.
The behavior was quite fascinating that individuals were likely to have
pension funds regardless of the other expenses that they must meet. Based
on the financial planning life cycle, respondents between the ages of 36 and
58 were more likely to have a pension plan, despite rising loan payments.
Gender variable does not significantly affect an individual decision in
making retirement planning. This can be observed from the respondent
readiness of pension funds plan. Retirement readiness is a cognitive marker
for behavior from rejection or support for change efforts and maturity points
in the form of preparedness, readiness, and maturity to be able to accept the
transition to a new lifestyle, in this case it always involves changing roles,

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changing desires and values, and changes to the individual's lifestyle that
manifest in the form of behavior (Safitri, 2013). From the sample, employees
who had retirement preparedness with female gender were 435 employees
(29.53%), and male were 430 employees (32.21%). Thus, it can be
concluded that employee retirement readiness in both genders were equally
for not being concern in preparing their future retirement planning.
CONCLUSION
The theories presented in this study can explain retirement planning
behavior. Based on the financial planning life cycle, retirement planning
becomes more important as people reach the age of 36, the career
development stage. The financial planning process can influence retirement
planning behavior because it evaluates financial status such as income,
savings, and loan payments. This study discovered that households with
more education, being younger, earning higher salaries, having less loan
obligation, and saving less were more likely to have saved for retirement (for
age group of 26-35).
Based on the findings of this study, the ideal age range for Indonesians
to begin retirement was between the ages of 36 and 58. This age group had
entered the career development stage and was more concerned with
financial planning, particularly retirement. Outcomes are consistent with the
financial planning life cycle, as awareness of retirement planning begins in
the phase of career development that is over 35 years old.
In the 35-58 age group, obtaining more education, as they get older, being
in marital life, and having more loan obligations would most likely influence
household decisions to have retirement planning. This study discovered that
when income increased in the early stages of life, it tended to determine
retirement planning. The theory of planned behavior dominated in explaining
retirement behavior, as evidenced by the strength of the influence of age,
marital status, and education level. People's awareness of retirement
planning behavior will increase as they get older, married, and more
educated.
The findings could help policymakers understand the factors that
influence retirement planning behavior. Any policy implementation aimed at
assisting individuals in adequately preparing for retirement should consider
the fact that different groups have different retirement saving behaviors. This
study showed once again that behavioral approaches play a significant role
in retirement planning behavior without ignoring the influence of such
financial variables. It is suggested to encourage retirement planning
behavior by increasing people's knowledge of retirement planning. These
findings help people understand the significance of retirement planning. In
general, the most influential variable is education level. It is necessary for
educational institutions to educate people about the importance of retirement
planning.
Although some facts were discovered, this study still had the following
limitations. First, due to a cross-sectional study, it cannot be observed over

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time, it can only capture retirement behavior at a specific point in time.
Second, while this study is being conducted using secondary data, using
primary data for specific purposes such as questioners will allow the study
to become more accurate in terms of the study objective.

REFERENCES
DeVaney, S. A., Chiremba, S. T. (2005). Comparing the Retirement Savings of the Baby
Boomers and Other Cohorts. Compensation and Working Conditions Online, U.S.
Department of Labor, Bureau of Labor Statistics, March 16, 1–12.
Fontes, A. (2011). Differences in the Likelihood of Ownership of Retirement Saving Assets by
the Foreign and Native-Born. Journal of Family and Economic Issues, 32(4), 612–624.
https://doi.org/10.1007/s10834-011-9262-3
García Mata, O. (2021). The effect of financial literacy and gender on retirement planning
among young adults. International Journal of Bank Marketing, 39(7), 1068–1090.
https://doi.org/10.1108/IJBM-10-2020-0518
Gitman, L. J., Joehnk, M. D., Billingsley, R. S. (2011). Person Financial Planning (Mason
(ed.); 12th ed.). Joe Sabatino.
Hira, T. K., Rock, W. L., Loibl, C. (2009). Determinants of retirement planning behaviour and
differences by age. International Journal of Consumer Studies, 33(3), 293–301.
https://doi.org/10.1111/j.1470-6431.2009.00742.x
Huberman, G., Iyengar, S. S., Jiang, W. (2007). Defined contribution pension plans:
Determinants of participation and contributions rates. Journal of Financial Services
Research, 31(1), 1–32. https://doi.org/10.1007/s10693-007-0003-6
Hurd, M. (2003). and Unexpected Events. In Washington D. C (Ed.), Fifth Annual Conference
of the Retirement Research Consortium. Michigan Retirement Research Centre.
Kimiyaghalam, F., Mansori, S., Safari, M., Yap, S. (2017). Parents’ Influence on Retirement
Planning in Malaysia. Family and Consumer Sciences Research Journal, 45(3), 315–325.
https://doi.org/10.1111/fcsr.12203
Lawson, D. R., Klontz, B. T. (2017). Integrating behavioral finance, financial psychology, and
financial therapy theory and techniques into the financial planning process. Chinese
Economic Planning: Translations From Chi-Hua Ching-Chi, July, 100–114.
Lum, Y. S., Lightfoot, E. (2003). The effect of health on retirement savings among older
workers. Social Work Research, 27(1), 31–44. https://doi.org/10.1093/swr/27.1.31
Mahdzan, N. S., Mohd-Any, A. A., Chan, M. K. (2017). The influence of financial literacy, risk
aversion and expectations on retirement planning and portfolio allocation in Malaysia.
Gadjah
Mada
International
Journal
of
Business,
19(3),
267–289.
https://doi.org/10.22146/gamaijb.24441
Mansor, M. F., Hong, C. C., Abu, N. H., Shaari, M. S. (2015). Demographic Factors
Associated with Retirement Planning: A Study of Employees in Malaysian Health Sectors.
Asian Social Science, 11(13), 108–116. https://doi.org/10.5539/ass.v11n13p108
Moorthy, Ze, K.N. (2012). A Study on the Retirement Planning Behaviour of. International
Journal of Academic Research in Economics and Managament Sciences. International
Journal of Academic Research in Economics and Management Sciences, 1(2), 54–72.
Onduko, E. M., Gweyi, M. O., Nyawira, J. (2015). Analysis of the Determinants on Retirement
Planning in Kenya: A Survey of Registered Pension Schemes in Nairobi. Research Journal
of
Finance
and
Accounting,
6(3),
208–216.
http://www.iiste.org/Journals/index.php/RJFA/article/view/19961
Rey-Ares, L., Fernández-López, S., Vivel-Búa, M. (2015). The determinants of privately
saving for retirement: the cases of Portugal and Spain. European Journal of Applied
Business
and
Management,
1(1),
69–92.
http://nidisag.isag.pt/index.php/IJAM/article/view/26
Safitri, B. R. (2013). Kesiapan Menghadapi Masa Pensiun Ditinjau dari Peran Gender
Karyawan.
Jurnal
Ilmiah
Psikologi
Terapan,
01(02),
191–204.
https://doi.org/https://doi.org/10.22219/jipt.v1i2.1577

81

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Vivel-Búa, M., Rey-Ares, L., Lado-Sestayo, R., Fernández-López, S. (2019). Financial
planning for retirement: the role of income. International Journal of Bank Marketing, 37(6),
1419–1440. https://doi.org/10.1108/IJBM-09-2018-0253
Zurlo, K. (2021). Session 2425 (paper). Innovation in Aging, 5(Supplement_1), 241–241.
https://doi.org/10.1093/geroni/igab046.930

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ASSESSING THE ROLE OF PERCEIVED VALUE ON
PURCHASE INTENTION ON LIVESTREAM PLATFORMS
Minh Anh Tran
Gia Dinh University,
Vietnam
minhta01@giadinh.edu.vn

Abstract
E-commerce livestreaming is one of the fastest-growing types of commerce
today. Along with the development of the IT industry, wireless networks
covering the whole country with low capacity costs have led to the strong
growth of livestreaming activities. This article evaluates the factors affecting
consumers' purchase intention through e-commerce livestreaming and
examines the mediating role of perceived value in those relationships. The
data were collected from April to June 2021 with 217 valid questionnaires.
The results show that the components of perceived value (functional and
emotional) positively mediate the relationship between interactivity, content,
entertainment, opinion leader and promotion and purchase intention. As a
result, entertainment has the most substantial impact on perceived
emotional value, and content has the most decisive effect on perceived
functional value. In contrast, promotions had the lowest impact on perceived
functional and emotional value. This surprising outcome shows the
difference between E-commerce livestreaming and other forms.

Key Words
Livestream; perceived value; purchase intention; interactivity; entertainment.

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

INTRODUCTION
Livestreaming is a form of online communication and transmission method
that collects, releases, and views video information simultaneously in real
time on the internet (Wang, 2017). Along with the development of the IT
industry, wireless networks covering the whole country with low capacity
costs have led to the strong growth of livestreaming activities. Therefore, an
increasing number of livestreaming platforms with superior features attract a
growing number of consumers. In recent years, the livestream has been
continuously improved with increasingly diversified content and rapid
development. Many industries today, such as education, games, finance,
and sports, use livestream tools to reach consumers. E-commerce
platforms, video-sharing platforms and short video platforms have launched
livestream features to use the advantages of livestreaming to attract users.
Some famous stores in livestream e-commerce, such as Jessica Alba's The
Honest Company (on Amazon Live), Wayfair (on YouTube), and The Fresh
Market, show that this business form has excellent potential.
E-commerce livestreaming is one of the fastest-growing types of
commerce today. It combines livestreaming and e-commerce to sell
products during online streaming (Wang, 2017). Kang et al. (2021) argue
that e-commerce livestreams are a form of e-commerce, with the primary
marketing tool being live broadcasts. In e-commerce livestreaming, the
master of ceremonies (MC) can interact in real time with the audience (Hou
et al., 2019). Therefore, e-commerce livestreaming has advantages over
traditional e-commerce in terms of interactivity and conversion rate (Xu et
al., 2021). E-commerce livestreaming gathers consumers together to create
a strong interaction with a high frequency between sellers and buyers.
Communicating according to the group effect helps e-commerce
livestreaming stimulate consumers to buy more than in traditional ecommerce (Mike, 2016). The multidimensional information characteristics,
sharing of experiential content, and the closing atmosphere of e-commerce
livestreaming effectively improve the order conversion rate. Factors
including the aspects of the e-commerce platform, the characteristics of the
interface of the MC livestreaming, and the interaction of the livestream MC
affect perceived usefulness, thereby positively affecting the intention to
continue to use the e-commerce livestreaming. According to Meng (2012),
compared to traditional e-commerce, the livestreamed e-commerce is
significantly superior in all aspects, such as product introduction, similarity,
direct cooperation, and enhancing the consumer experience.
Currently, most studies on e-commerce livestreaming by scholars have
researched the current status and policies of livestream e-commerce
development, the application of livestream e-commerce in other industries,
or the sales effectiveness of e-commerce livestreaming from a brand
perspective. However, there are few studies on e-commerce livestreaming
that analyse consumer behaviour. With the development of online shopping,
consumers' information collection methods and channels are highly diverse.
For example, the internet has changed consumers' shopping habits, and the
psychology of consumers has since altered accordingly. From the

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psychological aspect of consumers, this study evaluates the factors affecting
consumers' purchase intention through e-commerce livestreams, thereby
adding to studies related to consumer psychology and purchase intention.
LITERATURE REVIEW
Purchase Intention
Fishbein and Ajzen (1975) first introduced the concept of behavioural
intention, which manifests each person's willingness to perform a specified
behaviour. It is considered the premise that directly leads to behaviour.
Mirabi et al. (2015) argue that purchase intention is the impulse to buy a
particular product in a particular buying environment. Shah et al. (2012) gave
a comparative definition, asserting that purchase intention is a process of
choice, expressing the motives that indicate why the customer buys a
particular product. Ghosh (1990) argues that purchase intention is an
essential predictor of consumers' purchasing decisions. Since purchase
intention can help predict consumers' purchasing decisions, it is of practical
importance to study the factors affecting consumers' purchasing intentions.
Therefore, it allows managers to develop appropriate strategies in the sales
process to promote consumers' buying decisions.
Along with the rapid development, many scholars have researched online
shopping intention. According to Salisbury et al. (2001), online purchase
intention is one factor that motivates customers to buy online. According to
Close and Kukar-Kinney (2010), online purchase intention comes from
purchase intention. Thus, the intention to purchase online is the certainty of
consumers who will buy over the internet. Meskaran et al. (2013) defined
online purchase intention as a customer's willingness to purchase over the
internet.
Currently, when researching the online shopping intentions of consumers,
scholars focus on two factors: trust and perceived value. Chen (2012)
studied the influence of personal identity and trust on online purchase
intention, examining the factors affecting consumers' online purchase
intention. Personal value is measured by value, perceived sacrifice, and
trust, including ability, benevolence and honesty. Kawet et al. (2017) explain
that brand identity and trust greatly influence product purchase intention, and
part of the influence of value and trust will contribute to purchase intention.
Perceived value
From the 1990s onwards, the competition between businesses has become
increasingly fierce. As a result, the customer's position is increasingly
valued, and the importance of the customer's perceived value is increasingly
confirmed. Therefore, consumers' perceived value is the object of many
scholars' research. Zeithaml (1988) defined the perceived value of
customers as the overall assessment of the usefulness of a product based
on the perception of what is received and what is spent. Meanwhile,

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Anderson and Sullivan (1993) define customer perceived value as the
perceived monetary value of the technical, economic, service and social
benefits that customers receive compared to the price they pay for a product,
taking into consideration the prices and offers of available suppliers. Thus,
the buyer's perception of value is a description of the balance between the
quality of the product or the benefits they perceive from the product and the
cost they pay for the product. Butz and Goodstein (1996) argue that the
customer's perceived value is the emotional relationship established
between the customer and the supplier after the customer has used a
product or service of the supplier and find that the product or service creates
added value.
The concepts of customer perceived value of Zeithaml (1988) and
Woodruff (1997) are developed from a rational point of view, comparing two
aspects: benefits received and value spent. However, many researchers
believe that emotional factors also influence customers in the consumption
process. Therefore, many researchers measuring customers' perceived
value have used a multidimensional approach. Holbrook and Hirschman
(1982) pointed out two aspects of perceived value: emotions and
pragmatics. Sheth, Bruce and Barbara (1991) identified five dimensions:
functional value, social value, emotional value, intellectual value, and
conditional value. Sweeney and Soutar (2001) identified four dimensions:
social value, emotional value, perceived quality, and perceived price. Petrick
(2003) studies and tests the relationship between monetary and
nonmonetary prices, reputation, service quality and emotional response to
customers' overall perceived value of the service. Thus, when studying
consumers' perceived value, it is necessary to consider the rational
considerations of consumers. At the same time, paying attention to
consumers' emotional needs is also necessary.
In recent years, along with the explosive development of the internet and
the e-commerce industry, an increasing number of scholars have studied
consumers' perceived value when shopping online. When researching
aspects of consumers' perceived value when buying online, Dong and Yang
(2008) argued that perceived value has three dimensions: consequential
value, procedural value and emotional value. Deng (2015) divides perceived
value into four aspects with priority: social value, quality value, service value,
and price value. In studying the relationship between consumers' perceived
value and online interactions, Sun (2016) concluded that perceived value
includes three aspects: functional value, emotional value and perceived risk.
After referring to previous studies and considering multidimensional
research from rational and emotional perspectives, this article studies two
aspects of consumers' perceived value in online shopping: functional and
emotional.
The results of Xu et al. (2020) demonstrated that perceived perception
positively affects consumers' purchase intention. In an e-commerce
livestream, the MC's consultation will provide consumers with information
and knowledge about the product and, at the same time, share their own
user experiences, helping consumers to feel more about the functional value
of the product. Many studies also consider the perceived quality of products

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by consumers as an important factor constituting the perceived value of
consumers. For example, in the process of watching livestreaming, through
the process of self-observation, the introduction of MC and the interaction of
other buyers will shape the perceived functional value of the consumer
towards the product. When consumers realise that the quality of the product
is guaranteed, they will have a greater intention of buying it. Therefore, the
first hypothesis is as follows:
H1: Perceived functional value has a positive effect on purchase intention.
Research by Chen and Lin (2018) proves that perceived value positively
impacts consumers' purchase intention. Xu et al. (2020) also confirm that
perceived preference positively affects consumers' purchase intention.
While watching the livestream, customers have positive feelings such as fun
and excitement. Thus, the stimuli that consumers receive during livestream
viewing will change consumers' emotions, affecting consumers' purchasing
intentions. Therefore, the article proposes the following research hypothesis:
H2: Perceived emotional value has a positive effect on purchase intention.
Interactivity
Interactivity is a prominent feature of e-commerce livestreams, so it plays a
critical role in the e-commerce livestream environment. For example,
customers can interact directly with MC through the chat interface.
Interactivity is the exchange of activities between consumers and MCs or
between consumers through asking questions, answering, liking, sharing or
mini-games. Sun (2016) proves that interactivity positively affects perceived
functional and emotional value. In an e-commerce livestream, through the
interactive process, consumers will understand the product's features more
clearly and vividly, increasing their interest in the product. From the above
arguments, the following hypothesis is proposed:
H3a: Interactivity has a positive effect on perceived functional value.
H3b: Interactivity has a positive effect on perceived emotional value.
Promotion
Promotion is a business tactic often used in e-commerce livestreams. While
watching livestreams, consumers can enjoy exclusive discount offers. Online
stores often use deals or other promotional activities to stimulate consumers'
buying action. Promotions in this study are all promotional activities during
livestreaming, including preferential discounts, gifts, and vouchers,
exclusively for consumers watching the livestream. Hao et al. (2008), when
studying the impact of the discount effect on promotion, found that the form
of product price discounts brings higher perceived value to consumers, both
in terms of functional and emotional value. Therefore, the following
hypothesis is proposed:

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H4a: Promotion has a positive effect on perceived functional value.
H4b: Promotion has a positive effect on perceived emotional value.
Entertainment
Currently, people's quality of life continues to improve daily. Consumers
shop not only to meet their shopping needs but also to meet their
entertainment needs. Therefore, while watching livestreams, consumers
also have particular entertainment needs. The entertainment concept
includes stimuli that bring joy and comfort to consumers during livestream
viewing. Xu et al. (2020) proved that entertainment positively impacts
perceived emotional and functional value. During livestreaming, the seller
can use the form of conveyed content, humour and an attractive style of the
MC to create a fun and entertaining experience for consumers to enhance
the perceived emotional and functional value of product features. The related
hypotheses are as follows:
H5a: Entertainment has a positive effect on perceived functional value.
H5b: Entertainment has a positive effect on perceived emotional value.
Content
Content refers to useful information or knowledge about specific aspects
consumers receive while watching the livestream. During livestreaming, the
MC provides product information more comprehensively and vividly while
providing expert knowledge, which helps consumers make more reasonable
purchasing decisions. In addition, it improves shopping efficiency for
consumers. Xu et al. (2020) demonstrate that content positively affects
perceived emotional and functional value. Thus, the new hypotheses are
stated as follows:
H6a: Content has a positive effect on perceived functional value.
H6b: Content has a positive effect on perceived emotional value.
Opinion leader
Opinion leaders are people who regularly provide objective information in a
particular field that has a specific influence on many others. The public
opinion leader in this article is only the MC, who, during livestreaming, has
professionally provided information about products or services to
consumers. The results of Jia (2019) show that the consciousness, affection
and knowledge of livestream MCs positively affect consumers' trust. Meng
(2012) proves that opinion leaders' trust positively impacts professionalism,
product knowledge, interactivity, and popularity. In e-commerce
livestreaming, many brands have invited famous people such as singers,
actors, and influential people on social networks to take on the role of
livestream MCs. They are professional and highly effective people called

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opinion leaders. Research by Xu et al. (2020) has demonstrated that opinion
leaders positively impact perceived emotional and functional value. The
subsequent research hypotheses are stated as follows:
H7a: Opinion leadership has a positive effect on perceived functional value.
H7b: Opinion leadership has a positive effect on perceived emotional value.
RESEARCH METHODOLOGY
This article uses Google Forms to build a survey and then sends the link via
email, Zalo, Messenger, and Viber to the survey subjects. At the same time,
the author printed the paper survey to send to the respondents who could
not access it through the above methods.
The study used structural equation modelling (SEM) to test the
hypotheses. According to Hair et al. (2010), the minimum sample size should
be 50 or more, and the desired ratio is five observations for each variable.
According to Kline (2015), a sample size below 100 is small, from 100–200
is medium and above 200 is large. Thus, based on the opinion of Hair et al.
(2010), the minimum sample size of this study should be greater than
5x9=45 samples. According to Anderson and Gerbing (1988), a sample size
of 150 or more is acceptable. From the above arguments, the article collects
200 survey samples to increase the scales' reliability. The survey sample
was selected by convenience sampling, and the sample selection criteria
were those who had watched e-commerce livestreaming. The observed
variables in the scales all use a 5-point Likert scale with scores ranging from
1 to 5: 1 - Strongly disagree, 2 - Disagree, 3 - Neutral, 4 - Agree, to 5 Strongly agree.
The interactivity scale is adjusted from the study of Lin et al. (2017) and
Chen and Lin (2018), including four observed variables. The promotion scale
also has four observed variables, a combination of Jiang (2019) and Wang
(2020). The content (3 items), entertainment (3 items) and opinion leader (4
items) scales are inherited from Jiang (2019), Chen and Lin (2018) and Meng
(2012), respectively. The perceived functional value and perceived
emotional value scales with three items were borrowed from Meng (2012),
and the purchase intention scale was built from reference to the studies of
Lin et al. (2017) and Lin (2017).
The data sample was collected from April to June 2021. This study
collected 263 survey questionnaires, of which 217 votes passed the filtered
question, and the percentage of votes that failed the filtered question was
17.5% because they reported that they never watched the livestream.
Among the respondents, there were 166 female respondents, accounting for
76.5%. A total of 26.7% of respondents were 18- to 25-year-olds, 21.2%
were between 26 and 30 years old, 28.1% were between 31 and 35 years
old, and 23.5% were over 35 years old. Regarding income, 21.7% of
respondents received below 200 EUR/month, 30.4% had income from 200
EUR to under 400 EUR, 34.1% had income from 400 EUR to under 800
EUR, and 13.8% had revenue of 800 EUR or more. In terms of educational

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qualifications, 6.9% of the candidates have a high school degree, 18.9% of
the respondents have a college/vocational level, 61.3% of the respondents
have a university degree, 11.5% students have a Master's degree, and 1.4%
of the candidates have other educational qualifications. Regarding the
average number of online purchases a month, 17.5% of candidates shop
less than once, 49.3% buy from 1 to 3 times, and 33.2% of candidates shop
more than seven times a month.
RESULTS AND DISCUSSION
Data were analysed using partial least squares structural equation
modelling. Hair et al. (2019) suggested that two phases of the PLS-SEM
assessment process should be implemented. Phase 1 is a measurement
model assessment with analytical steps such as reliability, convergent and
discriminant validity, and multicollinearity. Phase 2, Structural model
assessment, includes an explanatory power check (R2) and path coefficient
assessment.
Measurement Model Assessment
According to Hair et al. (2017), when assessing the internal stability of the
scale, both Cronbach's alpha and the composite reliability coefficient should
be considered. The larger the Cronbach's alpha coefficient is, the higher the
internal consistency reliability. According to Hair et al. (2014), Cronbach's
alpha coefficient needs to be greater than 0.7. The author's data analysis
results in Table 1 show that the scales have Cronbach's alpha in the range
from 0.838 to 0.947, all greater than 0.7. In addition to using Cronbach's
alpha coefficient, the scale's reliability is also evaluated through the
composite reliability coefficient. Composite reliability is best when it has a
value of 0.7 or higher (Hair et al., 2010). In Table 1, the reliability of the scale
ranges from 0.892 to 0.966, both greater than 0.7, which shows that the
scales used are satisfactory.
Table 1: Reliability, convergent validity and multicollinearity test
CA

CR

AVE

Outer
loadins

Interactivity

0.838

0.892

0.673

0.790–0.845

Promotion

0.887

0.922

0.747

0.842–0.885

Entertainment

0.884

0.927

0.810

0.878–0.924

Content

0.882

0.927

0.809

0.880–0.917

Opinion leaders

0.880

0.917

0.735

0.826–0.881

Perceived functional value

0.947

0.966

0.905

0.943–0.960

VIF
1.716–
1.977
2.363–
2.767
2.224–
3.344
2.177–
2.860
2.074–
3.138
2.244–
2.991

R2

0.512

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Perceived emotional value

0.88

0.92

0.809

0.881–0.922

Purchase intention

0.93

0.95

0.885

0.935–0.945

2.200–
3.672
2.744–
3.234

0.644
0.627

Source: Own survey.

This study assessed convergent validity through the outer loadings of
observed variables and average variance extracted (AVE). The AVE
coefficient needs to be above 0.5, and the outer loading needs to be greater
than 0.7 for the scale to converge (Hair et al., 2017). The results in Table 1
show that the scales' AVE and outer loading indices are more significant than
0.7. This result indicates that the scale variables converge with the research
concepts.
Table 2: Fornell-Larcker criterion

Perceived
emotional
value
Perceived
functional
value
Entertainment
Promotion
Opinion leader
Content
Interactivity
Purchase intention

1

2

3

4

5

6

7

8

0.90
0
0.73
1
0.67
9
0.60
8
0.63
0
0.68
6
0.64
2
0.71
8

0.95
1
0.54
9
0.52
2
0.59
0
0.65
4
0.55
7
0.72
1

0.90
0
0.54
6
0.49
6
0.60
6
0.56
9
0.59
2

0.86
5
0.57
1
0.63
1
0.50
4
0.62
9

0.85
7
0.64
3
0.54
6
0.63
1

0.90
0
0.65
2
0.73
5

0.82
1
0.59
4

0.94
1

Source: Own survey.

In PLS-SEM, the discriminant validity is evaluated based on the FornellLarcker criteria. The square root of AVE must be greater than the
intercorrelation coefficient between concepts (Fornell & Lacker, 1981). The
results shown in Table 2 show that the square root AVE of each concept is
larger than the correlation coefficient of that concept and others. Therefore,
the scale meets the discriminant condition.
Structural Model Assessment
Table 3: Bootstrapping analysis
Path Coefficients

β

P Values

Results

Interactivity → Perceived functional value

0.123

0.070

Rejected

Interactivity → Perceived emotional value

0.186

0.007

Accepted

Promotion → Perceived functional value

0.060

0.427

Rejected

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Promotion → Perceived emotional value

0.128

0.024

Accepted

Entertainment → Perceived functional value

0.156

0.043

Accepted

Entertainment → Perceived emotional value

0.300

0.000

Accepted

Content → Perceived functional value

0.302

0.000

Accepted

Content → Perceived emotional value

0.179

0.011

Accepted

Opinion leader → Perceived functional value

0.218

0.003

Accepted

Opinion leader → Perceived emotional value

0.191

0.003

Accepted

Perceived functional value → Purchase intention

0.215

0.021

Accepted

Perceived emotional value → Purchase intention
Source: Own survey.

0.310

0.000

Accepted

R2 is an index that measures the extent to which the input variables
explain the change in the dependent variable. The value of R2 ranges from
0 to 1, and the closer it is to 1, the greater the independent variable explains
the dependent variable. According to Henseler et al. (2009), there are three
R2 ratings: 0.25 is weak, 0.5 is moderate, and 0.75 or higher is high. The
results in Table 1 show that R2 ranges from 0.512 to 0.644, which is
assessed as the average level of impact. The concepts of interaction,
promotion, content, opinion leader, and entertainment explained 64.5% of
the variation in perceived emotional value and 51.2% of the variation in
perceived functional value at statistical significance level of 5%; the rest is
unexplained due to other factors not included in the model. For example,
perceived emotional and perceived functional value explained 62.7% of the
variation in purchase intention via livestreaming.
Multicollinearity was assessed using the variance inflation factor (VIF). If
this index is less than 10, the model does not experience multicollinearity
among the independent variables (James et al., 2013). Thus, the results in
Table 1 show that the VIF value is less than 10, indicating no multicollinearity
between the independent variables.
The study uses the bootstrap method of resampling from the original
sample to generate a large enough number of secondary samples (bootstrap
samples), from which there is a basis for evaluating the accuracy of the
statistical parameters of the original model. The author uses the
bootstrapping method repeated 5,000 times and then considers the path
coefficient and the P value. The path coefficient value ranges from -1 to +1.
The closer this value is to +1, the stronger the positive relationship, and vice
versa. In addition to testing the hypothesis, the author considers the P value.
If the P value ≤ 0.05, the level of impact is statistically significant, and the
hypothesis is accepted; otherwise, if the P value > 0.05, the level of effect is
not statistically significant, and the hypothesis is rejected.
The path coefficient results in Table 3 show that interaction, promotion,
entertainment, content, and opinion leader positively impact perceived
emotional and functional value. Entertainment has the strongest impact on
perceived emotional value (β = 0.300), and content has the strongest impact
on perceived functional value (β = 0.302). In contrast, promotions had the
lowest impact on perceived functional value (β = 0.060) and perceived

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emotional value (β = 0.128). Perceived functional value and perceived
emotional value positively impact purchase intention through livestreaming,
in which perceived emotional value (β = 0.310) has the strongest impact on
purchase intention, followed by perceived functional value (β = 0.215).
Regarding the p values, the results in Table 3 show that the relationship
between interactivity and promotion with perceived functional value has p
values > 0.05. Hence, the relationship between these variables is not
statistically significant. The remaining relationships between the
independent and intermediate variables (perceived emotional value and
perceived functional value) all have p values < 0.05; thus, these hypotheses
are verified. The relationship between perceived emotional value and
perceived functional value with purchase intention through livestreaming is
statistically significant with p values < 0.05; thus, the hypotheses are
confirmed.
The results of this study show that interactivity has a positive impact on
consumers' perceived emotional value. This result is similar to the results of
Sun (2016). The interactive activities in livestreaming have made consumers
happy and excited, thereby increasing purchase intention. However,
promotion does not affect perceived functional value, in contrast to the
results of Hao et al. (2008). The reason may be that consumer psychology
does not believe in the quality of the product, so promotion does not affect
consumers' confidence, and advancements in livestreaming make
consumers feel excited because of low prices but do not affect consumers'
thoughts about product quality.
The results of this study are similar to those of Yao and Qin (2010) and
Xu et al. (2020). The study shows that entertainment, content and opinion
leaders positively affect consumers' perceived functional and emotional
value. This means that the higher the entertainment atmosphere during
livestreaming, the higher the customer's trust, sympathy and perception of
product quality. This study's entertainment concept includes stimuli that bring
a sense of joy and comfort to consumers during livestream viewing. Along
with socioeconomic development, when participating in shopping activities,
consumers also meet shopping needs and can relax and relieve stress.
Currently, the sales livestream program on e-commerce floors has diverse
content and is well invested in many entertainment programs, with rich
content, trend updates and the tastes of consumers, increasing the amount
of interaction with customers. This helps increase traffic with viewers,which
will result in more customers.
CONCLUSIONS
This study proves that interactivity positively affects perceived emotional
value, and content quality is the most important factor affecting consumer
trust, perceived functional value and perceived emotional value. However,
products still play a vital role in the e-commerce livestreaming program. For
example, when consumers watch the e-commerce livestreaming, the most
critical purpose is to learn about the product by asking questions to the MC

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and talking to other consumers. Therefore, during livestreaming, MCs need
to understand and improve their ability to introduce product features,
promptly answer consumers' questions, and provide them with more intuitive
information outside the product's image. Finally, they must highlight the
product's advantages and help consumers make purchasing decisions more
effectively. During the e-commerce livestreaming, the MC also must provide
more product-related knowledge to consumers so that they can both shop
and perceive added value while watching the livestreaming programs.
Research has shown that the higher the level of professionalism of the
opinion leader, the more consumers trust the product and the greater the
perceived functional and emotional value. Therefore, when brands and
sellers choose MCs, it is necessary to choose people with a certain degree
of popularity, influence and high professionalism in the product-related field
to attract more customers. In addition, MCs’ endorsement and influence help
them have a clearer perception of the product's value, improve consumer
confidence in the product, and increase the product's transaction volume
during the livestreaming.
Currently, the livestream feature is becoming increasingly popular, and
consumers increasingly demand the content and interactivity of livestream
programs. Monotonous product explanations, traditional comments and
answers, and some price reductions have not been able to meet users'
needs. Platforms providing livestream features need to be researched and
developed to promote innovative forms of livestreaming, such as using VR,
AR and other technologies to convert livestream programs from "twodimensional" to "three-dimensional". In addition, livestreaming provides
consumers with more accurate and efficient product information with a more
prosperous, immersive viewing experience.
Perceived functional value and perceived emotional value positively
influence consumers' purchase intention through livestreaming. Consumers
who watch e-commerce livestream programs can more intuitively feel the
quality of the product and are interested in the product, thereby generating
a desire to buy. However, products are still the core factor determining the
success of the livestream program. For example, e-commerce livestream
platforms need to clarify the quality standards of livestream products and, at
the same time, conduct tests on products introduced in livestreaming to
ensure that the quality of the products is up to standards. Thus, sellers need
to provide customers with products that meet their needs at a strictly
controlled rate, strengthen the attachment of consumers who buy via
livestreaming, and attract loyal customers, thereby promoting transactions
of other products.
REFERENCES
Anderson, E. W., Sullivan, M. W. (1993). The antecedents and consequences of customer
satisfaction for firms. Marketing science, 12(2), 125–143.
Anderson, J. C., Gerbing, D. W. (1988). Structural equation modeling in practice: A review
and recommended two-step approach. Psychological bulletin, 103(3), 411–423.

94

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Butz Jr, H. E., Goodstein, L. D. (1996). Measuring customer value: gaining the strategic
advantage. Organizational dynamics, 24(3), 63–77.
Chen, C. C., Lin, Y. C. (2018). What drives live-stream usage intention? The perspectives of
flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1),
293–303.
Chen, H. (2012). The Influence of Perceived Value and Trust on Online Buying Intention.
Journal of Computer, 7(7), 1655–1662.
Close, A. G., Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers' online
shopping cart use. Journal of Business Research, 63(9-10), 986–992.
Deng, F. (2015). Research on the Composition of Customer Perceived Value in Online
Shopping Mode. Business Economic Research, 30, 66–67.
Dong, D., Yang, Y. (2008). Theoretical Analysis of Consumer Perceived Value in the Network
Environment. Journal of Management, 5(6), 856.
Fishbein, M., Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory
and research. Reading, MA: Addison-Wesley.
Fornell, C., Larcker, D. F. (1981). Structural equation models with unobservable variables and
measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.
Ghosh, A. (1990). Retail management. Chicago: Dryden press.
Hair, J. F., Barry, J. B., Rolph, E. A. (2010). Multivariate data analysis. Pearson Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on
the wall: a comparative evaluation of composite-based structural equation modeling
methods. Journal of the academy of marketing science, 45(5), 616–632.
Hair, J. F., Risher, J. J., Sarstedt, M., Ringle, C. M. (2019). When to use and how to report
the results of PLS-SEM. European Business Review, 31(1), 2–24.
Hair, J. F., Sarstedt, M., Hopkins, L., Kuppelwieser, V. G. (2014). Partial least squares
structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121.
Hao, L., Gao, C., Jia, J. (2008). An Empirical Study on the Effect of Price Discount
Presentation on Promotion Effect. Management World, 10, 106–114.
Henseler, J., Ringle, C. M., Sinkovics, R. R. (2009). The use of partial least squares path
modeling in international marketing. In New challenges to international marketing. Emerald
Group Publishing Limited.
Holbrook, M. B., Hirschman, E. C. (1982). The experiential aspects of consumption:
Consumer fantasies, feelings and fun. Journal of Consumer Research, 9(2), 132–140.
Hou, F., Guan, Z., Li, B., Chong, A. Y. L. (2020). Factors influencing people’s continuous
watching intention and consumption intention in live streaming: Evidence from China.
Internet Research: Electronic Networking Applications and Policy, 30(1), 141-–163.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An introduction to statistical
learning (Vol. 112, p. 18). New York: Springer.
Jia, X. (2019). Exploring consumers’ purchase and engagement intention on E-Commerce
live streaming platform (Master's thesis, Beijing University of Posts and
Telecommunications).
Jiang, J. (2019). Analysis of Influencing Factors of Internet Celebrity Live Broadcast on
Consumer Purchasing Decision. (Master's thesis, Beijing University of Posts and
Telecommunications).
Kang, K., Lu, J., Guo, L., Li, W. (2021). The dynamic effect of interactivity on customer
engagement behavior through tie strength: Evidence from live streaming commerce
platforms. International Journal of Information Management, 56, 102251.
Kawet, O., Pangemanan, S. S., Tumiwa, J. (2017). Analyzing The Effect Of Perceived Value
And Trust On Purchase Intention (Case Study of Zalora). Jurnal EMBA: Jurnal Riset
Ekonomi, Manajemen, Bisnis dan Akuntansi, 5(2), 132450.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford
publications.
Meng, F. (2012). Research of Opinion Leaders' impact on purchase intention under social
commerce context. (Master's thesis, Nanjing University).
Meskaran, F., Ismail, Z., Shanmugam, B. (2013). Online purchase intention: Effects of trust
and security perception. Australian Journal of Basic and Applied Sciences, 7(6), 307–315.

95

Advances in Business-Related Scientific Research Journal, Volume 13, No. 2, 2022

Mike. (2016). The core demand of live streaming E-Commerce: how to effectively combine
the live streaming mode and E-commerce attributes. Retrieved 5 March 2022, from
http://www.woshipm.com/pd/458649.html.
Mirabi, V., Akbariyeh, H., Tahmasebifard, H. (2015). A study of factors affecting on customers
purchase intention case study: The agencies of bono brand tile in Tehran. Journal of
Multidisciplinary Engineering Science and Technology, 2(1), 267–273.
Lin, J., Hu, Q., Lu, Y. (2017). Effect of social commerce characteristics on Consumer
Decision-making Behavior: A Guanxi Management perspective. Journal of Business
Economics, 37(1), 52–63.
Lin, T. (2017). Analysis of fashion live broadcast communication strategy and communication
effect (Master's thesis, Wuhan University).
Petrick, J. F. (2003). Measuring cruise passengers' perceived value. Tourism analysis, 7(34), 251–258.
Salisbury, W. D., Pearson, R. A., Pearson, A. W., Miller, D. W. (2001). Perceived security and
World Wide Web purchase intention. Industrial Management & Data Systems, 101(4), 165–
177.
Shah, S. S. H., Aziz, J., Jaffari, A. R., Waris, S., Ejaz, W., Fatima, M., Sherazi, S. K. (2012).
The impact of brands on consumer purchase intentions. Asian Journal of Business
Management, 4(2), 105–110.
Sheth, J. N., Bruce, I. N., Barbara, L. G. (1991). Why We Buy What We Buy: A Theory of
Consumption Values. Journal of Business Research, 22, 159–170.
Sun, H. (2016). Research on the Mechanism of "Network Interactive Experience Chain" from
the Perspective of Consumer Perception. Business Economics Research, 16, 64–66.
Sweeney, J. C., Soutar, G. N. (2001). Consumer perceived value: The development of a
multiple item scale. Journal of retailing, 77(2), 203–220.
Wang, X. (2017). Live streaming from the perspective of communication. Visual, 2017(9).
Retrieved from https://www.zzqklm.com/w/hxlw/21872.html.
Wang, T. (2020). Research on Consumers' Purchase Intention in the E-commerce live
streaming. (Master's thesis, Minzu University of China).
Woodruff, R. B. (1997). Customer value: the next source for competitive advantage. Journal
of the academy of marketing science, 25(2), 139–153.
Xu, X., Huang, D., Shang, X. (2021). Social presence or physical presence? Determinants of
purchasing behaviour in tourism live-streamed shopping. Tourism Management
Perspectives, 40, 100917.
Xu, H., Qu, H., Cai, J. (2020). Influencing Factors of Apparel Consumers' Impulse Purchase
Intention in the Context of Network Broadcast. Journal of Donghua University, 1–11.
Yao, G., Qin, Z. (2010). Research on the influence of consumers on the experience of ecommerce companies' trust maintenance process. Nankai Management Review, 13(1), 99–
107.
Zeitham, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model
and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

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