Article

Factors of Multi-Level Marketing Success Strategies

Which Motivate Participants
Albert Makore Talak Moyo
https://orcid.org/0000-0001-8091-5128 Great Zimbabwe University
Great Zimbabwe University mmbtalak@gmail.com
makorea2009@gmail.com

Evelyn Madziba
Great Zimbabwe University
evelynmadziba@gmail.com

Abstract
Background: Remarkable successes have been registered throughout the world
by individuals engaged in multi-level marketing (MLM), also called network
marketing, which refers to individuals selling products to the public, often by
word of mouth and direct sales.
Purpose: The purpose of this study was to examine the determinants of MLM
success strategies in the Zimbabwean economy and to identify factors that
influence multi-level marketers in Zimbabwe.
Methodology: A quantitative approach using a survey questionnaire was used
to collect data which was then analysed using SPSS. A sample of 146 usable
responses drawn from Harare and Masvingo was used in the study. Statistical
techniques, which included exploratory factor analysis (EFA) and the
correlation matrix, were carried out to deduce the strategies associated with
achieving success as a distributor for a MML company.
Findings: The study findings suggest that success in MLM is dependent
primarily on the following factors: incentives for motivation; team-building
methods; and support strategies. It is from these factors that the study further
sought to identify the individual variables or combinations thereof that could be
endorsed as predominantly influencing the success of MLM in Zimbabwe.
Value: Empirical evidence is provided on the latent constructs or factors that
influence individuals to join MLM companies. As part of the practical
contribution, MLM practitioners should focus on the compensation plan, trust,
and commitment as key factors in motivating individuals to participate in MLM.

South African Business Review https://doi.org/10.25159/1998-8125/12783

https://unisapressjournals.co.za/index.php/SABR ISSN 1998-8125 (Online)
Volume 27 | 2023 | #12783 | 25 pages © The Author(s) 2023
Published by Unisa Press. This is an Open Access article distributed under the terms of the Creative
Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-
sa/4.0/)
 Makore, Moyo and Madziba

Keywords: multi-level marketing; network marketing; distributors; dendrites; direct

marketing

Introduction
Multi-level marketing (MLM), also called network marketing, refers to individuals
selling products to the public, often by word of mouth and direct sales. MLM is a
peculiar form of direct selling which the salespeople who are in business for themselves
utilise and their compensation assumes a multi-level structure (Christensen 2008). In
2021, it was estimated that approximately 128 million salespeople (called distributors
in MLM) were participating in network marketing globally, with only 5.48 million
active in Africa and the Middle East (Statista 2021). MLM has earned its association
with direct marketing because it gets the products into the hands of the end-user through
face-to-face selling, which occurs away from the manufacturer’s location. Some studies
have also indicated that MLM can be traced to relationship marketing philosophy, which
stresses long-term relationships with the customer rather than transactional relationships
(Jung, Ineson and Green 2013).

In Zimbabwe also, MLM has become a popular business option, although there are no
documented statistical records of participants in the business. In Asia, similar gains as
those realised in the United States (US) are being recorded through MLM. Rubino
(2005) claims a direct link between network marketing and the transformation being
experienced by many individuals, communities, and companies that are engaged in this
form of business. It is noted that network marketing has become a source of hope to
those who normally would not have stood a chance at being employed in the formal
sector Rubino (2005). The socially disadvantaged groups, such as women, widows, and
those who were previously unemployed and deemed unemployable, have found a viable
route through which many have risen from poverty to prosperity (Groß and Vriens
2019). In many cases, this has happened in the famous rags to riches style.

Zimbabwe has seen an influx of MLM companies, and they have brought an assortment
of products ranging from healthcare, skincare, kitchenware, clothing, and accessories to
agricultural implements; all of which are sold through what is referred to as MLM. Some
of the most common names in MLM found in Zimbabwe include the following: Forever
Living, Dynapharm, Tablecharm, Tiange, and World Ventures. The products are
distributed by individuals through social network channels. The local manufacturing
firms have not yet embraced MLM as a distribution strategy. The individuals who are
involved in MLM seem not to be making much of an impact in the business, and hence
there are few success stories.

Past research has recorded that recruitment will mostly enhance growth and earnings as
long as the other factors remain stable (Vander Nat and Keeo 2002). This is further
supported by Pang and Monterola (2017) who brought forth the concept of dendritic
formations in MLM. Exchanges do occur in those linked nodes, and hence risk becomes
a factor that needs to be dealt with by all the parties involved in the process. The fact
2
 Makore, Moyo and Madziba

that risk is a significant factor in the exchange process, means that measures have to be
instituted to protect and safeguard the exchange process (De Wulf and Odekerken-
Schröder 2001). The sharing of information in the business process may also affect the
viability of the exchange process, as noted by the transaction cost theory (Coase 1937).
To observe the relationship mechanisms which lead to trust and commitment in MLM,
the researchers drew upon the social exchange theory (SET) in terms of rewards (Cortez
and Johnston 2020) and the transaction cost economies theory in terms of transaction
costs (Ketokivi and Mahoney 2020).

The SET is premised on the interaction between individuals and other groups, and
emphasis is placed on resource dependence, resource availability, and power as a frame
of reference (Emerson 1976). The SET notes that individuals are driven by incentives
to cooperate in an exchange, hence other theories, such as the self-determination theory
(SDT) (Deci, Olafsen and Ryan 2017), may support the determination of the factors
enhancing MLM success strategies.

Risk is reduced in MLM due to the need for a long-term relationship, and this inhibits
the desire to engage in destructive behaviour that might damage trust leading to broken
relationships. Members in network marketing relationships employ a structured method
of achieving gains (Bowen and Jones 1986). As noted from the research carried out by
Lee and Loi (2016), several factors that affect network distributor satisfaction were
explored, focusing on the diffusion of business ideas, perceived quality of members
joining a network, training, support, perception of marketing offers, and the rewards
every month to assess the strategies which are helpful in MML.

Problem Statement and Research Questions

The controversies in MLM or network marketing depict an evidence-void gap
concerning the factors that stimulate the growth being experienced in MLM
organisations at a global level due to unrestrained market capitalism or neoliberalism
(Wrenn 2022) and also at regional and country levels (Beek 2019). The legal and ethical
challenges of MLM companies emanate from the operational focus, which can be either
recruitment or value provision, resulting in the entities being viewed as illegal pyramid
schemes due to recruitment focus and misleading promises (Groß and Vriens 2019;
Suwitho, Riharjo and Dewangga 2023). Hence, the participants’ satisfaction is a critical
construct or factor in determining continuity and growth in MLM, and this calls for
further contribution in analysing the attitudinal and behavioural loyalty aspects which
lead to satisfaction (Purcaru et al. 2022). This study will contribute to the body of
knowledge by suggesting factors that motivate individuals supported by content,
process, and reinforcement theories. Therefore, the following research question was put
forward to evaluate the relationships of the various predictors on the outcome variables:

RQ1: What are the factors that motivate individuals to join Multi-level Marketing
companies?

3
 Makore, Moyo and Madziba

The research layout looked at the introduction and theoretical background, dealing with
a literature review focusing on the factors that influence individuals’ intention to join
MML. The methodology focused on the survey design and selection of the data
collection method. The last section deals with the results (exploratory factor analysis
(EFA)) and the findings, followed by managerial implications, limitations and areas of
future research, and conclusion and recommendation.

Literature Review
A network is a set of multi-party relationships that can be simple or complex depending
on the number of partners involved. When the number of partners increases, the
relationship gets more complex, and it then calls for relationship management. These
relationships are characterised by interaction, and this interaction is in the form of
information exchange and collaboration based on commitment and trust (Anderson,
Håkansson and Johanson 1994; Buttle and Maklan 2019; Gummesson 2008; Morgan
and Hunt 1994). The network can be a social network of acquaintances (friends and
relatives) working together for the common good. MLM is premised on leveraging this
connectivity in order to source and distribute products. It mainly involves the
development of retail selling and distribution networks that grow exponentially as new
distributors are incorporated. In some instances, these networks develop into vast
empires benefiting the individual distributors in the network and the firm supplying the
products. The networks in MLM are largely quasi-informal, and the distributors (who
become partners) are bound together by the gains they are likely to receive in that
relationship and the contractual agreements signed (Albaum and Peterson 2011).

In MLM, the distributors earn money on their sales, as well as on the deals of people
they recruited into the business, and on the sales of people hired by their recruits. The
network positions resemble a supply chain with various nodes; however, the MLM
linkages are dendritic in form. Different hierarchical locations are the hallmark of a
successful MLM supply chain network. The positions include distributor, assistant
supervisor, supervisor manager, and finally director, although the titles vary depending
on individual companies. Perhaps the pivotal positions in this dendritic formation,
which are also the key drivers of the business, are those of the recruiter and the
prospector. Collaboration between the two determines the extent of success that is
achieved in the MLM company.

Vander Nat and Keeo (2002) define MLM as a process of selling goods or services
through social networks which is either directly or indirectly linked. The MLM method
of selling tends to sell exclusive products and places heavy emphasis on the recruitment
of many representatives who, in turn, are also expected to recruit new members. The
same pattern continues to duplicate itself leading to the downstream formation of social
network chains that continue to multiply in a dendritic formation. In these networks, the
recruited member purchases an absolute value of the company’s products as an initial
investment which also qualifies them for membership. The products can either be

4
 Makore, Moyo and Madziba

consumed by the member or sold to the market at a profit. Membership makes the
member eligible to be a distributor, and hence gain the privilege to buy products at a
discount. In the process, the distributor earns commissions or points and makes a direct
profit from selling the products. Higher revenue is received through the recruitment of
new members who become active distributors. This is because the recruiter earns a
commission from the sales of their downline recruits making the drive for recruitment
a critical activity in MLM. Consequently, the more members are recruited and added to
the network chain, the more explosive the dendritic formation (Pang and Monterola
2017). As MLM grows and changes occur in business models there is a need to identify
the factors that motivate individuals to join MLM companies.

The Self-Determination Theory

Various theories have been utilised to evaluate the recruitment motives of participants
in MLM companies, such as the technology acceptance model (TAM) (Davis 1985),
highlighting perceived usefulness and perceived ease of use as noted in a recent study
by Nadlifatin et al. (2022). The current study examined the recruitment motives utilising
affiliate motivation theories supported by past research studies (Purcaru et al. 2022;
Roman et al. 2021).

The SDT can address the link between behaviour and motivations focusing on three
fundamental needs consisting of autonomy, affiliation and competence (Deci and Ryan
2012). Autonomy focuses on an individual’s desire to freely engage in an activity and
be in control of the decision-making process. Affiliation is an individual’s desire to feel
connected to their environment, particularly the immediate surroundings. Competence
refers to an individual’s desire to be effective in the process of interacting with the
environment (Alzamora-Ruiz et al. 2020).

The two major motivation components of the SDT comprise intrinsic motivation and
extrinsic motivation. Intrinsic motivation is shown by curiosity and the desire to
discover and focus on challenging aspects (Gilal et al. 2019). The various needs that
individuals have create gaps that may be seen as the difference between the individual’s
current state and the desired state resulting in motivations to correct the imbalance
(Thøgersen 2005). Intrinsic motivations are also related to specific objectives such as
affiliation, personal development, and profitability (Alzamora-Ruiz et al. 2020).

The SDT has six mini-theories, namely: the cognitive evaluation theory (CET); the
organismic integration theory (OIT); the causality orientations theory (COT); the basic
psychological needs theory (BPNT); the goal content theory (GCT); and the
relationships motivation theory (RMT. The main focus of the OIT is individuals’
extrinsic motivation of which there are four forms comprising external regulation,
introjected regulation, identified regulation, and integrated regulation (Deci and Ryan
2012; Gilal et al. 2019; Ryan and Deci 2020). A reward is an external form of regulation
and is an example of extrinsic motivation that will be obtained from engaging in an
activity (Alzamora-Ruiz et al. 2020).
5
 Makore, Moyo and Madziba

Incentives
The process of distributing goods and selling various products through networks makes
MML unique (Selamet et al. 2020). Factors, such as the company’s image or good
reputation, service delivery, reward schemes, social satisfaction, personal goals, trust,
and commitment, have been explored in terms of how they are perceived by potential
MLM participants (Nga and Mun 2011; Pratistha 2017). In addition, the prominence of
reward schemes has received wide recognition as one of the key motivational factors to
join a MLM company (Syahrivar et al. 2020). Other factors are critical in profiling
potential participants in MLM. Several distinct profiles have been used to categorise
participants and these may be based on major segmentation variables, such as
demographics (age, gender, religion, educational level), and psychographic
characteristics, such as social status (Grant-Smith et al. 2021).

Motivation
The researchers also noted motivation as an essential factor in MLM. The team leader
carries the responsibility for coaching, training, mentoring, and ensuring that team
members downstream are highly motivated (Vander Nat and Keeo 2002). Quite often
the motives would be closely linked with some personal situation for which they will
be searching for a solution. Both monetary and non-monetary rewards have been found
to motivate team members to actively participate in an MLM business, and the simple
explanation for this is said to rest on the social relationships that are characteristic of the
business (Coughlan and Grayson 1998).

Another mini-theory of the SDT, the GCT asserts that individuals are driven by the
anticipated results of their pursuit. This is based on the premise that individuals have to
establish a clear vision of their goals, as a prerequisite for building sufficient will and
effort to pursue the laid-out plans (Mullins 2010). Goals must be challenging and
realistic to provide direction, focus, and also regulate behaviour. The GCT explains and
notes the key differences between intrinsic motivators, such as personal growth, close
relationships, and community feelings, and extrinsic motivators, such as money, fame
and image, and hence the need to analyse the effect of both intrinsic and extrinsic
motivators on network marketing (Gilal et al. 2019).

For individuals who are motivated by financial rewards, then the compensation plan sits
at the top of the list. Compensation plans used by MLM companies vary according to
the preference of the owners of the companies. However, four basic types of
compensation plans are commonly used by MLM companies, namely: binary, matrix,
breakaway, and unlived plans (Coughlan and Grayson 1998). Commonly, MLM
members are compensated based on the volumes of the products that they sell together
with their team members (downlines). Thus, the total compensation comprises the sales
generated by the member, direct recruits, and indirect recruits (Christensen 2008).

6
 Makore, Moyo and Madziba

Recruitment Strategies
MLM relies mostly on presentations for recruitment and product sales (Pride and Ferrell
2008). Before the growth of internet technology, MLM companies used to rely on the
door-to-door contact method for accessing potential recruits and customers. However,
new concepts have since been incorporated such as the party plan or group presentation
method (Pride and Ferrell 2008).

The presentation strategies incorporate the use of word of mouth and testimonials of
success stories which have proven to be quite crucial in delivering the message
successfully. Other members of the MLM company are encouraged to present their own
life stories which bear testimony of tangible evidence of their success that can be
achieved in MLM (Msosa 2022). To enhance their efforts members also incorporate
other elements of the promotion mix such as advertising to augment the purchase
decision process (Fill 2009).

Presentation as a strategy for recruitment has a ripple effect on the MLM process. Its
impact creates results not only regarding convincing prospective members to join but
also the would-be users of the products to purchase, which results in the build-up of
momentum towards the achievement of overall goals. Thus, multi-level marketers also
borrow certain concepts from psychology to maximise the ripple effects derived from
personal testimonies. The more presentations made using personal stories the more
significant the impact on individual participants’ desire to excel in the business as well
as added inspiration to buy and sell more products (Christensen 2008). The
presentations, therefore, can be equated to the fuel that drives success in MLM.

Team Building
Team building is yet another pillar in building a successful career in MLM. Team
building is seen as two or more people working interdependently towards a common
goal. Some of the key team-building attributes are coming together to share experiences
(Jarvenpaa, Knoll and Leidner 1998). The attributes of team building include a
commitment to shared goals, trust, well-defined roles, communication, collaboration,
and positive personal relationships (Hakanen and Soudunsaari 2012). The other key
factors identified as fundamental to the success of MLM are self-motivation, leadership,
entrepreneurship, business attitude, knowledge of running a business, business
expertise, long-term people orientation, and business ethics (Roman et al. 2021). These
are requisite in establishing a highly productive team. In MLM, teamwork enhances
individual members’ ability to solve the challenges related to the business. Teamwork
also fosters amongst members the adoption and pursuit of a shared vision, mission, and
values, while through enhanced group communication, members give and receive
feedback to and from one another. Thus, the team-building effort focuses on how MLM
members relate to and operate with one another.

7
 Makore, Moyo and Madziba

Compensation plans in MLM are the key drivers for the success of most enterprises
despite the complexity of the structures (Coughlan and Grayson 1998; Keong and
Dastane 2019). However, the lack of productivity in one individual or team has
detrimental effects on the entire dendritic formation (see Figure 1). The reverse is also
true. The role of team building in these dendritic relationships is to encourage members
to work as teams that are inspired towards the achievement of individual and group
reward goals. Each recruited member is inspired to form their social network teams that
collaborate in order to realise their goal of increasing sales and recruiting new members.
All of this is made possible by the trust that is shared by all team members. Past studies
have also observed that individuals in MLM often recruit people they already know
(Legara et al. 2008).

Figure 1: Dendritic relationships

Source: Adapted from Cuntz et al. (2010) and Zemanian (1986)

Similarly, prospective members are more comfortable joining a group in which they
have confidence in the fulfilment of their individual goals. It is a rare occurrence in
African society for an individual to join a social network of people to whom they are a
stranger. Perhaps the “Guanxi” concept is the closest description the researchers found
that suitably explains the team members’ relationships. The idea entails merely that it is
critical to creating friendships as these play a vital role in the process of establishing
business relationships (Bruckermann 2021; Cateora et al. 2019). The process of
maintaining close relationships has been noted to be driven by relatedness from the SDT
(Deci and Ryan 2015) and one of its mini-theories, the RMT, as these show that
relationships are essential for human functioning and well-being (Deci and Ryan 2015).
The work-life balance or flexibility in terms of working hours culminating in earning
extra money are some of the key drivers in joining MLM companies (Grant-Smith et al.
2021).

8
 Makore, Moyo and Madziba

Collaboration
Collaboration in business can enhance the process of generating financial benefits
(Möhlmann 2015). Collaboration, a term borrowed mainly from supply chain
management, entails two or more independent parties working jointly for their common
benefit. The partnership is defined as occurring when “two or more independent
companies work jointly to plan and execute supply chain operations with greater success
than when acting in isolation” (Nha Trang et al. 2022; Simatupang and Sridharan 2005).
Collaboration allows for synergy to develop among partners and encourages joint
planning and real-time information exchange.

MLM uses social networks to distribute products or services by individuals recruited

into the system to earn income for themselves and those who drafted them. The
supplying firm gains in the form of increased sales. Usually the retailing is done from
the backyard and is informal within individuals’ social circles. It is a form of direct
marketing, where individuals will sell and make their marketing efforts directly to
potential clients (Peterson and Wotruba 1996). Those who are recruited receive more
gains when they form their own distribution networks. For example, if a leader recruits
five people into the programme, those five are encouraged to recruit their own five. The
current study has labelled this development a dendrite-like social distribution network.
The continuous multiplication of the dendrites lies at the centre of the growth of the
supply chain network. This also has a positive effect on the financial rewards for all
who are involved in the business. Naturally, since all members of the dendritic
relationship stand to gain from the expansion of social dendrites, there is a tendency to
collaborate (Pang and Monterola 2017). In such a case, collaboration can be said to be
the glue that keeps the dendrites working towards a common goal.

MLM systems need to be viewed as social supply chain networks. Supply chain
networks are defined as assets of supply chains that flow goods and services from the
sources to the customers (Kim et al. 2011; Lamming et al. 2000). Supply chain networks
are characterised by supply chain collaboration. Collaboration sub-dimensions have
been cited as information sharing; incentive alignment; and decision synchronisation
(Cao and Zhang 2011; Simatupang and Sridharan 2005). From the literature, it has been
noted that trust, commitment, communication and collaboration produce positive
partnerships in companies and these are driven by people (Mohr and Spekman 1994).

Hence, the fundamental question that this study sought to extend was which strategies
can be recommended for effective MLM. Therefore, the study attempted to provide
local MLM practitioners and their prospects with the knowledge that could help them
to improve their chances for success in their business.

9
 Makore, Moyo and Madziba

Research Methodology
The data was collected from the respondents through a structured survey questionnaire.
The questionnaire items were developed based on research constructs, such as team
building, which were derived from Alzamora-Ruiz et al. (2020), Hakanen and
Soudunsaari (2012), Rafferty and Griffin (2004), and Roman et al. (2021); recruitment
strategies from Coughlan and Grayson (1998) and Vander Nat and Keeo (2002);
collaboration from Alzamora-Ruiz et al. (2020) and Nha Trang et al. (2022); and
motivation from Jain, Singla and Shashi (2015) and Lee and Loi (2016). The research
participants were members of several selected firms comprising, Forever Living,
Dynapharm, Tablecharm, Tiange, and World Ventures to deduce the success strategies
employed to grow the distribution of products and services.

A pilot study was carried out by collecting data from 25 participants in the selected
MLM companies to deal with possible errors in the questionnaire. Each question was
rated on a five-point Likert scale, with 5 indicating “strongly agree” and 1 indicating
“strongly disagree”. The data was analysed using IBM Statistical Product and Service
Solutions (SPSS) version 23.0 and ADANCO software.

An empirical cross-sectional research study was used to understand the motivational

factors of MLM in developing countries. Both intrinsic and extrinsic motivators were
analysed to establish the factors that offer the greatest motivation for individuals to
participate in MLM.

The research followed a quantitative research design as the nature of the problem
required that the researchers describe the strategies currently being used by MLM
companies. Data collection comprised researcher-administered questionnaires to ensure
a high response rate. Using a convenience sampling technique, 146 usable responses
were used from the 250 survey questionnaires distributed; hence, a 58.4% response rate
was noted.

Data Analysis and Results

Demographic Profiles of the Participants
The socio-demographic characteristics of the study participants showed that 28.1%
(41/146) of the participants were drawn from Forever Living; 8.9% (13/146) from
Tablecharm; 6.2% (9/146) from World Ventures; 35.6% (52/146) from Dynapharm;
6.8% (10/146) from Tiange; and the remaining from Greenworld and others made up
14.4% (21/146).

10
 Makore, Moyo and Madziba

Table 1: Demographic data showing participants’ monthly income, gender and

educational level

Monthly income Frequency Percent

0–499 dollars 85 58.2

500–999 dollars 41 28.1

1 000–2 999 dollars 13 8.9

3 000 dollars and above 7 4.8

Total 146 100

Gender Frequency Percent

Female 108 74

Male 38 26

Total 146 100

Educational level Frequency Percent

O/A level 51 34.9

Diploma 48 32.9

Graduate 25 17.1

Other 22 15.1

Total 146 100

Table 1 shows that 74% of the participants were female and 26% were male. The income
of 58.2% of the participants was in the low-income range (0–499 dollars) and only 4.8%
were in the high-income range (3 000 dollars and above). The table also depicts that
34.9% had an O-level or A-level education, 32.9% had a diploma level, 17.1% a
graduate level and 15.1 % had other qualifications.

Exploratory Factor Analysis

The data analysis and results presented the detailed results from the analysis of the data.
Exploratory factor analysis (EFA) includes the assessment of the suitability of the data,
factor extraction, factor rotation, and interpretation. EFA was used to select the
appropriate latent constructs or factors and to group the similar ones under appropriate
dimensions thereby reducing the number of factors.

11
 Makore, Moyo and Madziba

There are differences between the two methods of factor extraction in EFA, and hence
the study utilised EFA, and factors with fewer than three items with 0.5 or less loading
were dropped (Costello and Osborne 2005). Sample adequacy was also noted in terms
of the obtained Kaiser-Meyer-Olkin (KMO) value (0.858) as recommended (Shrestha
2021).

Assessment of the Suitability of the Data

The KMO test was used to measure the suitability of the data for EFA. Also, Bartlett’s
test of sphericity and correlation matrix were computed to determine the suitability of
the data (Kaiser 1974). The correlation matrix showed that there were not many items
that indicated correlations > 0.30 between the factors, and hence it could be
hypothesised that the data was suitable. Table 2 shows that the KMO value was equal
to 0.867 which indicated that sampling adequacy had been achieved and EFA was
appropriate for the data.

Table 2: KMO test and Bartlett’s test of sphericity

KMO measure of sampling adequacy 0.867

Bartlett’s test of sphericity Approx. chi-square 2385.395

df 210

Sig. 0

Bartlett’s test of sphericity for testing the adequacy of the correlation matrix was
significant at p < 0.001 and this was an indication that the correlation matrix had
significant correlations among some of the factors. Bartlett’s test of sphericity had a chi-
square (χ2) df = 210, 2385.395 and the obtained degree of significance had a p-value <
0.001.

Factor Extraction
The number of the initial unrotated factors to be extracted is determined by the KMO
test and the Scree test or plot. The eigenvalues associated with each factor are depicted
in Table 3 and the variance explained by those factors is also shown. Values below 0.4
were suppressed in the analysis. The extraction method utilised in the study was
principal axis factoring and 21 linear components were identified before extraction. The
four factors extracted accounted for a 60.335% variance. The first factor explained
31.906% of the total variance with an eigenvalue of 6.7. The second factor explained a
16.54% variance with an eigenvalue of 3.473. The third factor explained a 7.967%
variance with an eigenvalue of 1.673. The fourth factor explained a 3.922% variance
with an eigenvalue of 0.824.

12
 Makore, Moyo and Madziba

Table 3: Total variance explained and eigenvalues

Initial eigenvalues Extraction sums of Rotation sums of squared

squared loadings loadings

Cumulative %

Cumulative %

Cumulative %
% of variance

% of variance

% of variance
Factor

Total

Total

Total
1 7.504 35.734 35.734 7.257 34.558 34.558 6.7 31.906 31.906
2 3.389 16.14 51.874 2.991 14.242 48.8 3.473 16.54 48.446
3 2.16 10.284 62.158 1.653 7.87 56.67 1.673 7.967 56.413
4 1.248 5.941 68.099 0.77 3.665 60.335 0.824 3.922 60.335
5 0.887 4.225 72.324
6 0.764 3.639 75.963
7 0.703 3.35 79.313
8 0.658 3.133 82.446
9 0.59 2.81 85.256
10 0.519 2.472 87.727
11 0.467 2.223 89.95
12 0.414 1.973 91.924
13 0.384 1.828 93.752
14 0.349 1.661 95.413
15 0.274 1.307 96.72
16 0.237 1.13 97.851
17 0.178 0.847 98.698
18 0.111 0.53 99.228
19 0.081 0.383 99.611
20 0.05 0.239 99.85
21 0.031 0.15 100

Note: Extraction method: Principal axis factoring

Figure 2 shows the scree plot with eigenvalues on the y-axis against the 21 linear
components in their order of extraction on the x-axis.

13
 Makore, Moyo and Madziba

Figure 2: Scree plot

Exploratory Factor Analysis

In Table 4, the data was analysed using principal axis factoring and orthogonal varimax
rotation with KMO normalisation. The KMO values for the factors were above 0.5 and
the KMO was (0.858) indicating that the data was sufficient for EFA.

Using the eigenvalues cut-off value of 1.00, the four factors explained a cumulative
variance of 68.199%. Table 4 shows the factor loadings after the rotation. The purpose
of the EFA was to identify latent constructs or factors that influence the adoption of
MLM, and hence the rotated factor loadings and rotated eigenvalues are reported.

Table 4: Exploratory factor analysis

Rotated factor matrix Incentive Team Support Informa-

building tion
Factor 1 Factor 2 Factor 3 Factor 4

M2 Compensation plans or rewards 0.956

C2 Commitment to the project 0.948

C1 Trust in team members 0.944

C3 Reward sharing – doing projects 0.938

together
M1 Personal goals 0.91

M4 Low entry barriers 0.877

14
 Makore, Moyo and Madziba

P1 Fliers/Business cards 0.573

T1 Status/Social standing 0.513

C4 Idea sharing 0.501

C5 Communication 0.498

T5 Personality of the individuals 0.793

P5 Seminars/Conferences 0.728

T3 Leadership attributes or skills 0.71

P4 Group presentations 0.682

T4 Local vs distant friends (those you 0.671

do not know)
T2 Expertise/Professional skills 0.607

P6 Word of mouth/Sharing my 0.433

success story
P7 Telephone calls 0.637

P2 Online information 0.637

dissemination/Social media
P3 Advertisements 0.623

M3 Product knowledge 0.715

Rotated eigenvalues 7.499 3.412 2.171 1.239

% of variance 35.712 16.249 10.337 5.902

Cumulative % of the variance 35.712 51.961 62.297 68.199

Cronbach’s alpha 0.841 0.806 0.654

Notes:

1. Extraction method: Principal axis factoring

2. Rotation method: Varimax with KMO normalisation

3. A rotation converged in five iterations.

4. The coding indicates the various topics under which the factors are discussed:

(M) Motivation; (C) Collaboration; (P) Presentation; (T) Team building

15
 Makore, Moyo and Madziba

Factor Rotation and Interpretation

After the extraction and rotation, the researchers established a four-factor solution from
the data with eigenvalues greater than one. The results showed that four factors
accounted for a 60.335% proportion of the total variance shared by the 21 variables,
supported by the KMO value of 0.858. The four factors in the rotated solution were
based on varimax, the most common orthogonal rotation method.

Incentives for Motivation

The first construct with 10 variables was labelled incentives for motivation. The
variance explained by this construct was 31.906% of the total variation in the data. The
positive loadings of the variables ranged from 0.498 to 0.956 indicating a good
representation of the construct. The construct contained 10 variables comprising:
compensation plans or rewards; commitment to the project; trust in team members;
reward sharing – doing projects together; personal goals; low entry barriers;
fliers/business cards; status/social standing; idea sharing; and communication. The
major motivational factor in the study, which focused on variables that motivate
individuals to join MLM companies, is the potential to earn some money as part of
extrinsic motivation and possible financial independence. The study reasserts that
reward schemes are a prominent motivating factor and a critical regulatory factor
(Alzamora-Ruiz et al. 2020).

Team-building Methods
The second construct, team building, consisted of seven variables, namely: personality
of the individuals; seminars/conferences; leadership attributes or skills; group
presentations; local vs distant friends (those you do not know); expertise or professional
skills; and word of mouth/sharing my success story in MML, which are dominantly
intrinsic motivational factors. The variance explained by this construct was 16.54% of
the total variation in the data. The variables have loadings which support the latent factor
ranging from 0.433 to 0.793 demonstrating that team building is a crucial factor for
potential earnings despite MLM controversies (Roman et al. 2021).

Support
The third construct, support, consisted of three variables, namely: telephone calls;
online information dissemination or social media; and advertisements, which also
influence individuals to join MML companies. The variance explained by this construct
was 7.967% of the total variation in the data. The correlations of the variables with the
support factor ranged from 0.623 to 0.637 depicting good support for the latent factor.
Support also emanates from the interaction with established members, which is a source
of confidence and flexibility in creating value for potential customers (Grant-Smith et
al. 2021).

16
 Makore, Moyo and Madziba

Information
Information, the fourth construct, only accounted for a 3.922% variance comprising one
variable (product knowledge) with a 0.715 factor loading, an eigenvalue of 1.239 from
the initial extraction and this denotes the desire of the individuals to learn about new
products or services. This construct had only one item, possibly due to over-extraction.

Reliability and Validity Tests

The figures in Table 5 for the reliability tests were acceptable as they were all above the
0.7 Cronbach’s alpha criteria as established (Hair et al. 2019, 787). The assessment of
the quality criteria starts with the evaluation of the factor loadings, then the construct
validity and construct reliability are established (Hair et al. 2019, 787).

Table 5: Cronbach’s alpha and composite reliability

Construct Dijkstra-Henseler’s Jöreskog’s rho (ρc) Cronbach’s alpha

rho (ρA) (α)
Incentives 0.961 0.949 0.935

Team building 0.856 0.883 0.844

Support 0.671 0.811 0.648

To assess the reliability of the factors in terms of how effectively they are measuring
the various constructs, the Cronbach’s alpha is calculated and this is important in
analysing the consistency of responses across the items within a construct (Collier
2020). The Cronbach’s alpha ranged from 0.648 to 0.935, whereas the composite
reliability statistics ranged from 0.800 to 0.950. Both statistics were above the
recommended threshold level of 0.700 (Hair et al. 2017), and hence construct validity
was established.

Convergent Validity
Convergent validity was also established when the average variance extracted (AVE)
was ≥ 0.5 and the AVE values for incentives, team building and support were 0.660,
0523 and 0.592, respectively (see Table 6).

Table 6: Fornell and Larcker (1981) criterion

Construct Incentives Team building Support

Incentives 0.660

Team building 0.070 0.523

17
 Makore, Moyo and Madziba

Support 0.015 0.001 0.592

Note: Squared correlations; AVE in the diagonal

When the AVE value is greater than or equal to the recommended value of 0.5 it is an
indication that the items converge to measure the underlying construct, and hence
convergent validity is established (Fornell and Larcker 1981). Convergent validity
results based on the AVE statistics in the current study showed that all constructs had
an AVE greater than 0.50, and hence convergent validity was established. Table 6 shows
the AVE values for each of the constructs. Therefore, the results depicted evidence of
internal consistency of the scale used.

Proposed Model
The proposed conceptual model (see Figure 3) is based on the extracted factors from
the EFA process. Based on the KMO test and Bartlett’s test of sphericity, factor
extraction, the total variance explained and eigenvalues, the scree plot, EFA and the
reliability test for guidance, the following conceptual model was proposed (Denis 2019).

Figure 3: Research framework which may further be employed to analyse the

dimensions that influence the growth of MLM or network marketing

Managerial Implications
The study findings showed that the EFA used to extract those motivational factors that
could be considered highly effective has shown that incentives rank highly on the final
constructs. The respondents were asked to rate the use of various factors and the
following factors were portrayed as being effective in motivating individuals to join
MLM companies: compensation plan/reward; commitment to the project; trust in team
members; reward sharing-doing projects together; personal goals; low entry barriers;

18
 Makore, Moyo and Madziba

flyers and business cards; status/social standing; idea sharing; and communication
MLM team-building methods or strategies also contribute towards the process of
motivating MLM participants through factors, such as: personality of the individuals;
seminars/conferences; leadership attributes of skills; group presentations; local vs
distant friends (those you do not know); expertise/professional skills; and word of
mouth/sharing my success story. These intrinsic factors also require management
attention to address specific objectives such as affiliation, personal development, and
profitability.

Limitations and Future Research

The research has made contributions in the area of theory and various latent constructs
or factors have been identified which provide support in future research if they are
utilised in collaboration with other variables. The limitations of the study are primarily
in the sampling technique which was a non-probability technique thus restricting broad
inferences on the findings. The analysis of the research findings can lead to further
research of the variables using other analysis techniques such as confirmatory factor
analysis.

Conclusion and Recommendations

Much more than a traditional business, MLM is anchored on trust between the sponsor
(the leader of the team) and their distributors (downlines). In MLM, when a member is
recruited into the business, they become a partner of the individual who recruited them.
Their success in achieving their goals is mutually dependent. The team leader becomes,
in essence, the mentor, coach, trainer, motivator, and role model. Even after the recruit
has learned the business skills, they continue to look up to the leader as a compass for
the organisation’s moral fibre, accountability, and work habits (Christensen 2008). Trust
is therefore identified as a critical binding ingredient in MLM relationships. Thus, it can
also be said that trust acts as an enabler in the formation of the MLM relationships which
often cut across the lines of personality, physical distance, and social standing divide.
Trust forms a pillar in relations created purely based on achieving personal gains and a
legally binding document that is devoid of social ties. Many participants in MLM teams
are neither friends nor family, but they grow into such because of the working
relationship built through trust. MLM is a form of business that is still clouded by
negative images, with individuals and governments around the globe even questioning
its legality. Thus, the individuals joining this business must have trust and confidence
in the words of the person who is recruiting them.

The model tested in the current study suggests that MML relationships are dendritic and
do not necessarily result in linear relationships (Cuntz et al. 2010). Recruitment of new
members into MLM starts with motivation, and it has been noted that despite the high
loss rate, the focus remains on changing beliefs, attitudes and behaviour (Hiranpong,
Decharin and Thawesaengskulthai 2016). The resulting structure is quite complex yet

19
 Makore, Moyo and Madziba

profoundly interconnected and interdependent. The researchers’ validation process of

the proposed model is supported by literature (Legara et al. 2008).

References
Albaum, G., and R. A. Peterson. 2011. “Multilevel (Network) Marketing: An Objective View.”
The Marketing Review 11 (4): 347–361.
https://doi.org/10.1362/146934711X13210328715902

Alzamora-Ruiz, J., C. Guerrero-Medina, M. Martínez-Fiestas, and J. Serida-Nishimura. 2020.

“Why People Participate in Collaborative Consumption: An Exploratory Study of
Motivating Factors in a Latin American Economy.” Sustainability 12 (2): 1936.
https://doi.org/10.3390/su12051936

Anderson, J. C., H. Håkansson, and J. Johanson. 1994. “Dyadic Business Relationships within
a Business Network Context.” Journal of Marketing 58 (4): 1–15.
https://doi.org/10.1177/002224299405800401

Beek, J. 2019. “Travelling Multi-level Marketing Schemes and Whispers of Fraud in Kenya.”
Social Anthropology/Anthropologie Sociale 27 (3): 501–516.

Bowen, D. E., and G. R. Jones. 1986. “Transaction Cost Analysis of Service Organization-
Customer Exchange.” The Academy of Management Review 11 (2): 428–441.
https://doi.org/10.2307/258470

Bruckermann, C. 2021. “Network Marketing and State Legitimacy in China: Regulating Trust
from Physical Workplaces to Virtual Spaces.” Economic Anthropology 8 (1): 86–101.

Buttle, F., and S. Maklan. 2019. “Relationship Management.” Concepts and Technologies. 4th
ed. Abingdon: Routledge.

Cao, M., and Q. Zhang. 2011. “Supply Chain Collaboration: Impact on Collaborative
Advantage and Firm Performance.” Journal of Operations Management 29 (3): 163–180.

Cateora, P. R., R. B. Money, J. Grahaam, and M. C. Gilly. 2019. “International Marketing

Download.” https://b-ok.africa/book/5669335/612fc2

Christensen, M. 2008. Be a Recruiting Superstar: The Fast Track to Network Marketing

Millions. New York: HarperCollins.

Coase, R. H. 1937. “The Nature of the Firm.” Economica. https://doi.org/10.1111/j.1468-

0335.1937.tb00002.x

Collier, J. E. 2020. Applied Structural Equation Modeling Using AMOS: Basic to Advanced
Techniques. New York: Routledge. https://doi.org/10.4324/9781003018414

20
 Makore, Moyo and Madziba

Cortez, R. M., and W. J. Johnston. 2020. “The Coronavirus Crisis in B2B Settings: Crisis
Uniqueness and Managerial Implications Based on Social Exchange Theory.” Industrial
Marketing Management 88: 125–135.

Costello, A. B., and J. W. Osborne. 2005. “Best Practices in Exploratory Factor Analysis: Four
Recommendations for Getting the Most from Your Analysis.” Practical Assessment,
Research and Evaluation 10 (7). https://doi.org/10.7275/jyj1-4868

Coughlan, A. T., and K. Grayson. 1998. “Network Marketing Organizations: Compensation

Plans, Retail Network Growth, and Profitability.” International Journal of Research in
Marketing 15 (5): 401–426. https://doi.org/10.1016/S0167-8116(98)00014-7

Cuntz, H., F. Forstner, A. Borst, and M. Häusser. 2010. “One Rule to Grow Them All: A
General Theory of Neuronal Branching and Its Practical Application.” PLoS
Computational Biology 6 (8): e1000877. https://doi.org/10.1371/journal.pcbi.1000877

Davis, F. D. 1985. “A Technology Acceptance Model for Empirically Testing New End-user
Information Systems : Theory and Results.” Cambridge, MA: Massachusetts Institute of
Technology. https://dspace.mit.edu/handle/1721.1/15192

De Wulf, K., and G. Odekerken-Schröder. 2001. “A Critical Review of Theories Underlying

Relationship Marketing in the Context of Explaining Consumer Relationships.” Journal
for the Theory of Social Behaviour 31 (1): 73–101. https://doi.org/10.1111/1468-
5914.00147

Deci, E. L., and R. M. Ryan. 2012. “Self-Determination Theory.” In Handbook of Theories of

Social Psychology, edited by P. A. M. Van Lange, A. W. Kruglanski and E. Tory Higgins,
416–437. Vol. 1. Los Angeles: SAGE. https://doi.org/10.4135/9781446249215.n21

Deci, E. L., and R. M. Ryan. 2015. “Self-Determination Theory.” In International

Encyclopedia of the Social & Behavioral Sciences, 486–491. 2nd ed. Amsterdam: Elsevier.
https://doi.org/10.1016/B978-0-08-097086-8.26036-4

Deci, E. L., A. H. Olafsen, and R. M. Ryan. 2017. “Self-Determination Theory in Work

Organizations: The State of a Science.” Annual Review of Organizational Psychology and
Organizational Behavior 4 (1): 19–43. https://doi.org/10.1146/annurev-orgpsych-032516-
113108

Denis, D. J. 2019. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics.
https://doi.org/10.1002/9781119465775

Emerson, R. M. 1976. “Social Exchange Theory.” Annual Review of Sociology 2 (1): 335–362.
https://doi.org/10.1146/annurev.so.02.080176.002003

Fill, C. 2009. Marketing Communications: Interactivity, Communities and Content. Harlow:

Pearson Education.

21
 Makore, Moyo and Madziba

Fornell, C., and D. F. Larcker. 1981. “Evaluating Structural Equation Models with
Unobservable Variables and Measurement Error.” Journal of Marketing Research 18 (1):
39–50.

Gilal, F. G., J. Zhang, J. Paul, and N. G. Gilal. 2019. “The Role of Self-Determination Theory
in Marketing Science: An Integrative Review and Agenda for Research.” European
Management Journal 37 (1): 29–44.
https://doi.org/https://doi.org/10.1016/j.emj.2018.10.004

Grant-Smith, D., L. de Zwaan, B. Irmer, and A. Feldman. 2021. A Profile of MLM Consultants
in Australia: Financial Literacy and Other Characteristics. Queensland: Griffith
University.

Groß, C., and D. Vriens. 2019. “The Role of the Distributor Network in the Persistence of
Legal and Ethical Problems of Multi-Level Marketing Companies.” Journal of Business
Ethics 156 (2): 333–355. https://doi.org/10.1007/s10551-017-3556-9

Gummesson, E. 2008. “Extending the Service-Dominant Logic: From Customer Centricity to

Balanced Centricity.” Journal of the Academy of Marketing Science 36 (1): 15–17.
https://doi.org/10.1007/s11747-007-0065-x

Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2019. Multivariate Data Analysis.
Boston: Cengage Learning.

Hair, J. F., G. T. M. Hult, C. M. Ringle, and M. Sarstedt. 2021. A Primer on Partial Least
Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks: SAGE.

Hakanen, M., and A. Soudunsaari. 2012. “Building Trust in High-Performing Teams.”

Technology Innovation Management Review 2 (6): 38–41.
https://doi.org/10.22215/timreview/567

Hiranpong, R., P. Decharin, and N. Thawesaengskulthai. 2016. “Structural Equation Modeling

of a Potentially Successful Person in Network Marketing.” Kasetsart Journal of Social
Sciences 37 (1): 22–29. https://doi.org/10.1016/j.kjss.2016.01.009

Jain, S., B. B. Singla, and S. Shashi. 2015. “Motivational Factors in Multilevel Marketing
Business: A Confirmatory Approach.” Management Science Letters 5 (10): 903–914.
https://doi.org/10.5267/j.msl.2015.8.006

Jarvenpaa, S. L., K. Knoll, and D. E. Leidner. 1998. “Is Anybody out There? Antecedents of
Trust in Global Virtual Teams.” Journal of Management Information Systems 14 (4): 29–
64. https://doi.org/10.1080/07421222.1998.11518185

Jung, T. H., E. M. Ineson, and E. Green. 2013. “Online Social Networking: Relationship
Marketing in UK Hotels.” Journal of Marketing Management 29 (3–4): 393–420.
https://doi.org/10.1080/0267257X.2012.732597

22
 Makore, Moyo and Madziba

Kaiser, M. O. 1974. “Kaiser-Meyer-Olkin Measure for Identity Correlation Matrix.” Journal of

the Royal Statistical Society 52 (1): 296–298.

Keong, L. S., and O. Dastane. 2019. “Building a Sustainable Competitive Advantage for Multi-
Level Marketing (MLM) Firms: An Empirical Investigation of Contributing Factors.”
Journal of Distribution Science 17 (3): 5–19. https://doi.org/10.15722/jds.17.3.201903.5

Ketokivi, M., and J. T. Mahoney. 2020. “Transaction Cost Economics as a Theory of Supply
Chain Efficiency.” Production and Operations Management 29 (4): 1011–1031.

Kim, Y., T. Y. Choi, T. Yan, and K. Dooley. 2011. “Structural Investigation of Supply
Networks: A Social Network Analysis Approach.” Journal of Operations Management 29
(3): 194–211. https://doi.org/https://doi.org/10.1016/j.jom.2010.11.001

Lamming, R., T. Johnsen, J. Zheng, and C. Harland. 2000. “An Initial Classification of Supply
Networks.” International Journal of Operations and Production Management 20 (6): 675–
691. https://doi.org/10.1108/01443570010321667

Lee, K., and K-Y. Loi. 2016. “Towards Satisfying Distributors in Multilevel Marketing
Companies.” International Journal of Management and Applied Research 3 (1): 48–64.
https://doi.org/10.18646/2056.31.16-004

Legara, E. F., C. Monterola, D. E. Juanico, M. Litong-Palima, and C. Saloma. 2008. “Earning

Potential in Multilevel Marketing Enterprises.” Physica A: Statistical Mechanics and Its
Applications 387 (19–20): 4889–4895. https://doi.org/10.1016/j.physa.2008.04.009

Möhlmann, M. 2015. “Collaborative Consumption: Determinants of Satisfaction and the

Likelihood of Using a Sharing Economy Option Again.” Journal of Consumer Behaviour
14 (3): 193–207. https://doi.org/https://doi.org/10.1002/cb.1512

Mohr, J., and R. Spekman. 1994. “Characteristics of Partnership Success: Partnership

Attributes, Communication Behavior, and Conflict Resolution Techniques.” Strategic
Management Journal 15 (2): 135–152. https://doi.org/10.1002/smj.4250150205

Morgan, R. M., and S. D. Hunt. 1994. “The Commitment–Trust Theory of Relationship

Marketing.” Journal of Marketing 58 (3): 20–38. https://doi.org/10.2307/1252308

Msosa, S. K. 2022. “The Flip Side of Multi-level Marketing: A Diagnosis of Factors Leading
to the Mass Uptake of Unregulated Pyramid Schemes in South Africa.” Innovative
Marketing 18 (1): 142–151.

Mullins, L. J. 2010. Management and Organisational Behaviour. 9th ed. Harlow: Pearson
Education.

23
 Makore, Moyo and Madziba

Nadlifatin, R., S. F. Persada, M. Clarinda, G. A. Handiwibowo, Rr R. Laksitowati, Y. T.

Prasetyo, and A. A. N. Periwadi Redi. 2022. “Social Media-based Online Entrepreneurship
Approach on Millennials: A Measurement of Job Pursuit Intention on Multi-Level
Marketing.” Procedia Computer Science 197: 110–117.
https://doi.org/10.1016/j.procs.2021.12.124

Nga, J. K. H., and S. W. Mun. 2011. “The Influence of MLM Companies and Agent Attributes
on the Willingness to Undertake Multilevel Marketing as a Career Option among Youth.”
Journal of Research in Interactive Marketing 5 (1): 50–70.
https://doi.org/10.1108/17505931111121525

Nha Trang, N. T., T-T. Nguyen, H. V. Pham, T. T. Ahn Cao, T. H. Trinh Thi, and J. Shahreki.
2022. “Impacts of Collaborative Partnership on the Performance of Cold Supply Chains of
Agriculture and Foods: Literature Review.” Sustainability 14 (11): 6462.

Pang, J. C. S., and C. P. Monterola. 2017. “Dendritic Growth Model of Multilevel Marketing.”
Communications in Nonlinear Science and Numerical Simulation 43: 100–110.
https://doi.org/10.1016/j.cnsns.2016.06.030

Peterson, R. A., and T. R. Wotruba. 1996. “What Is Direct Selling? – Definition, Perspectives,
and Research Agenda.” Journal of Personal Selling and Sales Management 16 (4): 1–16.

Pratistha, B. 2017. “A Conceptual Evaluation of Traditional and Network Marketing.” In XVIII

Annual International Conference Proceedings, 237–244.
http://www.internationalconference.in/XVIII_AIC/TS5A/Pratishtha%20Boora%20_237-
244_.pdf

Pride, W., and O. C. Ferrell. 2008. Marketing. 14th ed. New York: Houghton Mifflin.

Purcaru, I-M., A-M. Urdea, C. P. Constantin, and G. Brătucu. 2022. “Building Long-Term
Business Sustainability: The Influence of Experiential Marketing on Sales Representatives’
Loyalty to Multi-Level Marketing Systems.” Sustainability 14 (15): 9507.
https://doi.org/10.3390/su14159507

Rafferty, A. E., and M. A. Griffin. 2004. “Dimensions of Transformational Leadership:

Conceptual and Empirical Extensions.” The Leadership Quarterly 15 (3): 329–354.
https://doi.org/https://doi.org/10.1016/j.leaqua.2004.02.009

Roman, M., M. Wasiak, M. Roman, K. Roman, A. Niedziółka, A. Krasnodębski, and S.

Królak. 2021. “Essence of the Compensation Plan in the Process of Motivation in Multi-
level Marketing (MLM). A Case Study.” Sustainability 13 (16): 8738.
https://doi.org/10.3390/su13168738

Rubino, J. 2005. The 7-Step System to Building a $1,000,000 Network Marketing Dynasty:
How to Achieve Financial Independence through Network Marketing. Hoboken: Wiley.

24
 Makore, Moyo and Madziba

Ryan, R. M., and E. L. Deci. 2020. “Intrinsic and Extrinsic Motivation from a Self-
Determination Theory Perspective: Definitions, Theory, Practices, and Future Directions.”
Contemporary Educational Psychology 61: 101860.
https://doi.org/10.1016/j.cedpsych.2020.101860

Selamet, T., T. N. Mursitama, A. Furinto, and P. Heriyati. 2020. “Indonesias’ Multilevel

Marketing Organization Competitiveness UNDER Turbulences: A Proposed Model.”
Systematic Reviews in Pharmacy 11 (8): 682–688.

Shrestha, N. 2021. “Factor Analysis as a Tool for Survey Analysis.” American Journal of
Applied Mathematics and Statistics 9 (1): 4–11. https://doi.org/10.12691/ajams-9-1-2

Simatupang, T. M., and R. Sridharan. 2005. “An Integrative Framework for Supply Chain
Collaboration.” International Journal of Logistics Management 16 (2): 257–274.
https://doi.org/10.1108/09574090510634548

Statista. 2021. “Size of the Global Direct Selling Community, by Region 2021.”
https://www.statista.com/statistics/293178/size-of-the-global-direct-selling-community-by-
region/

Suwitho, S., I. B. Riharjo, and D. A. Dewangga. 2023. “The Nexus between Ponzi Scheme and
Multi-Level Marketing Systems: Evidence in Indonesia.” Cogent Social Sciences 9 (1):
2178540. https://doi.org/10.1080/23311886.2023.2178540

Syahrivar, J., H. K. Tunjungsari, F. Selamat, and C. Chairy. 2020. “Factors Influencing Career
Choice in Islamic Multilevel Marketing: The Mediating Role of Company Credibility.” In
Proceedings of the 8th International Conference on Entrepreneurship and Business
Management (ICEBM 2019) UNTAR. Paris: Atlantis Press.
https://doi.org/10.2991/aebmr.k.200626.001

Thøgersen, J. 2005. “How May Consumer Policy Empower Consumers for Sustainable
Lifestyles?” Journal of Consumer Policy 28 (2): 143–177. https://doi.org/10.1007/s10603-
005-2982-8

Vander Nat, P. J., and W. W. Keep. 2002. “Marketing Fraud: An Approach for Differentiating
Multilevel Marketing from Pyramid Schemes.” Journal of Public Policy & Marketing 21
(1): 139–151. https://doi.org/10.1509/jppm.21.1.139.17603

Wrenn, M. V. 2022. “Overworked, Alienated, and Externalized: Multi-Level Marketing

Distributors in the Neoliberal Age.” Journal of Economic Issues 56 (2): 648–654.
https://doi.org/10.1080/00213624.2022.2066449

Zemanian, A. H. 1986. “Multilevel Marketing Networks in Less Developed Countries.”

Applied Mathematics and Computation 18 (3): 185–210.
https://doi.org/10.1016/0096-3003(86)90014-7

25