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Copyright UCT
The Influence of Culture on Consumer Behaviour:
Social Axioms and Mobile Telephone Adoption
Tennyson Chimbo
The Graduate School of Business
University of Cape Town
A Research Report
presented in partial fulfillment
of the requirements for the
Master of Business Administration Degree
Supervisor: Professor Steven Michael Burgess
December 2010
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Acknowledgements
This research report is not confidential. It can be used freely by the University of
Cape Town.
I wish to thank the following people for their assistance with this research report:
Professor Steven Michael Burgess, my supervisor, for his expert advice and support.
The community of Kgautswane, for being receptive to the idea of this research and
agreeing to be the subjects of the research. I would like to make special mention of Mrs.
Clara Masinga, Director of the Kgautswane Community Development Centre, for allowing
me to use the facilities at the Centre when conducting the field surveys.
My wife, Tendai, and daughters, Tariro and Mutsa, for being patient with me during
the many long hours spent working on this research report.
I would also like to thank the lecturers from the Anthropology Department at the
University of South Africa for translating the survey instruments from English to Sepedi, and
then back to English.
I certify that this research report is my own work, and that where the works of others
have been cited, the sources have been correctly referenced in the References list.
Signed:
________________________________
Tennyson Chimbo
December 2010
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The Influence of Culture on Consumer Behaviour: Social Axioms and Mobile
Telephone Adoption
Abstract
Rogers (2003) recently called for new conceptual frameworks to help explain the
diffusion of innovations. Leung and Bond (2002) recently argued that our understanding of
cross-cultural behaviour would be enhanced if more attention were devoted to the concept of
generalised beliefs. They proposed social axioms as a new culture concept operating at the
level of individuals and groups. They have orchestrated a collaborative endeavour across
more than 40 countries to develop measurement scales and test a new theory on social
axioms. Social axioms refer to generalised beliefs about life and how it works. The current
research answers Roger‟s call by examining the influence of social axioms on innovativeness
and the adoption of an innovative product by low-income, rural consumers. Two hundred
and seventy five cases from rural Limpopo Province completed Leung‟s (2002) 25-item SAS
survey questionnaire and a Wejnert (2002)-based, 14-item Diffusion of Innovations survey
questionnaire as part of the study.
The results of the study indicate that some dimensions of social axioms, namely
reward for application and social cynicism, show statistically significant prediction of
adoption of innovations through their total effects on adoption. Some dimensions of social
axioms also show statistically significant direct effects on personal innovativeness, which
also affects adoption. These results have significant implications for future research and
practice. Business scholars have the opportunity to use the results of this study as a basis for
improving the predictive power of the structural model developed in this study. Practitioners
of diffusion of innovations will then have yet another explanatory framework for consumer
behaviour.
Keywords: Social axioms, adoption, diffusion, innovativeness, beliefs, culture,
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Table of Contents
Acknowledgements ............................................................................................................................ 1
Abstract ................................................................................................................................................ 2
Introduction ......................................................................................................................................... 5
Research Area and Problem ............................................................................................................. 5
Research Questions and Scope ......................................................................................................... 5
Research Assumptions ...................................................................................................................... 7
Research Ethics ................................................................................................................................. 7
Literature Review ................................................................................................................................ 9
What Shapes Consumer Behaviour? ................................................................................................. 9
Value-Based Approaches to Cultural Variability ........................................................................... 11
What Are Social Axioms? ................................................................................................................ 11
Diffusion of Innovations .................................................................................................................. 13
Hypothesized effects ........................................................................................................................ 15
Conclusion ...................................................................................................................................... 18
Research Methodology ...................................................................................................................... 19
Research Approach and Strategy .................................................................................................... 19
Research Design, Data Collection Methods and Research Instruments ......................................... 19
Sample ............................................................................................................................................. 21
Data Analysis Methods ................................................................................................................... 24
Limitations of the study ................................................................................................................... 26
Research Findings, Analysis and Discussion ................................................................................... 28
Correlation Analysis of Manifest Variables .................................................................................... 28
Reliability and validity .................................................................................................................... 28
Measurement Model Evaluation ..................................................................................................... 32
Structural Model Evaluation ........................................................................................................... 34
Hypothesis Testing .......................................................................................................................... 37
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Discussion ....................................................................................................................................... 40
Conclusion and recommendations ................................................................................................... 41
References........................................................................................................................................... 44
Appendix A: Research Instruments ................................................................................................ 47
Survey Questionnaire - English Version ......................................................................................... 47
Survey Questionnaire - Sepedi Version ........................................................................................... 50
Appendix B: Correlation Matrix ..................................................................................................... 53
Appendix C: Descriptive Statistics .................................................................................................. 72
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Introduction
Research Area and Problem
What is the influence of culture on innovative consumer behaviour for technology
products in low-income communities? One answer to this question comes from the study of
value priorities and innovative consumer behaviour (Burgess, 1992). Value-based
approaches to cultural variability represent a mature area of research (Hofstede, 1980;
Schwartz, 1994; Schwartz, 1992; Singelis et al, 1999). In the current research, I draw on a
new and complementary culture theory, social axioms, which refer to generalised beliefs that
people have about life and how it works. Research shows that social axioms complement
value priorities (Bond et al, 2004) in predicting human behaviour. I also draw on the mature
domain of innovation diffusion research (Rogers, 1976; Rogers, 2004; Nakata, 2001). There
have been several calls for new theoretical frameworks in order to advance innovation
diffusion theory and maintain researcher interest (Deffuant et al, 2005; Rogers, 2004;
Murray, 2009; Wejnert, 2002). Therein lays the problem identified for this research: that a
gap exists in the development of new conceptual frameworks for diffusion research. More
particularly, a gap exists in the literature on the possible relationships between social axioms
research and diffusion research. This research represented preliminary attempts at closing
this latter gap.
Research Questions and Scope
The main question that was addressed by this research was: In what ways are the
dimensions of social axioms related to innovativeness? Here, innovativeness was understood
to mean the degree to which an adopter is relatively earlier than other units in the social
system in adopting an innovation (Rogers, 1976). Rogers (2004), as cited by Murray (2009),
defines diffusion of innovations as
“...the process through which an innovation, defined as an idea, practice, or object
perceived as new by an individual or other relevant unit of adoption, which is
communicated through certain channels over time among members of a social
system is diffused and adopted within wider social networks.”
Social axioms were recently proposed as a complementary, explanatory framework
for cultural variability in human behaviour, alongside the traditional, value-based approaches
of Schwartz (1994). Leung et al (2002) identified a pan-cultural set of five dimensions of
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generalized beliefs about the world and how it functions. These general beliefs are called
social axioms; social as they derive from social experiences and axioms as they are accepted,
without question or scientific validation, as a priori true premises. The five dimensions of
social axioms are reward for application, social complexity, fate control, religiosity, and
social cynicism.
The adoption of innovations necessarily follows on from the diffusion of innovations.
Adoption of innovations itself is a process that Rogers (2004) describes as consisting of five
stages. These include awareness, communication, application, and trial and adoption
(Rogers, 2004). Five secondary research questions emerge from a consideration of these five
stages of the process of adoption of innovations: 1) which dimensions of social axioms
impede or facilitate the process of innovation awareness? 2) Which dimensions of social
axioms impede or facilitate the process of innovation communication? 3) Which dimensions
of social axioms impede or facilitate the process of innovation application? 4) Which
dimensions of social axioms impede or facilitate the process of innovation trial? And lastly,
5) which dimensions of social axioms impede or facilitate the process of innovation
adoption? Questions that could be asked for the purposes of future research include the
following: a) To what extent do diffusion of innovation scales show variability of measures
due to gender difference? b) Does the combination of social axioms and diffusion of
innovation conceptual frameworks result in better or worse predictors of consumer adoption
behaviour?
The research questions considered in the previous paragraph are broad in nature,
being applicable to innovations of any kind. However, for the purposes of this study, the
innovation under consideration was limited to the mobile telephone. By some accounts, the
mobile penetration rate in South Africa is said to be more than 100% (Source: Statistics
South Africa, Mobile Penetration, 2008). According to this statistic, it would be reasonable
to expect widespread diffusion of the mobile telephone in South Africa. However, the
geographical area of study did not include the whole of South Africa. It was limited to the
rural Kgautswane area of Limpopo Province, with a population of about 100,000 (Source:
Statistics South Africa, Census 2001). The unit of study was limited to individual, human,
female adult residents of Kgautswane. Even though statistics show that 35% of mobile
telephone users in South Africa are below the age of 18 (Source: Statistics South Africa,
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Mobile Penetration, 2008), the unit of study was limited to adults in order for the researcher
to more easily comply with ethical requirements of research.
Research Assumptions
One assumption of this study was that mobile penetration in the Kgautswane
Community was high enough to provide a sizeable working sample. This assumption was
based on the broader assumption that official statistics on mobile penetration rates in South
Africa could be extrapolated to the local level. Granted, if the mobile penetration rate on the
country level was, by some accounts, more than 100%, it was not expected that it would, on
average, be more than 100% at the local level.
A second assumption of this study was that participant recall would sufficiently
introduce the missing time continuum in this single-snapshot diffusion of innovations study
to render the research results usable. Diffusion studies are more correctly performed on the
units of adoption over time. However, due to the time constraints of this study, it was not
possible to faithfully adhere to this diffusion research requirement, without jeopardising other
academic requirements of the study.
A third assumption of this study was that the mobile telephone was a personal
communications device, and not a group one. This assumption had a justifying effect on the
selection of the unit of adoption. It seemed reasonable to select the individual as the unit of
adoption based on this assumption. As a personal communications device, it seemed
reasonable to assume that decisions to adopt mobile telephony rested with the individual.
However, cognisance must be had of the possibility of some rural communities considering
the mobile telephone as a family or group communications device, in which case adoption
decisions would rest with the family or group. Such units of adoption were excluded from
this research. Perhaps they could be the subject of future research.
Research Ethics
In this study only adult human participants were employed to gather data from. The
adults were required to sign an informed consent form to indicate their voluntary acceptance
to participate in the study. The researcher explained to the participants that the research was
for educational purposes only, and that no individual identities would be revealed.
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The rest of this research report is organized into four chapters. The next chapter
reviews the literature on values, culture, generalized beliefs and diffusion theory and practice.
This is followed by a chapter on the research methodology employed. The next chapter
analyses and discusses the findings of the study. The research report is then ended by a
chapter with some concluding remarks and recommendations for future studies.
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Literature Review
This literature review will include three main areas: (a) the relationship between
consumer behaviour and culture as an expression of values, (b) the relationship between
values and social axioms, and (c) the relationship between social axioms and diffusion of
innovations. These three areas seem to provide sufficient coverage in the search for answers
to the main research question: In what ways do strongly-held, generalized cultural beliefs
mediate the relationship between personal innovativeness and the adoption of innovations?
The literature review will focus on the individual level of analysis, to the exclusion of group
or national level considerations.
The remainder of this chapter is organised as follows. The influence of culture on
consumer behaviour, as an expression of values, is reviewed first. This is followed by a
review of value-based approaches to cultural variability. The shortcomings in the value-
based approaches lead naturally to a review of social axioms as complementary explanatory
variables for cultural variability in human behaviour. This is followed by a review of
diffusion of innovations theory. Lastly, a review of the relationship between personal
innovativeness, the five dimensions of social axioms, and the adoption of innovations
follows.
What Shapes Consumer Behaviour?
Human behaviour, in general, represents responses to environmental stimuli. In the
particular case of consumer behaviour, it represents responses to the product and service
offerings of the market. Smith, Peterson, & Schwartz (2002) assert that behaviours always
play out within a particular context. One of the environmental contexts of a consumer is
societal culture. Nakata & Sivakumar (2001) say that cultural values shape the interpretation
of new ideas or practices, but that culture also facilitates or impedes the adoption and
implementation of new ideas or practices. They identify interpretation, adoption and
implementation as sequential steps that a consumer goes through when faced with a decision
situation that leads to action. However, the underlying assumption to this conceptualisation
is that human beings always act in rational ways, which one could argue as being far from the
reality of the complex nature of human experiences.
While these stages are very useful in understanding the effects of culture on
innovative behaviour, it must be remembered that Nakata and Sivakumar (2001) intentionally
present a simplified model in order to draw attention to specific diagnostic stages of the
consumer decision process. Consider the granularity of what could be called stages in
consumer decision-making and behaviour. A considerable body of research on consumer
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decision processes suggests that steps in the decision process are separated by fuzzy
boundaries, rather than sharp delineations. In practice, interpretation, adoption and
implementation may occur sequentially or concurrently or perhaps in an iterative fashion.
Individuals may decide alone or as part of a social group. Consumers may process different
types and amounts of information, due to differences in knowledge or products or adoption
requirements. They may decide against adoption. They act without pursuing active decision
making (Campbell, 1966). For instance, habitual behaviour does not require active
information processing and devotion of cognitive resources to decision making. Thus, it is
important to consider the complexity and heterogeneity within and across consumers, when
attempting to understand their responses to innovative product or service offerings.
Hofstede (1980) and Bond et al (1987) separately conceptualized five national culture
factors that influence human behaviour. These include individualism, uncertainty avoidance,
power distance, masculinity and Confucian dynamism or future orientation. Nakata &
Sivakumar (2001) propose that the propensity of members of a given culture to adopt an idea
or practice depends on the degree of congruence between the values inherent in the idea and
the values of the potential adopters. The greater the congruence between the two value-sets
the greater is the likelihood of idea adoption. Rogers (1983) says that compatibility between
the potential adopters‟ values, past experiences, current practices, and needs increases the
likelihood of adoption of technology innovations.
The contextualisation of human behaviour poses problems for researchers who may
wish to draw practical implications from the conceptualization of culture in terms of values
that are context-free (Smith, Peterson, & Schwartz, 2002). A discussion of value-based,
context-free approaches to cultural variability follows in the next section.
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Value-Based Approaches to Cultural Variability
Attempts to define and measure the concept of culture (Kroeber & Kluckhon, 1952;
Rohner, 1984) have been many and varied. Some approaches have been pitched at the
national level (Hofstede, 1980; Triandis, 1995, Chinese Culture Connection, 1987; Hofstede,
1991; Schwartz, 1994; Smith, Dugan, and Trompenaars, 1996; Smith and Bond, 1996), while
others have been pitched at the individual level (Schwartz, 1992; Bond, 1988). The national
and individual levels of analysis aimed at identifying value-based explanations for observed
cultural variability between nations and individuals, respectively. Common to both
approaches was the conceptualisation of culture as the expression of value-centred, shared
meanings assigned to things, persons and events in the environment of culture members
(Smith, Peterson, & Schwartz, 2002).
Leung et al (2008) describe values as generalized goals that serve a motivational
function. Values focus people‟s behaviour on goals that they deem to be important. People
gravitate toward goals indicated by their value profiles (Leung et al, 2008).
Until recently, value-based perspectives have represented the most predominant view
on cross-cultural research. Schwartz (1992) characterises values as life‟s guiding principles
in terms of defining what worthwhile pursuits people would like to pursue in life. Another
characterisation by Leung et al (2008) is that values answer the “what?” question in life. The
search for a conceptual framework to answer the “why?” question in life leads to social
axioms. Important progress on understanding how values influence behaviour has been
made, but richer, complementary frameworks are needed in order to conceptualise culture in
ways that values cannot (Leung et al, 2004). One such framework is based on generalized
cultural beliefs, or social axioms.
What Are Social Axioms?
Social axioms have recently been proposed as a complementary, explanatory
framework for cultural variability in human behaviour, alongside the traditional, value-based
dimensions of Schwartz (1992). Leung et al (2002) identified a pan-cultural set of five
dimensions of general beliefs about the world and how it functions. These general beliefs are
called social axioms; social as they derive from social experiences and axioms as they are
accepted, without question or scientific validation, as a priori true premises.
The five dimensions of social axioms are fate control, religiosity, reward for
application, social complexity, and social cynicism.
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Reward for application. Reward for application refers to the general belief that the
investment of human capital, effort and other resources ultimately leads to positive social
outcomes (Bond et al, 2004).
Social complexity. Social complexity suggests that there are no rigid rules in life,
that there are always multiple ways of solving a problem or achieving a desired outcome and
that inconsistency in human behaviour is common, acceptable, and indeed, to be expected
(Leung et al, 2002).
Fate control. Fate control suggests that life events are predetermined, predictable
and fated, but that there are ways in which people can influence the course of fated events in
their favour (Leung et al, 2002).
Religiosity. Religiosity speaks to the existence of a supernatural being reigning over
human beings and the importance of religious beliefs and religious institutions to human
functioning.
Social cynicism. Social cynicism is a general belief that is characterized by a
negative view of human nature, a biased view against some groups of people, a mistrust of
social institutions, and a general disregard of ethical means for achieving an end (Leung et al,
2002).
Social axioms research is still in its infancy. Leung et al (2002) do not claim
completeness of their five factor model. The authors suggest that local collaborators on the
on-going global social axioms studies should feel free to include local variability to the
observed dimensions of social axioms. Thus, in South Africa, Ancestor Relevance has been
identified as a potential dimension of social axioms (Maku, 2006). Confirmation of the
universality of this potential dimension of social axioms within the South African context, as
well as studying the implications of that dimension on social behaviour, represent possible
areas of future social axioms research.
Research shows that social axioms serve several major functions for humanity. Katz
(1960) and Kruglanski (1989) regard social axioms as important for human survival and
functioning. Social axioms also serve four major functions of human attitudes (Leung et al,
2002). These are attainment of important goals (instrumentality), preservation of self-worth
(ego-defensive), manifestation of values (value-expressive), and knowledge acquisition
(Leung et al, 2002). Based on these functionalist perspectives on the study of human
attitudes, it is suspected that social axioms also play a role in the diffusion of innovations,
which will be discussed next.
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Diffusion of Innovations
Diffusion of innovations theory describes the process through which an innovation is
diffused and adopted within wider social networks (Murray, 2009). Rogers (2004) argues
that diffusion is a universal process of planned social change, not constrained by the type of
innovation, the adopters, place or culture. Innovation adopters are categorized as innovators
(pioneers), early adopters, late adopters and laggards (last adopters) based on their relative
speed of adoption of an innovation.
Development of diffusion of innovations theory dates back to the Ryan and Gross
(1943) seminal study focused on hybrid seed corn in Iowa, United States of America. Since
then, more than 5000 empirical and non-empirical research publications have emerged from a
wide variety of academic disciplines (Rogers, 2004). Common to these publications have
been attempts to create the theoretical bases for a conceptual framework to describe the
generalized process of innovation adoption as the end result of the process of diffusion
(Murray, 2009; Wejnert, 2002; Deffuant et al, 2005; Deffuant et al, 2002; Deffuant, 2001).
One of these conceptual frameworks is reviewed next.
Wejnert’s conceptual framework. Wejnert (2002) provides a conceptual
framework of diffusion variables influencing the diffusion of innovations. These variables
explain how innovation adopters go about the process of deciding to adopt an innovation.
The conceptual framework identifies three sets of diffusion variables, which, in this review,
will be labelled as diffusion dimensions. These are characteristics of the innovation,
characteristics of innovators, and characteristics of the environmental context (Wejnert,
2002). The diffusion dimensions will be reviewed next.
Characteristics of the innovation. The „characteristics of the innovation‟ dimension
is made up of two variables, namely, public versus private consequences and benefits versus
costs (Wejnert, 2002). Public consequences relate to the impact of an innovation‟s adoption
on entities other than the adopter. Private consequences are experienced by private
individuals or small communities (Wejnert, 2002). The benefits versus costs variable relates
to the perceived benefits or costs of adopting the innovation.
Wejnert (2002) identifies the following diffusion variables as making up the
„characteristics of innovators‟ dimension:
Societal entity
Familiarity with the innovation
Status characteristics
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Socioeconomic characteristics
Position in social networks
Personal characteristics
The societal entity describes the nature of the potential adopter as an individual, or as
a small or large collective actor such as a nation, an organisation, a community, a group of
people or a family. The nature of the diffusion process differs depending on the nature of the
societal entity (Wejnert, 2002). The current research focuses on the individual as the unit of
adoption, assuming that the individual‟s choice may be influenced by others in the same close
social unit that influence purchase and consumption decisions in this product category.
Familiarity with the innovation is a variable that measures the degree of how radical
the innovation is to the potential adopter (Wejnert, 2002). Innovations that represent radical
points of departure increase the perceived risk and cost of adoption. The status characteristics
variable refers to the relative prominence of an actor within a population of actors. An
actor‟s relatively high social status is expected to increase the probability of innovation
adoption (Wejnert, 2002). Socioeconomic characteristics of individual innovators include
such things as education level and economic well-being (Wejnert, 2002). The rate of
innovation diffusion appears to be positively correlated with socioeconomic characteristics of
innovators that facilitate innovation adoption. Position in social networks determines the
kind of communication channels available to actors. In the case of individual actors,
interpersonal networks play a major role in the diffusion process. Network connectedness
and openness are variables that further explain position in social networks. Opinion leaders,
for example, increase the probability of innovation adoption by others with whom they have
interpersonal relations. Examples of personal characteristics of innovators include self-
confidence, independence and risk-taking (Wejnert, 2002). These personal characteristics are
expected to increase the probability of adoption of novel innovations.
Characteristics of the environmental context. The „characteristics of the
environmental context‟ dimension is made up of the following diffusion variables (Wejnert,
2002):
Geographical setting
Societal culture
Political conditions
Global uniformity
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Wejnert‟s (2002) conceptual framework provides an important theoretical framework
for the characterisation of the process of diffusion of innovations. Many other conceptual
frameworks have been developed and put into practice over the years (e.g. Granovetter &
Soong, 1983; Leathers & Smale, 1991). However, for simplicity, this literature review will
consider only Wejnert‟s (2002) characteristics of individual innovators in its review of the
relationship between social axioms and diffusion of innovations variables. Charters (1992)
provides guidelines on the recommended way to select and name variables. The justification
for considering only the characteristics of innovators dimension, to the exclusion of
characteristics of the innovation and characteristics of the contextual environment, is based
on the fact that social axioms are concerned with generalized beliefs of individuals or nations.
Social axioms are general; they are not constrained by the characteristics of the innovation.
Social axioms are also context-free (Leung, 2008); they are not constrained by environmental
context. It therefore seems justified to consider only the relationship between social axioms
and the characteristics of innovators.
Hypothesized effects
Although research on social axioms is in its infancy, it is possible to draw some
tentative hypotheses for assessment in the current research based on the foregoing literature
review. I will now conclude the literature review with a summary of the expected relations,
which are framed in formal hypotheses.
Effects of innovativeness on cellular telephone adoption. Diffusion theory (Rogers,
2003) holds that status characteristics, personal characteristics and socio-economic status of
innovators have a positive direct effect on adoption. However, the theory was derived in an
institutional context very different to Kgautswane. So, although there is no reason to expect
these relations to not hold among subsistence consumers, it is nevertheless important to
assess that these relations hold in the present research. Therefore:
H1: Status characteristics have a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane
H2: Personal characteristics have a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane.
H3: Socio-economic status has a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane.
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Effects of culture on innovativeness. Although the links between social axioms and
innovative consumer behaviour have not been the subject of research, several studies have
identified links between cultural values and innovativeness (e.g., Rogers, 2003). The
following relations are hypothesized:
H4: Reward for application has a positive direct effect on socioeconomic status of
subsistence consumers in Kgautswane.
H5: Fate control has a positive direct effect on socioeconomic status of subsistence
consumers in Kgautswane.
H6: Religiosity has a negative direct effect on status characteristics of subsistence
consumers in Kgautswane.
H7: Social complexity has a positive direct effect on personal characteristics of
subsistence consumers in Kgautswane.
H8: Social cynicism has a positive direct effect on socioeconomic status of subsistence
consumers in Kgautswane.
Effects of culture on cellular telephone adoption. Culture will be represented by the
five dimensions of social axioms.
Reward for application and adoption. The relations of reward for application and
cellular telephone adoption have not been studied, to my knowledge. However, reward for
application emphasizes the belief that the investment of human capital, effort and other
resources ultimately leads to positive social outcomes. These other resources could include
status, socioeconomic and personal characteristics of potential innovation adopters. The
investment of effort and other personal innovativeness resources of individuals facilitate the
adoption of innovations. In summary, I expect reward for application to have a positive total
effect on cellular telephone adoption.
H9: Reward for application has a positive total effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane.
Fate control and adoption. People who endorse fate control believe that life events
are predetermined, but that there are ways to influence the outcomes of fated events. Such
people could thus influence the outcomes of fated life events in their favour by using their
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status, socioeconomic and personal characteristics, which facilitates adoption. In summary, I
expect fate control to have a positive total effect on cellular telephone adoption.
H10: Fate control has a positive total effect on the adoption of cellular telephones by
subsistence consumers in Kgautswane.
Religiosity and adoption. People who endorse religiosity believe in the existence of a
higher being that rules over humanity. They also believe in the beneficial impacts of religious
institutions. Potential innovation adopters who perceive novelty and unfamiliarity in an
innovation may believe that such novelty is part of God‟s plan. They may thus preclude using
their status, socioeconomic and personal characteristics to seek out information through the
appropriate communication channels to establish greater familiarity with the innovation,
which deters the adoption of innovations. In summary, I expect religiosity to have a negative
total effect on cellular telephone adoption.
H11: Religiosity has a negative total effect on the adoption of cellular telephones by
subsistence consumers in Kgautswane.
Social complexity and adoption. People who are high on social complexity are likely
to thrive under conditions of ambiguity and novelty. They view the world in a complex
fashion (Leung et al, 2008) and they adopt a contingency approach to problem solving,
readily accepting that there are always more than one solution to a problem. People who are
high on social complexity display the personal characteristics of self-confidence,
independence and risk-taking. People with low status characteristics lead simple lives, with
simple outlooks on life. However, people who have high status characteristics endorse social
complexity. Higher education levels and higher economic means often lead to perceptions of
a complex world. It is therefore expected that the degree of social complexity is positively
correlated with an actor‟s personal innovativeness, which facilitates adoption. In summary, I
expect social complexity to have a positive total effect on cellular telephone adoption.
H12: Social complexity has a positive total effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane.
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Social cynicism and adoption. Social cynics are characterized by a negative view of
human nature, a biased view against some groups of people, a mistrust of social institutions,
and a general disregard of ethical means for achieving an end (Leung et al, 2002). Such
people could thus choose to be proactive and depend on themselves (to the exclusion of
mistrusted social institutions and other groups) and the resources available to them to
facilitate the diffusion of innovations. In their assessment of the coping strategies of
subsistence consumers, Ruth and Hsuing (2007) support this viewpoint. The authors find the
maintenance of channels of communication to trusted family members, and adherence to new
resource generation and access opportunities as some of the important coping strategies of
subsistence consumers. Thus, low status, socio-economic and personal characteristics (which
is the hallmark of social cynics) will be expected to lead to higher adoption rates. In
summary, I expect social cynicism to have a positive total effect on cellular telephone
adoption.
H13: Social cynicism has a positive total effect on the adoption of cellular telephones
by subsistence consumers in Kgautswane.
Conclusion
Thirteen theorized hypotheses have been stated in the above. These hypotheses
suggest that relationships exist between innovativeness and adoption, social axioms and
innovativeness, and social axioms and adoption. The next chapter describes the research
methodology based on which these hypotheses will be tested empirically.
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Research Methodology
This section describes the type of research approach and strategy that was employed.
It provides an overview of the primary data collection and sampling methodologies. The
section also outlines the data analysis methods, followed by a discussion of the limitations of
the study.
Research Approach and Strategy
In what ways are the dimensions of social axioms related to innovativeness? This
was the main research question of this study. The purpose of the research was to explore the
usefulness of social axioms as predictors of innovativeness. In this sense the research was
exploratory in nature. The research was based on a cross-sectional research approach, where
diffusion of innovations variables were measured at a specific point in time, instead of over a
period of time as would have been more representative of the reality of diffusion processes.
However, in order to somewhat make up for this shortcoming, the research used participant
recall, to introduce some semblance of time passage into the diffusion processes. This in its
own right introduced some questions as to the validity of the responses to the diffusion of
innovations scale employed.
Research Design, Data Collection Methods and Research Instruments
Research design. The type of research was descriptive, quantitative, co-relational
analyses of cross-sectional data gathered using a single method approach. Single-method
approaches often lack the richness of multi-method approaches. Cross-sectional data suffers
from the time deficit, whereby phenomena that naturally occur over time are instead
measured and represented as a single snapshot.
The research subjects were adult women (16 years or older) resident in the
Kgautswane rural area of Limpopo Province. The unit of analysis was the individual. The
reason for selecting the individual as the unit of analysis was based on the fact that the mobile
telephone is generally a personal communication device. Therefore, the decision to adopt
this communications technology would generally reside in the individual. However, this
decision does not lessen the importance of joint-decision making that may have to take place
where the mobile telephone is a family-owned, or community-owned and/or shared
communications device. Another reason for selecting the individual as the unit of analysis
was the fact that the dimensions of social axioms used in this research were also derived with
the individual as the unit of analysis. The assumption then is that congruence in the units of
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analysis between social axioms and diffusion of innovations variables improves the internal
validity and consistency of the derived hypotheses.
The research fieldwork was designed to take place in two distinct phases. The first
phase involved conducting a pilot study using 25 people who were to become the research
assistants. The purpose of this phase in the research design was to check common
understanding of the questionnaire and to familiarize the research assistants with the research
process. This phase took place during the afternoon of day 1 of the research fieldwork. The
second phase involved sample selection and administration of the survey. This took place on
day 2 of the research fieldwork. Thus, the participation of the research assistants in the
research fieldwork was designed to last two days.
Data collection methods. The kind of data collected was primary data. The survey
questionnaire was the primary data collection method. Data was collected anonymously from
the survey participants who were willing participants. The questionnaire was administered to
the survey participants by the research assistants under the supervision of the researcher. The
research assistants then went through the questionnaire with the survey participants,
explaining how to respond to the questionnaire. The research assistants fielded answers to
any questions that arose from this interaction. The participants were then given time to
complete the questionnaires on their own, after which the research assistants collected the
completed questionnaire. Participants were assured of anonymity and confidentiality of their
responses.
Research instruments and data preparation. The survey questionnaire was the main
data collection instrument. The survey instrument included two primary scales. I measure
social axioms using the 25-item Social Axioms Scale, Version 1 (Leung, et al., 2002). Five
dimensions of social axioms were included in this scale, with each dimension being measured
by 5 items. The SAS survey tapped into the predictor (independent) social axioms variables.
I borrowed from Wejnert‟s (2002) Conceptual Framework on the Characteristics of
Innovators scale to construct a 14-item scale measuring innovation diffusion characteristics.
The diffusion of innovations scale, tapped into the dependent variables. Both scales were
measured on a 5-point Likert scale, from strongly disbelieve (scored as 1) to strongly believe
(scored as 5). In order to lower response bias, respondents could choose “don‟t know” for
each question, which was scored as 6. Don‟t know responses were treated as missing data in
the analysis.
Completed surveys were captured in Microsoft Excel. Data preparation for analysis
consisted of capturing the survey responses into an Excel spreadsheet. The column headings
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of the Excel spreadsheet represented the variable names, for example SCy1, SCy2, (i.e.
Social Cynicism subscale item 1, Social Cynicism subscale item 2, etc.). There were a total
of 42 variables, of which 25 were independent variables, 14 were dependent variables and 3
filter variables, namely age, gender and ownership of a mobile telephone set. Age was
intended to filter out minors from participating in the survey. Gender was used as a control
variable to ensure that males were excluded from the survey. Mobile telephone set
ownership was intended to exclude from the survey participants who did not currently, or at
some point in the past, own a mobile telephone set. Each row of the spreadsheet represented
the responses of individual participants.
Both questionnaires were originally developed in the English language. These were
translated to Sepedi in order to facilitate understanding by the survey participants. The
method of back-translation was used to check the accuracy of the translation. Both
questionnaires can be found in Appendix A. A pilot survey was conducted to check validity
of the questionnaire. The results of the pilot study showed that there was some confusion as
to the meaning of the word “novel” in the first item of the innovativeness scale. This word
was changed to “unfamiliar” in order to make the item clearer.
The descriptive statistics of the study can be found in Appendix C.
Sample
The sample consisted of female adults resident in the Kgautswane area of the
Limpopo Province. A reliable population register is not available for this area and
convenience sampling was implemented in two phases.
Phase 1: Pre-test and interviewer training. Twenty-five adult school leavers took
part in a pilot study of the questionnaire to assess the face validity of the scale. Test-retest
reliability checks of the survey questionnaires were conducted on this group over a period of
two days. The results showed acceptable face validity of the scale. Some confusion as to the
meaning the word “novel” in the first item of the innovativeness scale was eliminated by
changing this word to read “unfamiliar”.
Phase 2: Main study. Research assistants administered the surveys to adult,
females over a single day in the third quarter of 2010. After discarding incorrectly completed
or incomplete surveys, the final sample size consisted of 275 adult females. The sample size
was determined based on the data analysis methods that were used. Data analysis methods
will be described in detail in the next section. However, brief mention of the selected data
analysis method is necessary here in order to dispense with the rationale for sample size
decisions. Partial Least Squares (PLS) Structural Equation Modeling (SEM) with latent
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variables, or simply PLS path modeling, was the data analysis method used for this research.
Sosik, Kahai, & Piovoso (2009) suggest as a rule of thumb in PLS path modeling that the
number of cases must be greater than both the number of variables in the largest block and
the number of latent variables in the model.
In the case of this research, the innovativeness scale had the largest number of
manifest variables (14). Chin (as cited in Sosik, Kahai, and Piovoso, 2009) suggests the so-
called ten times rule as the guiding principle in sample size considerations for PLS path
modeling. The ten times rule says that the sample size should be greater than or equal to the
greater of either the largest number of formative indicators or the largest number of structural
paths leading into a latent structure variable (Sosik, Kahai, & Piovoso, 2009). Based on these
considerations, a sample size of 50 would have satisfied Chin‟s (1997) ten times rule.
However, the researcher selected 250 as the target sample size. In addition, the subsequent
consolidation of the innovativeness scale into three latent variables more than met the sample
size requirements as suggested by Chin‟s (1997).
Sample characteristics. The target sample of 250 women belonged to a group of
more than 2000 women who had queued up at the Kgautswane Community Development
Centre one Friday morning in the last quarter of 2010 to receive their monthly child support
grants and maintenance payouts. The reason for selecting an all-female sample was based on
the women‟s easy accessibility to the researcher and also on economic circumstances in the
village of Kgautswane. Table 3.1 below summarizes the sample characteristics.
Table 3.1
Sample characteristics
Characteristic Number Percentage
Gender
Male
Female
0
275
0%
100%
Age
<16 years
>16 years
0
275
0%
100%
Mobile Telephone Adoption
Non-Adopters
Adopters
21
254
8%
92%
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Three important observations leave the visitor to Kgautswane inclined to
erroneously believe that this village is a hive of economic activity. Upon entry into the area,
the visitor immediately notices a large number of formal dwellings with direct-to-home
digital satellite television antennas on roof tops and wall mountings. The visitor also notices
the high penetration of electrification in the village and also the high frequency of vehicular
traffic running up and down the dirt road that passes through the village. The third important
observation is the presence of two mobile cellular towers in the village, approximately five
kilometres apart. This last observation makes the observer wonder at the source of
disposable income that the two mobile cellular operators compete for in this market.
However, upon enquiry as to the main source of livelihood of members of the community,
the observer is informed that the majority of households survive on social grants. Now, since
women are the majority recipients of social grants in the area, it was expected that they would
be the main sources of disposable income, some of which could be used to purchase mobile
cellular telephones. It was for this reason that the researcher felt justified to make only adult
females the subjects of this research. However, the importance of men in the community
cannot be ignored. Perhaps future research could consider including men into the sample.
No age profiling was done on the selected participants besides, to establish that all
participants were adults who each resided in the Kgautswane area of the Limpopo Province.
Sampling method. A non-probabilistic, convenience sampling method was used to
select participants of this study. Upon joining the queue, each woman received from the
queue assistant a card with a number printed on it ranging from 1 to the total number of
women present on the day. This number was to mark the position in the queue of this
particular woman. The women received their payouts in the order determined by this
number. Selection of participants was based on this queue position number.
The researcher generated 375 different pseudorandom numbers in the range 100 to
2000. These were the queue position numbers of the women who were to be approached to
participate in the survey. Selection from the back of the queue was intended to guarantee
sufficient time to administer the survey before the participant‟s turn at the pay point. A
slightly bigger number than the target 250 was selected in order to cater for possible spoilt
questionnaires or non-returns. Each of the twenty five research assistants was then given a
list of fifteen numbers which represented the queue position numbers of the women that they
were to approach to administer the survey. Out of the 375 administered questionnaires, 286
were accepted for further processing. The rest were rejected for erroneous completion. In
the rural community in which this research was conducted, low formal education levels were
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found to be a major hindrance to reliable data collection. As a result of data pre-processing, a
further 11 questionnaires were rejected for random completion. The effective sample size for
this study was therefore 275.The next section describes the data analysis methods.
Data Analysis Methods
Partial Least Squares (PLS) structural equation modeling (SEM) with latent
variables, or simply PLS path modeling, was the data analysis method selected for this
research. SmartPLS software, version 2.0(M3) Beta, (Ringle, Wende & Will, 2005), was
used to conduct PLS path modeling. Three main reasons explain the rationale for selecting
this data analytic technique among other alternative choices of data analysis methods. The
first reason relates to the prediction-oriented nature of the study. The second reason relates to
the statistical characteristics of the collectable data. The third reason was based on the
limitations of more traditional statistical methods. The rationale for the selection of PLS path
modeling as the data analysis method is discussed in more detail in the following paragraphs.
This is then followed by a discussion of the actual types of data analyses conducted, and their
justification.
The main purpose of this study was to investigate whether or not the dimensions of
social axioms were related to innovativeness. More specifically, I was interested in finding
out whether an individual‟s generalized beliefs (social axioms) could be used to predict the
adoption of innovations, through their effects on personal innovativeness. In this sense, the
research was prediction-oriented in nature. Generalized beliefs were represented by five
dimensions of social axioms, i.e., five latent variables, each measured by five manifest
variables. Personal innovativeness was measured by three latent variables, personal
characteristics (DPC), status characteristics (DSC) and socio-economic status (DSES). A
ninth latent variable, Adoption, served as the endogenous latent variable, measuring
innovation adoption by means of adoption experience (AE) manifest variables. The
Adoption model used consisted of single, direct effects of personal innovativeness latent
variables on the Adoption, endogenous latent variable. This was by no means the only
possible model of adoption, but perhaps the simplest.
Traditionally, exploratory and/or prediction-oriented studies of this nature have been
subjected to statistical analyses that involved some form of exploratory and/or confirmatory
factor analytic technique. However, many researchers cited in Preacher and MacCallum
(2003), (e.g., Fabrigar, Wegner, MacCallum, & Strahan, 1999; Floyd & Widaman, 1995;
Ford, MacCullum, & Tait, 1986; Lee & Comery, 1979; Widaman, 1993), have warned that
these traditional statistical methods had serious shortcomings if they were not applied with
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caution. One source of error in using such traditional methods was the assumption of perfect
linear relationships between latent variables and their corresponding manifest variables. In
real life, such linear relationships are but just approximations. Yet another potential source of
error was the use of principal components analysis with, mainly, orthogonal Varimax rotation
to determine retention of principal components with eigenvalues greater than 1. Orthogonal
Varimax rotation presupposes that manifest variables are statistically independent of each
other. In real life, it is hard to achieve pure statistical independence between measurement
variables. PLS path modeling avoids these problems and is well-suited for prediction-
oriented research that makes minimal demands on the statistical independence of manifest
variables.
In the past, scale reliability assessments were generally based on the Cronbach‟s
coefficient alpha. One source of error in this approach to scale reliability testing was the
adoption of 0.700 as an arbitrary threshold of acceptable Cronbach‟s coefficient alpha for
factors that could be retained in the model. The application of Cronbach‟s alpha in scale
reliability testing also placed huge demands on sample sizes. Whereas PLS path modeling is
equally well-suited for problems with small and large sample sizes, traditional statistical
methods require hundreds if not thousands of cases in a sample. In real life, smaller sample
sizes are much easier to achieve than larger ones, making PLS an ideal statistical approach in
small-sample size studies.
One underlying assumption of traditional statistical analysis methods is the statistical
normality of the data. In a research approach in which survey questionnaires are the main
data collection instrument, data normality can hardly be guaranteed. The application of
traditional statistical techniques when the normality assumption is violated leads to incorrect
and potentially misleading results (Preacher and MacCallum, 2003). PLS path modeling
places no demands on data normality (Henseler, Ringle, & Sinkovics, 2009).
Based on the forgoing discussion, PLS path modeling with latent variables was the
preferred data analysis method. However, yet another decision had to be made with regard to
the PLS mode employed. One of two possible modes could be selected: reflective mode or
formative mode. According to Henseler, Ringle, & Sinkovics (2009), the reflective mode is
used in situations where measurement scales are well developed, manifest variables reflect
their corresponding latent variables, and the research is prediction-oriented. The formative
mode is recommended in the early stages of theory development, when scale characteristics
have not yet stabilised, and when the research is exploration-oriented (Henseler, Ringle, &
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Sinkovics, 2009). The choice of mode determines the types of analyses that would be
necessary and the interpretation of the results thereof.
For the purposes of this research, the reflective mode was selected. The rationale for
this decision was based on the fact that both social axioms and diffusion studies were
established fields of social research. Besides, the purpose of the study was not focused on
scale or theory building, but rather on scale application and on prediction.
The specific statistical analyses conducted for this research are discussed in the next
chapter. These included correlation analysis, scale reliability and validity analysis,
measurement and structural model analysis, and hypothesis testing. The following section
discusses the limitations of the study.
Limitations of the study
Several constraints were encountered in carrying out this research. One of the
constraints was to do with the size and randomness of the selected sample. The other
constraint was time. A final constraint that was considered was the limitations of the data
analysis method used.
A purely random sample was difficult to achieve. The pre-conditions for one to
belong to the population of this study were that one had to be an adult residing in
Kgautswane and owning a mobile telephone. Perhaps one way to have derived this
population would have been to perform a census of the Kgautswane Community, but time
and other constraints did not allow this. Therefore a convenience sample was the next best
thing to work with (Bryman and Bell, 2007: 197).
The next possible way to derive some idea of the size of the population was for the
research assistants to randomly approach adults that they came across and ask them whether
they resided in Kgautswane and whether they owned a mobile telephone. At the end of the
exercise, some proportion of adult residents with mobile telephones to the total number
approached would be derived. This proportion would then be used to derive the estimated
research population size. However, one problem with this approach was that the Kgautswane
population size of approximately 100,000 does not include only adults, but everyone. So,
another estimate of the split between adults and minors was necessary. It is evident that with
each additional estimate made to arrive at an estimated population size, and ultimately,
sample size, the population and sample errors would also increase. The target sample size of
250 was eventually arrived at based on several considerations, including time availability for
both the research assistants and the researcher, cost constraints, allowance for non-returns,
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relative language and cultural homogeneity of the population, and guidelines regarding the
data analysis method used.
Time was another major constraint of this study. By their very nature, diffusion
studies involve phenomena that take place over time. Diffusion studies would thus best be
conducted using a longitudinal research approach. However, time and other resource
constraints did not allow this. A cross-sectional research approach was thus employed.
Participant recall was used to introduce some semblance of time passage into the study.
However, research shows that participant recall is not always reliable.
The model of innovation, consisting of only three latent variables, was also a limiting
factor for this research. Three personal innovativeness latent variables do not even begin to
cover the richness of model that can be achieved with a more inclusive innovativeness model.
Therefore, for future research, it would be necessary to consider more latent variables for the
innovativeness scale.
In summary, research constraints provide the context for evaluating the research
results. They also serve to highlight the limitations of the generalizability of the research
results. The next chapter presents a discussion and analysis of the findings of the study.
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Research Findings, Analysis and Discussion
The analysis begins with correlation analysis of the manifest variables of the study.
Reliability and validity analysis of the measurement scales then follow after this. Means,
standard deviations and correlations of summated scale variables are then analyzed. Three
types of analyses follow after this, including measurement model analysis, structural model
analysis and hypothesis testing. Measurement model analysis assesses the nature of the
relationships between each latent variable and its manifest indicators, whilst structural model
analysis assesses the relationships between the latent variables. Hypothesis testing was
conducted in order to evaluate the relationships that were originally surmised between the
dimensions of social axioms and innovativeness.
Correlation Analysis of Manifest Variables
The correlation matrix of the manifest variables of the study is shown in Appendix B.
Inspection of item correlations reveals the expected pattern of large and significant inter-
correlations for items within social axiom dimensions and low correlations between items
measuring different dimensions.
Before closing this section, it is necessary to acknowledge the relatively high inter-
subscale item-item correlation coefficients of the innovativeness scale. Such high correlation
coefficients could be lessened by more careful and rigorous design of the innovativeness
scale. Wejnert (2002) does not make any claims on the reliability and validity of the
measurement scales that arise from the use of the Wejnert (2002) Conceptual Framework on
the Characteristics of Innovators. Only a small subset of the Wejnert (2002) Conceptual
Framework has been considered here, specifically the one related to personal characteristics
of innovators. I did not take it upon myself, either, to design a complete innovativeness
measurement scale from the Wejnert (2002) Conceptual Framework. This was left for future
research. Rather, it was more important to demonstrate the modus operandi of investigating
the relationship between social axioms and innovativeness, without getting into the minute
details of optimal scale design.
Reliability and validity
The overall reliability of a reflective measurement model can be assessed by
evaluating the reliability of its indicators. Two types of reliability measures can be assessed.
These are internal consistency reliability and indicator reliability. Internal consistency
reliability is a measure of whether or not indicators measure the same latent construct.
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Indicator reliability is a measure of the proportion of each indicator‟s variance that is
explained by the respective latent variable.
Cronbach‟s coefficient alpha is the measure of internal consistency reliability that is
most commonly used. Research and practice recommends a threshold value of 0.700 for
models that are in the early stages of development. However, Burgess and Steenkamp (2006)
suggest that early-stage research in emergent markets could be advanced by accepting
Cronbach‟s coefficient alpha values as low as 0.400. Values of Cronbach‟s alpha closer to
0.900 are recommended for more mature models.
Composite reliability is another measure of internal consistency reliability that can
be used. Table 4.1 below shows the results of internal consistency reliability of the
measurement model. Based on an assessment of Cronbach‟s coefficient alpha and composite
reliability, the model shows acceptable levels of internal consistency reliability that are, in
fact, higher than what has been achieved before around the world and in South Africa. Such
scale performance could be explained by the data pre-processing that I did, which is
explained next.
Out of 375 administered questionnaires, 286 were accepted for further processing.
Twenty four percent (89) of the administered questionnaires were rejected for erroneous
completion. A questionnaire was declared erroneously completed if at least one question was
not responded to or if there were multiple responses to the same question. Of the 89 rejected
questionnaires, 63 (71%) were of the latter category. Such provision of multiple responses to
the same question was attributed to low literacy levels. A further 11 out of the accepted 286
questionnaires (4%) were rejected due to random completion. Randomness of completion
was established by checking for expected completion trends. For example, on the
questionnaire was a set of questions that somebody who believed in the existence of a
supreme being controlling the universe would respond to in one way and somebody that did
not believe in the same would be expected to respond in the opposite way. By checking for
consistency in the response trends, it was possible to eliminate respondents who had
responded to the survey questionnaire at random.
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Table 4.1
Internal consistency reliability
Indicator Composite
Reliability
AVE Cronbach‟s Alpha
Adoption 0.9388 0.7553 0.9174
DPC 0.9268 0.8086 0.8817
DSC 0.9127 0.7235 0.8728
DSES 0.9051 0.8267 0.7924
FC 0.8346 0.5132 0.8730
RA 0.9094 0.6678 0.8796
RY 0.8100 0.4728 0.8364
SC 0.8893 0.6267 0.8564
SCy 0.9032 0.6533 0.8693
Two types of reflective measurement model validity can be assessed. These are
convergent validity and discriminant validity. Both measures provide some assessment of
goodness-of-fit of the measurement model. Convergent validity is a measure of how well the
measurement items relate to the constructs that they measure. For convergent validity to be
achieved, each item should strongly correlate to the construct that it measures. Discriminant
validity is a measure of how weakly each item correlates to the constructs that it does not
measure.
Average variance extracted (AVE) is the measure of convergent validity that is most
commonly used. It measures the amount of variance captured by a latent construct in relation
to the variance due to random measurement error. A value of average variance extracted that
is greater than 0.50 is the recommended indication of convergent validity (Fornell and
Larcker, 1981). Table 4.1 shows that Religiosity is the only subscale with an average
variance extracted that is less than the recommended threshold value of 0.50. Another
assessment of convergent validity is based on the loadings of the manifest variables on their
respective latent variables. Table 4.2 reports the six items that have item loadings less than
0.70. Religiosity has three items out of a possible 5 that have factor loadings below 0.70,
which may explain why its average variance extracted is below 0.50. Even though Fate
Control and Social Complexity have at most two items each with item loading less than 0.70,
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the rest of their items significantly load on the respective latent variable, thus keeping their
AVE above 0.50.
Table 4.2
Item loadings less than 0.70
Item FC RY SC
FC3 0.60
FC4 0.52
RY1 0.65
RY2 0.60
RY4 0.44
SC3 0.46
The Fornell-Larcker criterion can be used to assess discriminant validity. The
Fornell-Larcker criterion says that a latent variable should better explain the variance of its
own indicators than the variance of other latent variables (Fornell and Larcker, 1981).
Discriminant validity is confirmed if the square root of the AVE in a construct cross-
correlation matrix is greater than the correlations between the latent variable and all other
latent variable constructs. Table 4.3 shows the results of discriminant validity assessment.
Table 4.3
Assessment of discriminant validity using cross-correlation matrix
DPC DSC DSES FC RA RY SC SCy
DPC 0.8992 0 0 0 0 0 0 0
DSC 0.8674 0.8506 0 0 0 0 0 0
DSES 0.7823 0.8014 0.9092 0 0 0 0 0
FC 0.0563 0.0969 -0.0103 0.7164 0 0 0 0
RA 0.1610 0.1152 0.1221 -0.0478 0.8172 0 0 0
RY -0.0908 -0.1228 -0.0599 -0.0207 0.1292 0.6876 0 0
SC 0.1382 0.0817 0.1254 -0.0514 0.1368 -0.0113 0.7916 0
SCy 0.1493 0.0827 0.1836 -0.0799 0.0827 0.0134 0.0897 0.8083
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Except for DSC, the square root of each AVE (diagonal entries) is greater than all
the other correlations in the same rows and columns for that AVE (see Table 4.3). This
confirms that the measurement model displays adequate discriminant validity, except for that
between DSC and DPC. The problem of low discriminant validity between DSC and DPC
could be a conceptual one. DSC measures status characteristics whilst DPC measures
personal characteristics. It is quite possible that, among the target sample, the distinction
between the two elements of the measurement scale was not sharp enough. It is probable that
some of the participants of the study perceived status characteristics as begetting personal
characteristics or vice versa; or personal characteristics giving rise to status characteristics.
More careful innovativeness scale design should eliminate the sources of discriminant
invalidity.
Measurement Model Evaluation
Table 4.4 below summarizes the measurement model relations. All measurement
items, except for FC1, FC2, FC3, FC4, RA2, RY1, RY2, RY4, SC2 and SC3 have
statistically significant loadings on their intended latent variable. Of particular concern are
the negative loadings displayed by the FC (FC3 and FC4) and RY (RY1 and RY4) items
highlighted in Table 4.4 below. The process of definitively identifying the cause for such
unexpected behaviour in the face of high internal consistency is complex. However, it is
noteworthy that, in the PLS path model shown in Figure 1, these same items display low
positive loadings on their respective latent variables. In the absence of a scientifically
founded argument, I can only but surmise as to the cause of the negative values highlighted in
Table 4.4 below. My first impulse is to surmise that this behaviour is consistent with the
effects of misunderstanding or misinterpretation of a measurement item by the subjects of the
research. The veracity of such a proposition could be established through further
confirmatory research using the same or similar subjects.
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Table 4.4
Measurement model
Sample
Mean
Standard
Deviation
Standard
Error t
AE1 <- Adoption 0.265 0.005 0.005 56.707
AE2 <- Adoption 0.235 0.004 0.004 58.957
AE3 <- Adoption 0.236 0.004 0.004 56.886
AE4 <- Adoption 0.210 0.006 0.006 35.851
AE5 <- Adoption 0.199 0.006 0.006 31.326
DPC1 <- DPC 0.345 0.008 0.008 40.890
DPC2 <- DPC 0.422 0.010 0.010 44.851
DPC3 <- DPC 0.342 0.010 0.010 34.321
DSC1 <- DSC 0.271 0.009 0.009 30.187
DSC2 <- DSC 0.267 0.009 0.009 31.266
DSC3 <- DSC 0.268 0.010 0.010 28.134
DSC4 <- DSC 0.365 0.011 0.011 32.787
DSES1 <- DSES 0.600 0.016 0.016 37.504
DSES2 <- DSES 0.498 0.015 0.015 33.985
FC1 <- FC 0.250 0.151 0.151 0.463
FC2 <- FC 0.339 0.242 0.242 1.438
FC3 <- FC -0.263 0.193 0.193 0.187
FC4 <- FC -0.271 0.206 0.206 0.594
FC5 <- FC 0.426 0.282 0.282 2.902
RA1 <- RA 0.167 0.081 0.081 2.121
RA2 <- RA 0.180 0.107 0.107 1.573
RA3 <- RA 0.362 0.141 0.141 2.351
RA4 <- RA 0.323 0.111 0.111 2.914
RA5 <- RA 0.229 0.088 0.088 2.550
RY1 <- RY -0.288 0.186 0.186 0.179
RY2 <- RY 0.226 0.164 0.164 1.420
RY3 <- RY 0.396 0.242 0.242 2.643
RY4 <- RY -0.326 0.263 0.263 0.846
RY5 <- RY 0.339 0.207 0.207 2.575
SC1 <- SC 0.251 0.084 0.084 3.193
SC2 <- SC 0.181 0.205 0.205 0.347
SC3 <- SC 0.188 0.183 0.183 0.263
SC4 <- SC 0.470 0.195 0.195 2.250
SC5 <- SC 0.338 0.133 0.133 2.583
SCy1 <- SCy 0.285 0.050 0.050 5.636
SCy2 <- SCy 0.374 0.103 0.103 3.518
SCy3 <- SCy 0.197 0.087 0.087 2.268
SCy4 <- SCy 0.226 0.071 0.071 3.209
SCy5 <- SCy 0.149 0.080 0.080 1.820
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Structural Model Evaluation
Total effects. An overall goodness-of-fit measure has not been widely adopted in PLS
path modeling. Chin, Marcolin, & Newsted (2003) suggest an examination of the R2, the
amount of variance explained for the latent endogenous variables, as one way of evaluating
the structural model. Chin, Marcolin, & Newsted (2003) describe R2 values of 0.67, 0.33 and
0.19 as “substantial”, “moderate”, and “weak”, respectively. According to this assessment,
85.6% (which is substantial) of the variation in the Adoption, endogenous latent variable, is
explained by its relationship with other latent variables of the structural model. Only 14.4%
of the variation in the Adoption, endogenous latent variable, is explained by random
measurement error. Cohen (1988) characterized effect sizes as small (d = 0.20), medium (d =
0.50) and large (d = 0.80), based on his d-statistic and power tables. Cohen‟s d-statistic
transforms into the Pearson correlation coefficient, using the following formula: r = (d2 / (d
2
+ 4))1/2
(Rosenthal & Rosnow, 2008, p.365). Thus, in the current research, Pearson
correlations corresponding to Cohen‟s typology are small (r = 0.10), medium (r = 0.24) and
large (r = 0.37). I will use 0.10, 0.25 and 0.35 for convenience to describe effect sizes.
Total effects can also be evaluated by assessment of the Total Effects Output from
the bootstrapping procedure. Table 4.6 below reports the total effects. The table shows that
reward for application has small but statistically significant effects on adoption and the three
personal innovativeness latent variables. The table also shows that social cynicism has small
but statistically significant effects on adoption, personal characteristics and socio-economic
status.
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Table 4.6
Assessment of total effects
Path
Sample
Mean
Standard
Deviation
Standard
Error t
DPC -> Adoption 0.22 0.06 0.06 3.51
DSC -> Adoption 0.37 0.06 0.06 6.16
DSES -> Adoption 0.40 0.06 0.06 6.75
FC -> Adoption 0.03 0.10 0.10 0.63
FC -> DPC 0.05 0.09 0.09 0.82
FC -> DSC 0.05 0.12 0.12 0.89
FC -> DSES 0.01 0.09 0.09 0.12
RA -> Adoption 0.12 0.05 0.05 2.26
RA -> DPC 0.15 0.05 0.05 2.80
RA -> DSC 0.13 0.06 0.06 2.11
RA -> DSES 0.11 0.06 0.06 1.73
RY -> Adoption -0.07 0.10 0.10 1.03
RY -> DPC -0.04 0.13 0.13 0.85
RY -> DSC -0.11 0.11 0.11 1.28
RY -> DSES -0.05 0.09 0.09 0.80
SC -> Adoption 0.09 0.08 0.08 1.11
SC -> DPC 0.11 0.08 0.08 1.39
SC -> DSC 0.07 0.08 0.08 0.82
SC -> DSES 0.11 0.09 0.09 1.08
SCy -> Adoption 0.13 0.05 0.05 2.51
SCy -> DPC 0.13 0.05 0.05 2.46
SCy -> DSC 0.08 0.06 0.06 1.36
SCy -> DSES 0.17 0.05 0.05 3.23
Direct effects. Direct effects can be assessed by an evaluation of path coefficients of
the structural model. High and statistically significant values indicate good model fit. Table
4.7 below presents the direct effects.
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Table 4.7
Assessment of direct effects
Path Sample
Mean
Standard
Deviation
Standard
Error
t
DPC -> Adoption 0.22 0.06 0.06 3.42
DSC -> Adoption 0.37 0.06 0.06 5.90
DSES -> Adoption 0.40 0.06 0.06 6.82
FC -> DPC 0.10 0.05 0.05 1.67
FC -> DSC 0.12 0.06 0.06 1.95
FC -> DSES 0.08 0.05 0.05 0.25
RA -> DPC 0.15 0.05 0.05 3.14
RA -> DSC 0.13 0.05 0.05 2.49
RA -> DSES 0.11 0.05 0.05 2.00
RY -> DPC -0.13 0.05 0.05 2.04
RY -> DSC -0.13 0.07 0.07 1.94
RY -> DSES -0.09 0.05 0.05 1.48
SC -> DPC 0.13 0.05 0.05 2.30
SC -> DSC 0.09 0.05 0.05 1.20
SC -> DSES 0.12 0.05 0.05 1.93
SCy -> DPC 0.14 0.05 0.05 2.67
SCy -> DSC 0.09 0.05 0.05 1.56
SCy -> DSES 0.18 0.05 0.05 3.38
Note: Reported are the mean results for 500 samples of bootstrap partial least squares
estimates
The significance of structural model path relationships, obtained from the
bootstrapping estimates, is shown in Table 4.7 above. The table shows that the three paths
from the reward for application latent variable to the three latent variables measuring
personal innovativeness (personal characteristics (DPC), status characteristics (DSC) and
socio-economic characteristics (DSES)) are statistically significant. Social complexity has a
statistically significant path to DPC (t=2.30 at p < 0.05), and to DSES (t=1.93 at p < 0.05).
Fate control has statistically significant paths to DPC and DSC, both at the 0.05 level. Social
cynicism shows significant path relationships to the personal characteristics (DPC) and
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socioeconomic status latent variables, both at the 0.01 level. Figure 1 below presents the
overall PLS path model.
Figure 1
PLS path model
Hypothesis Testing
In the literature review, thirteen theorized relationships between innovativeness and
adoption, culture and innovativeness, and culture and adoption were formulated as
hypotheses. In this section, these hypotheses are formally tested. In order to conduct a more
precise test of the hypotheses, these were tested simultaneously in a structural equation model
using the latent variable partial least squares approach (Fornell and Cha, 1994; Hulland,
1999). Each path through the structural model represents a different hypothesis test.
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Effects of innovativeness on cellular telephone adoption. The theory on innovativeness
holds that status characteristics, personal characteristics and socioeconomic characteristics
have a positive direct effect on cellular telephone adoption. Although this theory is not the
focus of the current research, it is not clear that these theorized relations will hold in the rural,
subsistence consumer context. In the current research, I tested three hypotheses to confirm
that these relations hold for subsistence consumers in Kgautswane (viz., H1 – H3). The
hypothesized direct effects can be tested by examining the direct effects of the relevant latent
variables, which are summarized in Table 4.7. The theorized relations are confirmed. More
formally:
H1: Status characteristics have a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane. Status characteristics
have a large (r = 0.363), positive, statistically significant effect on adoption (p
< 0.01). Hence reject the null hypothesis.
H2: Personal characteristics have a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane. Personal characteristics
have a small (r = 0.22), positive, statistically significant effect on adoption (p
< 0.01). Hence reject the null hypothesis.
H3: Socio-economic status has a positive direct effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane. Socioeconomic status
has a large (r = 0.40), positive, statistically significant effect on adoption (p <
0.01). Hence reject the null hypothesis.
Effects of culture on innovativeness. These hypotheses will be tested by an assessment
of the direct effects of social axioms latent variables on the relevant innovativeness latent
variables, as shown in Table 4.7
H4: Reward for application has a positive direct effect on socioeconomic status of
subsistence consumers in Kgautswane. Reward for application has a small
(r=0.11), positive, statistically significant (t= 2.00 at 0.05 level) direct effect
on socioeconomic status. Hence, reject the null hypothesis.
H5: Fate control has a positive direct effect on socioeconomic status of subsistence
consumers in Kgautswane. Fate control has a negligible (r=0.08), positive,
statistically insignificant (t= 0.25) direct effect on socioeconomic status.
Hence, fail to reject the null hypothesis.
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H6: Religiosity has a negative direct effect on status characteristics of subsistence
consumers in Kgautswane. Religiosity has a small (r=-0.14), negative,
statistically significant (t= 1.94 at 0.05 level) direct effect on status
characteristics. Hence, reject the null hypothesis.
H7: Social complexity has a positive direct effect on personal characteristics of
subsistence consumers in Kgautswane. Social complexity has a small (r=0.13),
positive, statistically significant (t= 2.30 at 0.05 level) direct effect on personal
characteristics. Hence, reject the null hypothesis.
H8: Social cynicism has a positive direct effect on socioeconomic status of subsistence
consumers in Kgautswane. Social cynicism has a small (r=0.18), positive,
statistically significant (t= 3.38 at 0.01 level) direct effect on socioeconomic
status. Hence, reject the null hypothesis.
Effects of culture on cellular telephone adoption. These hypotheses will be tested by
an assessment of the total effects from Table 4.6.
H9: Reward for application has a positive total effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane. Reward for application
has a small (r=0.12), positive, statistically significant (t= 2.26 at 0.05 level)
direct effect on adoption. Hence, reject the null hypothesis.
H10: Fate control has a positive total effect on the adoption of cellular telephones by
subsistence consumers in Kgautswane. Fate control has a negligible (r=0.03),
positive, statistically insignificant (t= 0.63) direct effect on adoption. Hence,
fail to reject the null hypothesis.
H11: Religiosity has a negative total effect on the adoption of cellular telephones by
subsistence consumers in Kgautswane. Religiosity has a tiny (r=-0.07),
negative, statistically insignificant (t= 1.03) direct effect on adoption. Hence,
fail to reject the null hypothesis.
Social complexity and adoption
H12: Social complexity has a positive total effect on the adoption of cellular
telephones by subsistence consumers in Kgautswane. Social complexity has a
negligible (r=0.09), positive, statistically insignificant (t= 1.11) direct effect on
adoption. Hence, fail to reject the null hypothesis.
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Social cynicism and adoption
H13: Social cynicism has a positive total effect on the adoption of cellular telephones
by subsistence consumers in Kgautswane. Social cynicism has a small
(r=0.13), positive, statistically significant (t= 2.51 at 0.01 level) direct effect
on adoption. Hence, reject the null hypothesis.
Discussion
Thirteen hypotheses were tested on the relationships between innovativeness and
adoption, social axioms and innovativeness and social axioms and adoption. Personal
innovativeness, measured by status characteristics, personal characteristics, and
socioeconomic status latent variables, was found to have statistically significant direct effects
on adoption. An assessment of the effects of culture on innovativeness showed that reward
for application, social complexity and social cynicism each had positive direct effects on
innovativeness, whilst religiosity was found to have negative, statistically significant effects
on innovativeness. The assessment of the effects of culture on adoption showed that reward
for application and social cynicism each had positive, total effects on adoption. The rest of
the social axioms dimensions (fate control, religiosity and social complexity) each had
negligible and statistically insignificant total effects on adoption.
The results of assessment of the effects of culture on innovativeness and culture on
adoption are particularly interesting for future research. One direction of future research
would be to assess the mediating effects of culture on adoption. Another direction of future
research would be to employ a more complete model of innovativeness by including more
latent variables to measure innovativeness.
The low convergent validity of the Religiosity scale was a big disappointment for
the current research. Data were collected on two occasions after the initial attempt did not
succeed due to suspected interviewer fraud. However, the SAS scale is valid in South Africa.
Notwithstanding the SAS performance, there is the outside chance that it may suffer
convergent invalidity effects arising from the complexity of the interaction of social
phenomena. The next chapter provides some concluding remarks to this research report.
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Conclusion and recommendations
The measurement model developed in this research report showed that some
dimensions of social axioms could significantly predict the adoption of innovations, through
their total effects on adoption. The significance of this result is two-fold. Firstly, and more
importantly, deeply-held generalized beliefs could serve as yet another explanatory
framework for consumer behaviour. Secondly, social, consumer and behavioral research
practitioners could draw on the mature theory and practice from culture and diffusion
research, and triangulate these with the knowledge emerging from the relatively new
discipline of social axioms to construct better models for predicting human behaviour.
A second significant result emerging from the measurement model developed in this
research was that some dimensions of social axioms had significant direct effects on
innovativeness, which also affects adoption. Scale reliability analysis found both the social
axioms scale and personal innovativeness scale used to be reliable. Convergent validity
assessment identified that, for the sample under test, religiosity showed low convergent
validity. This result could be explained by the fact that low-income, rural, subsistence South
African consumers generally believe in the intermediation of ancestors for their well-being,
which belief is contrary to the endorsement of religiosity. The social axioms scale has been
found to be valid in South Africa. However, as a social research scale, potentially affected by
the complexity of social phenomena, there is the outside chance that some of its assumptions
may lead to invalid scale effects. Discriminant validity assessments showed that there was
insufficient discriminant validity between the personal characteristics and social status
subscales of the personal innovativeness scale.
A third important result of this research was that surmised hypotheses on the
relationships between the dimensions of social axioms and innovativeness on the one hand
and social axioms and adoption on the other hand, could be confirmed by using statistical
methods. The importance of this result lies in the fact that, whilst statistical rigour could be
brought to bear on the observed relationships, interpretation of the statistical results could be
reduced to simple intuitive arguments. Thus for example, social cynicism was found to
significantly predict adoption. What this tells us is that the preponderance of social cynics to
view the world in a negative light is in fact a positive coping mechanism that could be
employed to facilitate adoption. Ruth & Hsuing (2007) show support for this viewpoint in
their interpretation of the consumption practices and processes of subsistence consumers in
South Africa.
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Several shortcomings were highlighted for the innovativeness scale used in this
research. A number of suggestions can be made to improve that scale. The first
improvement relates to the inclusion of more manifest variables in the personal
innovativeness scale. Initially, the innovativeness scale was to have been measured by four
latent variables, namely, familiarity with the innovation, status characteristics, socio-
economic status and personal characteristics. However, this was reduced to three latent
variables, namely, status characteristics, socio-economic status and personal characteristics,
with Adoption being introduced as an endogenous latent variable, being measured by five
adoption experience variables. However, this process of scale consolidation resulted in
undesirable inter-subscale correlations between personal innovativeness latent variables. For
future research, it is recommended that rigorous optimal scale design methods be used to
come up with a reliable innovativeness scale displaying both convergent and discriminant
scale validity.
The second improvement relates to better conceptualization of the innovativeness
scale. For example, the distinction between status characteristics and socioeconomic status
was not clear to some participants of the study. The distinction between personal
characteristics and status characteristics was also not sufficiently sharp for some of the
participants. Some participants believed that socio-economic indicators, such as education
level and financial well-being, were the defining characteristics of social status. Other
participants believed that personal characteristics were a source of status characteristics of
innovators. Thus, for some of the participants, the inclusion of both subscales in the survey
questionnaire would have resulted in perceived overlap of concepts. However, the intention
of the researcher was to separate personal social status afforded by the nature of the
relationships that participants maintained with other members of the community, from the
socio-economic status afforded by access to money and/or education. Obviously, the
researcher‟s intention was not adequately transferred to the measurement instrument; hence
the discriminant invalidity between the personal characteristics and social status subscales of
the personal innovativeness scale.
Several future directions for research have been suggested throughout this research
report. One important area involved improving the validity of the Innovativeness scale used.
Ways of improving this scale were suggested. One most important way of implementing
such improvements would be to develop the scale in close consultation with the target
population of any future study. Another important area of future research would be to
introduce some qualitative data collection, perhaps by means of structured interviews, in
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conjunction with administration of survey questionnaires. This process would be expected to
collect important data that would otherwise not be collectable using a survey questionnaire.
Rogers (1966) criticizes the preponderance of diffusion research based on cross-sectional
data as simplistic, and not sufficiently representative of the multi-dimensionality of diffusion
processes. Some semblance of qualitative research could add more longitudinal data to the
research and provide much needed triangulation to some of the quantitative research findings.
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Appendix A: Research Instruments
Survey Questionnaire - English Version
Questionnaire Number:_______
Age: ( ) Minor ( ) Adult
Gender: ( ) Male ( ) Female
Adoption: ( ) I own a mobile phone ( ) I do not own a mobile phone
I am conducting a survey research on the influence of general social beliefs on
personal innovativeness in the process of adoption of mobile telephony. I would like to seek
your co-operation to answer some questions. There are no right or wrong answers. Please
answer the questions according to your individual opinion. The results of the survey will only
be used for the purpose of research. No attempt will be made to identify you as an individual.
Your answers will be kept strictly confidential.
Completion Instructions:
The following statements relate to general social beliefs and personal
innovativeness. Please read each statement carefully and mark the response that most closely
reflects your individual opinion.
Example:
Strongly
disbelieve
Disbelieve No
opinion
Believe Strongly
believe
Don‟t
know
Failure is the beginning of success 1 2 3 4 5 6
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Str
on
gly
dis
bel
iev
e
Dis
bel
iev
e
No
op
inio
n
Bel
iev
e
Str
on
gly
bel
iev
e
Do
n’t
kn
ow
1 Religious faith contributes to good mental health. 1 2 3 4 5 6
2 Good luck follows if one survives a disaster. 1 2 3 4 5 6
3 Human behaviour changes with the social context. 1 2 3 4 5 6
4 Religion makes people escape from reality. 1 2 3 4 5 6
5 People may have opposite behaviours on different occasions. 1 2 3 4 5 6
6 Fate determines one's successes and failures. 1 2 3 4 5 6
7 There is a supreme being controlling the universe. 1 2 3 4 5 6
8 One who does not know how to plan his or her future will
eventually fail. 1 2 3 4 5 6
9 Individual characteristics, such as appearance and birthday, affect
one's fate. 1 2 3 4 5 6
10 Adversity can be overcome by effort. 1 2 3 4 5 6
11 Every problem has a solution. 1 2 3 4 5 6
12 There is usually only one way to solve a problem. 1 2 3 4 5 6
13 One's behaviour may be contrary to one‟s true feelings. 1 2 3 4 5 6
14 There are certain things we can do to help us improve our luck
and avoid unlucky things. 1 2 3 4 5 6
15 One will succeed if he/she really tries. 1 2 3 4 5 6
16 Current losses are not necessarily bad for one's long term future. 1 2 3 4 5 6
17 Power and status make people arrogant. 1 2 3 4 5 6
18 Powerful people tend to exploit others. 1 2 3 4 5 6
19 People will stop working hard after they secure a comfortable life. 1 2 3 4 5 6
20 Beliefs in a religion help one understand the meaning of life. 1 2 3 4 5 6
21 Kind-hearted people are easily bullied. 1 2 3 4 5 6
22 Beliefs in a religion make people good citizens. 1 2 3 4 5 6
23 Kind-hearted people usually suffer losses. 1 2 3 4 5 6
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24 There are many ways for people to predict what will happen in the
future. 1 2 3 4 5 6
25 Hard working people will achieve more in the end. 1 2 3 4 5 6
26 Members of my community often seek out my opinion on matters
that are important in their lives. 1 2 3 4 5 6
27 When I bought a mobile telephone handset for the first time, I
found the technology to be unfamiliar. 1 2 3 4 5 6
28 I consider myself to be popular among members of my
community. 1 2 3 4 5 6
29 When I bought my first mobile telephone handset, I considered
myself to be risk-taking, making the decision to purchase without
complete information about the benefits or dangers of the
technology.
1 2 3 4 5 6
30 I regularly travel outside the immediate area of Kgautswane. 1 2 3 4 5 6
31 A higher educational level makes it easier for one to make the
decision to purchase a mobile telephone handset. 1 2 3 4 5 6
32 I have high confidence in myself. 1 2 3 4 5 6
33 A healthier financial situation makes it easier for one to purchase a
mobile telephone handset. 1 2 3 4 5 6
34 When I purchased my first mobile telephone handset, I consulted
with other people with whom I am related before making the
purchase decision in order to make a decision that would please
them as well.
1 2 3 4 5 6
35 I consider myself to be an open-minded person. 1 2 3 4 5 6
36 When I bought my first mobile telephone handset, I made use of
my personal connections to get information on the technology. 1 2 3 4 5 6
37 I am acutely aware of my strengths and weaknesses. 1 2 3 4 5 6
38 When I bought my first mobile telephone handset, my decision to
purchase was one that I made independently, without the
participation of anybody else in the decision making process.
1 2 3 4 5 6
39 I am a respected member of my community. 1 2 3 4 5 6
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Survey Questionnaire - Sepedi Version
Questionnaire Number:_______
Nywaga: ( ) Monyane ( ) Mogolo
Bong: ( ) Botona ( ) Botshadi
Sellathekeng: ( ) Ke nayo ( ) Ga ke nayo
Ke mo monyakisishe wa dipotsisho tsa go hlohlolwetswa ga ditumelo go bohlale ba batho mo
go tsweletsago dinlla thekeng. Ke kgopela shomishano ya lena go araba dipotsisho. Ga ona
karabo ye e nepagetseng goba ye e fosagetsweng. Araba go ya ka maikutlo a gago. Meputso
ya dipotsisho e tlo shomishwa go maikemishetso a di nyakishisho. Dikarabo tsa gago e tlaba
sephiri.
Ditaetšo tša phadišano:
Mantsu a a latelago a agana le ditumelo le bohlale bo batho. Bala ka hlokomelo o kgethe
karabo yeo o bonago e nepagetse.
Mohlala:
Ke
gan
a k
e
tiiš
itše
Ga
ke
dum
ele
Ga
ke
dum
ele
ebil
e ga
ke
gan
e
Ke
a dum
ela
Ke
dum
ela
ke
tiiš
itše
Ga
ke
tseb
e
Go palelwa ke mathomo a go
tšwelela
1 2 3 4 5 6
Copyright UCT
51
Ke
gan
a k
e
tiiš
itše
Ga
ke
du
mel
e
Ga
ke
du
mel
e
ebil
e ga
ke
gan
e
Ke
a du
mel
a
Ke
du
mel
a ke
tiiš
itše
Ga
ke
tseb
e
1 Tumelo tša bodumedi di nale kamano e botse go dikgopolo tše di
nepagetšego. 1 2 3 4 5 6
2 Mahlatse a latela motho a efoga kotsi. 1 2 3 4 5 6
3 Maitshwaro a batho a fetoga maloka le leago. 1 2 3 4 5 6
4 Bodumedi bo dira gore batho ba tšhabele bommannete. 1 2 3 4 5 6
5 Batho ba ka tšweleletša maitshwro ao a fapanego mo mabakeng
oa a fapanego. 1 2 3 4 5 6
6 Pheletšo e tšwleletša go atlega le go palelwa ga motho. 1 2 3 4 5 6
7 Go nale yo a phagamilego a laolago lefase ka bophara. 1 2 3 4 5 6
8 Yo a sa kgonego go beakanyetša bokamoso bja gagwe o tla
palelwa mafelelong. 1 2 3 4 5 6
9 Dimelo tša motho bjalo ka lebopo le matswalo di ama mafetšo a
gagwe. 1 2 3 4 5 6
10 Mathata a ka fetšišwa ka go šoma ka matla. 1 2 3 4 5 6
11 Bothata bjo bongwe le bjo bongwe bo na le tharollo. 1 2 3 4 5 6
12 Ka mehla go tsela e tee feela ya go rarolla bothata. 1 2 3 4 5 6
13 Maitshwaro a motho a ka fapana le maikutlo a gagwe. 1 2 3 4 5 6
14 Go nale ditsela tšeo di ka re thušago gore re kaonafatše mahlatse a
rena gomme re kgaogane le tšeo di re bakelago madimabe. 1 2 3 4 5 6
15 Motho o tla tšwelela ge a leka atiišitše. 1 2 3 4 5 6
16 Ditobo tsa bjale ga di na kamano e mpe go bokamoso bjo botelele
bja motho. 1 2 3 4 5 6
17 Matla le maemo di dira gore motho a be le boganka. 1 2 3 4 5 6
18 Batho ba maatla ba atiša go šomiša bangwe. 1 2 3 4 5 6
19 Batho ba tla lesa go šoma ka thata morago ga go beakanya
maphelo a makaone. 1 2 3 4 5 6
20 Tumelo go tša sedumedi e thuša go lemoga bohlokwa bja bophelo. 1 2 3 4 5 6
21 Batho ba pelo-tshekegi ba hlokofatwša ga bonolo. 1 2 3 4 5 6
22 Tumelo go tša sedumedi go dira gore batho e be ba ba
tšhepagalago mo setšhabeng. 1 2 3 4 5 6
Copyright UCT
52
23 Batho ba pelo tše di lokilego ba lobišwa ka mehla. 1 2 3 4 5 6
24 Go nale ditsela tše dintši tšeo batho ba ka di šomišago go bonela
pele gore bokamoso bo re swaretše eng. 1 2 3 4 5 6
25 Batho bao ba šomago ka thata ba tla tšwelela kudu mafelelong. 1 2 3 4 5 6
26 Maloko a setšhaba sa gešo a kgopela dikgopolo tša ka ge a tšea
diphetho tše bohlokwa mo maphelong a bona. 1 2 3 4 5 6
27 Ge ke seno ithekela sellathekeng lekga la mathomo, ka lemoga
gore thekniki ke selo se sefsa. 1 2 3 4 5 6
28 Ke yo mongwe wa bao ba tumilego setšhabeng sa gešo. 1 2 3 4 5 6
29 Ge ke reka sellathekeng sa ka sa mathomo ke lemogile gore ke
tsena kotsing ka go tšea sephetho sa go reka ntle le maitemogelo a
a nepagetšego a ditlamorago tše di boste le tše di mpe tša thekniki.
1 2 3 4 5 6
30 Ke etela dinaga-mabapi tsa Kgautswane kgafetša. 1 2 3 4 5 6
31 Maemo a a phagamego a thuto a kgontšha motho go tšea sepheto
sa go reka sellathekeng. 1 2 3 4 5 6
32 Ke itshepa kudu. 1 2 3 4 5 6
33 Maemo a ma kaone ditšheleteng a kgontšha motho go reka
sellathekeng. 1 2 3 4 5 6
34 Ge ke reka sellathekeng sa ka sa mathomo ke rerišane le maloko a
gešo pele ke tšea sephetho sa go reka gore ke tšee sepheto seo se
tla ba thabišago.
1 2 3 4 5 6
35 Ke motho wa kgopolo ye e lokologilego. 1 2 3 4 5 6
36 Ge ke reka sellathekeng sa ka ke šomišitše kamagonyo tša ka go
hwetša tsebo gotšwa thekniking. 1 2 3 4 5 6
37 Ke na le boitemogelo bjo bo nepagetsego mabapi le bokgoni le
mafokodi a ka. 1 2 3 4 5 6
38 Ge ke reka sellathekeng sa ka sa mathomo ke tšere sephetho ka
noši ntle le go laolwa ke mongwe ka dikakanyo mo lenaneong la
go reka.
1 2 3 4 5 6
39 Ke leloko la go hlomphega setšhabeng sa gešo 1 2 3 4 5 6
Copyright UCT
53
Appendix B: Correlation Matrix
Table B.1
Correlation matrix of manifest variables
Item RY1 FC1 SC1 RY2 SC2 FC2 RY3 RA1 FC3
RY1 Pearson Correlation 1 .001 .059 .438**
.091 -.021 .590**
.081 -.054
Sig. (2-tailed) .984 .334 .000 .137 .733 .000 .182 .387
N 272 259 270 269 270 267 247 270 262
FC1 Pearson Correlation .001 1 .075 -.020 .012 .620**
.025 -.107 .703**
Sig. (2-tailed) .984 .234 .748 .851 .000 .699 .086 .000
N 259 259 257 257 257 255 237 258 253
SC1 Pearson Correlation .059 .075 1 -.048 .747**
.070 -.011 .043 .083
Sig. (2-tailed) .334 .234 .432 .000 .253 .863 .480 .181
N 270 257 273 271 271 267 246 272 262
RY2 Pearson Correlation .438**
-.020 -.048 1 -.037 .046 .423**
.075 .054
Sig. (2-tailed) .000 .748 .432 .541 .456 .000 .217 .383
N 269 257 271 272 270 266 246 271 261
SC2 Pearson Correlation .091 .012 .747**
-.037 1 .069 -.024 .039 -.005
Sig. (2-tailed) .137 .851 .000 .541 .261 .705 .519 .931
N 270 257 271 270 273 267 246 271 262
FC2 Pearson Correlation -.021 .620**
.070 .046 .069 1 -.019 -.052 .568**
Sig. (2-tailed) .733 .000 .253 .456 .261 .773 .395 .000
N 267 255 267 266 267 269 243 267 260
RY3 Pearson Correlation .590**
.025 -.011 .423**
-.024 -.019 1 .108 -.011
Sig. (2-tailed) .000 .699 .863 .000 .705 .773 .090 .862
N 247 237 246 246 246 243 247 245 243
RA1 Pearson Correlation .081 -.107 .043 .075 .039 -.052 .108 1 -.115
Sig. (2-tailed) .182 .086 .480 .217 .519 .395 .090 .063
N 270 258 272 271 271 267 245 273 262
FC3 Pearson Correlation -.054 .703**
.083 .054 -.005 .568**
-.011 -.115 1
Sig. (2-tailed) .387 .000 .181 .383 .931 .000 .862 .063
N 262 253 262 261 262 260 243 262 263
RA2 Pearson Correlation .128* -.127
* .038 .105 .044 .013 .145
* .670
** -.176
**
Sig. (2-tailed) .037 .042 .538 .085 .476 .836 .023 .000 .004
N 268 255 269 268 269 265 243 269 259
RA3 Pearson Correlation .134* .003 .025 .074 .077 .108 .042 .618
** -.039
Sig. (2-tailed) .032 .964 .693 .238 .218 .086 .516 .000 .541
N 258 246 258 257 258 255 236 258 249
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
54
Table B.1 (continued)
Correlation matrix of manifest variables
Item RA2 RA3 SC3 SC4 FC4 RA4 SC5 SCy1 SCy2
RY1 Pearson Correlation .128* .134
* -.074 .016 .222
** .177
** .072 .127
* .134
*
Sig. (2-tailed) .037 .032 .221 .799 .000 .003 .237 .037 .027
N 268 258 272 270 259 272 270 271 272
FC1 Pearson Correlation -.127* .003 .163
** -.057 .690
** -.041 -.002 -.086 -.045
Sig. (2-tailed) .042 .964 .008 .358 .000 .507 .972 .171 .467
N 255 246 259 258 247 259 257 258 259
SC1 Pearson Correlation .038 .025 .468**
.751**
.162**
.152* .683
** .071 .105
Sig. (2-tailed) .538 .693 .000 .000 .009 .012 .000 .244 .083
N 269 258 273 271 259 273 271 273 273
RY2 Pearson Correlation .105 .074 -.043 .012 .052 .043 .038 .013 .021
Sig. (2-tailed) .085 .238 .479 .841 .402 .475 .531 .830 .734
N 268 257 272 271 258 272 270 272 272
SC2 Pearson Correlation .044 .077 .294**
.640**
.106 .249**
.616**
.102 .085
Sig. (2-tailed) .476 .218 .000 .000 .088 .000 .000 .092 .161
N 269 258 273 272 260 273 270 272 273
FC2 Pearson Correlation .013 .108 .115 -.025 .490**
.051 -.004 .049 .063
Sig. (2-tailed) .836 .086 .059 .689 .000 .405 .954 .422 .300
N 265 255 269 267 257 269 267 268 269
RY3 Pearson Correlation .145* .042 -.050 -.008 .092 .096 -.035 .057 .039
Sig. (2-tailed) .023 .516 .437 .899 .159 .131 .585 .374 .543
N 243 236 247 247 238 247 245 246 247
RA1 Pearson Correlation .670**
.618**
-.029 .089 -.058 .643**
.085 .103 -.024
Sig. (2-tailed) .000 .000 .628 .143 .357 .000 .165 .090 .691
N 269 258 273 271 259 273 271 273 273
FC3 Pearson Correlation -.176**
-.039 .127* -.038 .554
** -.123
* -.001 -.046 -.042
Sig. (2-tailed) .004 .541 .040 .543 .000 .046 .991 .456 .496
N 259 249 263 262 254 263 261 262 263
RA2 Pearson Correlation 1 .538**
-.029 .104 -.033 .572**
.105 .136* .105
Sig. (2-tailed) .000 .634 .088 .600 .000 .087 .025 .086
N 271 256 271 269 257 271 268 270 271
RA3 Pearson Correlation .538**
1 -.105 .032 .047 .580**
.111 .089 -.030
Sig. (2-tailed) .000 .092 .608 .460 .000 .077 .153 .631
N 256 260 260 258 247 260 257 259 260
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item SCy3 RY4 SCy4 RY5 SCy5 FC5 RA5 DSC1 AE1
RY1 Pearson Correlation .053 .732**
.133* .723
** .075 .005 .180
** -.078 -.019
Sig. (2-tailed) .388 .000 .029 .000 .218 .932 .003 .203 .764
N 269 270 269 271 269 269 271 269 254
FC1 Pearson Correlation -.052 -.002 -.060 .018 -.074 .687**
-.107 .006 -.029
Sig. (2-tailed) .403 .969 .337 .774 .239 .000 .085 .921 .656
N 256 257 258 258 257 256 259 256 247
SC1 Pearson Correlation .043 .099 .092 .035 .009 -.003 .037 .037 .071
Sig. (2-tailed) .479 .105 .131 .565 .886 .955 .549 .550 .260
N 270 271 270 272 271 271 272 269 253
RY2 Pearson Correlation -.038 .278**
-.006 .351**
.074 .025 .065 -.077 -.027
Sig. (2-tailed) .538 .000 .920 .000 .226 .682 .286 .209 .672
N 269 270 270 271 271 270 271 268 252
SC2 Pearson Correlation -.034 .136* .142
* .051 -.007 -.061 .076 -.007 -.026
Sig. (2-tailed) .579 .026 .019 .403 .911 .321 .212 .906 .685
N 269 271 270 272 270 270 272 269 252
FC2 Pearson Correlation -.015 .011 .164**
-.038 .060 .533**
.021 .047 .032
Sig. (2-tailed) .803 .863 .007 .538 .326 .000 .728 .450 .616
N 266 268 267 268 266 266 269 265 250
RY3 Pearson Correlation -.045 .488**
.015 .533**
.060 -.021 .100 -.092 -.056
Sig. (2-tailed) .485 .000 .819 .000 .350 .742 .117 .149 .388
N 245 246 246 247 245 244 246 245 236
RA1 Pearson Correlation -.040 .028 .034 .071 .073 -.099 .666**
.035 .080
Sig. (2-tailed) .518 .643 .574 .244 .231 .104 .000 .567 .204
N 270 271 270 272 271 271 272 269 253
FC3 Pearson Correlation -.008 -.073 .004 -.039 -.010 .582**
-.089 -.012 -.005
Sig. (2-tailed) .904 .242 .955 .526 .877 .000 .152 .850 .939
N 260 262 261 262 260 260 263 259 249
RA2 Pearson Correlation .002 .139* .022 .136
* .105 -.161
** .596
** .047 .044
Sig. (2-tailed) .974 .023 .717 .025 .088 .008 .000 .441 .487
N 267 269 268 270 268 268 270 268 250
RA3 Pearson Correlation .048 .075 .157* .110 .051 .015 .528
** .161
** .095
Sig. (2-tailed) .443 .230 .012 .078 .419 .810 .000 .010 .141
N 256 259 257 259 257 257 259 257 242
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item DSC2 DPC1 DSC3 DSES1 DPC2 DSES2
RY1 Pearson Correlation -.043 -.042 -.068 -.028 .040 .092
Sig. (2-tailed) .489 .510 .267 .655 .511 .130
N 263 253 269 265 270 270
FC1 Pearson Correlation .001 -.044 .002 .005 .002 -.029
Sig. (2-tailed) .992 .492 .974 .935 .976 .646
N 251 247 257 253 258 257
SC1 Pearson Correlation .018 .022 -.003 .025 .051 .155*
Sig. (2-tailed) .772 .735 .956 .681 .402 .011
N 265 251 270 267 271 271
RY2 Pearson Correlation .000 -.051 -.024 -.049 .015 .007
Sig. (2-tailed) .995 .420 .691 .427 .804 .907
N 263 250 269 266 271 270
SC2 Pearson Correlation .013 -.085 -.076 -.044 .008 .099
Sig. (2-tailed) .829 .182 .213 .477 .899 .105
N 264 251 270 266 271 271
FC2 Pearson Correlation .038 -.012 -.009 .036 .012 .078
Sig. (2-tailed) .537 .855 .889 .565 .851 .206
N 260 249 267 262 267 267
RY3 Pearson Correlation -.131* -.021 -.102 -.081 -.104 .006
Sig. (2-tailed) .044 .747 .112 .212 .103 .925
N 239 235 245 241 246 245
RA1 Pearson Correlation .095 .012 .005 .078 .109 .085
Sig. (2-tailed) .126 .850 .930 .203 .073 .161
N 264 252 270 267 271 271
FC3 Pearson Correlation -.020 .003 .028 -.005 -.021 -.049
Sig. (2-tailed) .749 .963 .653 .943 .731 .431
N 255 249 261 256 261 261
RA2 Pearson Correlation .008 -.009 -.032 .015 .149* .121
*
Sig. (2-tailed) .895 .887 .607 .804 .015 .047
N 265 249 268 264 270 269
RA3 Pearson Correlation .191**
-.017 .070 .064 .239**
.082
Sig. (2-tailed) .002 .791 .266 .311 .000 .186
N 251 241 257 254 258 259
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item AE2 DPC3 AE3 DPC4 AE4 DSC4
RY1 Pearson Correlation .009 .045 .012 .089 .040 -.002
Sig. (2-tailed) .884 .465 .855 .144 .523 .975
N 253 270 253 269 253 265
FC1 Pearson Correlation -.056 -.008 -.011 .080 -.059 -.017
Sig. (2-tailed) .380 .894 .869 .200 .357 .789
N 247 258 247 257 247 253
SC1 Pearson Correlation .089 .073 .089 .137* -.025 .100
Sig. (2-tailed) .159 .234 .158 .024 .695 .103
N 252 271 252 270 251 267
RY2 Pearson Correlation -.012 -.061 -.015 .003 .012 -.029
Sig. (2-tailed) .852 .316 .811 .956 .850 .636
N 251 271 251 269 250 265
SC2 Pearson Correlation .009 .011 .007 .086 -.093 .046
Sig. (2-tailed) .891 .859 .913 .160 .141 .459
N 251 271 251 270 251 266
FC2 Pearson Correlation -.001 .027 .032 .063 .008 .059
Sig. (2-tailed) .983 .660 .615 .305 .903 .342
N 249 267 249 268 249 262
RY3 Pearson Correlation -.047 -.085 -.045 -.004 -.008 -.058
Sig. (2-tailed) .469 .182 .494 .949 .898 .368
N 235 246 235 245 235 241
RA1 Pearson Correlation .138* .059 .062 .013 .080 .076
Sig. (2-tailed) .029 .330 .323 .834 .205 .218
N 253 271 253 270 252 266
FC3 Pearson Correlation -.018 -.015 .038 .031 -.030 -.017
Sig. (2-tailed) .772 .813 .552 .614 .635 .786
N 249 261 249 262 249 257
RA2 Pearson Correlation .088 .076 .049 .029 .114 .099
Sig. (2-tailed) .164 .211 .445 .639 .073 .108
N 249 269 249 268 249 267
RA3 Pearson Correlation .168**
.142* .131
* .136
* .168
** .110
Sig. (2-tailed) .009 .022 .041 .028 .009 .081
N 241 259 241 258 241 253
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RY1 FC1 SC1 RY2 SC2 FC2 RY3 RA1 FC3
SC3 Pearson Correlation -.074 .163**
.468**
-.043 .294**
.115 -.050 -.029 .127*
Sig. (2-tailed) .221 .008 .000 .479 .000 .059 .437 .628 .040
N 272 259 273 272 273 269 247 273 263
SC4 Pearson Correlation .016 -.057 .751**
.012 .640**
-.025 -.008 .089 -.038
Sig. (2-tailed) .799 .358 .000 .841 .000 .689 .899 .143 .543
N 270 258 271 271 272 267 247 271 262
FC4 Pearson Correlation .222**
.690**
.162**
.052 .106 .490**
.092 -.058 .554**
Sig. (2-tailed) .000 .000 .009 .402 .088 .000 .159 .357 .000
N 259 247 259 258 260 257 238 259 254
RA4 Pearson Correlation .177**
-.041 .152* .043 .249
** .051 .096 .643
** -.123
*
Sig. (2-tailed) .003 .507 .012 .475 .000 .405 .131 .000 .046
N 272 259 273 272 273 269 247 273 263
SC5 Pearson Correlation .072 -.002 .683**
.038 .616**
-.004 -.035 .085 -.001
Sig. (2-tailed) .237 .972 .000 .531 .000 .954 .585 .165 .991
N 270 257 271 270 270 267 245 271 261
SCy1 Pearson Correlation .127* -.086 .071 .013 .102 .049 .057 .103 -.046
Sig. (2-tailed) .037 .171 .244 .830 .092 .422 .374 .090 .456
N 271 258 273 272 272 268 246 273 262
SCy2 Pearson Correlation .134* -.045 .105 .021 .085 .063 .039 -.024 -.042
Sig. (2-tailed) .027 .467 .083 .734 .161 .300 .543 .691 .496
N 272 259 273 272 273 269 247 273 263
SCy3 Pearson Correlation .053 -.052 .043 -.038 -.034 -.015 -.045 -.040 -.008
Sig. (2-tailed) .388 .403 .479 .538 .579 .803 .485 .518 .904
N 269 256 270 269 269 266 245 270 260
RY4 Pearson Correlation .732**
-.002 .099 .278**
.136* .011 .488
** .028 -.073
Sig. (2-tailed) .000 .969 .105 .000 .026 .863 .000 .643 .242
N 270 257 271 270 271 268 246 271 262
SCy4 Pearson Correlation .133* -.060 .092 -.006 .142
* .164
** .015 .034 .004
Sig. (2-tailed) .029 .337 .131 .920 .019 .007 .819 .574 .955
N 269 258 270 270 270 267 246 270 261
RY5 Pearson Correlation .723**
.018 .035 .351**
.051 -.038 .533**
.071 -.039
Sig. (2-tailed) .000 .774 .565 .000 .403 .538 .000 .244 .526
N 271 258 272 271 272 268 247 272 262
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RA2 RA3 SC3 SC4 FC4 RA4 SC5 SCy1 SCy2
SC3 Pearson Correlation -.029 -.105 1 .327**
.040 -.079 .365**
-.192**
-.206**
Sig. (2-tailed) .634 .092 .000 .515 .191 .000 .001 .001
N 271 260 275 273 261 275 272 274 275
SC4 Pearson Correlation .104 .032 .327**
1 .104 .165**
.581**
.105 .148*
Sig. (2-tailed) .088 .608 .000 .095 .006 .000 .085 .014
N 269 258 273 273 260 273 270 272 273
FC4 Pearson Correlation -.033 .047 .040 .104 1 .054 .106 .067 .092
Sig. (2-tailed) .600 .460 .515 .095 .387 .089 .281 .139
N 257 247 261 260 261 261 259 260 261
RA4 Pearson Correlation .572**
.580**
-.079 .165**
.054 1 .226**
.112 .065
Sig. (2-tailed) .000 .000 .191 .006 .387 .000 .064 .282
N 271 260 275 273 261 275 272 274 275
SC5 Pearson Correlation .105 .111 .365**
.581**
.106 .226**
1 .055 .101
Sig. (2-tailed) .087 .077 .000 .000 .089 .000 .363 .096
N 268 257 272 270 259 272 272 272 272
SCy1 Pearson Correlation .136* .089 -.192
** .105 .067 .112 .055 1 .746
**
Sig. (2-tailed) .025 .153 .001 .085 .281 .064 .363 .000
N 270 259 274 272 260 274 272 274 274
SCy2 Pearson Correlation .105 -.030 -.206**
.148* .092 .065 .101 .746
** 1
Sig. (2-tailed) .086 .631 .001 .014 .139 .282 .096 .000
N 271 260 275 273 261 275 272 274 275
SCy3 Pearson Correlation .002 .048 -.051 -.011 -.017 -.063 .015 .573**
.455**
Sig. (2-tailed) .974 .443 .400 .863 .784 .304 .802 .000 .000
N 267 256 271 269 258 271 270 271 271
RY4 Pearson Correlation .139* .075 -.004 .056 .188
** .181
** .160
** .108 .129
*
Sig. (2-tailed) .023 .230 .951 .360 .002 .003 .009 .075 .033
N 269 259 273 271 260 273 270 272 273
SCy4 Pearson Correlation .022 .157* -.062 .075 .110 .115 .058 .719
** .532
**
Sig. (2-tailed) .717 .012 .307 .218 .079 .058 .345 .000 .000
N 268 257 272 271 259 272 269 271 272
RY5 Pearson Correlation .136* .110 .034 .034 .210
** .119 .078 .025 .048
Sig. (2-tailed) .025 .078 .574 .582 .001 .050 .201 .687 .430
N 270 259 274 272 260 274 271 273 274
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item SCy3 RY4 SCy4 RY5 SCy5 FC5 RA5 DSC1 AE1
SC3 Pearson Correlation -.051 -.004 -.062 .034 -.021 .176**
-.127* .113 .009
Sig. (2-tailed) .400 .951 .307 .574 .728 .004 .035 .063 .882
N 271 273 272 274 272 272 274 271 254
SC4 Pearson Correlation -.011 .056 .075 .034 .030 -.121* .096 .035 .135
*
Sig. (2-tailed) .863 .360 .218 .582 .620 .047 .113 .569 .033
N 269 271 271 272 271 270 272 269 252
FC4 Pearson Correlation -.017 .188**
.110 .210**
.059 .566**
.059 -.061 -.032
Sig. (2-tailed) .784 .002 .079 .001 .348 .000 .347 .331 .618
N 258 260 259 260 258 258 260 257 244
RA4 Pearson Correlation -.063 .181**
.115 .119 -.028 -.045 .601**
.124* .102
Sig. (2-tailed) .304 .003 .058 .050 .643 .459 .000 .042 .104
N 271 273 272 274 272 272 274 271 254
SC5 Pearson Correlation .015 .160**
.058 .078 -.016 -.003 .150* .072 .060
Sig. (2-tailed) .802 .009 .345 .201 .800 .966 .013 .243 .339
N 270 270 269 271 270 270 271 268 253
SCy1 Pearson Correlation .573**
.108 .719**
.025 .654**
-.118 .153* .073 .122
Sig. (2-tailed) .000 .075 .000 .687 .000 .053 .012 .230 .051
N 271 272 271 273 272 272 273 270 254
SCy2 Pearson Correlation .455**
.129* .532
** .048 .517
** -.141
* .047 .047 .163
**
Sig. (2-tailed) .000 .033 .000 .430 .000 .020 .440 .445 .009
N 271 273 272 274 272 272 274 271 254
SCy3 Pearson Correlation 1 .061 .490**
.003 .490**
-.009 -.029 .099 .110
Sig. (2-tailed) .316 .000 .955 .000 .884 .641 .106 .081
N 271 269 268 271 269 269 270 267 253
RY4 Pearson Correlation .061 1 .104 .619**
.028 .064 .107 -.052 -.013
Sig. (2-tailed) .316 .088 .000 .643 .293 .078 .395 .841
N 269 273 270 272 270 270 272 269 253
SCy4 Pearson Correlation .490**
.104 1 .043 .575**
-.071 .113 .027 .090
Sig. (2-tailed) .000 .088 .480 .000 .245 .064 .663 .157
N 268 270 272 271 270 269 272 268 251
RY5 Pearson Correlation .003 .619**
.043 1 .027 .043 .169**
-.056 -.058
Sig. (2-tailed) .955 .000 .480 .660 .477 .005 .361 .358
N 271 272 271 274 271 271 273 270 254
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item DSC2 DPC1 DSC3 DSES1 DPC2 DSES2
SC3 Pearson Correlation .070 .099 .047 .000 .002 -.073
Sig. (2-tailed) .257 .114 .443 .998 .978 .231
N 266 253 272 268 273 273
SC4 Pearson Correlation .073 .049 .027 .104 .109 .203**
Sig. (2-tailed) .236 .437 .659 .091 .073 .001
N 264 251 270 266 272 271
FC4 Pearson Correlation -.020 .006 -.046 -.025 .016 .027
Sig. (2-tailed) .748 .924 .462 .695 .798 .664
N 252 243 258 254 259 259
RA4 Pearson Correlation .097 -.004 -.039 .074 .189**
.207**
Sig. (2-tailed) .114 .954 .524 .224 .002 .001
N 266 253 272 268 273 273
SC5 Pearson Correlation .027 .058 .000 .026 .127* .149
*
Sig. (2-tailed) .657 .361 .999 .674 .037 .014
N 263 251 269 266 270 270
SCy1 Pearson Correlation .032 .052 -.003 .088 .125* .218
**
Sig. (2-tailed) .603 .407 .965 .151 .039 .000
N 265 252 271 268 272 272
SCy2 Pearson Correlation -.009 .105 -.022 .139* .123
* .282
**
Sig. (2-tailed) .888 .096 .712 .023 .042 .000
N 266 253 272 268 273 273
SCy3 Pearson Correlation -.008 .021 .060 .093 .104 .069
Sig. (2-tailed) .891 .738 .326 .131 .088 .258
N 262 251 268 265 269 269
RY4 Pearson Correlation .022 -.048 -.074 -.026 .054 .090
Sig. (2-tailed) .721 .451 .226 .670 .373 .138
N 264 252 270 266 271 271
SCy4 Pearson Correlation .065 .023 -.020 .076 .104 .145*
Sig. (2-tailed) .291 .719 .741 .215 .089 .017
N 263 250 270 265 271 270
RY5 Pearson Correlation -.123* -.020 -.118 -.072 -.040 .016
Sig. (2-tailed) .046 .758 .052 .239 .515 .799
N 265 253 271 267 272 272
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item AE2 DPC3 AE3 DPC4 AE4 DSC4
SC3 Pearson Correlation -.005 .049 -.007 .039 -.030 -.006
Sig. (2-tailed) .937 .418 .910 .525 .634 .919
N 253 273 253 272 253 268
SC4 Pearson Correlation .104 .122* .114 .131* .041 .176**
Sig. (2-tailed) .101 .045 .071 .031 .516 .004
N 251 272 251 270 251 266
FC4 Pearson Correlation -.010 -.019 -.022 .080 .007 -.003
Sig. (2-tailed) .874 .765 .727 .197 .918 .960
N 243 259 243 259 243 254
RA4 Pearson Correlation .152* .138* .113 .078 .091 .165**
Sig. (2-tailed) .015 .023 .074 .202 .151 .007
N 253 273 253 272 253 268
SC5 Pearson Correlation .073 .099 .055 .183** .073 .112
Sig. (2-tailed) .248 .104 .383 .003 .248 .070
N 252 270 252 269 251 265
SCy1 Pearson Correlation .092 .125* .096 .094 .219** .150*
Sig. (2-tailed) .143 .040 .129 .123 .000 .014
N 253 272 253 271 252 267
SCy2 Pearson Correlation .103 .143* .134* .170** .261** .194**
Sig. (2-tailed) .102 .018 .033 .005 .000 .001
N 253 273 253 272 253 268
SCy3 Pearson Correlation .081 .082 .096 .111 .142* .086
Sig. (2-tailed) .200 .178 .130 .069 .024 .163
N 252 269 252 268 251 264
RY4 Pearson Correlation .006 .072 .005 .111 .007 .031
Sig. (2-tailed) .925 .239 .942 .068 .908 .609
N 252 271 252 272 252 266
SCy4 Pearson Correlation .099 .116 .060 .114 .157* .107
Sig. (2-tailed) .119 .057 .341 .062 .013 .081
N 250 271 250 270 250 265
RY5 Pearson Correlation -.042 -.060 -.057 .001 .005 -.061
Sig. (2-tailed) .505 .326 .364 .989 .935 .320
N 253 272 253 271 253 267
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RY1 FC1 SC1 RY2 SC2 FC2 RY3 RA1 FC3
SCy5 Pearson Correlation .075 -.074 .009 .074 -.007 .060 .060 .073 -.010
Sig. (2-tailed) .218 .239 .886 .226 .911 .326 .350 .231 .877
N 269 257 271 271 270 266 245 271 260
FC5 Pearson Correlation .005 .687** -.003 .025 -.061 .533** -.021 -.099 .582**
Sig. (2-tailed) .932 .000 .955 .682 .321 .000 .742 .104 .000
N 269 256 271 270 270 266 244 271 260
RA5 Pearson Correlation .180** -.107 .037 .065 .076 .021 .100 .666** -.089
Sig. (2-tailed) .003 .085 .549 .286 .212 .728 .117 .000 .152
N 271 259 272 271 272 269 246 272 263
DSC1 Pearson Correlation -.078 .006 .037 -.077 -.007 .047 -.092 .035 -.012
Sig. (2-tailed) .203 .921 .550 .209 .906 .450 .149 .567 .850
N 269 256 269 268 269 265 245 269 259
AE1 Pearson Correlation -.019 -.029 .071 -.027 -.026 .032 -.056 .080 -.005
Sig. (2-tailed) .764 .656 .260 .672 .685 .616 .388 .204 .939
N 254 247 253 252 252 250 236 253 249
DSC2 Pearson Correlation -.043 .001 .018 .000 .013 .038 -.131* .095 -.020
Sig. (2-tailed) .489 .992 .772 .995 .829 .537 .044 .126 .749
N 263 251 265 263 264 260 239 264 255
DPC1 Pearson Correlation -.042 -.044 .022 -.051 -.085 -.012 -.021 .012 .003
Sig. (2-tailed) .510 .492 .735 .420 .182 .855 .747 .850 .963
N 253 247 251 250 251 249 235 252 249
DSC3 Pearson Correlation -.068 .002 -.003 -.024 -.076 -.009 -.102 .005 .028
Sig. (2-tailed) .267 .974 .956 .691 .213 .889 .112 .930 .653
N 269 257 270 269 270 267 245 270 261
DSES1 Pearson Correlation -.028 .005 .025 -.049 -.044 .036 -.081 .078 -.005
Sig. (2-tailed) .655 .935 .681 .427 .477 .565 .212 .203 .943
N 265 253 267 266 266 262 241 267 256
DPC2 Pearson Correlation .040 .002 .051 .015 .008 .012 -.104 .109 -.021
Sig. (2-tailed) .511 .976 .402 .804 .899 .851 .103 .073 .731
N 270 258 271 271 271 267 246 271 261
DSES2 Pearson Correlation .092 -.029 .155* .007 .099 .078 .006 .085 -.049
Sig. (2-tailed) .130 .646 .011 .907 .105 .206 .925 .161 .431
N 270 257 271 270 271 267 245 271 261
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RA2 RA3 SC3 SC4 FC4 RA4 SC5 SCy1 SCy2
SCy5 Pearson Correlation .105 .051 -.021 .030 .059 -.028 -.016 .654**
.517**
Sig. (2-tailed) .088 .419 .728 .620 .348 .643 .800 .000 .000
N 268 257 272 271 258 272 270 272 272
FC5 Pearson Correlation -.161**
.015 .176**
-.121* .566
** -.045 -.003 -.118 -.141
*
Sig. (2-tailed) .008 .810 .004 .047 .000 .459 .966 .053 .020
N 268 257 272 270 258 272 270 272 272
RA5 Pearson Correlation .596**
.528**
-.127* .096 .059 .601
** .150
* .153
* .047
Sig. (2-tailed) .000 .000 .035 .113 .347 .000 .013 .012 .440
N 270 259 274 272 260 274 271 273 274
DSC1 Pearson Correlation .047 .161**
.113 .035 -.061 .124* .072 .073 .047
Sig. (2-tailed) .441 .010 .063 .569 .331 .042 .243 .230 .445
N 268 257 271 269 257 271 268 270 271
AE1 Pearson Correlation .044 .095 .009 .135* -.032 .102 .060 .122 .163
**
Sig. (2-tailed) .487 .141 .882 .033 .618 .104 .339 .051 .009
N 250 242 254 252 244 254 253 254 254
DSC2 Pearson Correlation .008 .191**
.070 .073 -.020 .097 .027 .032 -.009
Sig. (2-tailed) .895 .002 .257 .236 .748 .114 .657 .603 .888
N 265 251 266 264 252 266 263 265 266
DPC1 Pearson Correlation -.009 -.017 .099 .049 .006 -.004 .058 .052 .105
Sig. (2-tailed) .887 .791 .114 .437 .924 .954 .361 .407 .096
N 249 241 253 251 243 253 251 252 253
DSC3 Pearson Correlation -.032 .070 .047 .027 -.046 -.039 .000 -.003 -.022
Sig. (2-tailed) .607 .266 .443 .659 .462 .524 .999 .965 .712
N 268 257 272 270 258 272 269 271 272
DSES1 Pearson Correlation .015 .064 .000 .104 -.025 .074 .026 .088 .139*
Sig. (2-tailed) .804 .311 .998 .091 .695 .224 .674 .151 .023
N 264 254 268 266 254 268 266 268 268
DPC2 Pearson Correlation .149* .239
** .002 .109 .016 .189
** .127
* .125
* .123
*
Sig. (2-tailed) .015 .000 .978 .073 .798 .002 .037 .039 .042
N 270 258 273 272 259 273 270 272 273
DSES2 Pearson Correlation .121* .082 -.073 .203
** .027 .207
** .149
* .218
** .282
**
Sig. (2-tailed) .047 .186 .231 .001 .664 .001 .014 .000 .000
N 269 259 273 271 259 273 270 272 273
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item SCy3 RY4 SCy4 RY5 SCy5 FC5 RA5 DSC1 AE1
SCy5 Pearson Correlation .490**
.028 .575**
.027 1 -.087 .033 .022 .060
Sig. (2-tailed) .000 .643 .000 .660 .154 .594 .722 .342
N 269 270 270 271 272 271 271 268 252
FC5 Pearson Correlation -.009 .064 -.071 .043 -.087 1 -.090 .129* .016
Sig. (2-tailed) .884 .293 .245 .477 .154 .141 .035 .795
N 269 270 269 271 271 272 271 269 252
RA5 Pearson Correlation -.029 .107 .113 .169**
.033 -.090 1 .033 .093
Sig. (2-tailed) .641 .078 .064 .005 .594 .141 .591 .141
N 270 272 272 273 271 271 274 270 253
DSC1 Pearson Correlation .099 -.052 .027 -.056 .022 .129* .033 1 .695
**
Sig. (2-tailed) .106 .395 .663 .361 .722 .035 .591 .000
N 267 269 268 270 268 269 270 271 251
AE1 Pearson Correlation .110 -.013 .090 -.058 .060 .016 .093 .695**
1
Sig. (2-tailed) .081 .841 .157 .358 .342 .795 .141 .000
N 253 253 251 254 252 252 253 251 254
DSC2 Pearson Correlation -.008 .022 .065 -.123* .020 .113 .080 .587
** .674
**
Sig. (2-tailed) .891 .721 .291 .046 .746 .067 .193 .000 .000
N 262 264 263 265 263 264 265 264 245
DPC1 Pearson Correlation .021 -.048 .023 -.020 .062 .080 -.015 .547**
.673**
Sig. (2-tailed) .738 .451 .719 .758 .329 .207 .813 .000 .000
N 251 252 250 253 250 250 252 250 252
DSC3 Pearson Correlation .060 -.074 -.020 -.118 -.009 .129* -.021 .616
** .707
**
Sig. (2-tailed) .326 .226 .741 .052 .886 .035 .732 .000 .000
N 268 270 270 271 269 269 272 269 251
DSES1 Pearson Correlation .093 -.026 .076 -.072 .110 .021 .042 .656**
.925**
Sig. (2-tailed) .131 .670 .215 .239 .073 .738 .496 .000 .000
N 265 266 265 267 267 267 267 264 252
DPC2 Pearson Correlation .104 .054 .104 -.040 .032 .027 .144* .570
** .689
**
Sig. (2-tailed) .088 .373 .089 .515 .603 .660 .017 .000 .000
N 269 271 271 272 271 270 272 269 252
DSES2 Pearson Correlation .069 .090 .145* .016 .068 -.102 .158
** .487
** .700
**
Sig. (2-tailed) .258 .138 .017 .799 .269 .093 .009 .000 .000
N 269 271 270 272 270 270 272 269 252
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Table B.1 (continued)
Correlation matrix of manifest variables
Item DSC2 DPC1 DSC3 DSES1 DPC2 DSES2
SCy5 Pearson Correlation .020 .062 -.009 .110 .032 .068
Sig. (2-tailed) .746 .329 .886 .073 .603 .269
N 263 250 269 267 271 270
FC5 Pearson Correlation .113 .080 .129* .021 .027 -.102
Sig. (2-tailed) .067 .207 .035 .738 .660 .093
N 264 250 269 267 270 270
RA5 Pearson Correlation .080 -.015 -.021 .042 .144* .158
**
Sig. (2-tailed) .193 .813 .732 .496 .017 .009
N 265 252 272 267 272 272
DSC1 Pearson Correlation .587**
.547**
.616**
.656**
.570**
.487**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 264 250 269 264 269 269
AE1 Pearson Correlation .674**
.673**
.707**
.925**
.689**
.700**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 245 252 251 252 252 252
DSC2 Pearson Correlation 1 .576**
.604**
.630**
.645**
.431**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 266 244 263 259 265 264
DPC1 Pearson Correlation .576**
1 .607**
.643**
.546**
.530**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 244 253 250 250 251 251
DSC3 Pearson Correlation .604**
.607**
1 .653**
.540**
.407**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 263 250 272 265 270 271
DSES1 Pearson Correlation .630**
.643**
.653**
1 .618**
.668**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 259 250 265 268 266 267
DPC2 Pearson Correlation .645**
.546**
.540**
.618**
1 .513**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 265 251 270 266 273 271
DSES2 Pearson Correlation .431**
.530**
.407**
.668**
.513**
1
Sig. (2-tailed) .000 .000 .000 .000 .000
N 264 251 271 267 271 273
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item AE2 DPC3 AE3 DPC4 AE4 DSC4
SCy5 Pearson Correlation .025 .049 .031 .038 .149* .096
Sig. (2-tailed) .696 .426 .630 .540 .019 .119
N 251 271 251 269 250 265
FC5 Pearson Correlation .036 .037 .029 .091 .008 -.006
Sig. (2-tailed) .571 .550 .647 .135 .894 .926
N 251 270 251 269 250 265
RA5 Pearson Correlation .169**
.099 .094 .062 .152* .108
Sig. (2-tailed) .007 .102 .135 .310 .016 .078
N 252 272 252 272 252 267
DSC1 Pearson Correlation .626**
.681**
.628**
.573**
.547**
.702**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 250 269 250 268 250 265
AE1 Pearson Correlation .871**
.929**
.870**
.705**
.713**
.953**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 253 252 253 252 252 247
DSC2 Pearson Correlation .647**
.708**
.577**
.573**
.545**
.670**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 244 264 244 263 244 264
DPC1 Pearson Correlation .571**
.647**
.606**
.544**
.574**
.689**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 252 251 252 251 253 246
DSC3 Pearson Correlation .667**
.684**
.664**
.601**
.546**
.661**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 250 270 250 270 250 265
DSES1 Pearson Correlation .807**
.857**
.803**
.637**
.675**
.907**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 251 267 251 265 250 261
DPC2 Pearson Correlation .632**
.758**
.667**
.650**
.592**
.698**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 251 272 251 270 251 267
DSES2 Pearson Correlation .625**
.632**
.595**
.558**
.612**
.706**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 251 272 251 270 251 266
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RY1 FC1 SC1 RY2 SC2 FC2 RY3 RA1 FC3
AE2 Pearson Correlation .009 -.056 .089 -.012 .009 -.001 -.047 .138* -.018
Sig. (2-tailed) .884 .380 .159 .852 .891 .983 .469 .029 .772
N 253 247 252 251 251 249 235 253 249
DPC3 Pearson Correlation .045 -.008 .073 -.061 .011 .027 -.085 .059 -.015
Sig. (2-tailed) .465 .894 .234 .316 .859 .660 .182 .330 .813
N 270 258 271 271 271 267 246 271 261
AE3 Pearson Correlation .012 -.011 .089 -.015 .007 .032 -.045 .062 .038
Sig. (2-tailed) .855 .869 .158 .811 .913 .615 .494 .323 .552
N 253 247 252 251 251 249 235 253 249
DPC4 Pearson Correlation .089 .080 .137* .003 .086 .063 -.004 .013 .031
Sig. (2-tailed) .144 .200 .024 .956 .160 .305 .949 .834 .614
N 269 257 270 269 270 268 245 270 262
AE4 Pearson Correlation .040 -.059 -.025 .012 -.093 .008 -.008 .080 -.030
Sig. (2-tailed) .523 .357 .695 .850 .141 .903 .898 .205 .635
N 253 247 251 250 251 249 235 252 249
DSC4 Pearson Correlation -.002 -.017 .100 -.029 .046 .059 -.058 .076 -.017
Sig. (2-tailed) .975 .789 .103 .636 .459 .342 .368 .218 .786
N 265 253 267 265 266 262 241 266 257
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item RA2 RA3 SC3 SC4 FC4 RA4 SC5 SCy1 SCy2
AE2 Pearson Correlation .088 .168**
-.005 .104 -.010 .152* .073 .092 .103
Sig. (2-tailed) .164 .009 .937 .101 .874 .015 .248 .143 .102
N 249 241 253 251 243 253 252 253 253
DPC3 Pearson Correlation .076 .142* .049 .122
* -.019 .138
* .099 .125
* .143
*
Sig. (2-tailed) .211 .022 .418 .045 .765 .023 .104 .040 .018
N 269 259 273 272 259 273 270 272 273
AE3 Pearson Correlation .049 .131* -.007 .114 -.022 .113 .055 .096 .134
*
Sig. (2-tailed) .445 .041 .910 .071 .727 .074 .383 .129 .033
N 249 241 253 251 243 253 252 253 253
DPC4 Pearson Correlation .029 .136* .039 .131
* .080 .078 .183
** .094 .170
**
Sig. (2-tailed) .639 .028 .525 .031 .197 .202 .003 .123 .005
N 268 258 272 270 259 272 269 271 272
AE4 Pearson Correlation .114 .168**
-.030 .041 .007 .091 .073 .219**
.261**
Sig. (2-tailed) .073 .009 .634 .516 .918 .151 .248 .000 .000
N 249 241 253 251 243 253 251 252 253
DSC4 Pearson Correlation .099 .110 -.006 .176**
-.003 .165**
.112 .150* .194
**
Sig. (2-tailed) .108 .081 .919 .004 .960 .007 .070 .014 .001
N 267 253 268 266 254 268 265 267 268
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item SCy3 RY4 SCy4 RY5 SCy5 FC5 RA5 DSC1 AE1
AE2 Pearson Correlation .081 .006 .099 -.042 .025 .036 .169**
.626**
.871**
Sig. (2-tailed) .200 .925 .119 .505 .696 .571 .007 .000 .000
N 252 252 250 253 251 251 252 250 253
DPC3 Pearson Correlation .082 .072 .116 -.060 .049 .037 .099 .681**
.929**
Sig. (2-tailed) .178 .239 .057 .326 .426 .550 .102 .000 .000
N 269 271 271 272 271 270 272 269 252
AE3 Pearson Correlation .096 .005 .060 -.057 .031 .029 .094 .628**
.870**
Sig. (2-tailed) .130 .942 .341 .364 .630 .647 .135 .000 .000
N 252 252 250 253 251 251 252 250 253
DPC4 Pearson Correlation .111 .111 .114 .001 .038 .091 .062 .573**
.705**
Sig. (2-tailed) .069 .068 .062 .989 .540 .135 .310 .000 .000
N 268 272 270 271 269 269 272 268 252
AE4 Pearson Correlation .142* .007 .157
* .005 .149
* .008 .152
* .547
** .713
**
Sig. (2-tailed) .024 .908 .013 .935 .019 .894 .016 .000 .000
N 251 252 250 253 250 250 252 250 252
DSC4 Pearson Correlation .086 .031 .107 -.061 .096 -.006 .108 .702**
.953**
Sig. (2-tailed) .163 .609 .081 .320 .119 .926 .078 .000 .000
N 264 266 265 267 265 265 267 265 247
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
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Table B.1 (continued)
Correlation matrix of manifest variables
Item DSC2 DPC1 DSC3 DSES1 DPC2 DSES2 AE2 DPC3 AE3 DPC4 AE4 DSC4
AE2 Pearson Correlation .647**
.571**
.667**
.807**
.632**
.625**
1 .803**
.751**
.627**
.643**
.822**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 244 252 250 251 251 251 253 251 253 251 252 246
DPC3 Pearson Correlation .708**
.647**
.684**
.857**
.758**
.632**
.803**
1 .827**
.746**
.699**
.927**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 264 251 270 267 272 272 251 273 251 270 251 266
AE3 Pearson Correlation .577**
.606**
.664**
.803**
.667**
.595**
.751**
.827**
1 .635**
.632**
.849**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 244 252 250 251 251 251 253 251 253 251 252 246
DPC4 Pearson Correlation .573**
.544**
.601**
.637**
.650**
.558**
.627**
.746**
.635**
1 .603**
.720**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 263 251 270 265 270 270 251 270 251 272 251 265
AE4 Pearson Correlation .545**
.574**
.546**
.675**
.592**
.612**
.643**
.699**
.632**
.603**
1 .727**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 244 253 250 250 251 251 252 251 252 251 253 246
DSC4 Pearson Correlation .670**
.689**
.661**
.907**
.698**
.706**
.822**
.927**
.849**
.720**
.727**
1
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 264 246 265 261 267 266 246 266 246 265 246 268
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Copyright UCT
72
Appendix C: Descriptive Statistics
Item N Range Minimum Maximum Sum Mean S.E. Std Dev Variance Skewness S.E. Kurtosis S.E.
RY1 272 4 1 5 843 3.10 .089 1.473 2.171 -.103 .148 -1.407 .294
FC1 259 4 1 5 748 2.89 .087 1.395 1.945 .055 .151 -1.256 .302
SC1 273 4 1 5 892 3.27 .084 1.395 1.947 -.323 .147 -1.202 .294
RY2 272 4 1 5 822 3.02 .074 1.221 1.490 -.067 .148 -.673 .294
SC2 273 4 1 5 866 3.17 .073 1.214 1.474 -.185 .147 -.765 .294
FC2 269 4 1 5 803 2.99 .076 1.240 1.537 .028 .149 -.740 .296
RY3 247 4 1 5 761 3.08 .080 1.256 1.579 -.079 .155 -.826 .309
RA1 273 4 1 5 905 3.32 .082 1.360 1.849 -.383 .147 -1.090 .294
FC3 263 4 1 5 757 2.88 .075 1.220 1.489 .070 .150 -.661 .299
RA2 271 4 1 5 906 3.34 .069 1.131 1.278 -.349 .148 -.384 .295
RA3 260 4 1 5 852 3.28 .082 1.315 1.730 -.195 .151 -.941 .301
SC3 275 4 1 5 781 2.84 .077 1.271 1.617 .261 .147 -.779 .293
SC4 273 4 1 5 874 3.20 .072 1.194 1.426 -.330 .147 -.538 .294
FC4 261 4 1 5 798 3.06 .081 1.304 1.701 -.065 .151 -.962 .300
RA4 275 4 1 5 939 3.41 .074 1.224 1.499 -.389 .147 -.602 .293
SC5 272 4 1 5 843 3.10 .075 1.230 1.514 -.143 .148 -.649 .294
SCy1 274 4 1 5 900 3.28 .087 1.447 2.094 -.237 .147 -1.316 .293
SCy2 275 4 1 5 895 3.25 .074 1.235 1.526 -.097 .147 -.748 .293
SCy3 271 4 1 5 825 3.04 .070 1.147 1.317 -.028 .148 -.478 .295
RY4 273 4 1 5 848 3.11 .073 1.200 1.441 -.103 .147 -.635 .294
SCy4 272 4 1 5 879 3.23 .070 1.153 1.330 -.143 .148 -.575 .294
RY5 274 4 1 5 816 2.98 .077 1.272 1.619 -.023 .147 -.896 .293
SCy5 272 4 1 5 864 3.18 .072 1.187 1.408 -.172 .148 -.565 .294
FC5 272 4 1 5 805 2.96 .076 1.252 1.567 -.026 .148 -.859 .294
RA5 274 4 1 5 958 3.50 .068 1.123 1.262 -.366 .147 -.416 .293
DSC1 271 4 1 5 819 3.02 .076 1.256 1.577 .037 .148 -.910 .295
AE1 254 4 1 5 790 3.11 .094 1.492 2.225 -.133 .153 -1.527 .304
DSC2 266 4 1 5 804 3.02 .075 1.225 1.501 -.068 .149 -.829 .298
DPC1 253 4 1 5 772 3.05 .085 1.357 1.843 -.084 .153 -1.111 .305
DSC3 272 4 1 5 826 3.04 .085 1.401 1.962 .007 .148 -1.258 .294
DSES1 268 4 1 5 819 3.06 .091 1.497 2.240 -.035 .149 -1.527 .297
DPC2 273 4 1 5 830 3.04 .082 1.362 1.855 -.029 .147 -1.160 .294
DSES2 273 4 1 5 874 3.20 .084 1.383 1.911 -.131 .147 -1.207 .294
AE2 253 4 1 5 793 3.13 .087 1.379 1.903 -.088 .153 -1.263 .305
DPC3 273 4 1 5 855 3.13 .090 1.494 2.233 -.108 .147 -1.513 .294
AE3 253 4 1 5 769 3.04 .088 1.397 1.951 -.097 .153 -1.236 .305
DPC4 272 4 1 5 851 3.13 .080 1.312 1.721 -.141 .148 -1.062 .294
AE4 253 4 1 5 813 3.21 .081 1.295 1.676 -.193 .153 -.972 .305
DSC4 268 4 1 5 857 3.20 .090 1.467 2.152 -.232 .149 -1.443 .297
Note: There are 193 cases with no missing data. S.E. = standard error.