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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

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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

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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).

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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).

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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).

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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).

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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).

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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).

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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).

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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).

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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

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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.