The Influence of Values on the Intention and Usage of Mobile Phone

Technology

A Case of Tanzanian SMEs

Renatus Mushi

, Deirdre Lillis

, Almar Ennis

and Said Jafari

Department of Computer Science, Dublin Institute of Technology (DIT), Dublin, Ireland

Department of Geography, A College of Dublin City University (DCU), Dublin, Ireland

Department of Information Technology, The Institute of Finance Management (IFM), Dar es Salaam, Tanzania

Keywords: ICT, SMEs, Mobile Phone Technology, SEM, AMOS and TAM.

Abstract: Mobile phone technology has been relied upon in performing a number of activities in the SMEs. In less

developed regions, computing infrastructures are very poor thereby depending highly on mobile phones.

The improvement of technology in the mobile phones contexts has seen more applications and services

being accessed through them. This gives SMEs, especially in developing countries, a preferable alternative

to desktop computing technology. However, to maximise the usability of mobile phone technology in SMEs

context, key factors which influence users’ perception on its acceptance need to be explained clearly. This

study explains the factors influencing employees’ intentions and use of mobile phone technology in SMEs,

by extending the Technology Acceptance Model (TAM) with values. The analysis results show that the

values of mobile phone technology in SMEs have a significant effect on the behaviour intention to use. This

suggests that stakeholders specifically vendors, policy makers, managers and mobile network operators

should take their part in handling the challenges and enforcing the benefits of mobile phones since they

constitute to the overall intention and usage. This study uses SEM with 459 employees including managers

and ordinary employees in different sectors which perform tourism activities in Tanzania. Data analysis is

performed by using SEM through AMOS. Implications of the research and future studies are also

highlighted in this paper.

1 INTRODUCTION

Mobile phone technology has been dependable

technological option in Small and Medium

Enterprises (SMEs). This is due to its operational

relief as compared to desktop computing technology.

For example, desktop computing technologies are

perceived to be too expensive, difficult to maintain

and have a high level of sophistication which

demand skilled labour (López-Nicolás et al., 2008;

Nah et al., 2005).

Maugis et al (Maugis et al., 2005) assert that,

under technology leapfrogging, developing countries

need not to replicate an invest on the technological

fixed infrastructure like their developed compatriots.

Instead, they can rely on mobile technologies as a

way of achieving their goals. The increasing

popularity in the use of mobile technology in

organisations can be attributed to the wide usage in

applications such as mobile brokerage services

(Looney et al., 2004) as well as mobile payment and

banking services (Mawona and Mpogole, 2013;

Rumanyika, 2015).

The success of any technology can be influenced

by its perception towards users (Onyango et al.,

2014; van Biljon and Kotzé, 2007a). To effectively

explain the adoption behaviour of technologies,

several models have been proposed (Oliveira and

Martins, 2011). These models highlight the factors

which influence the intention and use of

technologies and have been tested in several

contextual properties. The example of the models

which best explains individual adoption include the

Theory of Reasonable Action (TRA), the Theory of

Planned Behaviour (TPB) (Ajzen, 1991), the Unified

Theory of Acceptance and Use of Technology

(UTAUT) (Venkatesh et al., 2003) and Technology

Adoption Model (TAM)(Davis, 1989a). These

models/theories can be applied to explain the

intentions of using mobile phone technology by

Tanzania tourism SMEs. This study extends TAM

by adding another construct which explain the

116

Mushi, R., Lillis, D., Ennis, A. and Jafari, S.

The Inﬂuence of Values on the Intention and Usage of Mobile Phone - A Case of Tanzanian SMEs.

DOI: 10.5220/0006463201160123

In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - Volume 2: ICE-B, pages 116-123

ISBN: 978-989-758-257-8

values of mobile phone technology in an attempt to

evaluate its suitability on explaining the acceptance

of mobile phone technology in SMEs perspective.

The rest of this article is organised as follows:

Section two discusses the Tanzanian SMEs followed

by highlights of technology adoption model. Section

four discusses the uses of mobile phones in the

SMEs context, section five presents the values of

mobile phone in Tanzanian SMEs while section six

discusses the hypothesis formulation. Research

methodology is presented in section seven followed

by results and discussions in section eight. Section

nine is a conclusion for this research article.

2 TANZANIAN SMEs

SMEs are companies which are characterised by

their staff numbers and financial resources (Hourali

et al., 2008; Makame et al., 2014). In Tanzania,

SMEs comprise of companies with head counts of

up to 99 and financial resources of up to

444,000USD (URT, 2013).The Tanzania Small

Industries Development Organisation (SIDO) uses

the definition which is identified in the SME policy

(see table 1) while pointing out that in case a

company belong to more than one category, its

financial investment will determine its position

(URT, 2013).Another definition of SME is defined

by the Tanzania Revenue Authority (TRA) in which

they define them as enterprises whose taxable

turnover is less than TZS 40 Million (USD 22,500).

This study define SMEs as the businesses entities of

one up to 99 employees and capital investment of

zero up to 800 million (444,000 USD).

3 TECHNOLOGY ACCEPTANCE

MODEL

The original TAM was intending to identify the

factors that facilitate integration of technologies into

an organisation and discover why users accept or

reject a technology (Davis, 1989a). Development of

TAM was based on adopting the concepts of the

theory of reasoned action (TRA) (Fishbein and

Ajzen, 1975) which is a more generalised theory to

be used to explain specific contexts (Lindsay et al.,

2011). In social psychology, TRA has been used to

explain the why people performs a particular

behaviour in situations of ‘reasoned action’ through

identifications of causal relationships which exists

between beliefs, attitudes, intentions and behaviour

(Kwon and Chidambaram, 2000; Pedersen, 2005).

Since then, TAM has been used to identify factors

contributing towards acceptance of technologies.

TAM theorises that when users are given a piece of

technology, there are several factors which influence

their decisions on how and when they will use such

technology (Davis, 1989a; Yueh et al., 2015). TAM

explains the acceptance of technology by two key

perceived attributes or measures: perceived

usefulness (PU) and perceived ease of use (PEU).

According to Davis (1989a), PU is whether the

technology will enhance the user’s job performance

whereas PEOU relates to whether using the system

will be free from effort. The integrity of the original

TAM has been tested through a number of empirical

research, which extends the model to different

settings, providing consistency and good re-test

reliability, confirming the its validity (Lindsay et al.,

2011; Venkatesh and Davis, 2000). In this regard,

TAM is chosen to be the best model to provide a

framework of exploring the issues which motivates

the adoption of mobile phone technology in

Tanzania tourism SMEs by extending it with the

values.

4 MOBILE PHONE USAGE IN

SMEs

Mobile phone technology allows accessing the

computing services and internet through the mobile

devices in the wireless medium (López-Nicolás et

al., 2008; Mashenene, 2015). This mobility nature

allows users to access computing services anywhere

and at any time (Sun et al., 2013). Mobile phone

technology needs reliable telecommunications infra-

structure which can support technologies such as

Wireless Application Protocol (WAP), Bluetooth,

3G, and General Packet Radio Service (GPRS) as

well as the devices which will act as a client on the

user side such as mobile phones, Personal Digital

Assistants (PDAs) etc. (Nah et al., 2005; Sheng et

al., 2010). In the case of Tanzania, mobile

technology is being used heavily by people in SMEs.

A study conducted in 2014 shows that the main

functionalities used by SMEs include making and

receiving calls, mobile money services and SMS

(Venkatakrishnan, 2014). The usage of internet and

associated mobile apps for business purposes shows

an increasing trend.

A key feature of the mobile sector in Tanzania is

that mobile money services used in Tanzania uses

text SMS to allow a mobile phone subscriber to

The Inﬂuence of Values on the Intention and Usage of Mobile Phone - A Case of Tanzanian SMEs

117

Table 1: The description of definition of Small, medium and large enterprise in a Tanzanian Context (Adopted from

Tanzania SMEs POLICY (URT, 2013).

Type of

Enterprise Micro Small Medium Large

No. of Employees

0-4 5-49 50-99 100 and above

Working Capital

0-2,777

USD

>2,777-111,100

USD

>111,100-

444,400USD >444,400USD

send/receive funds, send/receive airtime balances,

performing merchandise bill payments (like

electricity, TV, water etc.) to the respective

companies as well as performing transactions of

funds between the bank accounts and their mobile

phone accounts (like M-pesa and Tigo Pesa).

Vankatakrishnan (2014) summarise the mobile

services in Tanzania as Person to Person (P2P)

(remittances), Person to Business (P2B) (bill

payments, loan repayments, etc.) and Person to

Government (P2G) (tax payments).

5 VALUES OF MOBILE PHONE

TECHNOLOGY IN SMES

This study extends TAM model with another

construct which represent values of mobile phone

technology in the SMEs context. These values have

been explored by the use of Value-Focused Thinking

(VFT) by Mushi et al (2016). In such explorative

study, the values are expressed in terms of

perceptions of users concerning their wishes,

concerns, problems and benefits encoun-tered as

they use mobile phones to perform their duties.

These values are:

• Maximise Mobile Network Coverage and

Quality

• Minimise Acquisition and Operational Costs

• Maximise Collaboration and Sharing of

Information

• Maximise Security in Provision of Mobile

Money Services

• Maximise Battery Life and Processing Capacity

of Mobile Phones.

This study takes over from where Mushi et al (2016)

left. The study of Mushi et al (2016) is an

explorative study which reveals the values of mobile

phones in the Tanzanian SMEs without showing

how such values influences the behaviour intention

and use of mobile phones. This study extends Mushi

et al (2016) by conducting a survey involving

managers and ordinary employees of SMEs by

extending TAM with such values and test the

statistical significance of the relationships of the

factors.

6 HYPOTHESIS FORMULATION

Some of the studies have tested TAM in different

context. In mobile phone acceptance, a number of

studies have found that PU of the technology and

PEU do directly influence the behaviour intention

(BI) of users in attempts to adopt a new technology

(Kim, 2008; Tassabehji et al., 2008; van Biljon and

Kotzé, 2007a). Also, most of empirical research

have shown that PEU is the antecedent of PU while

suggesting that, through PU, PEU indirectly tends to

influence the intention to adopt technology and

finally its usage(Gallego et al., 2008a; Peng et al.,

2012; van Biljon and Kotzé, 2007b). Therefore the

following hypothesis should be true for this study:

H1a: Perceived ease of use (PEU) of mobile phones

in SMEs will positively influence the employees’

perceived usefulness (PU)

H1b: Perceived ease of use (PEU) of mobile phones

in SMEs will positively influence the employee’

behaviour intention (BI)

H1c: Perceived usefulness (PU) of mobile phones in

SMEs will positively influence the employee’

behaviour intention (BI)

H1d: Employees behavioural Intention (BI) of using

mobile phones on Tanzania tourism SMEs will

influence its actual usage (U)

This study assumes that if the perceived values are

enhanced, the intention to use mobile phones by

employees and managers will be enhanced. There

the following hypothesis is suggested:

H2d: Perceived Values (PV) of mobile phone

technology in Tanzania Tourism SMEs will

positively influence Behavioural Intention (BI)

ICE-B 2017 - 14th International Conference on e-Business

118

Figure 1: Conceptual Framework.

Based on hypothesises which are proposed in this

study, the conceptual framework looks as seen in

figure 1.

7 RESEARCH METHODOLOGY

This study involves data collected from three regions

in Tanzania: Dar es salaam, Zanzibar and

Kilimanjaro which accounts to 459 respondents.

Purposive sampling was used to select the districts

and sectors of interests. The SME sectors were

comprised of bars, safari and tours companies, sports

and recreational companies, studio and film

production companies, restaurants, lodges and car

rental companies. Structural Equation Modelling

(SEM) was used employed to analyse the mobile

phone technology acceptance model. SEM has been

considered to be superior to the first multivariate

generation methods because it takes care the

measurement errors in the items (Astrachan et al.,

2014; Awang, 2015). This implies that, SEM

produces the parameters which are error-free thereby

enhancing clearer information to the decision

makers. In addition, SEM evaluates al multi-level

dependence relationships simultaneously in which

dependent variable (DV) becomes independent

variable (IV) forming a sub-relationship within the

same analysis (Astrachan et al., 2014).

In SEM, a model can be evaluated by using

either partial least square (PLS-SEM) or covariance

based (CB-SEM). PLS-SEM is mostly used in the

exploratory studies (in which there are no pre-tested

relationships) while CB-SEM is used in the

confirmatory studies (which have pre-tested

relationships)(Sarstedt et al., 2014). Since there is a

prior developed conceptual framework for this

study, the need to confirm it would prefer to use CB-

SEM. Furthermore, CB-SEM is considered to be

stricter in testing the parameters than PLS (Hair Jr

and Hult, 2013). In this case, the demand for clear

generalizable end results favours the selection of

CB-SEM to test the pre-defined hypothesises in this

study.

The popular software which are normally used to

perform analysis for CB-SEM are SAS, LISREL,

MPLUS and AMOS (Awang, 2015). Byrne (2013)

asserts that, among these popular software, AMOS is

has the best graphic interface which allows easy

modelling of constructs and the associated observed

variables than the rest; which used commands. With

AMOS graphic interface, data analysis is performed

easily while results are more of error-free (Awang,

2015). This study, therefore, uses AMOS in the data

analysis. Specifically, IBM AMOS version 22,

which belong to the latest versions was used.

Advanced features of this version include ability to

perform computations and generate results quickly

as well as being user friendly when compared to the

older versions (Arbuckle, 2013).

8 RESULTS AND DISCUSSIONS

The assessment for completeness was performed for

the 500 questionnaires which were distributed. Only

473 questionnaires were collected from respondents.

14 cases were missing more than 60% of the

contents therefore they were discarded completely.

49 missing values from the 35 cases were assessed

for any pattern for missing values by using Little’s

MCAR test. The Missing Value Analysis (MVA)

show that the missing values are non-significant (χ2

(1751.944) =1721, p=0.332), which means that the

The Inﬂuence of Values on the Intention and Usage of Mobile Phone - A Case of Tanzanian SMEs

119

values are missing completely at random. Therefore,

the replacement of the missing values was

performed using Expectation Maximization (EM)

algorithm to get a complete set of 459 cases.

The availability of multivariate outliers in this

study was identified by squared mahalanobis

distance (D

). Since there were few signs of

multivariate outliers, cooks distance was assessed to

see the overall impact of the outliers. However, the

magnitude of cooks distance was less than 1

showing there is no significant impact on outliers

(Cook, 1977).. The results show that all kurtosis

values have values less than 3, which is a

recommended threshold for a reasonable level of

normality (West et al., 1995).

The correlation is identified to be very high

between perceived usefulness and perceived ease of

use (r=0.583). This shows that as employees

perceive that the mobile phones are easy to use are

likely to perceive that it is useful too. In another

hand, the least correlation is between behavior

intention and actual use. They also show that they

have both influence each other but with less margin.

Multicollinearity refers to a situation where

predictors or constructs appears to correlate highly

with other predictors (Martz, 2013). The assessment

of multicollinearity was performed. Each of the

constructs have tolerance of more than 0.5 whereas

the Variance Inflation Factor (VIF) is less than two.

As for tolerance, a value of 0.10 is recommended as

the minimum level (Tabachnick et al., 2001).

However, a value of 0.25 can be seen used in the

literature (Huber and Stephens, 1993). Also,

according to Hair et al (2008), VIF should not

exceed 10. Therefore, all the constructs in this

analysis have not exceeded the collinearity

requirements.

Reliability of the cosntructs was assessed by using

cronbachs’ alpha where the minimum value was

0.72 which suggests that the model is reliable

enough (Loewenthal, 1996; Malhotra, 2010).

Discriminant validity tests aims at checking

whether answers from different individuals to the

questionnaire items are either lightly correlated or

not correlated at all with other latent variables (Chin,

2003). For a factor to achieve good discriminant

validity, its value of Average Variance Extracted

(AVE) should be larger than any of the correlation

coefficients between such factor and each of the rest

(Bernstein and Nunnally, 1994; Chin, 2003; Gefen et

al., 2000). It is asserted that if AVE appear to be less

than a correlation coefficient with a certain another

factor, the two factors are highly correlated and

therefore do not measure well-separated latent

concepts (Gefen et al., 2000). However, the results

in the SPSS have shown a number of correlation

coefficients in the correlation matrix having values

greater than the AVEs. This shows that there are

factors which are highly correlated. This correlation

situation can be caused by the following

possibilities; Firstly, latent factors which compose

one concept or phenomenon in the real world cannot

be absolutely independent. In this study, this has

become even more realized because of language

barriers in which the items were collected in another

(English) language followed by translating and then

answered by another language (Swahili). Secondly,

the ICT related vocabularies are mostly tending to

be too technical for linguistic professional for them

to translate to their most appropriate Swahili

language so as to be understood by a Tanzanian

layman.

Since the analysis process is handled by SEM,

most of measurement errors re handled

automatically, while there is also an opportunity of

discarding/dropping off the items which causes

trouble in the measurement model. In addition, SEM

gives opportunity of identifying the error causative

items through modification indices thereby pointing

the possibilities of adjusting a fitness of the model.

Therefore, in this study, discriminant validity is

handled through Modification Indices and

monitoring the fit indices of the measurement

model.

Hair et al (Hair et al., 1998) asserts that

whenever factor loadings associated with indicators

for all respective latent variables are 0.5 or above the

convergent validity of a measurement model is

generally considered to be acceptable. On the other

hand, Awang (2015) asserts that, for a model to

achieve convergent validity its fitness indices should

meet the required levels. Therefore, since there are

no items which have less than 0.5 factors loading

then the model have acceptable convergent validity

status. As for the case of fitness indices, they are

well within the threshold values in which the

absolute fit, incremental fit as well as parsimonious

fit have been reached as seen by having CFI=0.981

(should be>0.9), TLI=0.976(should be .0.9),

NFI=0.936(should be .0.9), RMSEA=0.029(should

be ,0.08) and Chisq/df=1.379( should be

<3.0)(Awang, 2015; Hooper et al., 2008).

In addition, the results were tested for common

method variance (CMV). The method used was

Hermans one factor method. The results show that,

after loading only one factor, the maximum variance

is 27.450%. Since this value is less than 50%, the

results are not bounded to common method variance

ICE-B 2017 - 14th International Conference on e-Business

120

as per Eichhorn (2014) recommendations. The

regression weight in the resulted model suggests the

following significance relationships:

A) Positive direct Influence of Perceived Ease

of Use on Perceived Usefulness(H1a)

This study hypothesized that perceived ease of

use have direct influence to the perceived

usefulness. This have been also supported by a

number of studies in the context of acceptance of

mobile phone technology (see(Gallego et al., 2008b;

Kwon and Chidambaram, 2000)). Similarly the

results of this study support this hypothesis. That

means, the more the employee perceive that mobile

phones are easy to use, they tend to perceive that it

is useful to them.

One of the studies which did not find the

relationship between perceived ease of use and

perceived usefulness is the study which investigated

the acceptance of integrating mobile commerce in

into an organizational processes(Gribbins et al.,

2003).

B) Direct influence of Perceived Usefulness on

behavior Intention (H1c)

The relationship between perceived usefulness

and behavior intention in the Tanzanian SMEs was

not supported. This suggests that the perception on

the usefulness of mobile phone technology in their

activities does not influence their intention to use it

in future. This observation is in line with a study on

the employee acceptance of integrating mobile

commerce in their workplaces in which perceived

usefulness did not have significant influence on their

behavior intention(Gribbins et al., 2003). This is

against of other findings which have shown that

perceived usefulness have positive and significant

relationship with behavior intention in other contexts

of mobile phone usage (see (Gribbins et al., 2003;

Kim, 2008; Prieto et al., 2015).

C) Direct influence of Perceived Ease of Use

on behavior Intention (H1b)

This hypothesis was supported. This study is in

line with the context of acceptance of smart phones

(Chen et al., 2009) and employees acceptance of

mobile commerce (Gribbins et al., 2003). This

implies that as employees of SMEs perceive that its

ease to use mobile phones then it will possibly be

useful in their work.

D) Direct influence of Perceived Values on

Behavior Intention (H2d)

The values of mobile phones in Tanzanian SMEs

were introduced in this study. The SEM results show

that PV has a significant positive influence on

behavior intention. This implies that as if challenges

on using mobile phones are resolved and benefits of

using mobile phones are realized, then the

employees are likely to intend to use mobile phones

to perform their work obligations in the near future.

E) Direct influence of behavior Intention on

Actual Use ( H1d)

An intention to use a technology has shown to

influence its actual usefulness in many contexts

(see(Davis, 1989b; Kwon and Chidambaram, 2000;

Venkatesh and Davis, 2000) and (Byomire and

Maiga, 2015)). This relationship was also tested to

investigate whether the context of Tanzanian

Tourism SMEs can support it. The results show that

there is a statistically significant relationship

between behavior intention and actual usage of

mobile phone technology in SMEs. This implies

that, as employees feel intending to use mobile

phone in performing their SME obligations, they

will actually use it.

9 CONCLUSION

This article discusses the relationship between

factors influencing employee intention and usage of

mobile phone technology by extending TAM.

Specifically, this study shows that the perceived

values have a statistical significant effect on the

intention to use a mobile phone in Tanzanian SMEs.

This suggests that through solving the challenges

and enhancing the benefits expressed by employees,

their intentions will be enhanced. Therefore, this

study suggests the stakeholders to act upon making

sure that the values of mobile phone technology in

the context of Tanzanian SMEs are well dealt with.

In addition, this study has shown that the use of

mobile phone technology by employees of SMEs

does not directly influence the intentions to use. This

suggest that, in order to promote the use of

technology in Tanzanian SMEs, more emphasis

should be put to making sure that it is easy to use as

well as values are handled well since they are the

key predictors of intention and usage. Further

studies may involve extending a study and identify

the strongest value among the existing ones as a way

of setting up strategic decisions in handling them.

Another study also could involve testing this model

in other different contexts and see any meaningful

information which can emerge on such study.

The Inﬂuence of Values on the Intention and Usage of Mobile Phone - A Case of Tanzanian SMEs

121

REFERENCES

Ajzen, I., 1991. The theory of planned behavior.

Organizational behavior and human decision processes

50, 179–211.

Arbuckle, J. L., 2013. IBM SPSS AMOS 22 Users’ Guide:

IBM Corp.

Astrachan, C. B., Patel, V.K., Wanzenried, G., 2014. A

comparative study of CB-SEM and PLS-SEM for

theory development in family firm research. Journal of

Family Business Strategy 5, 116–128.

Awang, Z., 2015. SEM made simple: A gentle approach to

learning Structural Equation Modeling. MPWS Rich

Publication.

Bernstein, I. H., Nunnally, J. C., 1994. Psychometric

theory. New York: McGraw-Hill. Oliva, TA, Oliver,

RL, & MacMillan, IC (1992). A catastrophe model for

developing service satisfaction strategies. Journal of

Marketing 56, 83–95.

Byomire, G., Maiga, G., 2015. A model for mobile phone

adoption in maternal healthcare, in: IST-Africa

Conference, 2015. IEEE, pp. 1–8.

Byrne, B. M., 2013. Structural equation modeling with

LISREL, PRELIS, and SIMPLIS: Basic concepts,

applications, and programming. Psychology Press.

Chen, J. V., Yen, D. C., Chen, K., 2009. The acceptance

and diffusion of the innovative smart phone use: A

case study of a delivery service company in logistics.

Information & Management 46, 241–248.

Chin, W. W., 2003. Issues and opinions on structural

equation modeling.

Cook, R. D., 1977. Detection of influential observation in

linear regression. Technometrics 19, 15–18.

Davis, F. D., 1989a. Perceived usefulness, perceived ease

of use, and user acceptance of information technology.

MIS quarterly 319–340.

Davis, F. D., 1989b. Perceived usefulness, perceived ease

of use, and user acceptance of information technology.

MIS quarterly 319–340.

Eichhorn, B. R., 2014. Common Method Variance

Techniques. Cleveland State University, Department

of Operations & Supply Chain Management.

Cleveland, OH: SAS Institute Inc.

Fishbein, M., Ajzen, I., 1975. Belief, attitude, intention

and behavior: An introduction to theory and research.

Gallego, M. D., Luna, P., Bueno, S., 2008a. User

acceptance model of open source software.

Computers in Human Behavior 24, 2199–2216.

Gallego, M.D., Luna, P., Bueno, S., 2008b. User

acceptance model of open source software. Computers

in Human Behavior 24, 2199–2216.

Gefen, D., Straub, D., Boudreau, M.-C., 2000. Structural

equation modeling and regression: Guidelines for

research practice. Communications of the association

for information systems 4, 7.

Gribbins, M., Shaw, M., Gebauer, J., 2003. An

investigation into employees’ acceptance of

integrating mobile commerce into organizational

processes. AMCIS 2003 Proceedings 11.

Hair, J. F., Anderson, R. E., Tatham, R. L., Black, W. C.,

1998. Multivariate data analysis, 5th. NY: Prentice

Hall International.

Hair, J. F., Wolfinbarger, M. F., Ortinau, D. J., Bush, R.P.,

2008. Essentials of marketing research. McGraw-

Hill/Higher Education.

Hair Jr, J. F., Hult, G. T., 2013. M, Ringle, C. M, &

Sarstedt, M. A primer on partial least squares

structural equation modeling (PLS-SEM). Thousand

Oaks: Sage Publications.[Links].

Hooper, D., Coughlan, J., Mullen, M., 2008. Structural

equation modelling: Guidelines for determining model

fit. Articles 2.

Hourali, M., Fathian, M., Montazeri, A., Hourali, M.,

2008. A Model for E-Readiness Assessment of Iranian

Small and Medium Enterprises. Journal of Faculty of

Engineering, Vol. 41, No. 7., 969–985.

Huber, E., Stephens, J. D., 1993. Political parties and

public pensions: a quantitative analysis. Acta

Sociologica 36, 309–325.

Kim, S. H., 2008. Moderating effects of job relevance and

experience on mobile wireless technology acceptance:

Adoption of a smartphone by individuals. Information

& Management 45, 387–393.

Kwon, H. S., Chidambaram, L., 2000. A test of the

technology acceptance model: The case of cellular

telephone adoption, in: System Sciences, 2000.

Proceedings of the 33rd Annual Hawaii International

Conference on. IEEE, p. 7–pp.

Lindsay, R., Jackson, T. W., Cooke, L., 2011. Adapted

technology acceptance model for mobile policing.

Journal of Systems and Information Technology 13,

389–407.

Loewenthal, K. M., 1996. An introduction to

psychological tests and scales.

Looney, C. A., Jessup, L. M., Valacich, J. S., 2004.

Emerging business models for mobile brokerage

services. Communications of the ACM 47, 71–77.

López-Nicolás, C., Molina-Castillo, F. J., Bouwman, H.,

2008. An assessment of advanced mobile services

acceptance: Contributions from TAM and diffusion

theory models. Information & Management 45, 359–

364.

Makame, W. H., Kang, J., Park, S., 2014. Factors

influencing electronic commerce adoption in

developing countries: The case of Tanzania. South

African Journal of Business Management 45, 83–96.

Malhotra, N.K., 2010. Marketing research: an applied

orientation.

Martz, E., 2013. Enough Is Enough! Handling

Multicollinearity in Regression Analysis [WWW

Document]. URL http://blog.minitab.com/blog/

understanding-statistics/handling-multicollinearity-in-

regression-analysis (accessed 4.7.17).

Maugis, V., Choucri, N., Madnick, S.E., Siegel, M.D.,

Gillett, S.E., Haghseta, F., Zhu, H., Best, M.L., 2005.

Global e-readiness—For what? Readiness for e-

banking. Information Technology for Development

11, 313–342.

Mawona, A., Mpogole, H., 2013. ICT and financial

ICE-B 2017 - 14th International Conference on e-Business

122

inclusion: Adoption of mobile phone banking among

small business owners in Iringa, Tanzania, in: IST-

Africa Conference and Exhibition (IST-Africa), 2013.

Presented at the IST-Africa Conference and Exhibition

(IST-Africa), 2013, pp. 1–9.

Mushi, R., Lillis, D., Almar, E., Jafari, S., 2016. Exploring

the Values of Mobile Phone Technology in Tanzania

Tourism SMEs. Conference papers.

Nah, F.F.-H., Siau, K., Sheng, H., 2005. TheVALUEof

Mobile Applications: A Utility Company Study.

Communications of the ACM 48, 85.

Oliveira, T., Martins, M. F., 2011. Literature Review of

Information Technology Adoption Models at Firm

Level. The Electronic Journal Information Systems

Evaluation Volume 14 Issue 1, (pp110- 121).

Onyango, R. A., Ongus, R. W., Awuor, F. M., Nyamboga,

C., 2014. Impact of Adoption and Use of Mobile

Phone Technology on the Performance of Micro and

Small Enterprises in Kisii Municipality Kenya. World

Journal of Computer Application and Technology 2,

34–42.

Pedersen, P. E., 2005. Adoption of mobile Internet

services: An exploratory study of mobile commerce

early adopters. Journal of organizational computing

and electronic commerce 15, 203–222.

Peng, R., Xiong, L., Yang, Z., 2012. Exploring Tourist

Adoption of Tourism Mobile Payment: An Empirical

Analysis.

Prieto, J. C. S., Migueláñez, S. O., García-Peñalvo, F. J.,

2015. Mobile acceptance among pre-service teachers:

a descriptive study using a TAM-based model, in:

Proceedings of the 3rd International Conference on

Technological Ecosystems for Enhancing

Multiculturality. ACM, pp. 131–137.

Rumanyika, J. D., 2015. Obstacles Towards Adoption of

Mobile Banking in Tanzania: A Review.

Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., Hair, J.

F., 2014. Partial least squares structural equation

modeling (PLS-SEM): A useful tool for family

business researchers. Journal of Family Business

Strategy 5, 105–115.

Tabachnick, B. G., Fidell, L. S., Osterlind, S. J., 2001.

Using multivariate statistics.

Tassabehji, R., Wallace, J., Srivastava, A., 2008.

Corporate Acceptance of M-Technology in the Service

Sector: A Case Study. AMCIS 2008 Proceedings 208.

URT, 2013. Tanzania SME Development Policy 2003:

“ten years after”, Implementation Review.

van Biljon, J., Kotzé, P., 2007a. Modelling the Factors

That Influence Mobile Phone Adoption, in:

Proceedings of the 2007 Annual Research Conference

of the South African Institute of Computer Scientists

and Information Technologists on IT Research in

Developing Countries. ACM, New York, NY, USA, pp.

152–161. doi:10.1145/ 1292491.1292509

van Biljon, J., Kotzé, P., 2007b. Modelling the Factors

That Influence Mobile Phone Adoption, in:

Proceedings of the 2007 Annual Research Conference

of the South African Institute of Computer Scientists

and Information Technologists on IT Research in

Developing Countries, SAICSIT ’07. ACM, New York,

NY, USA, pp. 152–161. doi:10.1145/1292491.1292509

Venkatesh, V., Davis, F. D., 2000. A theoretical extension

of the technology acceptance model: Four longitudinal

field studies. Management science 46, 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D.,

2003. User acceptance of information technology:

Toward a unified view. MIS quarterly 425–478.

West, S. G., Finch, J. F., Curran, P. J., 1995. Structural

equation models with nonnormal variables: Problems

and remedies.

Yueh, H.-P., Lu, M.-H., Lin, W., 2015. Employees’

acceptance of mobile technology in a workplace: An

empirical study using SEM and fsQCA. Journal of

Business Research.

The Inﬂuence of Values on the Intention and Usage of Mobile Phone - A Case of Tanzanian SMEs

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