CHAPTER ONE

1.0 INTRODUCTION

1.1 Background to the Study

The Global System of Mobile Communications (GSM) is a second-generation digital technology, it was originally developed in Europe and in less than ten years after the commercial launch, it developed into the world’s leading and fastest growing mobile standard (GSM Association, 2006). According to (Lonergan et al 2004), it was reported that at the beginning of 2004, there were over 1.3 billion mobile phone users worldwide and by 2007, the demand for mobile services would have grown at an average annual rate of 9.1%.

The GSM Association estimates that the GSM technology is used by more than one in five people of the world's population, which represent approximately 77% of the world’s cellular market and is estimated to account for 73% of the world’s digital market and 72% of the world’s wireless market (GSM Association, 2006). This growth principally results from the establishment of new networks in developing countries rather than from an increase in mobile access lines in developed countries (Serenko and Turel, 2006). African countries are actively involved in the establishment of the mobile services and specifically Nigeria, which is the focus of this study.

Since 1990s, the telecommunications sector has become an important key in the development of the economy of developed countries.This results from the saturated markets, de-regulation of telecommunications industry (removal of monopoly rights, especially enjoyed by state-owned telecoms networks), and increasing number of mobile service providers, enormous technical development and intense market competition. (Gerpott et al 2000).

Szyperski and Loebbecke (1999) wrote that this increasing economic importance and benefits of telecommunications firms motivated many management scholars (especially marketing experts) to devote attention to this sector. Wilfert (1999), Gerpott (1998), and Booz. Allen and Hamilton (1995) pointed out that marketing strategies are very important in telecommunications services because once customers have subscribed to a particular

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telecommunications service provider, their long-term link with this provider is of greater importance to the success of the company than they are in other industry sectors. Hence, service providers need to form a continuous lasting relationship with their customers to know them better and satisfy their needs adequately.

This paradigm shift has undauntedly led to the growing interest in customer relationship management initiatives that aim at ensuring customer identification and interactions, customization and personalisation that unservedly lead to customer satisfaction, retention and profitability, among other things (Thompson.2004, Xu et al ,2002:Dyche,2001:Ryals & Knox,2001: Stone,2000).

Organisations are therefore increasingly being more customer-centric and are much interested not just in acquiring new customers, but more importantly, retaining existing customers. This is perhaps because Customer’s satisfaction holds the potential for increasing an organization’s customer base, increase the use of more volatile customer mix and increase the firm’s reputation (Fornell, 1992, Levesque and McDaugall, 1996). One path to achieving customers’ satisfaction is through customers’ service.

Zeithaml and Bitner (2003) defined customers service as a series of activities designed to enhance the level of customer’s satisfaction that is, the feeling that a product or service has met customer’s expectation. Customer’s service varies by product, industry and customer. It however, assume important dimension in service delivery and sales of product. This is because Service firm such as Telecommunication firms, have no inventory of finished goods to buffer production from random demand variability (Dutta and Roy, 2006). Nevertheless, it is a demand for corporation survival; profitability and growth that service firm hold their own in competition.

The shift to devoting considerable attention and resources to customer acquisition and retention through customer satisfaction is not different with four of mobile telecommunication networks in Nigeria namely (MTN, Zain (former Celtel, Vmobile), Etisalat, and Globacom.Though competition has been keen in the industry, each of the mobile networks has been growing in customer acquisition since Nigeria deregulated its telecommunication in 1992.

Each of the telecom network companies is continually improving upon the quality of their service delivery in order to survive the high competition in the industry. Since Survival and growth or financial outcome is driven by customer loyalty and retention which in turn is driven by customer satisfaction and value (Rust and Oliver, 1994; Wang and Hing-Po Lo, 2002), delivering quality service and customer satisfaction have been important goals and

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pursuit for each of the four expanding Mobile Telecom Networks (MTNs) as well as the regulators of the industry.

RESEARCH QUESTIONS

Which dimensions of service quality are important to customers of MTNs in Nigeria’s?

What is the switching intention among customers of MTNs in Nigeria?

1.3 RESEARCH OBJECTI VES

To describe using overall satisfaction measure, customer satisfaction with service quality delivered by MTNs in Nigeria irrespective of which mobile telecom network customers subscribe to.

To describe customer satisfaction with dimensions of service quality in MTNs in Nigeria.

To examine switching intention among customers of MTNs within Nigeria.

1.4 Structure of Thesis

The study is organised into six chapters. Chapter one is the introductory chapter that covers the Background to the study, Research Questions, Research Aims/Objectives and Structure of thesis.

Chapter two is a review of literature. It covers mobile telecommunication, state of telecommunication in Nigeria, Regulatory Bodies in Nigeria’s Telecom and Overall sector Growth. Chapter three identify the hypotheses and explains in details the main constructs and Concepts and Theoretical Framework as well as their indicators and measurement in this study.

Chapter four is the Methodology section. It focuses on the research perspectives, data collection: population sampling, research instruments, access strategies and credibility of the study. Chapter five is presentation of data and analysis of results and findings. Chapter six is the summary, conclusion and implications.

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CHAPTER TWO:

2.0 LITERATURE REVIEW

2.1.1 Industry /Sector Overview

Mobile telecommunications industry has grown exponentially over the last two decades (Kenny and Keremane, 2007). In a country, mobile sector has become a critical indicator of economic development. Mobile technology provides a unique opportunity for the developing countries where telephone diffusion has been very low. Specifically, due to its comparatively low investment requirements, mobile telecommunications allows these countries to take advantage of technology innovations to provide communications services in the areas with limited or no telephone network (Noll, 2000; Thompson and Garbacz, 2007).

However, the potential of mobile telecommunications has not been fully utilised in many parts of the world. This is evident from its uneven pattern of diffusion, over 94% penetration rate in Europe in contrast to that of 22% in Africa (ITU, 2007).

Today mobile telecommunications industry has become a more and more important part of the world economy. With over 2.3 billion subscribers, the user base is still increasing at the rate of about 1 million new subscribers per day (Alam and Prasad, 2008).

2.1.2 State of Mobile Telecommunication in Nigeria

2.1.3 Brief Historical Overview of Nigeria’s Telecom Industry

Nigeria, a developing country, in 1992 introduced its first mobile phone services, through the joint venture between NITEL and DSL of Canada to form Mobile Telecommunications Service (MTS), (Ndukwe, 2005, pp 26). In January 2001, the regulatory body NCC, modernised and expanded the mobile telecommunications network and services by granting GSM license to three service providers; MTN Nigeria, Econet Wireless Vmobile,(now Zain), and the first national carrier, NITEL (initially MTS, privatised to form Mtel). In 2002, the second national carrier, Globacom was also granted license to commence operation. Since the launch of the GSM, the number of subscribers in Nigeria has greatly increased. Ndukwe (2005, pp 37-38, 40) reported that between 1998 and 2000, the number of mobile lines was 35,000 but grew to over 11 million as of March 2005, with a growth rate of more than a million lines annually since 2002. This translated to an increase from the total density of 0.4 lines per 100 inhabitants in 1998 to 9.47 lines per 100 inhabitants currently.

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Additionally, this sector has attracted an investment of over US $8 billion and has greatly increased the number of employed people directly (those working with the GSM companies) or indirectly (this includes various levels of dealerships, cell phone vendors, repair shops, suppliers of accessories, fixed and mobile call shops and street recharge card hawkers) (Hoff, 2006).

The number of the employed people is reported to be over 300,000 Nigerians in 2005 (Ndukwe, 2005). Other benefits include easy, affordable and quick access to phone by different categories of the population, reduced frequency of travelling, etc (Adomi, 2003), and all these benefits contribute to the socio- economic development of the country.

Based on the annual growth rate of the subscribers, and increasing teledensity, Nigeria is one of the fastest growing telecoms market in Africa (Hoff, 2006).

Additionally, the population count of over 130 million people and GDP per capita and PPP valuation of US $1,776 (estimated in 2005) (OECD, 2006) presents a massive growth potential for the mobile telecoms sector and the customer base is estimated to reach 23 million subscribers in 2007 and 32 million subscribers in 2009 (Hoff, 2006). This anticipated increase in the customer base will translate into better social and economic development, resulting from more financial investments from the service operators.

Despite the economic and social benefits of the mobile telecommunications to Nigerian economy and market, unlike the developed countries, there is no marketing or management research attention to this .According to Serenko and Turel (2006), customer satisfaction measurement addresses both users and public interests and such studies can assist in economic and social development. Therefore, there is need to gain more understanding in the area of customer satisfaction.

Customer behaviours and attitudes are greatly influenced by demographic, situational, environmental and psychological factors and these factors can be used by companies and policy makers to develop strategies to meet different needs of the different customer segments. Hence, there is need to gain more understanding of the influence of these factors on customer satisfaction. (Jackson et al 1996; Platow et al, 1997; and Homburg and Giering, 2001

2.2 Regulatory Bodies in Nigeria’s Telecom Industry

The Nigerian Communications Commission is an independent National Regulatory Authority for the telecommunications industry in Nigeria. According to (NCC 2006),the Commission is

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responsible for creating and enabling environment for competition among operators in the industry as well as ensuring the provision of qualitative and efficient telecommunications services throughout the country.

The broad business and purpose of the NCC as derived from the enabling Decree 75 of 1992 and it helps to facilitate private sector participation in telecommunications service delivery, coordinate and regulate the activities of all the operators to ensure consistency in availability and survey of service delivery and fair pricing. NCC mission is to support a market driven telecommunications industry and promote universal access (NCC, 2006).

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CHAPTER THREE

3.0 AREA OF MARKETING THEORY

3.1 CUSTOMER SATISFACTION AND SERVICE QUALITY IN MOBILE

TELECOMMUNICATION SERVICES

With the growth of the mobile telecommunication services around the world, a significant body of literature has emerged over the past several years. For example, Turel and Serenko (2006) empirically investigated customer satisfaction with mobile services in Canada. They adapted the American Customer Satisfaction Model to identify the antecedents and consequences of customer satisfaction for young cellular subscribers. They developed and estimated a model using a PLS (partial least square) path modelling developed by Chin (1998, 2001).

The results indicated that perceived service quality and perceived value are the key constructs affecting the customer's satisfaction with mobile services. Satisfaction in turn leads to customer loyalty. Woo and Fock (1999) investigated determinants of customer satisfaction in the Hong Kong mobile phone services sector. They conducted an exploratory factor analysis on 20 attributes followed by confirmatory factor analysis and obtained four determinants of customer satisfaction viz. transmission quality and network coverage, pricing policy, staff competence and customer service.

In their study in the New Zealand's telecom services industry Danaher and Gallagher (1997) identified that certain attributes of the personnel delivering the service, such as friendliness and competency, more strongly influence the overall service quality than other factors viz. clear voice and time taken to respond. In another study Wang et al. (2004) investigated the impact of quality-related factors on customer value and customer satisfaction using structural equation modeling (SEM) in China. They used the SERVQUAL (Parasuraman et al. 1988) factors (reliable, tangible, responsive, assurance and empathy) to measure service quality, but added "network quality" as another antecedent of customers' perceived service quality. Results indicated that all the service quality factors had significant and positive impact on customer satisfaction.

Also customer perceived value had a moderating effect on the service quality and customer satisfaction link. Similarly, Lai et al. (2007) tested the SERVQUAL model in China's mobile communication industry using exploratory and confirmatory factor analysis. They found that

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the SERVQUAL instrument is a valid means for measuring service quality. They also identified "service convenience" as an important additional dimension of service quality in China's mobile services sector. Johnson and Sirikit (2002) conducted their study on both landline and mobile users of Thai telecommunication industry using the service quality dimensions (reliability, responsiveness, assurance, empathy and tangibles). Tangibles emerged as the most important factor, but no significant link was found between the service quality ratings and the customers' behavioural intentions.

Athanassopoulos and Iliakopoulos' (2003) study of the residential customers of a European telecommunication company revealed that customer perceived performance (i.e., satisfaction, recommendation to others, relationship and value for money) were affected by product performance satisfaction, directory enquiries, branch network, billing and corporate image. Gerpott et al. (2000), through a structural equation modelling approach, found that customer retention, customer loyalty and customer satisfaction are important goals for the telecommunications operators in the German mobile telecommunications market. Results also indicated that network quality, assessment of price and personal benefits had positive and significant effect on customer satisfaction.

Mobile service price, personal service benefit perceptions and number portability had the strongest effects on customer retention as well. Kim et al (2004) investigated the effect of different service features and switching barriers on customer satisfaction and customer loyalty in the Korean mobile telecommunication services sector. They used SEM to test their proposed structural model.

The results indicated that customer satisfaction is significantly and positively affected by call quality, value added services and customer support. They also found that customer satisfaction and switching barrier had a significant and positive impact on customer loyalty.

Aydin and Ozer (2005) used the SEM technique to study the impact of service quality, perceived value, customer expectations and complaint handling on customer satisfaction in the Turkish mobile telephone market. The results showed that service quality, customer expectations and complaint handling had positive and significant effect on customer satisfaction.

Service quality had the strongest effect than other constructs in their model. All these studies have looked at different facets of service quality or service features affecting customer satisfaction. In this study we consider the service-related factors in the Nigerian mobile telecommunications sector.

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3.1.1 Customer Satisfaction

Customer satisfaction has been extensively studied in the field of marketing over the last two decades (Oliver 1980, 1981, 1999; Fomell et al 1996; Anderson and Fomell 1996; Yi 1989; Johnson et al 2001; Anderson et al 2004). It is by far the most commonly used customer-oriented metric by managers (Gupta and Zeithaml 2006) because it is generic and can be measured universally for all products and services including nonprofit and public services (Zeithaml et al 2006; Johnson and Fomell 1996).

After an extensive and critical review of the customer satisfaction literature, Yi (1989) conceptualized customer satisfaction as an attitude like judgment following a purchase act or based on a series of consumer-product interactions. The definition highlights that customer satisfaction is essentially the customer's judgment about the extent to which a product or service meets or falls short of expectations.

The literature has also emphasized the disconfirmation of expectations paradigm to a great extent (Oliver 1996; Yi 1989). This explains that the consumer compares the product/service with a pre-consumption expectation. Tse and Wilton (1988) defined customer satisfaction as "the customer's response to the evaluation of the perceived discrepancy between prior expectations (or some form of performance) and the actual performance of the product as perceived after its consumption." This definition conforms with definition of Oliver (1977).

Customer satisfaction research has developed around two broad types of evaluations: (1) transaction-specific satisfaction (2) cumulative satisfaction or an overall satisfaction concept which is similar to the attitude (Johnson et al 2001). Traditionally, satisfaction was considered to be transaction-specific, which is a result of the immediate post purchase judgment or affective reaction (Oliver 1993). De Ruyter et al. (1997) used the transaction-specific concept and showed the relationship between perceived quality and satisfaction.

A more economic psychology-based approach to satisfaction has been developed in the literature over the last decade or so which is cumulative satisfaction concept. This concept defines customer satisfaction as a customer's overall experience to date with a product or service provider (Johnson et al.2001). Studies done by Anderson and Fomell (1994); Fomell et al. (1996); Johnson et al. (2001) etc. have used the overall customer satisfaction concept.

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According to these studies satisfaction is viewed as an "overall evaluation based on the total purchase and consumption experience with a good or service over time (Anderson et al. 1994, p.54)." More and more satisfaction studies are now using the overall evaluation of

satisfaction concept which develops over all the experiences a customer has with the firm (Gupta and Zeithaml 2006).

More so, previous studies of conventional retailing have pointed out that service quality positively influences customer satisfaction (Johnson & Fornell, 1991; Kristensen et al., 1999; Cronin et al., 2000). Similar conclusions have been proposed in the studies of website and online shopping (Kuo, 2003; Lee & Lin, 2005; Collier & Bienstock, 2006; Hsu, 2006; Park & Kim, 2006; Bauer et al., 2006). Among the studies of the telecom industry, Wang et al. (2004) investigated the telecom industry in China, and Kim et al. (2004), Tung (2004), and Turel and Serenko (2006) investigated the mobile services in South Korea, Singapore, and Canada respectively. These studies also supported that service quality positively influences customer satisfaction. Thus, Hypothesis 1 is proposed as follows:

H1: Service quality positively influences customer satisfaction in MTNs mobile telecom Network

3.1.2 Service Quality

In the services marketing literature, service quality is defined as the overall assessment of a service by the customers.

Parasuraman et al (1985) conceptualized service quality as perceptions resulting from the comparison of customer expectations and actual service performance.

Parasuraman et al (1988) went ahead to define Service quality as a "global judgment, or attitude, relating to the superiority of the service.

They further pointed out that service quality perceptions are not solely the outcomes of service but it also involves the evaluation of the service delivery process by the customers. Lehtinen and Lehtinen (1982) conceptualized service quality as a three dimensional construct viz. "physical", "interactive" and "corporate." Physical quality is the quality dimension which originates from the physical elements of service like physical product and physical support.

Also, Service quality, consumers’ appraisal of overall quality or service excellence, may influence decisions to remain with or switch service providers. (Bitner and Hubbert, 1994; Cronin and Taylor, 1992; Zeithaml, Berry and Parasuraman, 1996) These studies, as well as conventional wisdom, indicate that by improving service quality, firms will satisfy their customers and hence retain them. For example, customers who perceived high service quality with their banks were more likely to stay with the banks than those who perceived low service quality were (Caruana, 2002).

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Likewise, a study across four industries – computer manufacturing, life insurance, automobile insurance, and retail chains – showed positive and negative behavioural intentions related to perceived service quality (Zeithaml, Berry and Parasuraman, 1996). Favourable service quality increased positive intentions such as remaining with the firms and willingness to pay more, and reduced negative intentions such as switching to other firms and complaining. Unfavourable service quality related to negative intentions such as exit and negative word-of-mouth.

For mobile telecommunications, service quality also relates to loyalty. The positive relationship between service quality and mobile loyalty cuts across nationalities including French (J. Lee, Lee and Feick, 2001), Chinese (Wang and Lo, 2002), and German (Gerpott, Rams and Schindler, 2001). Extending these studies to MTNs mobile users in Nigeria:

Hypothesis (H2): Service quality relates positively to mobile loyalty.

3.1.3 Customer Value

According to Wang and Lo (2002), More and more firms are searching for new ways to achieve, retain, upgrade and leverage competitive advantages, given the fact that customers are becoming more demanding, competition is getting more intense and technology is changing more rapidly.

As some researchers have concluded (Day, 1990: Naver and Slater, 1990), creating superior customer value is a major goal for market-driven firms. In fact, delivery superior customer value is inevitably becoming one of the most important success factors for any firm now and in the future because of its significant impact on behaviour intensions of customers.

As a result, many firms are transforming their focus from looking internally within the organisation for improvement by way of quality management, downsizing, business process reengineering or lean production and agile manufacturing to pursue superior customer value delivery (Band ,1991:Day,Gale,1994:Naumann ,1995:Butz and Goodstein,1996:Woodruf ,1997).

According to Wong and Lo (2002) the significance of customer value is widely recognized, the growing body of research about customer value is quite fragmented and the definition of customer value is divergent.

Perceived value is the customer’s overall assessment of the utility of a product based on perception of what is received and what is given.( Zeithaml 1988)

Doods et al. (1991) argues that the cognitive trade-off between perceptions of quality and sacrifice results in perceptions of value

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Butz and Goodstein (1996) define it as emotional bond established between a customer and a producer after the customer has used a salient product or service produced by that supplier.

Woodruff (1997) defines as a customer perceived preference for and evaluation of those products attributes, attribute performances and consequences arising from use that facilitate achieving the customer’s goals and purposes in use situations based on customer perspective on value derived from empirical research into how customers really think about value.

However, it is obvious that there are some areas of agreement among the different definitions mentioned above. For example, customer value is inherent in or linked through the use to some products or services: customer value is something perceived by customers rather than objectivity determined by sellers or other stakeholders and those perception processes typically involve a trade-off between what customers receive such as quality, benefits and utilities and what they give up such as price, sacrifices including opportunity cost, maintenance and learning cost.

So in this study, we concur with the majority researchers who define customer value in terms of get (benefit) and give (sacrifice) components (Woodruff,1997:Slater1997:Berry and Yadav,1996:Ravald and Gronroos,1996:Hass,1995:Mazumdar,1993:Slater and Narver,1992:Narver and Slater,1990:Day,1990:Zeithaml,1988) although some researchers argue that perceived value is made of only benefits (Hunt and Morgan ,1995:Hamel and Prahalad,1994).

There is a clear indication that any factors influencing the benefits customer can get or sacrifices customers have to endure will cause different evaluation of customer value even though different customers may form different opinions over time, for example, for product related factors such as image, time/effort/energy and solidarity are all customer value drivers or sources (Lapierre,2000: Ravald and Gronroos ,1996:Bolton and Drew,1991: Zeithaml,1988).

According to the different researches of the relationships between service quality and customer’s perceived value in conventional retailing and online shopping, most of the empirical studies have pointed out that service quality will positively influence perceived value (Cronin et al 1997, Cronin et al 2000, Brady et al 2001; Bauer et al 2006).

In addition to the above researches are Wang et al. (2004) and Turel and Serenko (2006) studies of the telecom industry which investigated the mobile services in China and Canada and found out that service quality is positively related to perceived value. Thus, Hypothesis 3 is proposed as follows:

H3: Service quality positively influences perceived value in MTNs mobile network

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3.1.4 CUSTOMER LOYALTY

Coyne (1989) stated that customer satisfaction has measurable impact on customer loyalty in that when satisfaction reaches a certain level; on the high side, loyalty increases dramatically; at the same time, when satisfaction falls to a certain point, loyalty reduces equally dramatically. Yi (1990) expressed that the impact of customer satisfaction on customer loyalty by stating that “customer satisfaction influences purchase intentions as well as post-purchase attitude”. In other word, satisfaction is related to behavioral loyalty, which includes continuing purchases from the same company, word of mouth recommendation, increased scope of relationship.

Fornell (1992) found out that there is a positive relationship between customer satisfaction and customer loyalty but this connection is not always a linear relation. This relationship depends on factors such as market regulation, switching costs, brand equity, existence of loyalty programs, proprietary technology, and product differentiation at the industry level. Jones and Sasser (1995) proposed that link between satisfaction and loyalty can be classified into four different groups: loyalist/apostle (high satisfaction, high loyalty), defector/ terrorist (low satisfaction, low loyalty), mercenary (high satisfaction, low loyalty), and hostage (low satisfaction, high loyalty).

Roger Hallowell (1996) confirmed the link between customer loyalty (in the context of behavioral loyalty) and customer satisfaction. Oliver (1999) stated that the relationship between satisfaction and loyalty is that satisfaction is transformed into loyalty with the assistance of a myriad of other factors. However, this relationship is complex and asymmetric.

High levels of satisfaction lead to high levels of attitudinal loyalty. Attitudinal loyalty involves different feelings, which create a customer’s overall attachment to a product, service, or company (Lovelock et al., 2001). Gerpott et al. (2001) in their study of the German mobile telecommunication found that customer satisfaction is positively related to customer loyalty, and both factors are important parameters in the mobile telecommunications industry. Turel and Serenko, 2006, in their study of Canadian mobile telecommunications also confirmed this finding.

3.2 Switching Costs

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Unlike service quality and value, which entice customers to stay by enhancing loyalty, switching costs deter customers from leaving via inconveniences and penalties (Burnham, Frels and Mahajan, 2003; Jones, Mothersbaugh and Beatty, 2007). The efficacy of switching costs in deterring switching occurs across industries including online brokerage (Chen and Hitt, 2002), travel, medical, and hairstyling services (Patterson and Smith, 2003).

Similarly, a survey of UK bank customers revealed that dissatisfied customers remained because they perceived time, effort, and uncertainty costs as higher than the potential benefits from switching banks (Panther and Farquhar, 2004).

Switching costs are also effective deterrents with mobile services (Caruana, 2004; Gerpott, Rams and Schindler, 2001). To reduce switching, thereby enhancing loyalty, mobile service providers often erect barriers such as disallowing number portability (Haucap, 2003) – retaining one’s phone number when switching mobile service providers – and committing subscribers to long-term contracts with punitive penalties for premature termination (Valletti and Cave, 1998). These switching costs may even be more critical than price, service quality, and loyalty programs in retaining mobile service subscribers ( Lee and Murphy, 2005). Thus for Nigeria MTNs mobile Users:

Hypothesis (H4): Switching costs relate positively to mobile loyalty.

3.2.1 SERVICE QUALITY AND CUSTOMER SATISFACTION

During the past few decades service quality has become a major area of attention to practitioners, managers, and researchers owing to its strong impact on business performance, lowering costs, improving customer satisfaction, customer loyalty and profitability (Leonard & Sasser, 1982; Cronin & Taylor, 1992; Chang & Chen, 1998; Newman, 2001; Sureshchandar, Rajendran, & Anantharaman, 2002). According to Brown and Swart (1989), customers prefer organizations that deliver higher service quality, and suppliers can charge a premium for service qualities.

Furthermore, customer-perceived service quality has been given increased attention in recent years, due to its specific contribution to business competitiveness and developing satisfied customers. However, satisfaction is reported as a feeling which results from a process of evaluating what was received against that expected, the purchase decision itself and the fulfillment of needs or want (Kotler, 1991).

Researchers have also identified customer satisfaction, from a multi-dimensional nature, and view overall satisfaction as a function of satisfaction with multiple experiences with the service provider (Sureshchandar et al., 2002). Therefore, it can be concluded that overall satisfaction is based on the information from all previous experiences with the service

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provider and is viewed as a function of all previous transaction-specific satisfactions (Teas, 1993; Parasuraman et al., 1994).

3.2.2 Service Quality DimensionsParasuraman et al. (1988) identified five dimensions of service quality (viz. reliability, responsiveness, assurance, empathy, and tangibles) that link specific service characteristicsto consumers’ expectations.(a) Tangibles - physical facilities, equipment and appearance of personnel;(b) Empathy - caring, individualized attention;(c) Assurance - knowledge and courtesy of employees and their ability to convey trust and confidence;(d) Reliability - ability to perform the promised service dependably and accurately; and(e) Responsiveness - willingness to help customers and provide prompt service.

After a comprehensive review of service quality studies, Asubonteng, McCleary, and Swan (1996) concluded that the number of service quality dimensions varies in different industries. For example, Kettinger and Lee (1994) identified four dimensions in a study of information systems (IS) quality, which did not have tangible dimension. Cronin and Taylor (1992) developed a one-factor measurement instrument instead of the five-factor measures proposed by Parasuraman et al. (1988).

Besides SERVQUAL, Sureshchandar, Rajendran, and Anantharaman (2003) have identified five factors of service quality from the customers’ perspective.Those are: a) Core service or service product, b) Human element of service delivery, c) Systematization of service delivery: non- human element, d) Tangibles of service, and e) Social responsibility. After a close inspection it could be safely concluded that the newly defined construct of service quality by Sureshchandar et al. (2003) has some resemblance with the definition provided by Parasuraman et al. (1988).

3.2.3 Definition and importance of measuring service quality

During the 1980s, service quality received a great deal of attention as a key strategic factor for product differentiation to increase market share and boost profits (Phillips, Chang, & Buzzell, 1983; Buzzell & Gale, 1987). Thus, researchers focused on the process in which consumers evaluate service quality. Earlier researchers suggest that customers assess service quality by comparing what they feel a seller should offer and compare it against the seller’s actual service performance (Grönroos, 1982; Lehtinen & Lehtinen, 1982).

However, Bitner, Booms and Mohr (1994) define service quality as “the consumer’s overall impression of the relative inferiority/superiority of the organization and its services.” Other researchers view service quality as “a function of the differences between expectation and performance along the quality dimensions” (Parasuraman, Zeithaml, & Berry, 1985, 1988, 1991), “a relativistic and cognitive discrepancy between experience-based norms and

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performances concerning service benefits” (Roest & Pieters, 1997), and “a form of attitude representing a long-run overall evaluation” (Cronin & Taylor, 1994; Taylor & Cronin, 1994).

According to Berry, Parasuraman and Zeithaml (1988), service quality has become a significant differentiator and the most powerful competitive weapon which all the service organizations want to possess, and is found to be measured regularly (Reichheld & Sasser, 1990) and most accurately through the eyes of the customer.

Therefore, measuring perceived service quality is considered to be the fundamental in developing a customer oriented strategy that ensures the long-term survival of the firm concerning customer satisfaction (MacStravic, 1997). Some studies, however, have been undertaken to identify quality dimensions and detailed aspects of services and their relationship with customer satisfaction. The SERVQUAL approach further integrates the two constructs and suggests that perceived service quality is an antecedent to satisfaction. Spreng and Mackoy (1996) showed that service quality leads to customer satisfaction.

3.3 THEORETICAL RESEARCH FRAMEWORK

Researchers and managers thrive for learning details about components of perceived service quality for the kind of business they are in for obvious reasons such as customer satisfaction and increased profitability. In this context, different models of service quality gain specific importance as they not only help in learning factors associated with it, but also provide a direction for improvements.

However, the idea of linking service quality and customer satisfaction has existed for a long time. Studies indicate that there are links among customer satisfaction, service quality, customer value, customer loyalty, and profitability (Grönroos, 1982; Parasuraman et al., 1985; Sewell & Brown, 1990; Heskett, Sasser, & Schlesinger, 1997; Anderson & Mittal, 2000).

As for the relationship between service quality and customer satisfaction, Oliver (1993) first suggested that service quality would be antecedent to customer satisfaction regardless of whether theses constructs were measured for a given experience or over time. Research exists (Anderson & Sullivan, 1993; Anderson et al., 1994; Spreng & Mackoy, 1996) to empirically support this idea, wherein customer satisfaction is a consequence of service quality. However, at some point there must be “diminishing returns to increasing customer satisfaction” (Anderson et al., 1994).

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3.3.1 Customer Satisfaction Theories

There has been a growing interest in studying customer satisfaction over the last few decades. This is evident from the large number books, journal articles, and research studies dealing with different aspects of the subject in different disciplines.

Owing to the crucial of customer satisfaction, it is generally accepted that its determinants must be analyzed and be compared across firms, industries, sectors and nations (Fornell et al., 1996). For this purpose, different models have been developed for determining various influencers of customer satisfaction.

Social psychologists, marketing researchers, and students of consumer behaviour, have extensively studied the concepts of customer satisfaction and dissatisfaction. The increasing importance of quality in both service and manufacturing industries has also created a proliferation of research, with more than 15,000 academic and trade articles having been published on the topic of customer satisfaction in the past two decades (Peterson and Wilson, 1992). Several conferences have been devoted to the subject and extensive literature reviews have been published (Day, 1977; Hunt, 1977; Latour and Peat, 1979; Smart, 1982; Ross, et., 1987, Barsky, 1992: Oh and Parks, 1997).

Nearly all customer satisfaction theories are based on a structural model, which comprises the antecedents and consequences of customer satisfaction. Some of these models are going to be described here.

3.3.2 Customer Satisfaction Indices

Customer satisfaction indices have been developed and applied in several countries (for example, Sweden, the USA, Russia, Switzerland, Norway, Taiwan and Germany) for measuring and analyzing customer satisfaction. A national customer satisfaction index is a market oriented performance measure, which can be seen as complementary to traditional performance measures, such as return on investment, profits and market shares or Kaplan and Norton’s balanced score card approach. These satisfaction indices help both consumers and product managers to assess the quality of products and services, help companies to benchmark their operations, and assist policy makers with decisions about quality-related aspects (Hackl et al., 2000, p.999).

The Swedish customer satisfaction barometer (SCSB) was the first national customer satisfaction index to provide a harmonized cross-company, cross-industry, national measurement tool for customer satisfaction and evaluation of the quality of products and services. It is based on annual survey data from customers of about 100 leading companies in some 30 industries (Fornell, 1992, p.6). In 1994, the National Quality Research Centre in the USA (NQRC) adapted the Swedish index, to produce its own America customer satisfaction index. This ACSI was designed to be representative of the economy as a whole, covers more than 200 firms, with 1994 sales of more than $2.7 trillion, competing in over 40

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industries in the seven major consumer sectors of the economy. It estimates customer satisfaction for each company in the national economy equivalents (Fornell et al., 1996,p.7).

Following the Swedish and American indices, the European Organization for Quality (EQO), European Foundation for Quality Management (EFQM), European Academic Network for Customer-Oriented Quality Analysis and the European Commission developed the European Satisfaction Index (ECSI). A pilot study was conducted in 12 European countries in 1999.

In all of these indices, customer expectations are considered as one of the determinants of customer satisfaction. But some other models more than CSIs completed this factor by considering customer expectation disconfirmation rather than expectation. Because expectation by itself can’t result in customer satisfaction or dissatisfaction but, the level of disconfirming his expectations can lead to understand our customer’s satisfaction.

3.3.3 Expectation Disconfirmation Theory

The importance of expectations in the consumer decision process has been documented in the literature. Expectations play a major role in the consumer decision making process (Van Raaij, 1991; Spreng et al., 1993). In the pre purchase stage, expectations influence consumer decisions on which brand or type of product or service to buy. During consumption, expectations can be affected by the attitudes of service personnel, other customers and equipment. In the post purchase stage, expectations form the basis of evaluations of satisfaction ( Oliver,1980; Kurtz and Clow,1998) and service quality (Parasuraman et al., 1998; Brown and Swartz 1989).

Most studies have treated expectations as a static variable that exerts both a direct and indirect influence on customer satisfaction (Cadotte et al.,1987; Tse and Wilton, 1988). In these studies, customer expectations were formed prior to consumption; they formed as comparative referents for quality judgements, for determining customer satisfaction, and ultimately for behavioural intentions. It has been suggested that marketers who wish to understand and favourably influence customer satisfaction need to understand and influence expectations (Anderson,1993). Success in influencing customer satisfaction may depend, in part, on understanding how customer expectations are viewed as dynamic since they may change as a result of the customer’s experience.

A review of the literature suggests that consumers may use multiple types of expectations in their satisfaction evaluation process (Cadotte et al., 1987: Tse and Wilton,1988). However, the types most often referred to are predictive expectations and normative expectations. Predictive expectations are generally defined as consumer beliefs about the level of service that a specific service firm would be likely to offer. These expectations are frequently used as a standard of reference against which satisfaction judgements are made (Churchill and Suprenant,1982). Normative expectations are generally conceptualized as consumers’ ideals level of service which can be referred as desires too.

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As mentioned before, numerous researchers have attempted to apply customer satisfaction theories developed by consumer behaviourists in different areas and industries.

In the 1980s, customer satisfaction was explained by the expectation disconfirmation theory. While there are a variety of approaches to the explanation of customer satisfaction/dissatisfaction, the most widely used is the expectation disconfirmation theory (Oliver, 1980).

As explained by this theory in the late 1980s, in the marketing context, customer satisfaction is a collective outcome of perception, evaluation, and psychological reactions to the consumption experience with a product/service (Yi, 1990). This theory suggests that satisfaction is determined by the intensity and direction of the gap between expectations and perceived performance. As such, expectations are defined as a set of beliefs held by users about a product/service’s performance (Teas, 1993; Szajna and Scamell, 1993).

The expectation disconfirmation model suggests that customers determine satisfaction with a service by comparing perceptions of performance against their expectations. If there is a disparity between the expectations and the perceptions of performance, disconfirmation results, which in turn may affect the level of customer satisfaction (Oliver,1980). There is considerable empirical evidence to suggest that consumers who perceive that a performance matches or exceeds their predictive expectation are more likely to feel satisfied with the service than those whose expectations are not met (Gupta and Stewart, 1996; Anderson and Sullivan, 1993).

This model was somehow the dominant paradigm of satisfaction/dissatisfaction formation for many years .It specifies that consumer satisfaction/dissatisfaction results from a comparison of expectations concerning the quality of consumption, with the actual consumption experiences (Oliver,1980). This model provides the theoretical basis for understanding the formation of consumer satisfaction (Anderson and Sullivan, 1993; Churchill and Surprenant, 1982) and the assessment of service quality (Brown and Swartz, 1989; Parasuraman et al., 1988).

According to this theory, which has been tested and confirmed in several studies (Oliver and DeSarbo, 1988; Tse and Wilton, 1988), customers purchase goods and services with pre-purchase expectations about anticipated performance. Once the product or service has been purchased and used, outcomes are compared against expectations. When outcome matches expectations, confirmation occurs. Disconfirmation occurs when there are differences between expectations and outcomes.

Satisfaction is also of great interest to practitioners because of its important effect on customer retention Patterson et al., 1997; Neal, 1999).

As discussed before, in the marketing literature (e.g. Churchill and Suprenant, 1982; Oliver and DeSarbo, 1988), the disconfirmation theory emerges as the primary foundation for

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satisfaction models. According to this theory, satisfaction is determined by the discrepancy between perceived performance and cognitive standards such as developed and validated in the context of physical products (mainly brand names) where customers were familiar with the attributes of the products and could develop expectations based on their prior experience/knowledge.

Expectation disconfirmation occurs in three stages: 1) positive disconfirmation where perceived performance exceeds expectations; 2) confirmation, where perceived performance meets expectations; and 3) negative disconfirmation, where perceived performance falls below expectations. An individual is more likely to be satisfied if the service meets (confirmation) or exceeds (positive disconfirmation) his/her expectations (Oliver and Desarbo, 1988). On the other hand, he/she is more likely to be dissatisfied if the service performance falls below his/her expectations (negative disconfirmation).

By proposing expectation disconfirmation as the sole determinant of satisfaction, this theory does not account for the possibility that the confirmation of high expectations is more likely to lead satisfaction than the confirmation of low expectations. To resolve this drawback, Tse and Wilton (1988) included perceived performance as an additional determinant of satisfaction. Their rationale was that if actual perceived is expected and confirmed to be low, it may still negatively affect satisfaction and override the impact of confirmation or positive disconfirmation, resulting in dissastification. The authors found perceived performance to be a direct and independent determinant of satisfaction.

Negative disconfirmation occurs when product/service performance is less than expected. Positive disconfirmation occurs when product/service performance is better than expected. Satisfaction is caused by confirmation or positive disconfirmation of consumer

expectation and dissatisfaction is caused by negative disconfirmation of consumer expectations.

3.3.3.1 The Expectation Disconfirmation Model

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Perceived

Performance

Expectation

DisconfirmationDesire Disconfirmation

Overall Satisfaction

3.4 Disconfirmation Theory

Researchers have debated the roles of expectations and desires in explaining satisfaction. McKinney et al. (2002) applied the expectation disconfirmation theory without taking into account the potential role of desire disconfirmation that may possibly be a salient factor in satisfaction formation. Other studies (e.g.Suh et.al., 1994; Sprieng et. Al., 1996) argued for the superiority of desires over expectations as a comparison standard, but did not operationalize or empirically validate the proposed desire disconfirmation models.

These studies suggested that desires should be used instead of expectations rather than in addition to expectations. But expectations and desires are different concepts that can both play important roles in explaining satisfaction. The main argument used by the desire disconfirmation proponents (e.g Suh et al., 1994) is that services that exceed the expected levels, but not the desired levels, may still lead to feelings of dissatisfaction.

Conversely, one can argue that a customer’s desires for a particular service may be lower than his/her expectations (i.e., the service is not really wanted by the customer). In such a case, meeting the customer’s desired level of service while failing to meet his/her expected level (e.g., based on what the merchant promised to deliver) may also lead to dissatisfaction. The customer may still feel dissatisfied if he/she expectations are not fulfilled, independently of his/her desires. Some researchers therefore agree with Chin & Lee (2000) and Khalifa and Liu (2002) on the need to include both desires and expectations as comparison standards for disconfirmation.

Prior research did not provide conclusive results regarding what cognitive standard to use in explaining or predicting satisfaction. Some researchers argued for the superiority of desires over expectations, while others argued for the simultaneous use of both comparison standards. Therefore to address these problems, Khalifa and Liu (2000) developed the disconfirmation model that includes expectation ,desire disconfirmation, and perceived performance simultaneously as determinants of satisfaction, differentiating between satisfaction at adoption and post-adoption satisfaction.

As said above, more recently, Chin and Lee (2000) and Khalifa and Liu (2000) developed models that include both expectations and desires in explaining overall satisfaction with information systems and with online services, respectively. They both adopted direct measures of overall satisfaction using reflective items (i.e “Overall I am satisfied with…”), arguing that expectations and desires might have direct and independent effects over satisfaction. While Chin and Lee (2002) provided the argument, it was Khalifa and Liu (2002) who empirically verified it- through their examination of satisfaction with online services in the adoption stage.

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Their results showed that desires and expectations are both important factors that need to be considered simultaneously in explaining satisfaction.

Their research presents important theoretical and practical contributions. On the theoretical side, they provided a better conceptualisation of the formation of satisfaction by examining its evolution and the variability of its determinants. On the practical side, their empirical results provide a better understanding of the respective roles and relative importance of the determinants of satisfaction.

When not well defined, expectations play a minimal role as a comparison standard, and desires therefore become more salient determinants of satisfaction since their formation is less dependent on past experience/knowledge. Hence they argued that the expectation disconfirmation theory developed in the marketing literature should be further defined by adding desire disconfirmation to more fully explain/predict satisfaction and is going to be called as disconfirmation theory.

Figure 3.4.1 The Disconfirmation Model

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Expectation Disconfirmation

Desire Disconfirmation

Perceived Performance

Overall Satisfaction

3.5 The disconfirmation Model

Both desires and expectations are employed as the comparison standards in the disconfirmation process. They serve as benchmarks for assessment of actual perceived performance. The empirical results have important implications for practitioners. As perceived performance, expectation disconfirmation, and desire disconfirmation are all significant determinants of satisfaction, practitioners should pay attention not only to customers’ expectations and product or service performance, but also to customers’ desires. Therefore, to improve overall satisfaction, it is important to elicit and strive to satisfy customer desires in addition to their expectations.

It is therefore essential to create a good match between the value proposition and customers’ desires and expectations. Due to previous studies, this research believes that perceived performance has both a direct effect on satisfaction (Tse and Wilton,1988; Spreng et al., 1996; Patterson et al., 1997) and mediated effects through expectation disconfirmation and desire disconfirmation (Oliver and Desarbo, 1988; Suh et al., 1994). Satisfaction is more probable with higher perceived performance. Alternatively, perceived performance also affects satisfaction through determining the outcome of expectation/desire disconfirmation. Higher performance is more likely to meet or exceed desires/expectations, leading to more positive disconfirmation and hence higher satisfaction.

Consistent with Khalifa and Liu (2002), Chin and Lee (2002) and Suh et al. (1994), it also hypothesizes that desire disconfirmation will affect satisfaction significantly and positively. When the formation of concrete expectations is restricted, e.g by lack of experience or knowledge, desires may emerge to be the salient benchmarks for judging satisfaction.

Spreng et al. (1993) consider that consumer expectations play two important roles in the determination of satisfaction.

First, expectations strongly influence consumers’ choices of brand or type of product or service. Second, expectations serve as reference points with which subsequent perceptions of performance are compared. In some cases, when consumers find it difficult to judge a product or service performance due to its complexity, expectations play a major role in the judgement process (Anderson and Sullivan, 1993). Olshavsky and Miller (1976) explained that for some complex products with a considerable ambiguity, consumers’ perceptions of performance may tend to assimilate towards expectations.

The importance of expectations in evaluations of service quality has been documented in the service literature (Sweeney et al., 1992; Brown and Swartz, 1989). Consumer expectations, which some prefer to call “standards” (Iacobucci et al., 1994), serve as the benchmark against which quality of a service is evaluated. Clow and Vorhies (1993) consider

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the long-term success of service firms to be dependent on the skilful management of service expectations.

As telecommunications evolve from systems providers to service providers, the perceived performance can be evaluated through the “service quality” of the mobile service.

It is important to know that measurements of service quality and customer satisfaction have received considerable attention in the last two decades (Cronin and Taylor, 1992; Parasuraman et al., 1985, 1991; Rust et al., 1994). Indeed, Peterson and Wilson (1992) report that over 15,000 articles on customer satisfaction have been written in the past 20 years. This interest of improving customer satisfaction is not without reason, with the realization that high service quality results in higher customer satisafaction, retention and increased market share (Buzzell and Gale, 1987; Danaher and Rust, 1996; Rust et al., 1994).

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4.0 CHAPTER FOUR

4.1 RESEARCH METHODOLOGY

This chapter will present a detailed idea about how the research will be conducted. This includes research approach, research design, data collection, sample selection method and data analysis methods. At the end of this methodology part validity and reliability issues will be discussed to follow the quality standards of the research.

4.1.1 RESEARCH PURPOSE

The research purpose is a broad statement of what the research hopes to achieve. According to the purpose, research could be broadly divided into exploratory, descriptive and explanatory (Saunders, Lewis & Thornhill 2003).

4.1.2 Exploratory research

Exploratory research are concerned with identifying the real nature of research problems and, perhaps, of formulating relevant hypothesis for later tests. (Chisnall, 2005).

Exploratory research gives valuable insight, results in a firm grasp of the essential character and purpose of specific research surveys, and encourages the development of creative, alternative research design.

According to (Malhotra & Birks, 2007) exploratory research is characterised by a flexible and evolving approach to understand marketing phenomena that are inherently difficult to measure. Its main objective is to provide insight into an understanding of marketing phenomena and its used in instances where the subject of the study cannot be measured in a quantitative manner or the process of measurement cannot realistically represent particular qualities.

Oppenheim 1992, recommends that these early exploratory interviews should be tape-recorded and listened to later by the research team, so that all involved in the programme are able to derive full value from the views expressed by respondent, including noting any personal hygiene or honesty.

4.1.3 Descriptive research

Descriptive research designs are usually structured and specifically designed to measure the characteristics described in a research question. Hypothesis, derived from the theory, usually serve to guide the process and provide a list of what needs to be measured (Hair, Babin, Money & Samuel 2003).

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The objective of descriptive research is to portray an accurate profile of persons, events of situations. It is necessary to have a clear picture of the phenomena on which researcher wish to collect data prior to the collection of the data (Saunders, Lewis & Thornhill 2003).

Studies that establish causal relationships between variables may be termed exploratory studies. The emphasis here is on studying a situation or a problem in order to explain the relationship between variables (Saunders, Lewis & Thornhill 2003). Exploratory studies are designed to test whether one event causes another (Hair, Babin, Money & Samuel 2003).The purpose of the research is descriptive. The data has been collected through questionnaire.

Descriptive research, in contrast to exploratory research, stem from substantial prior knowledge of marketing variables. Questions should be designed to secure specific kinds of information, related, perhaps to product performance, market share, competitive strategies, distribution etc for this type of research to be productive (Chisnall, 2005).

4.1.4 Research approach

The knowledge claims, the strategies and the method all contribute to a research approach that tends to be more quantitative, qualitative or mixed (Creswell 2003)

The quantitative approach will be used in this research.

Quantitative approach is one in which the investigator primarily uses post positivist claims for developing knowledge (that is cause and effect thinking, reduction to specific variables and hypotheses and questions, use of instrument and observation, and the test of theories), employs strategies of inquiry such as experiments that yield statistical data (Creswell 2003).

In quantitative research, data is quantified and statistical methods are used in the data analysis. It aims to give results that are representative to the whole population. (Malhotra & Birks 2000, 155-156) Typical difference between the methods is how different stages of research are separated. The quantitative method makes a clear distinction between the different stages of data collection, preparation and analysis, whereas the qualitative method does not. (Uusitalo 1991, 80) Instead of being exclusive, qualitative and quantitative methods should be seen as complementary to one and another (Hirsjärvi et al 2005).

According to (Denzin and Lincoln 1994) qualitative research is multi method in focus, involving an interpretive, naturalistic approach to its subject matter. In other words, qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.

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According to (Yin,1994) qualitative methods are often related to case studies, where the aim is to receive thorough information and thereby obtain a deeper understanding of the research purpose.

Qualitative research can, for example, be used to precede quantitative research in identifying the appropriate variables that can then be measured. Conversely, a quantitative research may be conducted to discover meaningful differences between groups, which can then be analyzed in qualitative manner to gain understanding of the reasons for differences between the groups. It is also possible to use the methods simultaneously. (Hirsjärvi et al 2005; Malhotra & Birks 2000)

Since the purpose is to determine customer satisfaction through service quality, quantitative research is found to be more appropriate for this study.

4.1.5 Data Collection Method

The study is based on both secondary and primary data in order to find sufficient and describing data. As a result of wanting the information gathered to be focused on specific research question a survey questionnaire will be administered.

Data collection is a fundamental step in research. In data collection, sampled data are collected through various means that provide a basis for analyzing the market behaviour of a general population from which the data are sampled.

When a report is written the research can be based on primary or secondary data or both of them. Secondary data is information taken from other researchers. Secondary data are data that have already been collected for purposes other than the problem at hand. (Malhotra & Birks, 2007)

Many times secondary data can be easier or more practical to use because of the availability of already existing information. (Lundahl and Skarvad, 1992). The collection and analysis of secondary data help to define the marketing research problem and develop an approach. It can also be an essential component of a successful research design by locating and analysing relevant secondary data before collecting primary data. (Malhotra & Birks, 2007)

Primary data are data originated by a researcher for the specific purpose of addressing the problem at hand. (Malhotra & Birks, 2007) Primary data is, data collected for the first time and for a specific purpose. There are two main methods by which primary data can be collected-observation and communication. Observation method is used to get both past and current information. For example, instead of asking respondents about their current behaviour, we may observe it and record observations. (Richard D. Crisp)

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4.2 Sampling Method

Data were collected using self-administered questionnaires from the MTNs mobile phone users in the city of Lagos. A convenience sampling (Non probability) method was used to select respondents for the study. 100 questionnaires were administered in total. The rating scale varied form “Very Satisfied” to “No Opinion”.

The questionnaires were administered on the streets of Lagos at Ikeja area of residence, and the choice of this method of data collection was of high priority because the residents of Lagos State are mostly very busy people, who leave their homes for work or trade very early in the morning (5.00am) and return late (some people return as late as between 22.00-23.00pm). There will probably be little or no available time to attend to the questionnaires if dropped at their homes and failure of power supply (electricity) is very common at nights.

4.2.1 SELECTING THE SAMPLING METHOD

Selection of the sampling method to use in a study depends on a number of related theoretical and practical issues. Traditional sampling method can be divided into two categories: probability and non-probability sampling (Samuel et. al., 2003)

Probability sampling is most commonly associated with survey-based research where researcher needs to make inferences from the sample about a population to answer the research questions or to meet research objectives (Saunders et. al., 2003). In probability sampling, sampling elements are selected randomly and the probability of being selected is determined ahead of time by the researcher. If done properly, probability sampling ensures that the sample is representative (Hair et. al., 2003)

Non-probability sampling provides a range of alternative techniques based on the researcher subjective judgment (Saunders et.al., 2003). In non-probability sampling the selection of elements for the sample is not necessary made with the aim of being statistically representative of the population. Rather the researcher uses the subjective methods such as personal experience, convenience, expert judgment and so on to select the elements in the sample. As a result the probability of any element of the population being chosen is not known (Samuel et. al. 2003)

According to Samuel et. al., 2003 most non-probability sampling methods are:

4.2.1.1 Convenience Sampling

Convenience sampling involves select sample numbers who can provide required information and who are more available to participate in the study. Convenience samples enable the researcher to complete a large number of interviews and quickly. (Hair et. al., 2003)

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4.2.1.2 Judgment Sampling

Researcher’s judgment is used to select sample element and it involves for a specific purpose. Group of people who have knowledge about particular problem they can be selected as sample element. Sometimes it referred as a purpose sample because it involves a specific purpose. (Hair et. al 2003)

4.2.1.3 Quota Sampling

Objective of quota sampling is to have proportional representation of the strata of the target population for the total sample and the certain characteristics describe the dimensions of the population (Cooper & Schindler 2003). In quota sampling the researcher defines the strata of the target population, determines the total sample size and set a quota for the sample elements from each stratum. The findings from the sampling cannot be generalized because of the choice of elements is not done using a probability sampling methods (Samouel et. al, 2003)

4.3 RESEARCH INSTRUMENT

Questionnaire is the instrument used in this study to collect data. The questionnaire employed the typical form of fixed-response alternative questions that required the respondent to select from a predetermined set of answers to every question. According to Malhotra and Birks (2003, pp. 224), this survey approach is the most common method of primary data collection in marketing research and the advantages are simple administration and data consistency.

The survey questionnaires were administered on the streets (mode of data collection): the questionnaires were filled out mostly by the people themselves. Malhotra and Birks (2003) showed in their evaluation of comparative survey techniques that street interviews have high flexibility of data collection, high degree of diversity of questions due to interaction and high response rate, moderate sample control, moderate quantity of data, moderate to high great potential to probe respondents, moderate to high great potential to build rapport, moderate to high speed and cost of data collection. These qualities were responsible for the choice of this survey technique for this study.

The questionnaires employed the Likert non-comparative scaling technique. It is a widely used rating scale which requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements or questions (Albaum, 1997). This rating scale is easy to construct and administer and respondents readily understand how to use the scale (Malhotra and Birks, 2003).

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The Likert scale used in this study is odd numbered (as proposed by Spagna, 1984); balanced (the number of favorable and unfavorable categories is equal). This view is proposed by Watson (1992), who reported the balanced state helps to obtain an objective data; has non-forced choices “no opinion” to improve the accuracy of the data (as proposed by Hasnich, 1992); and 5-scaled categories which conforms to the traditional guidelines reported by Aaker (1997). He proposed that the categories scale should be between 5 and 9.

The questions seeks respondents feelings about overall customer satisfactions, satisfaction for dimensions service quality, importance of dimensions of service quality, and switching intension of customers. In all, the questionnaire had four parts consisting of fifty-five (55) items; five (5) related to respondents’ identification data, four (4) for overall customer satisfaction with service delivery, thirty-seven (37) related to customer satisfaction with dimensions of SERVQUAL and eight (8) related to importance of SERVQUAL dimensions and one (1) related to switching intention of customers.

4.4 SELECTION OF RESPONDENTS

The location is based firstly on my experience of the area and secondly on the Wikipedia article (2006) on the history of Lagos State. Ikeja is the capital choice of residence for state parastatals, corporate bodies, top state officers, civil officers, businessmen and averagely rich people. It is the choice of residence for civil officers, business people, etc.

A total number of 100 people were interviewed for this study. This number is in accordance with the views of Dillman (2000) and Hill et al. (2003), who reported that a sample size of 100 and above is sufficient to present good concise research findings and also, provide good representation of the population or organization or any subject investigated. Selection is by convenience sampling (Non-probability sampling); interception of mobile users (questionnaires were handled out to every passerby and interested people waited to fill the forms) on streets in the central area of the chosen location on their way to work, lunch, school and shopping centers, etc. The points of data collection were changed within the chosen central location to minimize bias. 100 respondents were administered the questionnaires.

4.5 DATA ANALYSIS

After collecting all the data the process of analysis begins. To summarize and rearrange the data several interrelated procedure are performed during the data analysis stage (Zikmund, 2000).

For quantitative data analysis, statistical tools of Microsoft excel and SPSS are used for data input and analysis. The statistics results were presented by tabular form with detail description.

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4.6 VALIDITY AND RELIABITY

In order to reducing the possibility of getting the answer wrong, attention need to be paid to particular on research design: reliability and validity (Saunders et.al., 2003).

4.7 VALIDITY

Validity is concerned with whether the findings are really about what they appear to be about (Saunders et.al., 2003). Validity defined as the extent to which data collection method or methods accurately measure what they were intended to measure (Saunders et.al., 2003). Cooper & Schindler (2003) believe that validity refers to the extent to which a test measures what we actually wish to measure. There are two major forms: external and internal validity. The external validity of research findings refers to the data’s ability to be generalized across persons, settings, and times. Internal validity is the ability of a research instrument to measure what is purposed to measure. (Cooper & Schindler, 2003).

4.4.2 RELIABILITY

According to Saunders et al (2003), reliability refers to the degree to which data collection method or methods will yield consistent findings, similar observations would be made or conclusions reached by other researchers or there is transparency in how sense was made from the raw data. Cooper & Schindler (2003) have defined reliability as many things to many people, but in most contexts the notion of consistency emerges. Reliability is a necessary contributor to validity but is not a sufficient condition for validity. Numbers of different steps were taken to ensure the reliability of the study: the theories that have been selected for the study was clearly described and research question was formulated based on the previous theory. Data will be collected based on the frame of reference that was drawn from the discussed theories. The objective is to make sure that if another investigator will follow the same procedures and used the same questionnaires objects, the same conclusions would be made.

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CHAPTER FIVE

5.0 DATA ANALYSIS

This chapter consists of two parts: Data presentation and Discussion. Data presentation covers data on respondents’ characteristics, customer satisfaction measurement and satisfaction with service quality dimensions, relative importance of service quality dimensions and switching intention. The discussion is an analysis of hypotheses, results and findings to answer the research questions.

Since the characteristics of the respondents influence the results, here’s the presentation of descriptive data of respondents.

Frequency Percent Valid PercentCumulative

Percent

Valid Male 53 53.0 53.0 53.0

Female 47 47.0 47.0 100.0

Total 100 100.0 100.0

Table 5.1.1Respondent’s Gender

The respondents’ gender as displayed in Table 5.1.1 indicates that the males (53%) were slightly more than the females (45%). This further implies that there was a good representation of both genders in the sample.

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Frequency Percent Valid PercentCumulative

Percent

Valid Civil servant 15 15.0 15.0 15.0

Students 45 45.0 45.0 60.0

Businessman/woman

19 19.0 19.0 79.0

other 21 21.0 21.0 100.0

Total 100 100.0 100.0

Table 5.1.2 Respondents’ Occupation

Table 5.1.2 depicts respondents’ Occupation. Most of the respondents were students

representing 45% followed by others representing 21% while 19% and 15% were business

persons and civil servants respectively.

Frequency Percent Valid PercentCumulative

Percent

Valid Below 20 12 12.0 12.0 12.0

20-29 44 44.0 44.0 56.0

30-39 30 30.0 30.0 86.0

40-49 9 9.0 9.0 95.0

50 and above 5 5.0 5.0 100.0

Total 100 100.0 100.0

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Table 5.1.3 Age of respondents

Table 5.1.3 indicates the respondents’ age. It is obvious that most of them were in the young adult age and economically active group, between the ages of 20 and 39 consisting of 74% (44% and 30%), while the rest constitute 12% made up of respondents below 20, between 40 and 49, and 50 years plus.

Frequency Percent Valid PercentCumulative

Percent

Valid Below N100 4 4.0 4.0 4.0

N100-200 16 16.0 16.0 20.0

N 100-300 32 32.0 32.0 52.0

Above N300 48 48.0 48.0 100.0

Total 100 100.0 100.0

Table 5.1.4 Income Level of respondents

Table 5.1.4 displays the respondents’ income levels. It indicates that generally, a relative larger number of the respondents 48% were in the high income groups of which 48% earned above N300 per month and 32% were non-income earners, probably because they come from the student group. About one-third constituting 16% of the respondents were earning below N200 and N100 per month.

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Frequency Percent

Valid WASSCE 2 2.0

Post-Secondary 7 7.0

Diploma/HNDIploma 16 16.0

Bachelor's degree 39 39.0

Post-graduate Diploma/Masters

32 32.0

phD 4 4.0

Total 100 100

Total 100 100.0

Table 5.1.5 Respondents’ Education

Figure 5.1.5 depicts respondents’ level of education. They indicate the education levels were normally distributed. All the respondents were educated, with two-thirds constituting 55% of the respondents having Higher National Diploma (HND,(16%) and Bachelor’s degree (39%). The rest representing (45%) were in the extremes, consisting of those with at least education up to Post-Secondary level (9%) and those with post-graduate and doctoral education (36%). The statistics further indicate that most of the respondents (91%) had higher education while a relatively small number (9%) had at least high school education.

5.2 Measuring Customer Satisfaction with Service Quality

In measuring customer satisfaction with service quality, desire and expectation disconfirmations, and overall satisfaction were used. Categorically, customer satisfaction was measured irrespective of mobile network.

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5.2.1 Disconfirmation models

Disconfirmation models are models that state that customer satisfaction occurs when there is confirmation or disconfirmation as result of a customer comparing his/her service performance or perceived service quality with his/her expectations or desire or some cognitive standards (Parasuraman, et al., 1998, Gronroos 2000, 2001).

Within the Disconfirmation school, it has been empirically established that expectation disconfirmation should be used in addition to and not instead of desire disconfirmation in explaining or analysing customer satisfaction (Khalifa and Liu, 2002). So satisfaction will be measured using both desire and expectation disconfirmation scales. The indicators for these variables are shown in Table 5.2.1

DD DESIRE DISCONFIRMATION

How well did the services you received from your network compare with the ideal/desired services?

ED EXPECTATION DISCONFIRMATION

To what extent have your mobile network services met your expectations?

Table 5.2.1 Variables for Desire and Expectation Disconfirmation

5.2.2 Overall Satisfaction

Customer satisfaction can be measured using overall satisfaction measures. Overall satisfaction refers to the overall evaluation of the services quality delivered by an organisation. The indicators of this measure is one question that ask customers to rate their overall satisfaction of the service received. This will be measured using a single question (Table 3.4.2.2) to which respondents will be asked to rate their satisfaction on a five-point likert-scale: Very Dissatisfied, Neutral, Satisfied and Very satisfied.

OCS OVERALL CUSTOMER SATISFACTION

Overall, tell how satisfied you are with the service delivery of your network.

Table 5.2.2 Indicator for Measuring Overall Satisfaction

5.2.3 Indicator and Measurement of Switching Intention of Customers

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Customer switching intention is important to consider in analysing customer satisfaction as it is one of the outcome of customer satisfaction. In this study switching intention will be measured by using a single question (Table 3.4.3) to which respondents will be asked to rate their switching intention on a five-point liker-scale: Definitely yes, a bit yes, Neutral, a bit No and Definitely No.

SI SWITCHING INTENTION

Do you have any intention of switching to use a better network’s services?

Table 5.2.4 Indicator for Measuring Switching Intention

5.3 Results of disconfirmation measures and overall customer satisfaction measure

Customers were asked to rate their satisfaction with service quality using desire disconfirmation (DD), expectation Disconfirmation (ED) measures and overall Customer satisfaction (OCS) measures. The ED measure had a five-point likert scale: “much worse than expected”, “worse than expected”, “equal to expectation”, better than expected and “much worse than expected”. The scale for DD measure was also five-point likert scale: “very dissatisfied”, “dissatisfied”, “neutral”, “satisfied”, and “very satisfied” where: 1 representing the lowest and 5 representing the highest.

5.4 Description using overall satisfaction measure, customer satisfaction with service quality delivered by MTNs in Nigeria irrespective of which mobile telecom network customers subscribe to.

The following (Table 5.4.1) shows a descriptive statistics of three measures: ED, DD and OCS.

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Table 5.4.1Descriptive Statistics of Satisfaction Measures

Table 5.4.1 indicates that the mean rating of customer satisfaction using DD measure is 2.69 with standard deviation of 1.022 while ED measure, the mean is 2.33 with standard deviation of .842. Using OCS, the mean rating of customers was 3.03 with standard deviation of 1.185, being the highest.

H1: Service quality positively influences customer satisfaction in MTNs mobile telecom Network

N Minimum Maximum Mean Std. Deviation

Customer Satisfaction 100 1 5 2.69 1.022

Networks service Quality

100 1 5 3.03 1.185

Valid N (listwise) 100

Table 5.4.2 Descriptive Statistics of Service quality and customer satisfaction

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N Minimum Maximum Mean Std. Deviation

Sample Audience expectation of mobile network services (ED)

100 1 5 2.33 .842

Respondents desired services (DD)

100 1 5 2.69 1.022

Networks service delivery (OCS)

100 1 5 3.03 1.185

Table 5.4.2 above also indicates that service quality influence customer satisfaction in MTNs mobile telecom Network, indicating that when telecom companies provide good service quality customer satisfaction can be enhanced. Service quality positively influenced customer satisfaction. In other words, higher service quality can lead to higher customer satisfaction.

Hypothesis (H2): Service quality relates positively to mobile loyalty.

H3: Service quality positively influences perceived value in MTNs mobile network

Hypothesis (H4): Switching costs relate positively to mobile loyalty.

Model

Unstandardized Coefficients

Standardized Coefficients

B Std. Error Beta t Sig.

1 (Constant) 1.549 .328 4.715 .000

Switching Cost(H4) .195 .071 .247 2.758 .007

Networks service Quality (H3)

.104 .074 .126 1.406 .163

Respondents (value) (H2)

.397 .081 .440 4.920 .000

Table 5.4.3 Linear Regression with Loyalty as Dependent Variable

The results in the table 5.2.3 above showed that Value and switching costs related significantly and positively to loyalty, thereby supporting H2 and H4. Furthermore, value related to mobile loyalty more than service quality and switching costs did. However,

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Service Quality was insignificant and failed to support H3.Service and values tend to be strong when consumers view products as mainly functional tools (Ahtola, 1985; Batra and Ahtola, 1990; Voss, Spangenberg and Grohmann, 2003). In contrast, consumers who enjoy using products as hedonic toys tend to be less concerned with cognitive factors such as service quality and value.

5.5 Customer Satisfaction with Service Quality Dimensions

A detailed descriptive statistics of the results of customer rating of their satisfaction with four service quality dimensions can be found in Appendix C

5.5.1 Importance of SERVQUAL DimensionsCustomers were asked to rate the importance of service quality dimensions on a five-point likert scale: “Not at all important”, “Not important”, “Neither important nor unimportant”, “Important”, and “Very important” where: 1 representing the slowest and 5 representing the highest.

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A summary of descriptive statistics is presented in Table 5.5.1.

Dimensions N Minimum Maximum Mean Std. Deviation

Tangibles 100 1 5 3.35 1.132

Assurance 100 1 5 3.24 1.129

Responsiveness 100 1 5 3.47 4.167

Empathy 100 1 5 3.30 1.243

Reliability 100 1 5 3.45 1.077

Economy 100 1 5 3.38 1.204

Technical 100 1 5 3.45 1.114

Image 100 1 5 3.19 1.203

Valid 100

Table 5.5.1 Summary Descriptive Statistics of Important SERVQUAL Dimensions

As to which dimensions are perceived by customers as more important than others, Table 5.5.2shows ranking of service quality of service quality dimensions in order of customers’ priority.

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DIMENSIONS RANKINGS

(IN ASCENDING ORDER)

Reliable 1

Technical Quality 2

Responsiveness 3

Assurance 4

Economy 5

Image 6

Empathy 7

Tangibles 8

Table 5.5.2 prioritised Dimensions of SERQUAL in MTNs in Nigeria

Table 5.3.4 indicates that the most important service quality dimension to the customers is Reliability, followed by Technical quality, Responsiveness, Assurance, Economy, Image and Empathy being least important. “Tangibles” dimension, as earlier indicated in Table 5.3.3 is unimportant to the customers.

5.6 Switching intentions within and between mobile networks

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Customers were asked to rate their intention to switch to a better mobile network operator on a five-point likert scale: “definitely yes”, “a bit yes”, “neutral”, “a bit no” and “definitely yes”. These were coded 1 to 4 respectively. However, customers are not willing to switch from a specific mobile telecom network to another. A summary of results is presented in a cross tabulation and descriptive statistics for switching intentions in table 5.6.1

Count

Mobile Networks of Sample Audience

Zain MTN Globacom Etisalat Total

Respondents switching intentions

definitely yes 2 3 3 2 10

a bit yes 4 15 1 2 22

neutral 7 16 6 2 31

a bit no 2 10 5 2 19

definitely no 3 14 1 0 18

Total 18 58 16 8 100

Table 5.6.2 Cross tabulation of Switching among Mobile Networks in Nigeria

In Table 5.4.1 shows results of customer rating among the four Mobile Networks in this study. For ZAIN, 33.3% are willing to switch to another network while 38.9% and 27.8% are neutral and not willing to switch respectively. For MTN, 31.0% of the customers are willing to switch while 27.6% and 41.4% are neutral and not willing to switch respectively. For Globacom, 25% are willing to switch while 37.5% and 37.5% are neutral and not willing to

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switch respectively. For Etisalat, 50% are willing to switch, while 25% and 25% are neutral and not willing to switch respectively.

In all 32% of customers are willing to switch to another network while 31% and 37% are neutral and not willing to switch respectively in the Mobile Telecom market.

Chi-Square Tests

Value dfAsymp. Sig.

(2-sided)

Pearson Chi-Square 13.518a 12 .333

Likelihood Ratio 15.317 12 .225

Linear-by-Linear Association

.985 1 .321

N of Valid Cases 100

5.6.2b 14 cells (70.0%) have expected count less than 5. The minimum expected count is .80.

5.6.2c Symmetric Measures

Value Approx. Sig.

Nominal by Nominal

Phi .368 .333

Cramer's V .212 .333

N of Valid Cases 100

Table 5.6.2c indicate that a Chi square of .333 was obtained, however the 14 cells (70.0%) have expected count less than 5.Therefore, it can be concluded that there is no significant relationship between Switching intentions and mobile network sample audience.

5.7 Research Questions

5.7.1 Which dimensions of service quality are important to customers of MTNs in Nigeria?

In prioritising the SERVQUAL dimensions, the mean ranking of the dimensions (Table 5.5.2) indicated that Reliability is the most important dimension, followed by Technical quality, Responsiveness, Assurance, Economy, Image , while Empathy was the least important dimension.

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In relation to other research work, three of most important SERVQUAL dimensions: technical quality, empathy and reliability were found among the strongly rated SERVQUAL dimensions in Iran Mobile Telecom Market (IMTM) in the work of Satari S. (2007), but technical quality and reliability were found to be more important than the others in Iran Aseman Airline (IAA) by Borzorgi M.M (2007). However, “tangibles” which Borzorgi (2007) found to be more important in IAA was not important at all in MTNs in Nigeria and received lower rating in Iran’s Mobile Telecom Market. Again, while assurance responsiveness dimensions that were found less important in Nigeria’s MTNs received similar ratings in IAA and IMTN respectively. The trends in these comparative findings are consistent with the conclusion of Chowdhary N. and Prakash M., (2007, p. 506) that “no simple generalization of relative importance of determinants of service quality is possible that importance of determinants of quality for customers would vary across different service types”.

5.7.2 What is the switching intention among customers of MTNs in Nigeria?

Switching intention (SI) statistics (Table 5.4.1) reveal that 6% of Zain customers are willing to switch to another network, followed by 18% of MTNs customers who are willing to switch, and 4% and another 4% of Globacom and Etisalat who are willing to switch. Thus, a considerable number of Zain customers are not willing to switch. This is supported by the fact a smaller proportion of the respondents of Zain are not willing to switch.

Conversely, the rating of switching intention for MTNs, Globacom and Etisalat shows that 16%, 6%, and 2% respectively are not willing to switch. These findings are supported by the fact that generally their outcomes are neutral and not willing to switch (5%, 24%, 6%, 2%, 31%, 37% respectively) as have been analysed.

Therefore, it can be said that switching intentions is not equal among the mobile telecom networks in Nigeria.

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CHAPTER SIX

6.0 SUMMARY, CONCLUSION AND IMPLICATIONS

This concluding chapter summarises the purpose and objectives of the study, the major findings and conclusions, discusses the implications for marketing, and makes recommendation for further research.

6.1 Summary of Findings and Conclusions

In this study the purpose was to measure customer satisfaction with service quality delivered by Nigeria’s Mobile Telecom Networks irrespective of mobile telecom network using expectation disconfirmation measures and overall satisfaction measures. The study examined customer satisfaction with service quality dimensions, prioritised SERVQUAL dimensions and switching intentions among customers of MTNs in Nigeria.

Out of the 100 sample population, 100 responded to the questionnaire administered. Based on the objectives of data analysis and discussion of results and findings, the following are the summary of major findings and conclusions of this study:

Irrespective of mobile telecom network in Nigeria, the measures pointed that customer satisfaction is low and not equal to or better than desired or expectation, so the customers are not satisfied with service quality delivered by MTNs in Nigeria.

Overall customer satisfaction is significantly different among Mobile Telecom Networks in Nigeria.

According to customer priority, Reliability is the most important dimension, followed by Technical quality, Responsibility, Assurance, Economy, Image, while Empathy is the least important dimension. “Tangibles” is significantly unimportant to the customers.

Switching cost of customers is different among the mobile telecom networks in Nigeria, but the customers of MTNs are significantly more willing to switch than those Zain, Globacom and Etisalat.

6.2 Implications of the Findings

6.2.1 To Industry Regulators and Policy Makers

It has been found in this study that generally customer satisfaction with service quality is low or less than expected and desired in the Nigeria MTNs. This imply that policy makers and industry regulators such as Nigerian Communications Commission Authority in Nigeria, need to be awakened to this empirical fact and take pragmatic steps to ensure that mobile telecom network operators in Nigeria improve their efficiency and effectiveness in the provision of telecommunication services that meet and exceed customer need, desire and expectation.

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This can be done by sensitising and encouraging the various mobile network companies to focus more attention and resources on more important service quality dimensions for which customers are not satisfied and to focus little attention on unimportant and less important dimensions.

In this regard, first of all, efforts and resources should be focused on improving Reliability, Technical quality, empathy and economy of the service quality delivered. Within these SERVQUAL dimensions, more management efforts and intensive strategy must be geared towards improving upon important dimensions.

Moreover, the service quality should be improved by making the services more economical so that customers can afford and have better value for their money or sacrifices made for using the mobile network services. By pursuing this, the service quality and therefore customer satisfaction would be improved in the “economy” dimension.

Also in “responsiveness” service quality, management should develop strategies to improve upon the “ability to tell customers exactly when services will be performed”, “ability to prompt customer services” and to attend to customers’’ need/problems”, “employees willingness to help customers in emergency situations” and “easy contact and approachability of employees”.

Furthermore, some attention and effort should be given to sustain the assurance dimension especially employees’ use of required skills and knowledge in answering customers questions.

Finally, in Nigeria’s MTNs, the regulators should encourage marketers through marketing seminars and workshops to seek meeting and exceeding not only the expectations of customers but also customer desired set of service quality and experiences. Thus, satisfaction may be more significantly affected by the degree to which their expectations, rather than their desires, are met (Khalifa, Liu, 2003). Conversely, the effect of expectation disconfirmation on satisfaction may be considerably weakened when confidence in expectations is low or minimal. In other words, confidence in expectations may moderate the relationships between expectation/desire disconfirmation and satisfaction.

6.2.2 To the Mobile Network Company

Specifically, the findings of this study imply that the management of MTNs must seriously take knowledge of their customer dissatisfaction with their service quality and work harder to develop effective strategies to improve the situation, work towards exceeding the expectation and desired service quality of their customers and consider its customer switching intention since the study indicates that switching intention is significantly different among the MTNs in Nigeria and especially that, the customers of the company are willing to switch to use better network services from other competitor mobile telecom in Nigeria.

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6.3 Final Conclusion

The final conclusion of this study is that generally customers are not satisfied with service quality delivered by mobile telecom networks in Nigeria or that their satisfaction is considerably low.

6.4 Recommendations for Further Research

This study mainly assessed and analysed customer satisfaction with service quality in Nigeria’s MTNs. It is recommended that future research should:

Examine customer satisfaction with specific service areas delivered across mobile telecom networks such as the delivery of MMS, SMS, Internet Services, customised services, customer services, e.t.c.

Examine customer satisfaction with fixed lines or prepaid telecom services.

Develop and verify a model of customer satisfaction for Nigeria’s telecom Industry or verify disconfirmation theories in other different industry settings.

6.5 Limitations of the study

The study was a questionnaire-based survey and used a quantitative approach. Its therefore, recommended that different models and methodology should be used for a similar study and compare the results.

In addition, another major limitation of the study is that a relatively smaller sample of the target population was used and limited to literates.

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APPENDICE A

QUESTIONNAIRE

Dear mobile network subscriber, this questionnaire is designed to collect information about how you feel about the service delivery of your mobile network in Nigeria. (MTN), at least for the last 12 months. Your responses will be treated confidential and used for only academic purpose. I am a student of London Metropolitan University, London.

SECTION A

CUSTOMER SATISFACTION WITH SERVICE DELIVERY

1. Which mobile network(s) do you use? Tick all the networks you use.( ) Zain ( ) MTN ( ) Globacom ( ) Etisalat

2. Which mobile telecom network services do you use most often?( ) MTN ( ) Zain ( ) Globacom ( ) Etisalat

Use the responses from 1-5 to answer, where: 1 representing the lowest and 5 representing the highest

3. To what extent has your mobile network services met your expectations?Very satisfied Satisfied Dissatisfied Very dissatisfied No opinion 1 2 3 4 5

4. How well was the service you received from your network compared to the desired services?Very Satisfied Satisfied Dissatisfied Very dissatisfied No opinion 1 2 3 4 5

5. Do you have the intention of switching to a better network?( ) Definitely yes ( ) a bit yes ( ) Neutral ( ) a bit No ( ) Definitely No SECTION BCUSTOMER SATISFACTION WITH SERVICE QUALITY DIMENSIONSIn your opinion, how does the service quality of your mobile network meet your expectations in terms of the following dimensions? Use the responses from 1-5 to answer, where:1- Much worse than expected 2- Worse than expected 3- Equal to expectation 4-Better than expected 5- Much better than expected

DIMENSIONS Circle only one option in 1-5

How tangible is the use of your mobile telecom network’s services in terms of

TA1 Your network’s ability to give you access to information, SIM card (chip), reload cards

1 2 3 4 5

TA2 Provision of visually attractive, offices, equipment and materials like starter packs and reloads cards

1 2 3 4 5

TA3 Network’s ability to providing variety of entertainment facilities, e.t.c

1 2 3 4 5

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TA4 Appearance and uniforms of employees of your network . 1 2 3 4 5

How reliable is the use of your mobile telecom network’s services in terms of

RL1 How timely is the delivery of SMS, MMS, Voice message and other services of your network

1 2 3 4 5

RL2 How truthful (keeping to promises) is your mobile network to you? 1 2 3 4 5

RL3 How dependable and consistent is your network in network in solving customers’ complaints?

1 2 3 4 5

RL4 How able is your network to perform services right the first time? 1 2 3 4 5

RL5 How able is your network to insist on error-free records 1 2 3 4 5

How responsive is the use of your mobile telecom network’s services in terms of:

RS1 How is your network able to tell customers exactly when services will be performed?

1 2 3 4 5

RS2 How able is your network to give prompt customer services and attend to customers’ needs/problems?

1 2 3 4 5

RS3 How are employees’ willing to help customers in emergency situations?

1 2 3 4 5

RS4 How are the employees approachable and easy to contact? 1 2 3 4 5

RS5 Employees’ ability to communicate clearly with you 1 2 3 4 5

How epithetical is the use of your mobile telecom network’s services in terms of

EM1 Having convenient periods & terms for activation, recharge, and accounts suspension, free call times

1 2 3 4 5

EM2 Having operating hours convenient to all customers 1 2 3 4 5

EM3 Having sound loyalty programme to recognise you as a frequent customer

1 2 3 4 5

EM4 Having the customers’ best interest at heart 1 2 3 4 5

EM5 Giving individual customer attention by employees 1 2 3 4 5

EM6 Efforts to understand specific customer needs 1 2 3 4 5

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EM7 Apologising for inconvenience caused to customers 1 2 3 4 5

How assurance is the use of your mobile telecom network’s services in terms of

AS1 Ability to provide variety of value added services-Music, access to internet, SMS, MMS, e.t.c

1 2 3 4 5

AS2 Sincerity and patience in resolving customers’ complaints/problems

1 2 3 4 5

AS3 The behaviour of employees in instilling confidence in customers 1 2 3 4 5

AS4 Employees’ use of required skills and knowledge to answer customers’ questions.

1 2 3 4 5

How economical is the use of your mobile telecom network’s services in terms of

EC1 Reloading card and their denominations? 1 2 3 4 5

EC2 The call charge per minute/second? 1 2 3 4 5

How technical is the use of your mobile telecom network’s services in terms of

TQ1 Successful in completion of calls, SMS, MMS, line activation, credit reloading, etc.

1 2 3 4 5

TQ2 Employees have technological knowledge and skills in solving customer problems

1 2 3 4 5

TQ3 Network clarity and speed for call and other services 1 2 3 4 5

TQ4 Network innovativeness- ability to use current technology to improve services

1 2 3 4 5

TQ5 Providing adequate network coverage 1 2 3 4 5

How imagery is the use of your mobile telecom network’s services in terms of

IM1 How successful is your mobile network company? 1 2 3 4 5

IM2 What is the reputation of your mobile network company? 1 2 3 4 5

IM3 What is the brand image of your mobile network? 1 2 3 4 5

IM4 How socially responsible is your mobile network? 1 2 3 4 5

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IMPORTANCE OF DIMENSIONS OF SERVICE QUALITYIn receiving or using services of your network, how important is each of the following dimensions to you? Use scale 1-5 to answer, where: 1- Not at important 2- Not important 3- Neither important nor Unimportant 4- Important 5- Very Important

DIMENSIONS Circle only one option:1-5

(The appealing nature of physical environment, reload cards etc) 1 2 3 4 5

(Assurance of sincerity, efficiency and variety of services) 1 2 3 4 5

(Attending to customer needs and complaints promptly any time) 1 2 3 4 5

(Showing of respect, care and understanding to customers’ needs) 1 2 3 4 5

(Competence to give timely, reliable services and truthful to promises 1 2 3 4 5

(Giving customer value for services received 1 2 3 4 5

(Having good network clarity & coverage for call completion/services) 1 2 3 4 5

( having a good reputation of company and brand name) 1 2 3 4 5

6. Overall, please tell how satisfied or dissatisfied you are with the service delivery of your network by circling one of these that best describes your feelings and perceptions.

Very Dissatisfied Dissatisfied Neither Satisfied Very Satisfied

Please tick ( ) the appropriate box for your answers.

SECTION C

7. Gender ( ) male ( ) female8. Age:

( ) below 20 ( ) 20-29 ( ) 30-39 ( ) 40-49 ( ) 50 and above9. Occupation

( ) civil servant ( ) student ( ) businessman/woman ( ) other10. Income:

( ) Below N 100 ( ) N 100-200 ( ) N 100-300 ( ) above N 30011. Educational Level:

( ) WASSCE ( ) Post-Secondary ( ) Diploma/HNDiploma ( ) Bachelor’s degree( ) Post-graduate Diploma/ Masters ( ) PhD

Thank you for taking time to complete this questionnaire!

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APPENDIX B

FREQUENCIES OF RATING FOR DISCONFIRMATION MEASURES AND OVERALL SATISFACTION MEASURES IRRESPECTIVE OF MOBILE TELECOM NETWORK

Desire Disconfirmation

Frequency Percent Valid PercentCumulative

Percent

Valid Much than desired

5 5.0 5.0 5.0

Worse than desired

52 52.0 52.0 57.0

Equal to desire 17 17.0 17.0 74.0

Better than desired

21 21.0 21.0 95.0

Much better than desired

5 5.0 5.0 100.0

Total 100 100.0 100.0

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Expectation Disconfirmation

Frequency Percent Valid PercentCumulative

Percent

Valid Much worse than expected

6 6.0 6.0 6.0

Worse than expected

70 70.0 70.0 76.0

Equal to expectation

12 12.0 12.0 88.0

Better than expected

9 9.0 9.0 97.0

Much better than expected

3 3.0 3.0 100.0

Total 100 100.0 100.0

Overall Customer Satisfaction

Frequency Percent Valid PercentCumulative

Percent

Valid Very dissatisfied 13 13.0 13.0 13.0

Dissatisfied 21 21.0 21.0 34.0

Neither 24 24.0 24.0 58.0

Stisfied 34 34.0 34.0 92.0

Very Satisfied 8 8.0 8.0 100.0

Total 100 100.0 100.0

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APPENDIX C

DESCRIPTIVE STATISTICS OF SATIFACTION RATING FOR EACH DIMENSION OF SERVICE QUALITY

N Minimum Maximum Mean Std. Deviation

TA1 100 1 5 2.89 .909

TA2 100 1 5 3.03 .958

TA3 100 1 5 3.02 1.044

TA4 100 1 5 3.28 1.092

RL1 100 1 5 3.05 1.029

RL2 100 1 5 3.02 .985

RL3 100 1 5 2.97 .948

RL4 100 1 5 2.91 .996

RL5 100 1 5 3.01 .980

RS1 100 1 5 3.00 1.005

RS2 100 1 5 2.96 1.072

RS3 100 1 5 2.75 1.048

RS4 100 1 6 2.85 1.149

RS5 100 1 5 2.99 1.020

EM1 99 1 5 3.14 1.050

EM2 100 1 5 2.89 .973

EM3 100 1 5 2.96 .974

EM4 100 1 5 2.81 1.080

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EM5 100 1 5 2.98 1.025

EM6 100 1 5 2.83 1.138

EM7 100 1 5 2.88 1.174

AS1 100 1 5 2.95 1.192

AS2 100 1 5 2.98 .964

AS3 100 1 5 3.07 1.085

AS4 99 1 5 3.07 1.136

EC1 100 1 5 3.08 1.079

EC2 100 1 6 3.12 1.122

TQ1 100 1 5 2.95 .968

TQ2 100 1 5 2.88 .956

TQ3 100 1 5 3.15 .968

TQ4 100 1 5 2.94 1.013

TQ5 100 1 5 2.97 1.020

IM1 100 1 5 3.01 1.087

IM2 100 1 5 3.06 1.127

IM3 100 1 5 3.19 1.116

IM4 100 1 5 3.19 1.107

TANGIBLE 100 1 5 3.35 1.132

ASSURANCE 100 1 5 3.24 1.129

RESPONSIVENESS 100 1 5 3.47 1.167

EMPATHY 100 1 5 3.30 1.243

RELIABILITY 100 1 5 3.45 1.077

ECONOMY 100 1 5 3.38 1.204

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TECHNICAL 100 1 5 3.45 1.114

IMAGE 100 1 5 3.19 1.203

Valid N (listwise) 100

APPENDIX D

REGRESSION ANALYSIS INVOLVING SWITCHING VALUE LOAYLTY AND OVERALL CUSTOMER SATISFACTION

Model Summary

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

1 .528a .279 .256 .840

a. Predictors: (Constant), Having Respondents best interest at heart, Respondents switching intentions, Networks service delivery

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ANOVAb

ModelSum of Squares df Mean Square F Sig.

1 Regression 26.156 3 8.719 12.366 .000a

Residual 67.684 96 .705

Total 93.840 99

a. Predictors: (Constant), Having Respondents best interest at heart, Respondents switching intentions, Networks service delivery

b. Dependent Variable: Sound loyalty programmes to recognise frequent Respondents

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Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

B Std. Error Beta t Sig.

1 (Constant) 1.549 .328 4.715 .000

Respondents switching intentions (H4)

.195 .071 .247 2.758 .007

Networks service delivery (H3)

.104 .074 .126 1.406 .163

Having Respondents best interest at heart(H2)

.397 .081 .440 4.920 .000

Table Linear Regression with Loyalty as Dependent Variable

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