Customers’ Views on Service Dimensions

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Executive Summary

Two important factors for understanding how to improve the success of service-oriented businesses are customer satisfaction and service quality. This analogy is true for telecommunication firms because their success depends on their service quality. Therefore, to quantify their success, they need to measure their service quality. However, service is intangible and difficult to measure. To solve this problem, this paper shows that the best way to measure service quality is to measure customer perspectives. Relative to this goal, this paper analyses the theoretical aspects of service quality by explaining the concept and evaluating different tools for measuring it. The theoretical background of this paper evaluates technical service models and establishes that many telecommunication firms use the SERVQUAL model to assess their service quality. However, few research studies have investigated whether this model also applies to the Saudi telecommunication industry. To bridge this research gap, this paper explores how SERVQUAL’s service gaps can measure service quality in the Saudi Arabian telecommunications industry. The research questions evaluate the top variables that reflect customer satisfaction and investigate the effects of quality on customer satisfaction. The study obtained data using self-completion questionnaires (based on the SERVQUAL model). The SPSS technique analysed the data and revealed that the SERVQUAL model was an unreliable measure of service quality in the Saudi Arabian telecommunications industry. Moreover, it revealed the failure of the SERVQUAL model to come up with reliable measures of service quality because some of its service items associated with different service dimensions.

This section of the dissertation introduces the research topic and helps the readers to understand the research focus. This chapter also explains the motivation for undertaking this study and its importance to academicians and service-oriented businesses. Furthermore, this chapter familiarises the readers with the empirical context of the study. It explains why it is important to measure service quality and how companies can benefit from doing so. This way, readers can understand the research gap and that informs this study. Similarly, by understanding the purpose and research questions outlined in this chapter, they can comprehend the roadmap used to fill it.

Introduction

Service quality is an important aspect of business operations. Indeed, managers, practitioners and researchers agree that it affects different aspects of a business’s performance, including a company’s operational efficiency and customer loyalty (Chowdhary & Prakash 2007). Intense global competition and the fast-paced nature of the global economy have created the need for companies to understand customer perceptions about their services (Carpenter & Moore 2006). This fact shows the importance of service quality to the survival and growth of modern businesses. Moreover, it highlights the need for providing quality services to customers, if businesses want to maintain a competitive advantage (Chowdhary & Prakash 2007). Based on this fact, it is pertinent for service-oriented organisations to understand how improved service quality could help them meet their organisational goals.

Chowdhary & Prakash (2007) say that using advanced technology and adopting the best service quality measures (for monitoring customer perceptions and expectations) is the best way for organisations to gain a competitive edge in the cutthroat global business environment. Although customer perceptions and expectations share a close relationship with customer satisfaction, the latter is an important indicator of positive organisational performance because it affects different aspects of a company’s operations, including profitability and business management acumen (among others). Therefore, researchers and managers often strive to learn new techniques for improving their service models to improve organisational competencies and profitability. This realisation prompted many researchers to affirm the need for companies to understand service quality indicators as tools for improving customer satisfaction. For example, Carpenter & Moore (2006) say this realisation comes from decades of focusing on product improvement strategies, at the expense of service improvement strategies. Based on this analysis, companies need to understand that service marketing is as important as product marketing.

While it is interesting to study how service quality affects different aspects of a company’s operations, it is more interesting to study service quality perceptions in the telecommunications industry because, today, this industry contributes to the gross domestic products of many countries around the world. Secondly, the telecommunications industry oils the wheels of today’s modern economy. Moreover, the service challenges that characterise the telecommunications industry have created the need to understand how these challenges would affect other global economic aspects that depend on the telecommunications industry.

Based on the above dynamics, this paper focuses on studying service quality indicators in the Saudi Arabian telecommunications industry, which is the largest telecommunications industry in the Gulf region (Delta Partners Group 2014). Experts say the industry is worth about $11.6 billion (2010 estimates) (Delta Partners Group 2014). They also project that the industry will grow by about $1billion annually. Comparatively, this figure sums the growth projections of all other sub-sectors of the GCC telecommunications industry. Today, the Kingdom of Saudi Arabia (KSA) telecommunication sector accounts for a significant part of the country’s economy. Specifically, its contribution has grown in the past few years, after the government introduced trade liberalisation laws in 2005 (Delta Partners Group 2014). Since then, it has evolved and now boasts of offering high-tech telecommunication services, including a well-established voice and data services. Six pillars of growth (a youthful customer base, a growing demand for internet and broadband services, innovative telecom applications and services, virtual banking, integral lifestyle choices, and a strong need for sophisticated information, communication and technology services) are at the centre of the industry’s success (Delta Partners Group 2014). Based on this understanding, the findings of this study can greatly improve how telecommunication companies model their service provision models to exploit these industry dynamics. The main benefit of this analysis is its ability to provide directions for service improvements in the telecommunication industry.

Purpose of Study

The purpose of this paper is to understand the main service dimensions for assessing service quality in the Saudi telecommunication industry. To do so, this paper measures the perceived service quality of telecommunication services and the performance of the various service quality dimensions (of the SERVQUAL model) in the Saudi telecommunications industry. Here, it is important to understand that the research questions also seek to understand the main service measurement tool for assessing service quality in this industry, evaluate the top variables that reflect customer satisfaction, and investigate the effect of quality on customer satisfaction. In line with these questions, the study evaluates the SERVQUAL model by investigating how it measures service quality in the telecommunications sector (as a theoretical contribution to comprehending how the SERVQUAL model works). Similarly, this paper also strives to understand the customer perceptions of service quality that apply to consumers in the Saudi telecommunications industry. The research objective of this paper appears below

Research Objectives

Main Objective

What are the Most Significant Service Quality Dimensions that have the Strongest Impact on Customer Satisfaction?

Research Questions

• What is the most Adopted measurement Scale to Assess Service Quality Dimensions?
• What Top Variables Reflect customer satisfaction?
• What is the Effect of Quality on Customer Satisfaction?

Importance of Study

Nejati (2011) says it is important to measure service gaps because it improves different aspects of an organisation’s performance. For example, researchers have affirmed its use in organisational planning and decision-making (Nejati 2011). Others say service quality information is beneficial in resource allocation and priority setting (Nejati 2011). Many scholars say abundant volumes of literature affirm service quality gaps in different service dimensions (Wicks & Roethlein 2009). Most of their studies are consistent with one another. However, there may be some slight variations regarding their consistency measurements across different service dimensions. For example, service quality dimensions that have the highest service gaps may show different outcomes from those that have lower quality gaps (Nejati 2011). Researchers have voiced several reasons for these varying outcomes.

However, social and cultural differences, family backgrounds and other demographic characteristics may explain many of these differences (Nejati 2011). The main message in this analysis is the influence of social factors on customer perceptions and expectations. Based on this analogy, Wicks & Roethlein (2009) cautions researchers against generalising their service quality findings across different contexts. Similarly, based on the same principle, some researchers prefer independent and contextualised studies to measure service quality dimensions. This study borrows from the same recoemmendation to investigate the main service dimensions in KSA.

Indeed, since defining the relationship between service quality and organisational performance is an important area of research in marketing literature, this paper’s findings are useful in improving the quality of services offered by telecommunication companies. Similarly, such companies could identify the most important quality enhancement measures for improving their bottom-line financial performance. Douglas & Connor (2003) define this benefit as the “return on quality.” Furthermore, based on the highly competitive nature of the Saudi telecommunication industry, telecommunication firms are seeking new and profitable ways of differentiating themselves, mainly through service improvements.

This is why there is a strong need to understand service quality measures in this industry because it is service-oriented and customers are highly sensitive to the kind of services these companies offer. This information would be useful to close the service quality gaps that may be impeding efforts to improve the performance of telecommunication companies. Furthermore, information obtained from this paper will help such companies identify the service quality gaps they should focus on. Such information would also be useful in identifying the strengths and weaknesses of the service quality model used by many firms in the Saudi telecommunications industry. Overall, this paper strives to understand the expectations and perceptions of service quality for Saudi telecommunication companies to improve customer satisfaction.

Delimitation of Study

To understand the gist of this dissertation, it is important to define our research scope and clarify the context of this paper. This paper focuses on the Saudi Arabian telecommunication industry by understanding how customers perceive the quality of services offered by firms that operate in this industry. Although, the size and market share of telecommunication firms matter to customers, and the types of services offered in the industry, this paper assumes that the telecommunication firms operating in the Saudi Arabian telecommunication industry offer the same retail assistance services to their customers. Therefore, this study is limited to the telecommunications industry in Saudi Arabia because its sample population mainly comes from this market. Moreover, the study focuses on sampling the views of customers who have shared experiences with Saudi telecommunication firms. Therefore, in the context of this study, the word “customers” refers to people who have used the services of Saudi telecommunication companies.

Research Interest

I have been a marketer in the telecommunications industry for more than a decade now. My experience in this industry created an interest in this research field. Since marketers seldom use the SERVQUAL assessment tool in this industry, I became curious about the best tool for measuring service quality in the telecommunications sector. Moreover, customer satisfaction is very important in the service sector because many technology-based firms are service-oriented. Moreover, since service quality links with many intangible products in the telecommunications industry, it is important to identify an assessment tool that could effectively measure these intangible factors.

Structure of the Study

This dissertation contains six chapters. The first chapter is the introduction chapter, which explains the direction of the dissertation and the reasons for conducting the study. The second chapter is the literature review section, which evaluates past research findings about the research topic. The methodology chapter is the third chapter. It explains how I conducted the study. Its findings appear in the findings and analysis chapter. The last two chapters are the discussion and conclusion chapters. They discuss and summarise the research findings respectively.

Literature Review

This section of the paper reviews previous literatures regarding the research topic. It involves understanding the theoretical underpinning of the research topic, among other quality and customer satisfaction issues that are important to the study focus. Furthermore, this section of the paper defines important concepts of study that may help or impede our understanding of the research questions. Overall, this theoretical framework provides the conceptual framework for undertaking the empirical investigations that appear in later sections of this dissertation.

The Service Concept

Eshghi, Roy & Ganguli (2008) say that service is a difficult concept to understand because management literatures convey multiple meanings. For example, some people see it as a description of how organisations deliver their services, while others see it as the by-product of service-oriented businesses. Regardless of these definitions, Einarsson (2008) believes that a service is an intangible output that associates with a similarly intangible activity, as opposed to an intangible product. However, critics have said this definition is still ambiguous, because some intangible services have some tangible attributes, such as equipments and employees (Garcıa & Caro 2010). Fen & Meillian (2005) say this criticism is important in understanding service-oriented businesses because they must consider the physical attributes of quality if they want to provide quality services. Nonetheless, Garcıa & Caro (2010) believe people should convey the service concept as a pigment of a customer’s perception regarding the output “service.” In this regard, they say that since customers are part of the service delivery process, they often prefer to receive quality service outputs (Garcıa & Caro 2010).

According to Giese & Cote (2002), the service concept has only two main perceptions – the customer and the service provider. The latter perception views service as a process that contains many elements, including core delivery, service operations, and service-attentiveness (among other factors) (Giese & Cote 2002). Comparatively, customers view service delivery as a phenomenon that includes their personal experiences. In their view, they see service delivery as a system that consists of core needs, customer choices, and emotional content. To them, these factors affect different service outputs, thereby creating different customer experiences.

Why is it Difficult to Measure Service?

Beamish & Ashford (2008) say the main problem with measuring service quality in the service industry is the intangibility of the services offered. Indeed, unlike products, a researcher can use no physical attribute to measure customer perspectives and expectations of service quality (in service-oriented industries). This way, it is similarly difficult for customers to understand what they receive from these companies. Indeed, as Johns (1999) says, consumers cannot touch, smell, or hear anything as they consume the “products” of service-oriented companies. This makes it difficult for them to understand the nature of output that such companies offer. This analogy applies to telecommunication companies because customers mainly make calls and do not receive other physical products from these companies. Although subsequent sections of this paper outline “product” as a service quality dimension for the telecommunication companies sampled in this study, “service” is the main output of such firms.

To understand the difficulty of measuring services in the telecommunications industry, it is similarly important to use the above analogy to understand the challenges researchers encounter when measuring intangible services in grocery stores. Such companies focus on offering physical products. Therefore, instead of focusing on understanding the intangibility of their services, Beamish & Ashford (2008) say that most researchers are better off including tangible elements in their service quality analyses. Another complication that arises when measuring service quality is the lack of heterogeneity in customer service provision (Johns 1999). This concept stems from the failure of human beings to offer the same level of services across different customer populations. This challenge mainly arises in the service delivery process. For example, it is difficult for one shopkeeper to deliver one service quality to multiple clients, in the same manner. Furthermore, most of these clients have different behaviours and are likely to perceive the same service differently.

Service “perishability” also creates a challenge to researchers who want to measure service quality because unlike products, if a customer does not use a service, when a service provider offers it, he cannot use it again. Although some researchers dispute the “perishability” of services in this regard (by saying that the concept does not suffice in some service industries), it is almost impossible for a client to store a service for a later date (Beamish & Ashford 2008). Inseparability is another challenge that emerges when measuring service quality because it is similarly almost impossible to separate the purchase and consumption processes. Mainly, this attribute applies to the telecommunication sector because customers often consume a service when they pay for it. By extension, this attribute means that the clients are involved in the service production and delivery processes.

Quality Concept

Similar to service delivery, Ghylin, et al. (2006) say “quality” has many types of definitions. The lack of a common definition stems from the different perspectives and orientations that define quality. In the context of this study, we must define quality within the precincts of the telecommunications industry. This way, the concept should focus on multiple dimensions of product and services in the sector. However, it is pertinent to understand that the manufacturing and product industries have different definitions of quality. Academicians and practitioners fuel this divide. Nonetheless, the disjointed understanding of quality stems from the intangibility of the concept. This attribute makes it difficult to identify one measure for assessing it. Huddleston et al. (2008) say quality is an attribute of an entity. They also say it defines people’s characters, product dynamics, or their degrees of excellence (Huddleston et al. 2008). Moreover, since, it is a type of social status; people need to understand it through a ranking metric. Based on this understanding, Haider (2001) says, “Quality is product performance, which results in customer satisfaction, freedom from product deficiencies, which avoids customer dissatisfaction” (p. 5).

Service Quality

Many researchers agree on the importance of service quality in differentiating service-oriented businesses. This section of the paper acknowledges this fact and emphasises the need for improving service quality as a prerequisite of customer satisfaction improvements. Academicians and Practitioners have created a lot of interest about service quality in this regard (Zahorik & Keiningham 1995; Ghylin, et al. 2006). Many marketing scholars define service quality as the perceived quality of goods and services offered by different companies (Zahorik & Keiningham 1995; Ghylin, et al. 2006). Similarly, others define it as the quantity of effort that a company makes to meet its customer service expectations (Chowdhary & Prakash 2007). Using this logic, Parasuraman, Zeithaml and Berry (1985a) define service quality as “The discrepancy between consumers’ perceptions of services offered by a particular firm and their expectations about firms offering such services” (p. 44). Customer expectation is central to this study because if a company exceeds the expectations of the customers, it means they have a high service quality. Similarly, if they do not meet these expectations, they have a low service quality.

Many factors affect a customer’s service quality expectations. Brady & Cronin (2001) say personal experiences and word-of-mouth communications are the main influences of customer perceptions. Nonetheless, Johns (1999) defines it as a customer’s overall assessment of the service. Moreover, he says there is a need for companies to understand this definition because it is the only way they would understand how to improve their services to meet their customers’ expectations (Johns 1999). Nonetheless, understanding the concept requires people to understand that service quality involves many dynamics of service quality, including intangibility and heterogeneity (among other factors) (Brady & Cronin 2001). By understanding how these factors contribute to service quality, it would be easy to measure the concept.

In the context of this study, service quality refers to the difference between customer expectations and service performance. However, this difference applies to a time when both a service provider and a customer have not met. Here, customer expectations refer to a customer’s desire to seek quality products or services. Stated differently, customer expectations refer to what a service provider “should offer,” as opposed to what they “would offer.” The perceived service is both technical and functional, but, overall, it refers to how a customer views a service.

Service Quality Models

This chapter shows no significant definition regarding what service quality entails. However, this paper adopts the definition of Kumar, Kee & Manshor (2009) of the concept, which defines it as the difference between the expectations and perceptions of service. Nonetheless, it is pertinent to understand that measuring service quality is a common management topic. This interest stems from the need to establish reliable metrics for measuring customer views about a company’s performance. Similarly, this interest stems from the need for associating service quality with key organisational outcomes. These factors have created the need to develop effective service quality models. This paper explains them below

Technical and Service Quality Models

Technical and service quality models demand that all organisations should understand the perceptions that their customers have about their products and services (Gilmore 2003). This demand is a key factor in understanding the models because they perceive customer perception as a benchmark of the service standards that an organisation should meet. Therefore, if organisations match this standard, they are likely to have satisfied customers. Rust, Zahorik & Keiningham (1995) say technical and service quality models have only three pillars that support service quality – technical quality, functional quality, and image. The diagram below shows how these three pillars interrelate to create a service quality model.

The technical quality refers to what customers get from their company (quality of goods or services). Quality enables customers to evaluate the service quality standards that a company offers. The functional quality closely relates to the technical quality because it describes how customers get their technical outcomes. Furthermore, it has the same effect as the technical quality on customer perceptions. Image is a product of the last two types of service provisions highlighted above. Ladhari (2009) believes that it is the most important pillar of the service quality model. However, it is not only vulnerable to technical and service quality factors alone; traditions, company cultures, social ideologies (among other factors) affect it.

SERVQUAL Model

Three American scholars, Parasuraman, Zeithaml and Berry (1988) developed the SERVQUAL Model to understand the correct metrics for evaluating an organisation’s service quality. At its inception, the scholars identified ten metrics for measuring service quality. However, they later revised and reduced them to five dimensions – “reliability, responsiveness, empathy, tangibles and assurances” (Buttle 1996, p. 34). When developing these dimensions, their main concern was the metrics’ ability to capture access to quality services and understand customer characteristics. Based on this need, the researchers made several other modifications to the model. For example, they replaced the word, “should” with “would” when referring to how such service dimensions denote service quality. They also reduced the total number of items underscoring the model to 21 (Parasuraman, Zeithaml and Berry 1988). However, the five-dimensional structure of the model remained the same. The Gap model was a central pillar of the SEVRQUAL technique

The Gap model outlines the difference between customer perceptions and expectations. Parasuraman, Zeithaml and Berry (1988) say this gap exists at five levels. The first gap exists between what managers and consumers expect. Often, it arises when managers have no idea about the types of services to offer their customers. The second gap denotes the perceived level of service quality that the managers provide and their customers’ expectations. The main difference between this second service provision gap and the first service provision gap is the perceived value of service quality. Usually, specifications of improper service quality standards denote this gap. The third gap outlines the service performance gap (Parasuraman, Zeithaml and Berry 1988). It is the difference between what an organisation specifies as the service quality standards and what its employees provide to the customers. The fourth gap exists when there is a difference between the delivery of service quality and the communicated specifications of the same (Seth & Deshmukh 2005). Here, experts usually investigate whether the promised service quality meets the quality of services provided (Parasuraman, Zeithaml and Berry 1988). The fifth gap exists when there is a difference between what customers expect and their perceptions of the services offered. This gap is also a product of the above-mentioned gaps because it depends on the size and direction of the four gaps mentioned above. The diagram below shows how these five gaps interrelate

According to the diagram above, the five gaps span across two points of contact in the service delivery model – consumers and marketers. The first, second, third, and fourth gaps depend on marketing strategies, while the fifth gap depends on consumer expectations (Seth & Deshmukh 2005). The delineation of the first four gaps identified in this diagram also extended the model to focus on communication and control factors in an organisation. Lee, Lee & Yoo (2000) say ten dimensions may affect the appearance of a gap – competence, courtesy, credibility, security, access, communication, understanding customers, tangibles, reliability, and responsiveness.

Cronin & Taylor (1994) came up with four important equations that help us to understand how the above intrigues affect service quality. The first equation stipulated that the SERVQUAL model is equal to performance minus expectations. The second equation outlined the weighted SERVQUAL model by saying it is a product of the SERVQUAL model and its importance (Seth & Deshmukh 2005). The third equation says SERVPERF is equal to performance (Abdullah 2006). Here, it is important to understand that the SERVPERF model differs from the SERVQUAL model because it focuses on attitude-based measures for assessing service quality (Cronin & Taylor 1994). In this regard, this model focuses on understanding customer feelings about service quality. Researchers say the model is good for measuring service quality, but they criticise if for its failure to provide recommendations (or basis for recommendations) to improve service quality (Cronin & Taylor 1994). This way, the model does not provide service providers with an opportunity to improve their services.

Lastly, the fourth equation says the weighted SERVPERF model is a product of its importance and performance (Buttle 1996). Based on the above equations, it is important to point out that the SERVPERF model measures customer satisfaction based on the metrics outlined by the SERVQUAL model (Rodrigues & Barkur 2011). Moreover, based on its measures, it closely conforms to the findings of researchers who have analysed satisfaction and attitude measures about service quality (Seth & Deshmukh 2005). While the SERVQUAL model has traditionally contained five service quality elements, different researchers have included new service perspectives of the model to their analysis. For example, some researchers have increased the service dimensions of the model to suit their analysis. Others have reduced the model’s scope by eliminating some of its elements. The SERVQUAL-P model emerged in this regard. Sebastianelli & Tamimi (2002) say the SERVQUAL-P model reduces the original components of service quality to four factors – reliability, responsiveness, personalisation, and tangibles. Here, it is important to understand that the SERVQUAL-P model differs from the SERVQUAL model because it contains the “personalisation” dimension, which underscores the importance of understanding the social content of interaction between companies and their customers.

Where have other People Used the SERVQUAL Model before?

Researchers have developed different conceptual models to explain the importance of measuring customer service metrics in organisational performance. Relative to this fact, Seth & Deshmukh (2005) say, “It is envisaged that conceptual models enable management to identify quality problems and thus help in planning for the launch of quality improvement programs, thereby improving the efficiency, profitability and overall performance” (p. 914). The National Regulatory Research Institute (1996) says the SERVQUAL model is the dominant service quality-measuring tool in marketing research. However, other researchers have used it in many industries to measure the quality of services they offer. Examples of its uses exist in the retail industry, dental services, hotel services, automobile industry, and the education sector (among others) (Bougoure & Lee 2009; Naik, Gantasala & Prabhakar 2010).

Although the above studies show an abstract and broad assessment of how other researchers have used the SERVQUAL model to measure service quality, it is difficult to have a deep understanding of how they used the framework without having in-depth investigations of these studies. For example, Gupta & Zeithaml (2006) used the SERVQUAL technique to evaluate service quality standards in the Malaysian banking industry. Unlike previous researches, which used only five assessment measures for the model, the researchers modified its items to create six dimensions of service quality (tangibility, reliability, responsiveness, assurance, empathy and convenience) (Gupta & Zeithaml 2006). They included the sixth element (convenience) because it was an important service quality factor for bank customers. Using a 26-item questionnaire, the researchers sought to investigate their respondents’ views of service quality. Their findings led Gupta & Zeithaml (2006) to say that four critical factors (tangibility, reliability, convenience and competence) determined service quality in the banking sector. Within the P-E continuum, these four factors showed significant differences. Tangibility showed the smallest gap between the two variables while convenience showed the largest gap (Gupta & Zeithaml 2006). Their findings also showed the need for banks to deliver their services competently. Similarly, they showed the need for assuring customers that they would provide high quality services. Similarly, they outlined the need to provide convenient services to their customers.

Away from the banking sector, Curry & Sinclair (2002) used the SERVQUAL technique to assess service quality in the health sector. They used the model to evaluate their perceptions about the health services offered in the physiotherapy department. Initially, the researchers considered the ten original criteria for evaluation, but later revised them to include only five (Curry & Sinclair 2002). Using a 22-item survey, the researchers explored the expectations and perceptions of their customers. This way, they sought to measure the five gaps of the SERVQUAL model. Their findings showed that the customers appreciated the services offered by the facility, although their perceptual values were negative (Curry & Sinclair 2002). This outcome meant there was a lot of room for improving the services offered. Unlike the banking survey (discussed above), Curry & Sinclair (2002) found out that assurance and empathy were the main service dimensions in physiotherapy. Despite the criticisms surrounding the SERVQUAL model, the authors said it was an effective tool for measuring service quality and company performance (Curry & Sinclair 2002).

In a different study, Lu, Zhang & Wang (2009) used the SERVQUAL model to measure service quality in the information technology sector. Unlike similar studies, the researchers used a large sample of respondents to measure their first research gap. The second gap aimed to understand the service quality gaps that existed in the industry. They focused this study on three educational institutions in the United Arab Emirates (UAE) (Lu, Zhang & Wang 2009). Their findings showed an imbalanced fit among service dimensions. Similarly, they also found out that the SERVQUAL model was an effective measure of service quality in the education sector. For example, the model correctly measured service quality in the above-mentioned studies. Similar studies have occurred in the hospitality industry. For example, Bojanic & Rosen (1994) conducted a study to analyse the efficacy of the SERVQUAL model in measuring the services of five three star hotels in the UAE and found out that competency, courtesy and assurance were the main factors that outline service quality measures.

Studies that are associated (more) with the focus of this paper (the telecommunications industry), and have adopted the SERVQUAL model, are also many. For example, Negi (2009) used the SERVQUAL model to measure service quality in the telecommunications industry. He found out that the main service quality dimensions were reliability, empathy and network quality (Negi 2009). Similarly, Qin, Prybutok & Zhao (2010) and Eshghi, Roy & Ganguli (2008) used the model to measure the quality of services among Indian, Ethiopian, and Chinese telecommunication firms. In these researches, the scholars used the technique to measure different aspects of quality, including network quality, value-added services, pricing plans, employee competencies, billing systems, customer services, and convenience. They found out that network quality and employee competencies had the strongest correlation with service quality (Qin, Prybutok & Zhao 2010; Eshghi, Roy & Ganguli 2008).

The above research studies have not fully explored the scope of service dimensions that SERVQUAL could cover. For example, many researchers have not used the technique to cover innovation as an important service quality dimension (innovation is an important dimension of service delivery in the telecommunication industry as well). Based on this understanding, Hosseini, Zadeh and Bideh (2013) say that these researchers should undertake strive to understand how the SERVQUAL tool affects other service quality dimensions that are unlisted in its original format. In line with these findings, our focus of study aims to understand if the SERVQUAL model would show the same results, as outlined above. However, unlike the above-mentioned study, this paper focuses on the Saudi telecommunications sector. As highlighted in the first chapter of this paper, its findings will show if the model is a perfect measure of service quality and customer satisfaction constructs in this sector. The analysis would also help to identify the service quality gaps of Saudi telecommunications firms and understand how to bridge them.

Criticisms of the SERVQUAL Model

Although marketers have used the SERVQUAL model to improve their service quality metrics, some people have criticised it for different reasons. This section of the paper outlines the main criticisms of the model according to the following bases.

Theoretical Criticisms

Shahin (2005) says the SERVQUAL model has received criticisms from people who have paradigmatic objections to its structure. For example, critics say, because the model depends on a disconfirmation paradigm, it is weak (Shahin 2005). Instead, they would prefer an attitudinal paradigm to underscore the model. The same critics also say since the model does not borrow from known economic, statistical, and psychological theories, it is inappropriate to use it in the wider marketing literature (Shahin 2005). Critics have also failed to support the SERVQUAL model because of the lack of enough empirical literature to suggest that most customers use the gap between the perceived and expected service qualities to assess a company’s service quality (Kumar, Kee & Charles 2010). Therefore, they do not support the Gap model as a significant pillar of the SERVQUAL tool.

The dependency on process orientation by the SERVQUAL model also fuels more criticisms about the SERVQUAL technique because some critics believe that the process should focus more on outcomes of the service encounter as opposed to the processes that underlie it (Kumar, Kee & Charles 2010). Lastly, critics of the SERVQUAL model argue that the multidimensionality of the service quality framework undermines the credibility of the model because its five dimensions are not universally accepted (Shahin 2005). In other words, they argue that these five quality dimensions are only real if researchers contextualise them. Using this analogy, they say it is wrong to load service factors within the five dimensions, abstractly (Shahin 2005). Moreover, there is a high degree of inter-correlation among the five factors. Therefore, it would be difficult to distinguish them from one another. Broadly, these theoretical criticisms have created enough impetus for highlighting operational criticisms of the SERVQUAL model.

Operational Criticisms

Critics have argued that the SERVQUAL model has several operational weaknesses because of expectations, polarity, and variance weaknesses (Shahin 2005). For example, regarding expectations, they say the SERVQUAL technique assumes that most customers use expectations to evaluate service quality, but in the real sense, they use standards to do the same (Shahin 2005). Furthermore, they say that by relying on expectations, the model becomes ambiguous, because the expectation is polysemic (has multiple meanings) (Sower & Fair 2005). This way, they believe the SERVQUAL model fails to measure customer expectations (Shahin 2005). Besides these criticisms, some pundits also believe that the five categories of the model (item compositions) cannot effectively capture all the dynamics of service quality (Sower & Fair 2005). Regarding the polarity concern, critics say respondent errors occur when the SERVQUAL model uses polarity of items to measure service quality (Sower & Fair 2005). Using the same argument, Lam (1997) believes that the model relies on a flawed seven-point Likert scale to measure customer perceptions. Sower & Fair (2005) also believe that the constant reliance on two service quality factors (expectations and perceptions), is often confusing for many users of the SERVQUAL model. Lastly, the researchers say many SERVQUAL scores fail to provide the correct proportion of item variances of service quality score (Sower & Fair 2005).

How Else Can we Measure Service Quality?

Since the GAP model had its shortcomings, other researchers came up with alternative models for measuring service quality. For example, Teas (cited in Seth & Deshmukh 2005) developed the evaluated performance model to address some of the challenges of the SERVQUAL model. This model evaluated the difference between what customers ideally want from their service providers and their perceived values of a service. It differed from other models because it did not include customer expectations in its analysis. When asked why its proponents did not (simply) evaluate the difference between performance and expectations, the researchers said this approach had a questionable validity because of problems associated with how to define the two concepts (performance and expectations) (Seth & Deshmukh 2005). They also had an issue with the conceptual problems and the theoretical justification of the expectations component of the performance-expectations (P E) framework. Similarly, the researchers said using perceived and expected service qualities as the main attributes for measuring service quality required the revised expectation measures to match with ideal quantities of the service attributes (Seth & Deshmukh 2005).

Sahin et al. (2006) introduced a new service quality measure, which hinges on three pillars – interaction quality, environmental quality, and outcome quality. These three pillars have new sub-dimensions. For example, attitude, behaviour, and expertise make up the interaction quality measurement. Ambient conditions, design factors, and social factors also outline the physical environment quality (Seth & Deshmukh 2005). Similarly, waiting time, tangibles and valence outline the outcome quality. Proponents of this service quality model say hierarchical and multidimensional models explain service quality better than other models do (Seth & Deshmukh 2005). In their view, they say these factors explain how to define service quality perceptions, how service quality perceptions emerge, and why it is important to understand how “place” positions itself in the service quality model. By understanding these factors, Negi (2009) believes, it would be easier for companies to improve customer experiences.

After considering all the findings outlined above, Reimer & Kuehn (2005) came up with six critical factors that could effectively measure a customer’s perceived service quality. They included the human aspects of service delivery, core services (content and features), social responsibility, systemisation of service delivery, tangibles of service, and service marketing (Reimer & Kuehn 2005). In their assessment, the researchers found out that these factors contributed to how customers perceived service quality, thereby affecting their satisfaction and loyalty in the same manner (Reimer & Kuehn 2005). They also said that the same factors influenced customer satisfaction and customer loyalty. Relative to this assertion, Saravanan & Rao (2007) believe that customer satisfaction depends on customer views about the technical and functional qualities of a service, an organisation’s service product, the service delivery process, and the service environment.

In a different analysis, Swan (2001) identifies five main strategies that most companies can use to measure service quality. The first method is the expectancy disconfirmation approach. It measures customer expectations and their customer service experiences. Stated differently, it explains the gap between customer services and service performance (Swan 2001). Usually, researchers who use this method simply ask their respondents to recall the types of services they experienced from a company. The second strategy for measuring service quality, as outlined by Swan (2001), is the performance only approach. It investigates customer satisfaction levels after a service encounter. Swan (2001) said the third approach for measuring service quality is the technical and functional dichotomy approach. This approach singles out two factors that affect service quality – technical quality and functional quality.

The technical quality refers to attributes such as the durability of a product, or the security of a service. The functional quality refers to intangible attributes about a product, or service, such as the speed of delivery and the helpfulness of a product or service (among others) (Swan 2001). The fourth approach is the service quality vs. service satisfaction approach. This model mainly focuses on two interrelated factors – transition specific assessment and the overall assessment (Swan 2001). The latter evaluates the overall quality of a product, or service, while the former investigates specific features of a product or service. The attribute importance approach is the fifth measure of service quality. To explain how it works, Teas (1993) says, “This approach focuses its relative weight on the importance that a consumer places on the attributes found to be linked with service satisfaction” (p. 20).

Top Values that Affect Customer Satisfaction

Different societies, industries, and people have different views about customer satisfaction. However, an abstract definition of the concept shows that it underscores the value of a good or service (Van der Wal, Pampallis & Bond 2002). In an attempt to find out the customer satisfaction levels of a group of military personnel in Turkey, Van der Wal, Pampallis & Bond (2002) said that some demographics were indifferent to customer satisfaction standards. Similar studies conducted in China showed that cultural and social factors greatly affected customer satisfaction levels (Van der Wal, Pampallis & Bond 2002). Similarly, Wicks & Roethlein (2009) conducted a different study on customers of computer products and arrived at the same conclusion. However, in their study, they highlighted more than 30 items for evaluating service quality dimensions. Some of them included product, flexibility, reliability, priorities determination, and security (among others) (Wicks & Roethlein 2009).

Independent studies merge some of these findings and show that “human needs, service quality, products, and product usability” (Wicks & Roethlein 2009, p. 82) are among the greatest influences of customer satisfaction. However, they acknowledge that some demographics may require some of these variables, more than others, to be satisfied (Wicks & Roethlein 2009, p. 82). Based on the above factors, Beamish & Ashford (2008) went a step further to categorise the main dimensions of customer satisfaction in three categories – basic factors, performance factors, and excitement factors. Basic factors outline the minimum attributes that a product or service should have to satisfy a customer. In other words, without these factors, a customer would show dissatisfaction. This analogy shows that basic factors do not necessarily cause satisfaction, but they prevent a customer from being dissatisfied. Comparatively, performance factors lead to customer satisfaction because they highlight unexpected attributes about a service (that a customer could not have expected) (Beamish & Ashford 2008). Lastly, excitement factors are the opposite of basic factors because, although they increase customer satisfaction, they do not (necessarily) cause customer dissatisfaction.

Relationship between Service Quality and Customer Satisfaction

Schneider & White (2004) say that, like service quality, people should see customer satisfaction as a multidimensional attribute. They say so because customer satisfaction can occur at different levels (for a customer and an organisation). Moreover, companies have their customer operations along the continuum of service quality. This fact explains why service quality always aligns with customer satisfaction (they share a direct correlation) (Schneider & White 2004). In other words, when there is a highly perceived level of service quality, there is usually a similarly highly perceived level of customer satisfaction. Using this relationship, Schneider & White (2004) say service quality leads to customer satisfaction. This line of argument affirms the assertions made by, Ozer, Argan & Argan (2013), which show that customer satisfaction depends on service quality.

Schneider & White (2004) say that for a long time, researchers have established a strong relationship between company services and customer satisfaction. For example, Gunjan et al. (2011) used the correlation between the two variables to investigate how customer services affected customer satisfaction in the mobile telecommunications sector. Their study revealed that network quality and service provider reliability were the main factors that affected customer quality (Gunjan et al. 2011). However, the study also revealed the need to use three service dimensions (tangibles, empathy and assurance) when measuring how service quality affects customer satisfaction in the sector. Although this study mainly concentrated on the mobile telephone market (only), observers believe that its findings represent the relationship between customer service quality and customer satisfaction in the wider telecommunications sector (Gunjan et al. 2011). In an unrelated sphere of analysis, Lu, Zhang & Wang (2009) say the relationship between service quality and customer satisfaction standards have a profound impact on the intention of customers to patronize an organisation. By extension, their analogy shows that customer satisfaction and service quality have a profound impact on a company’s success in a competitive business environment. Overall, these findings show that most customers are often happy when they receive quality services.

Our Case

Despite the criticisms of the SERVQUAL model, this paper still evaluates its use in the Saudi Arabian telecommunications industry because many researchers, such as McDaniel & Gates (1998) and Iwaarden & Wiele (2003) believe that its good reliability and validity make it a good measure of customer perceptions of service quality. Since this study focuses on understanding customer perceptions (using the SERVQUAL technique) in the Saudi Arabian telecommunications industry, we are going to measure service quality using the service dimensions proposed by Parasuraman, Zeithaml & Berry (1985a) and their model. Since Saudi telecommunication firms package some of their services as products, we are going to use the concept within the context of this study (as well) to improve the validity of our study. This contribution is important because telecommunication firms compete based on their products. Similarly, customers perceive service quality based on the same measure. Using this understanding, this paper will use the concept (product) as a measure of variety and quality. This analysis creates an effective framework for understanding the gap between expectations and experiences about product varieties and product qualities in the Saudi telecommunication industry. Based on this understanding, this paper adds “product” as the sixth service dimension for assessing the customer perceptions of service quality in the Saudi telecommunications industry. To understand these service dimensions, effectively, the study uses a 23-item survey (see appendix one).

Conceptual Framework

As shown in this paper, previous researchers have used the SERVQUAL model to measure customer satisfaction and service quality in the telecommunications industry (Beamish & Ashford 2008; Qin, Prybutok & Zhao 2010; Eshghi, Roy & Ganguli 2008). Similar to this study, some of these researchers have used the modified versions of the model to measure the same issue. For example, this paper shows that many researchers who used the SERVQUAL model in the Malaysian banking sector included “convenience” as an extra service quality measure (Qin, Prybutok & Zhao 2010; Eshghi, Roy & Ganguli 2008). This paper also adopts the same approach because the generic version of the SERVQUAL model would not effectively capture all the service quality dynamics of the Saudi telecommunications industry. Carman (1990) supports this service addition, because he highlights “product” as a technical quality dimension for measuring service quality in the telecommunications industry. This paper uses the same reasoning to measure service quality and customer satisfaction levels. This strategy stems from the interrelation between both attributes. The SERVQUAL model also acknowledges the same fact by integrating the two constructs (Parasuraman, Zeithaml & Berry 1985a). Stated differently, it perceives service quality as an antecedent of customer satisfaction. The 23-item questionnaire for this paper captures all these intrigues.

Methodology

This section of the paper explains how I conducted the research and the motivations for pursuing different data collection and analysis methods.

Research Strategy

Long et al. (2000) say the decision regarding whether to choose the quantitative or qualitative research strategies depends on the knowledge and reality surrounding a research topic. Furthermore, Long et al. (2000) say this process depends on the systems that underlie how we obtain research knowledge, or how we construct reality. These intrigues shaped the research process and contributed to the formulation of the underlying assumptions that informed the selection of research tools. The research study was a quantitative research. A quantitative research emphasises the need for quantifying data. In the context of this paper, it supported our research approach because it accommodated a deductive approach to understanding the relationship between theory and research. Moreover, it provided a natural and scientific theoretical model of positivism. Since the quantitative research approach views social reality as an external and objective conception of service quality (in marketing research), it helped the researcher to answer the research questions by making it possible to measure variables of the SERVQUAL model. This framework allowed the study to accommodate differences among people, which explain differing perceptions of service quality (Salkind 2003).

This framework also provided the study with a yardstick for understanding the differences between customer perceptions (among the different customer groups). Furthermore, it provided the study with an accurate measure of the degree of relationship between different service quality variables. These attributes improved the credibility of the study by improving its reliability and validity. Therefore, the research strategy was useful to this study because it demystified the SERVQUAL model, explained how its attributes applied to this study, and why the same attributes could effectively measure customer perceptions. Furthermore, the quantitative study design was pivotal in this analysis because it allowed the researcher to generalise the study’s findings within the context of the study focus. However, since the quantitative research design was highly scientific, it is pertinent to understand its biases and values (in the research design process), to come up with reliable research findings.

Research Design

This section of the paper explains the framework used for collecting and analysing the research information obtained in this study. Bryman & Bell (2007) say the most effective research designs give priority to the causal connections between variables. They also say that effective research designs allow the researcher to generalise the research findings to a larger population sample, but still validate the study findings, within the study context (Bryman & Bell 2007). Hague (2004) says there are five main types of research designs – experimental design, cross-sectional (social survey) design, longitudinal design, case study design, and the comparative design. This paper used the cross-sectional research design. It allowed me to analyse multiple cases in one research. Moreover, it provided the opportunity to get quantitative and qualitative research data about two or more variables. This provision provided an opportunity for understanding the patterns of association among the research variables.

This research design was also appropriate for this paper because many researchers have used it in similar studies (about the SERVQUAL technique), which investigated service quality and customer satisfaction issues. Although this fact made it difficult for this paper to delimit its findings uniquely, the research design was still useful during the questionnaire design stage. Stated differently, it helped to categorise different variables, which were able to capture different types of research data from the respondents, in the research questionnaire. Moreover, the research design made it easy to analyse the different dimensions of the SERVQUAL model that investigated service quality standards. It was difficult to use other types of research designs because, in social survey research, it is difficult to manipulate research variables, as would be the case if we used other research designs, such as the experimental research design (Salkind 2003). For example, it is difficult to change social variables, such as gender and age.

Research Approach

This paper merged theory and data to come up with the research findings. Panneerselvam (2004) says that such processes can only occur by adopting inductive and deductive approaches. This paper chose the deductive approach because, naturally, it denotes the relationship between theory and research (Panneerselvam 2004). This approach denotes the hypothesis deduction process, based on what researchers know (Nargundkar 2003). However, empirical tests evaluate this hypothesis, as this paper will show in subsequent sections of the study.

Since this paper borrowed its framework from existing models, it was appropriate to use the deductive technique to come up with the research findings. The theoretical model was the SERVQUAL model, which measured the perceived and expected service quality. Based on this understanding, the main research problem focused on understanding if this assessment tool (the SERVQUAL model) could correctly measure service quality in the telecommunications sector. From this research framework, I was able to collect data regarding the expected and perceived service quality for customers of Saudi Arabian telecommunication firms. This process contributed to the questionnaire design process by making it easy to understand how customers perceived service quality and how to identify the most effective dimensions of customer satisfaction. These findings could help in creating solutions for the telecommunications sector and providing enough impetus for improving their services. This process is deductive because it stems from the SERVQUAL model and outlines how Saudi telecommunication firms could improve their service standards, in line with the findings.

Therefore, this paper borrowed different attributes of the SERVQUAL model to understand the main service dimensions that characterise the Saudi telecommunication industry (Parasuraman, Zeithaml & Berry 1988). This means that its indices outlined the service quality measures. This strategy stemmed from the importance of understanding service quality in the telecommunications industry (plus the factors that affect service perceptions among customers). As shown in earlier sections of this paper, evidence of other researchers using the SERVQUAL model in other telecommunications industries (besides Saudi Arabia) exists, but there is no evidence of its use in the Kingdom. It is pertinent to close this research gap, especially because researchers have not done this type of research in the Middle East. This paper also takes a keen understanding on identifying the service quality dimensions that appeal to Saudi Arabian customers. This study answers these questions by using quantifiable data collected from research participants in the sector. The findings would help us to understand how customers perceive service quality and how service dimensions bring customer satisfaction to them.

Data Collection

The main instruments for collecting data were questionnaires, telephone interviews, and secondary literature from books, journals and credible websites. The research respondents got the questionnaires using a non-probability sample.

In-depth Interviews

Ten call centre managers participated in the in-depth interviews. The interaction sought to understand their perceptions about the efficacy of the SERVQUAL technique in measuring customer satisfaction in the telecommunications industry. The same interviews helped to gain a deep understanding of the respondents’ views regarding the dimensions of perceived service value for mobile telecommunication customers.

Secondary Data

This research obtained secondary data from KSA publications of customer service quality. The secondary data included the customer service views of more than 5,000 users of KSA services. The data related to customer service statistics of the year 2012. It covered different types of services offered by the company, including the decision to buy mobile SIM cards and all the processes that lead to the use, or deactivation, of mobile communications. However, this study only used raw data from the company publications after the service providers gave permission to do so. However, the paper withholds the names of the company subsidiaries for confidentiality purposes. Nonetheless, the data obtained was useful to the study because it allowed us to compare the company findings with information obtained from the literature review.

Literature Review

This paper focused on getting secondary research materials from reputable and credible information sources. Mainly, it sourced information from books, journals and reputable websites. These information sources highlighted the history of the SERVQUAL technique and its use in other industries, such as the banking and retail sectors.

Quantitative Survey

This paper used the quantitative survey method to come up with empirical findings that would establish the most appropriate dimensions of service quality. The survey included 100 respondents. The study used a probability sampling method to select them (I used the snowball and convenient sampling methods to get the research participants). The quantitative survey occurred through self-completed questionnaires. The data collection method was appropriate for this paper because it was cheaper to administer, quicker to administer, and gave the respondents autonomy to answer the research questions. Their findings helped to investigate which of the ten dimensions of service quality, in the SERVQUAL model, captured service quality indicators in the telecommunications industry. The main drawbacks that emerged from using this method were lower response rates, submission of incomplete questionnaires, and possible biases in responses.

Data Analysis

This study used the ten dimensions of service quality outlined in the SERVQUAL assessment model to link different study elements. However, since this paper uses the quantitative design, it was only natural to use quantitative assessment tools for analysing the data. These tools involved descriptive and inferential statistical tools. In this regard, there were varieties of statistical assessment tools used, such as the SPSS technique. Although SPSS has its drawbacks, my familiarity with it and its user-friendly nature made it the primary data analysis tool. In this regard, the paper borrowed from the findings of Parasuraman, Zeithaml and Berry (1988) and used descriptive statistics to analyse the data. The statistical mean was one of these indices.

Simply, the mean refers to the average of all the values obtained in the data analysis process. It represents a distribution of many discrete and continuous variables. The study also used the standard deviation metrics to analyse the data. The standard deviation denotes the level of variability that a given set of statistics project. It mainly occurs across different values and measures. If the set of values derived from the study concentrate towards the mean, it means that the statistics have a high peak. However, if they deviate further from the mean, it means that there is a low peak. This paper used the kurtosis metric in this regard. Lastly, this paper used the factor analysis to investigate if there were any relatable variables in the SERVQUAL model. This analysis was pivotal in helping to understand whether the model was a good measure of analysing service quality standards in the telecommunications industry, or not.

Ethical Considerations

This study used the information obtained from the respondents by observing a high level of confidentiality. The names and identities of the respondents are confidential. Since the data collection process occurred with a high degree of open-mindedness, the study treated all information obtained from the respondents with high regard.

Findings and Analysis

The purpose of conducting the primary research using the 23-item questionnaire was to answer the main research question – what are the most significant service quality dimensions that have the strongest impact on customer satisfaction? Furthermore, this method aimed to investigate if the customers were satisfied with the same services. The data analysis process occurred in two stages. The first stage was a preliminary data assessment process, which described a preliminary analysis of the research findings. This process also involved understanding how the data collected related to the demographics captured in the data collection process. The main purpose of doing so was to make it easy to understand the data obtained.

Factor analysis was the main data analysis method, which sought to investigate if the SERVQUAL technique measured service quality in the Saudi telecommunications industry. The study also used the gap analysis to summarise the significance between expected and perceived service qualities among KSA customers. To do so, this paper subtracted the perceived service quality values from the expected values of each service dimension. The reliability and value of the SERVQUAL model depended on six dimensions. This paper also used the cronbach alpha to measure each item in the model (the values of cronbach alpha metrics spanned across 0-1). Since this study involved several measures of internal reliability, it was pertinent to measure the metrics as the first part of the data analysis process. This process guaranteed the credibility of the findings. Stated differently, this process made sure that the respondents’ views on one indicator were similar to the views portrayed on other indicators. The personal profiles of the respondents sampled appear below

Table 1: Demographic Characteristics of the Respondents.

Sample Population

Men comprised the majority of the respondents who participated in the study. This gender bias possibly stems from the engagement of many men in Saudi’s economic and political space. Most of the respondents were subscribers of STC. Only a few of them (12%) subscribed to Zain telecommunication services. However, this distribution did not have any correlation with their monthly spending on telecommunication services because most of the respondents used between R101 Riyals and R199 on mobile telecommunication products and services, monthly. The minority used less than R49 on similar services. Lastly, most of the respondents had spent less than a year with their telecommunication service providers. About 34% of the respondents had spent less than half this period with their service providers.

Reliability Coefficient

After analysing the findings obtained in the study, this paper came up with a total reliability scale of 0.9 for the service assessment model. This figure was closely similar to the same value depicted in studies conducted by Parasuraman, Zeithaml and Berry (1988). Since the reliability coefficient value was 0.89, it is safe to assume that the study had a high internal consistency. This outcome makes it easy to presume a high reliability of the six dimensions of service quality used in this paper. The table below outlines the findings obtained after analysing every service dimension used in the paper

Table 2: Reliability scale of Service Dimensions.

The above process evaluated if the analysed dimensions were genuine, or not. For example, when the cronbach’s alpha increased after deleting one dimension from the analysis, it was safe to assume that the analysed dimensions were unreliable. However, if the cronbach’s alpha decreased, the possibility that the analysed dimension was genuine is high. Overall, the analysis showed a declining reliability when I deleted an item. However, this was not the case for EM2.

After looking at the value of all the six coefficients mentioned above, we see that some of the coefficients reported values that were below 0.7. For example, tangibles and responsiveness had values that were below this figure. This fact could signify that some of the dimensions were slightly similar. The product dimension also posted similar results and dynamics (as mentioned above). Nonetheless, the other dimensions studied showed coefficient values that were higher than 0.7. This fact meant that these dimensions depicted the true measure of service quality.

Expectations and Perceptions

This paper measured customer expectations through the 5-point Likert scale. Higher values reflect high customer expectations, or perceptions, of the services offered. The responses obtained from the sample population showed that customer expectations often surpassed service quality perceptions. Many researchers have also experienced this outcome. For example, Cronin, Brady & Hult (2000) say it is common for customer expectations to exceed the perceived levels of service quality offered. However, when this occurs, it means companies have to improve their services.

The items with the highest values were network coverage, employee behaviours, confidentiality issues, and convenient working hours. These items had scores of 4.67, 4.64, 4.61, and 4.54 respectively. The scores of other service items were close to the above values. This means that Saudi customers expect a high quality of services from their telecommunication service providers. The items with the highest (perceived) service quality scores were kept promises, ability of network coverage to meet customer needs, easy activation of telecommunication services, and availability of adequate customer service outlets. These items had scores of 3.76, 3.73, 3.65, and 3.23 respectively. A broad assessment of perceptual values did not show much variation among the 23 items studied. However, their scores were largely lower that the scores reported after investigating customer expectations.

Analysing Differences between Expected and Perceived Service Qualities through the Factor Analysis

Brochado (2009) says that most researchers use the factor analysis method for data reduction purposes. In line with this purpose, this paper used the technique to identify patterns of correlation between different service quality items. This logic makes it correct to assume that highly correlated items often share a correlation pattern. Similarly, the same factors do not often influence the same items. Exploratory and confirmatory factor analyses are the main types of analyses, which influence customer responses, or ascertain if there is a correlation between different responses. Since this paper already has a predetermined number of service dimensions, it used the confirmatory data analysis method. The test scores varied between “zero” and “one.” A “zero” value shows a large partial correlation value. Often, when researchers come up with this value, there is no need for conducting a factor analysis.

However, when there is a value of close to “1,” it means there is a reliable correlation pattern. Moreover, such analyses are likely to show distinct and reliable factors in the factor analysis. This study revealed a factor analysis of 0.822. This meant that the factor analysis was relevant for the study. It suggests that there are common factors that underlie the 23 items in the SERVQUAL model. In other words, by identifying common factors, we assume that the service quality items appeal to specific analytical groups. There is a varied correlation between each factor and measurement. It depends on whether one factor influences more than one dimension. Items that have a score of 0.4 (or below) have a low pattern of correlation and are insignificant to our analysis. The factor analysis explores this correlation in detail. For example, higher loads make it easy to define the service dimensions of one factor. Factors that have a negative value highlight an inverse impact. The following table shows the results of the factor analysis

Table 3: Factor Analysis.

The table above shows how each service quality item relates to one service factor component in the analysis. As shown above, this study excluded factors that had a value of less than 0.45 from the analysis because they were insignificant to the study. The above findings show how we could group items of different dimensions in one factor. Similarly, items that come from one dimension may appear in different factors. For example, EM4 and RN4 fall in more than one factor. This analysis shows that the SERVQUAL model fails to measure service quality in the Saudi Arabian telecommunications industry because it fails to pool similar factors in the same category (to measure the same thing). Nonetheless, our analysis shows that only items that have a tangible dimension fall within the same category, as highlighted below

Table 4: Cumulative and Variance Value Analysis.

The above table shows how the data obtained fit into the six factors, which outlined the service quality. The total variance percentage obtained in the study was 66.860. The first factor carried 35.256% of the data. This figure shows that most of the data obtained fit in the data described. The other five data groups, outlined in the study, had percentages that were below 10%. This meant a low level of data fit.

Gap Score Analysis

The gap score analysis sought to find out how customers perceived the services offered in the Saudi telecommunications industry. The same analysis sought to find out the service quality dimensions that the customers liked. Lovelock & Wright (1999) said high gap scores denoted high customer satisfaction levels. They also said gap scores highlight the differences between expected and perceived service dimensions (Lovelock & Wright 1999). When there is a small difference between the levels of perceived and expected service levels, it is correct to assume that there is a high level of service quality. The largest gap scores emerged after analysing the differences between perceived and expected levels of services regarding response rates, sincerity in solving customer issues, employee behaviour, and using new technology in service provision. These items had scores of “one.”

Here, we calculated the gap scores, based on the differences between customer expectations and perceptions of the telecommunication services offered by the telecommunication companies. Generally, this study’s findings show that the customers’ perceptions of service quality did not meet their expectations. This means that all the gap scores were negative. The largest mean gaps emerged after measuring reliability, responsiveness, and assurance. The three service dimensions had scores of -1, 0902, -1, 0034, and – 0, 8676. The lowest mean gaps emerged after measuring products, tangibles, and empathy. They had values of – 0, 4835, -0, 6276, and -0, 6875 respectively. Overall, these findings show that the service quality perceptions were lower than the service quality expectations. According to Carrillat, Jaramillo & Mulki (2007), researchers can easily measure service quality by obtaining the average score of the SERVQUAL dimensions. Here, it is important to understand that we used the service quality dimensions (technical and functional service qualities) of Carrillat, Jaramillo & Mulki (2007) to add “product” as a sixth attribute. The table below shows the scores for the six dimensions mentioned in this paper.

Table 5: Gap Score Analysis.

The table above shows a consistent set of standard deviation scores for all the six dimensions studied in this paper. This means there has been a wide range of opinions about service quality given by the respondents.

Perceptions of Service Dimensions

Tangibles (TA)

The tangibles variable had a mean score of 0.6. Its average score was -0.6276. Since this service measure posted a standard deviation of 1.05786, it means the spread, from the mean value, was high. This value also had a positively skewed value because this measure was -0.137. This output meant that clustering occurred away from the mean value.

Reliability (RL)

The mean for the reliability dimension was -1.0901. This output meant that most customers perceived the service quality dimension as unsatisfactory. Similar to the tangibles variable, the reliability variable also had its gaps spread away from the mean. The standard deviation was 1.12941. There was a positive skewness of the reliability metric because it was -0.676. The kurtosis value was 0.891. These figures show that the reliability metric had a slight deviation to the right of the mean.

Responsibility (RE)

Broadly, most Saudi customers were unsatisfied with the service quality offered by the Saudi telecommunication firms. The service gap was -1.0034, the standard deviation was 1.18744, and the median and mode had gaps of -0.76 and -0.6, respectively. This figure shows no significant variations from the mean value. The kurtosis value was 0.987. This meant that clustering occurred at a different point from the mean.

Assurance (AS)

Similar to reliability, the sample studied showed a high sense of dissatisfaction with the “assurance” service dimension. The average gap for this service dimension was -0.8676. The mode was -0.6. Although the study revealed more than one modal class, the least value was -0.6. This value was higher than the mean. The median gap and standard of deviation values were -0.76 and 1.11835 respectively. These figures showed no significant deviations from the mean.

Empathy (EM)

The empathy metric had a statistical mean value of -2. Comparatively, the service dimension had a mode of -0.7 (the gap score was -0.6875). There was a slight deviation, which occurred to the right of the statistical mean because the kurtosis value was 1.268. Similarly, the distribution was -0.959.

Products (PR)

The product dimension had the least gap among all the other service dimensions studied in this paper. The mean and modal gaps were both 0.6. The standard deviation for this service dimension was 1.3850. “Product” had the highest standard deviation from all other service dimensions. Moreover, it had the greatest deviation from the mean.

What is the General Perception of Service Quality?

The table above shows that the customers of Saudi telecommunication services expect more from their service providers than what the companies offer. The negative mean of -0.7933 affirms this fact. Similarly, the highest gap was -1.63. Here, it is important to show that the standard deviation was lower than the individual analyses on the same. This means that the Saudi customer population is somewhat homogenous. They also had a value of -0.853 (kurtosis value). Similarly, these gaps clustered away from the mean. Broadly, the standards of deviation varied around a common average, meaning that the results for all the six service dimensions were consistent. Similarly, this finding suggests that the customers had a range of opinions about the service quality offered by Saudi telecommunication firms. Their opinions mainly showed that their perceptions of the services offered by these firms were low. Particularly, the findings showed that the services offered by the telecommunication firms did not meet their expectations. Hence, customer dissatisfaction was evident. Many factors could have caused this dissatisfaction, but overpromising and the failure to deliver quality services are among the main reasons for this situation. The following section of this paper discusses this matter in detail.

Discussion

This paper has already investigated the differences between customer perceptions and customer expectations in the telecommunications industry. The five point Likert scale showed that the respondents scored 3.677 on the service metrics. This figure shows that the customers have a high expectation of the types of services they need from Saudi telecommunication firms. An independent investigation of individual service dimensions shows that reliability has the highest score. This means that most customers want reliable telecommunication services from Saudi firms. This outcome aligns with the findings of Gronroos (1982), which suggest the need for telecommunication companies to provide reliable services for their clients. The assurance and empathy dimensions also have a high score of more than 3.0. This outcome shows how customers are keen to have service providers that are empathic and assuring. The score of more than 3.0 shows high customer expectations. However, the service gaps that emerged from the empathy dimension show that most Saudi telecommunication firms fail to provide timely services to their clients. It also shows that these firms are insensitive to the needs and requirements of their customers. Lastly, it shows their lack of understanding of the complaints raised by customers about the existing company services.

Independent ratings of customer expectations give an overall score of 4.23. On a scale of 1-5, this score means that the Saudi customers expect very high customer service standards. Considering the customer perceptions of telecommunication services in Saudi Arabia mirror the SERVPERF model, which aligns customer perceptions with customer satisfaction, we see that customer expectations are higher than customer perceptions. However, the difference between these two indices is marginal. If we base this analysis on the individual service dimensions analysed in this paper, we see that most customers are interested in reliability and empathy, with an average score of 3.749. “Product” had the lowest score of 2.492. If we had used the SERVPERF model, we would have assumed that most customers were satisfied with the quality of services offered by Saudi telecommunication firms.

This is because the score is above average and it assumes that quality has a direct correlation with customer satisfaction. According to Duff & Hair (2008), increased service quality leads to increased customer satisfaction. This fact aligns with the findings of other researchers, such as Kumar, Kee & Charles (2010) who believe that customer satisfaction depends on the service quality provided by service providers. Their findings provide a reliable metric for understanding customer satisfaction. Similar to how customer expectation affects customer satisfaction, Nejati & Nejati (2008) also say that customer perceptions affect customer satisfaction the same way. Since, the satisfaction score derived in this paper is slightly above 3.0, it means that most Saudi customers are barely satisfied with the types of services offered by their telecommunication firms. In fact, these findings show that the telecommunication firms need to work hard to cover about 30% of the customer expectation gap.

Since Parasuraman, Zeithaml & Berry (1990) introduced the gap measure to explain service quality; our findings show that quality is an important measure of customer satisfaction. Nonetheless, it is pertinent to understand that this analogy restricts inference of customer satisfaction with service quality. Instead, it proposes a gap score between customer expectations and customer perceptions. This paper has used the same model to understand the difference between customer expectations and customer perceptions of telecommunication services in Saudi Arabia. Its outcome shows that the study highlighted many negative service quality gaps. Alternatively, this finding means that most customers expect more than what many Saudi telecommunication firms offer.

In the strict sense of this analogy, we see that most customers are dissatisfied with the quality of services offered by these firms. This finding also stems from the relationship that customer satisfaction has with service quality because the latter is an antecedent of the former. Therefore, a low or poor customer satisfaction level means that most customers are dissatisfied with the quality of services offered by Saudi telecommunication companies. Many researchers have also come up with such conclusions (Kumar, Kee & Charles 2010). They suggest that such outcomes come from the increased demand for quality services (among customers) and (possibly) the poor relationship between customers and their service providers. If we extrapolate these findings to the Saudi telecommunications industry, we can deduce that most customers are having a lower tolerance for poor services. According to Parasuraman, Zeithaml & Berry (1985b), the experience between a customer and his service provider is likely to cause this outcome.

Broadly, the findings of this study show many negative gaps in most of the customer service dimensions – meaning that the overall quality of telecommunication services offered in the kingdom is poor. In this regard, it is correct to say that most customers are dissatisfied with the quality of services offered by Saudi telecommunication firms. By extension, this finding shows that most firms need to improve all the service dimensions highlighted in this paper to bridge any gaps that may cause customer dissatisfaction.

Research Quality

A valid business research should show that the research reliably achieves its research objectives (the research should be highly consistent). Reliability and validity were important measures for this paper. However, some dimensions of the SERVQUAL model did not exemplify this fact. This means they did not show proper cohesiveness when measuring service quality. Nonetheless, most of the metrics used in the study measured service quality well. The factor analysis was also a reliable measure for affirming the validity of the paper. However, this analysis showed that the factor analysis was not a good measure for assessing service quality because some service quality dimensions failed to fit in one factor.

The last issue in understanding our quality criterion is the ability to replicate the findings of this study. This attribute refers to the likelihood that our research provides the same findings if we used it on a different population sample, at a different time. This paper outlined the procedures used to undertake the research, such as the sample respondents, concept designs and the research administration process. Researchers often outline the same specifics in quantitative research designs that adopt a cross-sectional design. The ability to replicate this study is satisfactory because I believe that the methodology used in this paper was reliable, since the paper collected trustworthy information and formed reliable information sources from it. Moreover, using a cross-sectional research design and adopting a probability sample strengthened the external validity of our study. Stated differently, it is easy to generalise our research information across a larger sample in Saudi Arabia. However, the possibility of changing customer preferences may affect future research outcomes. Overall, it is correct to consider our findings “probable” because the methodology used to make sure that all the responses obtained from the research was unbiased. Therefore, the study mainly relied on primary data and only occasionally used secondary data to substantiate some of the findings obtained. This means that the study’s findings are honest.

Conclusion

This section of the study summarises the main findings of this paper by aligning the main points of the study with the research objectives and research questions. This section of the paper also outlines the research limitations and the implications of the study. Finally, it suggests areas of future research.

Summary of the Findings

The purpose of this paper was to understand the main service dimensions for assessing service quality in the Saudi telecommunication industry. The research questions also sought to understand the main service measurement tool for assessing service quality in this industry, evaluate the top variables that reflect customer satisfaction, and investigate the effect of quality on customer satisfaction. To answer these research questions, the paper used the findings from the literature review section to focus on the SERVQUAL model as the main metrics for measuring service quality. It also considered the SERVPERF and the Serviscape models as measures of service quality, but because of their limited use in the telecommunications industry, this study paid little attention to them. The SERVQUAL model outlined five main service dimensions for assessing service quality. However, because of the changing nature of products and services in the Saudi telecommunications industry, this paper included a sixth dimension in the service quality analysis – product. Reliability, assurance and empathy emerged as having the greatest impact on customer satisfaction. This outcome means that these dimensions are the top variables that reflect customer satisfaction.

The data analysis method showed that the service measurement model was not suitable for this study. For example, some items fit into one factor, while others required different factors for the same analysis. Although, the study showed that the SERVQUAL model was the most commonly used metrics for assessing service quality, this analysis showed that the model was not good for our analysis. However, the tangible item fits into one factor. This outcome shows a discriminate validity in our study where our findings differ from the outcomes realized by Parasuraman, Zeithaml and Berry (1988). Nonetheless, the SERVQUAL model provided a significant level of reliability, which almost reaches the same levels as depicted by Parasuraman, Zeithaml and Berry (1988) in their study. However, the other service dimensions (tangibles, responsiveness, and product) did not exhibit a high level of reliability, as portrayed by Parasuraman, Zeithaml and Berry (1988). This outcome shows that some items were incoherent to form useful service dimensions. Based on the above findings, we can see that the SERVQUAL model is not a good tool for measuring service quality in the Saudi telecommunications sector.

The gap score showed that there was a low service quality perception among the customers sampled (hence a high customer dissatisfaction). This information made it easy to answer our research question, which sought to investigate the effect of quality on customer satisfaction. The gap score analysis showed that quality had a direct effect on customer satisfaction because the service quality gaps that created low customer quality caused customer dissatisfaction. After evaluating the findings of the gap score analysis, we can see that no service quality dimension contributed to customer satisfaction. In fact, in this analysis, expectations exceeded customer perceptions. Based on this analysis, this paper highlights the need for the Saudi telecommunication companies to improve their services and (consequently) increase their customers’ satisfaction levels. Indeed, since customers expect more from them, they would not maintain competitive businesses if they do not improve their services.

What Limits Characterised the Study

This study was subject to different environmental and internal consistency factors. Although, it was easy to generalise its findings across the larger sample of the Saudi telecommunication industry (because the paper relied on probability sampling techniques), this paper could have questionable consumer perceptions because the Saudi telecommunication firms sampled in the study often have a customer base that transcends the local market. In an unrelated analysis, as shown in earlier chapters of this report, this research study represented different types of information regarding mobile services by sourcing information from interviews, literature reviews, and surveys. The main drawback of the research was the potential inability of the sampled responses to reflect the views of all KSA customers. However, to mitigate this concern, the paper included secondary research to compare with the primary findings. Furthermore, the pilot study (conducted during the research proposal) tested the efficacy of the questionnaires and helped to make sure that the information gathered was cognisant of the main purpose of the paper. Information obtained from the questionnaires improved the credibility of the findings because if we generalised the findings of the research across the entire telecommunications industry (beyond the sample study), we would not have yielded credible findings.

The non-discriminatory sample of Saudi telecommunication firms chosen for this paper could also affect our research findings because consumer perceptions often change, depending on the size of the telecommunication firms. For example, customers of “big” telecommunication companies often expect higher quality services than the customers of small telecommunication companies do. Nonetheless, it is pertinent to understand that these limitations are insignificant to the importance of undertaking this study. In fact, researchers should carry out such studies frequently because customer perceptions often change. This makes it easier to monitor service quality to minimise service weaknesses and maximise its strengths.

Implications of this Study

The purpose of this paper was to understand the main service dimensions for assessing service quality in the Saudi telecommunication industry. The research questions also sought to understand the main service measurement tool for assessing service quality in this industry, evaluate the top variables that reflect customer satisfaction, and investigate the effect of quality on customer satisfaction. Understanding how customers perceive service quality is important for many service-oriented companies. This is true because managers can (now) easily get the right information for improving their service qualities. This paper shows that many researchers have used the SERVQUAL technique (more than other service measurement tools) to measure service quality. Indeed, by using the SERVQUAL model, managers could easily understand the service quality measures that would create a positive impact on their services, or the satisfaction levels of their customers.

This way, they would understand their greatest strengths and weaknesses in the industry and make improvements accordingly. Although the literature review section guided this analysis to focus on the SERVQUAL technique as the main metrics for measuring service quality in the telecommunications industry, our analysis showed that the model was not a good measure for assessing service quality in the Saudi telecommunications space because some service dimensions were unreliable measures of service quality. This finding means that market researchers should use different models and methodologies for measuring service quality in this industry. The product dimension emerged as the weakest measure of service quality because it reported the lowest reliability.

Our findings also showed how many customers had higher expectations of service quality than what they perceived. Although the different service dimensions highlighted in this paper provided different scores, it is important for the firms to improve all their service quality dimensions to have a better customer satisfaction rating. Indeed, since this paper shows that telecommunication firms in Saudi Arabia fail to meet their customers’ service expectations, such firms should invest more resources in training their personnel to provide global standards in service delivery. Similarly, this paper recommends that such organisations should improve their communication strategies to improve their relationships with their clients. Collectively, these factors should reduce the service quality gaps reported in this paper.

Suggestions for Future Research

It is important to undertake further research to understand the service quality concept in more detail. Furthermore, it is pertinent to understand how this concept works, how it measures service quality, and how it could increase an organisation’s productivity and growth. However, future research should use a large sample to better generalise the findings to a larger population. Furthermore, it is advisable to conduct the research in a different and multicultural region to see if the same findings will suffice (people with different cultures often have different customer service expectations). Nonetheless, since this study focused on understanding the main service quality dimensions in the Saudi telecommunications industry, it is a noteworthy study because few researchers have investigated the same research issue in the kingdom. Therefore, it could contribute to existing volumes of literature about service quality in the developing world (with a particular emphasis on the Middle East).

Appendix

Appendix One: Questionnaire

The questionnaire has four sections, expectations and experience.

Section A

Section B Expectations

This section will enquire about your opinion of mobile provider (Operator) service quality. Please, indicate to which extent you think the service provider ‘should’ hold the following attributes. You should rank each statement as follows by circling your choice:

Section C: Perceptions

This section will enquire about your opinion about the actual perceptions of service quality experienced by your current mobile service provide.

Section D: Identifying Service Quality Attributes

Please rank the factors that could influence your opinion in evaluating the service quality offered by a mobile service provider

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