CUSTOMER SATISFACTION WITH MOBILE OPERATORS SERVICES IN LITHUANIAN RURAL AREAS

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1 CUSTOMER SATISFACTION WITH MOBILE OPERATORS SERVICES IN LITHUANIAN RURAL AREAS Lina Pileliene 1, PhD; Viktorija Grigaliunaite 1 Vytautas Magnus University Abstract. In tough competitive conditions of Lithuanian mobile services market, customer satisfaction becomes one of the most important factors for customer retention and attraction. Lithuanian mobile market can be described as being in a maturity stage of its life-cycle: the prices and services of different mobile operators are quite similar. However, the network coverage and signal strength differs main differences can be observed in rural areas of the country. Therefore, the scientific problem solved in the article is: what factors affect customer satisfaction with mobile operators services in Lithuanian rural areas? The aim of this paper is to assess customer satisfaction with mobile operators services in Lithuanian rural areas determining its antecedents and consequences. Questionnaire survey is provided to reach the aim. The research results show that the level of customer satisfaction with mobile operators services in Lithuanian rural areas is moderate. Customer perceived quality and perceived value are found to be two most important factors affecting customer Lithuanian rural areas. Despite the moderate customer satisfaction, the level of customer loyalty with mobile operators services in Lithuanian rural areas is high. Research results indicate that customer expectations and companies image are managed properly and do not need improvement in order to enhance customer satisfaction and / or loyalty with mobile operators services in Lithuanian rural areas. Moreover, customer complaints are also managed properly. Key words: customer satisfaction, Lithuania, mobile operator. JEL code: M31, L89, O18 Introduction The topic of customer satisfaction is becoming one of the most widely discussed in marketing scholar as well as business society. Many companies assess their customer satisfaction achieving to improve the services they provide and determine the key failures leading their customers to defect. Based on a bundle of previous researches (Chu-Mei L. et al., 2014), many customer satisfaction index models (i.e. service-specific, industry-specific, countryspecific, region-specific, nation-specific etc.) are created in order to enable and facilitate satisfaction measurement. Therefore, specific elements having impact on customer satisfaction are established; however, the size of the impact is situation-specific and has to be analysed in a framework of each particular situation. Mobile communication market is one of the biggest markets in Lithuania. Based on a smooth development of the sector, the competition is also very tough. Rapidly changing consumer lifestyle and habits determine the steady growth of the demand for mobile services. As the mobile market has already reached the maturity stage of its life-cycle, the prices and services provided are quite similar. However, the network coverage and signal strength differs. These differences are often more evident in rural areas of Lithuania. As the possibility for customers to change their mobile operator keeping the current phone number forces companies to pay greater attention to customer needs, customer satisfaction becomes a vital factor achieving to retain existing customers; moreover, through the positive word-of-mouth it becomes an important factor leading to new customer attraction. Therefore, the scientific problem solved in the article is: what factors affect customer Lithuanian rural areas? The aim of this paper is to assess customer Lithuanian rural areas determining its antecedents and consequences. Three tasks were set to reach the aim of the article: to assess the level of customer satisfaction with mobile operators services in Lithuanian rural areas; to determine the factors affecting customer Lithuanian rural areas; 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 330

2 to determine the consequences of customer Lithuanian rural areas. In order to reach the aim of the article, the questionnaire survey (based on a standard European Customer Satisfaction Index) is provided. Descriptive statistical analysis and structural equation modelling (SEM) using partial least squares (PLS) path modelling methodology were applied for statistical analysis of survey data. Research results and discussion To reach the aim of the article, the main part is organized into three chapters: substantiation of the topic; methodology of the research; and research results. The empirical research provides guidelines for mobile operators how to enhance customer satisfaction and loyalty. 1. Substantiation of the topic According to the data of Communications Regulatory Authority of the Republic of Lithuania (2016), in Lithuania there are three main mobile operators: Omnitel, Tele2, and Bite Lietuva. According to Puras G. (2016), in the beginning of 2015 there were 4184,1 thousand mobile subscribers in Lithuania (144.8 per cent), which is almost twice as many as in a year 2004 (2174 thousands mobile subscribers), and almost thirteen times as many as in a year 2000 (329 thousands of subscribers). Considering that the amount of mobile subscribers exceeds the population of Lithuania, the market can be called mature. The services provided by mobile operators can be divided into three main groups. The core services are described by a possibility to send / receive calls and text messages at any time and any place; supplementary services, e.g., internet connection, call forwarding, voice mail, mobile banking, data storage, etc.; changing mobile operator keeping the same phone number. Therefore, the latter kind of services enables customers to defect from their mobile operator at any time without having trouble of changing phone number. This kind of situation is very convenient for customers who always seek for better prices and services; moreover, it forces mobile operators to keep high service quality and to provide better value for money. Considering the core services provided by mobile operators, the network coverage and signal strength are becoming the most common quality indicators. Network coverage in urban areas of Lithuania is quite well developed, however in rural territories it is still insufficient. Network coverage by operators is provided in Table 1. Network coverage and signal strength Mobile operator Bite Lietuva Omnitel Tele2 Table 1 Signal strength Weak Moderate Strong Weak Moderate Strong Weak Moderate Strong Lithuanian territory, % Source: Communications Regulatory Authority of the Republic of Lithuania (2016) As it can be seen in Table 1, the network coverage reaches about 100 per cent of the territory of the country; however, in per cent of the area signal strength is indicated to be weak. Presumably, the lower coverage and weak signal can be observed in the rural areas of the country. According to a theory of customer satisfaction, the valuation of all the service-related attributes and features can be performed by applying a customer satisfaction index model. Traditional customer satisfaction index models often contain such variables as customer expectations and perceived quality, which are supposed to affect perceived value; also in addition, company image 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 331

3 can be considered as an antecedent of customer satisfaction. As possible consequences of customer satisfaction in various customer satisfaction indices models can be found such variables as customer loyalty (Ruiz Díaz G., 2017) and customer complaints (Johnson M. D. et al., 2001). However, the influence of the established factors and their combination is situation-specific. Different factors have to be considered as important according to each situation. Knowing the most important factors affecting customer Lithuanian rural areas can help these companies to allocate their investments and key attention areas, thus gaining a competitive advantage in a market. 2. Methodology of the research For the analysis of customer satisfaction with areas, standard European Customer Satisfaction Index (Bayol M.-P. et al., 2000) is applied. Latter model contains seven latent variables and is expressed by six structural equations: 1) Customer Expectations=β 20 + β 21 Image + ζ 2 ; 2) Perceived Quality=β 30 + β 32 Customer Expectations + ζ 3 ; 3) Perceived Value=β 40 + β 42 Customer Expectations + β 43 Perceived Quality + ζ 4 ; 4) Customer Satisfaction=β 50 + β 51 Image + β 52 Customer Expectations + β 53 Perceived Quality+ β 54 Perceived Value + ζ 5 ; 5) Customer Complaints=β 60 + β 65 Customer Satisfaction + ζ 6 ; 6) Customer Loyalty=β 70 + β 71 Image + β 75 Customer Satisfaction + β 76 Customer Complaints + ζ 7. The chosen measurement model is a reflective one. Each latent variable is measured by corresponding manifest variables, which compose the questionnaire of the research. Hence, questionnaire is comprised of two parts: 1) 21 manifest variables; 2) questions related to respondents demographic characteristics. During the research, 10-point evaluation scale was applied for the evaluation of manifest variables. The research was held in Lithuania in Only citizens of Lithuania who live in rural areas could participate in the research. Authors applied stratified random sampling method. Since the object of the research relates to Lithuania s level, which is divided into 10 counties, 25 respondents from rural area of each county were surveyed in person. Hence, the total sample size is per cent of female and 44 per cent of male participated in the survey. 30 per cent of the respondents were at the age group of years old, 46 per cent at the age group of 26-35, 24 per cent at the age group of 36 and more years old. Descriptive statistical analysis and structural equation modelling (SEM) using partial least squares (PLS) path modelling methodology were applied for statistical analysis. IBM SPSS Statistics V.20 and SmartPLS V.3 (Ringle C.M. et al., 2015) software products were used for the statistical analysis of the research results. 3. Research results The analysis of the research results includes the evaluation of the reflective measurement model, structural model, testing of research hypotheses and assessment of variables performance. As it can be seen from Table 2, all the values of Cronbach s Alpha and Composite Reliability are above the threshold value of 0.7, thus indicating no lack of internal consistency reliability in the measurement model. Moreover, the values of average variance extracted (AVE) are above the threshold value of 0.5, hence the degree of convergent validity is sufficient as well. For the assessment of individual indicator reliability, indicators outer loadings are taken into consideration. All of the values of indicators outer loadings are above the threshold value of 0.7 and statistically significant (p < 0.05), indicating that individual indicators are reliable. 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 332

4 For the assessment of discriminant validity in the reflective measurement model, three criteria are applied: Fornell-Larcker criterion, crossloadings and heterotrait-monotrait ratio of correlations (HTMT 0.90 ). Considering the crossloadings criteria, all of the indicators outer loadings with their corresponding latent constructs are greater than their outer loadings with all the remaining constructs. Based on the Fornell-Larcker criterion, each construct s squared root value of average variance extracted is higher than its correlations with all other latent constructs. Considering the HTMT 0.90 criteria, the values of HTMT are lower than the predefined threshold value of 0.90, substantiating that there is no lack of discriminant validity in the reflective measurement model. Consequently, reflective measurement model is assessed as reliable and valid. Variable Evaluation of reflective measurement model Cronbach s Alpha Composite Reliability Table 2 AVE Image Expectations Perceived quality Perceived value Satisfaction Loyalty The evaluation of variance inflation factor (VIF), Cohen f 2 effect size, Stone-Geisser Q 2, and coefficient of determination (R 2 ) are made for the assessment of the structural model. As it can be seen from Table 3 below, the values of coefficient of determination (R 2 ) are all greater than 25 per cent. The R 2 values of variables satisfaction and loyalty are respectively 84 per cent and 68 per cent, hence indicating that the amount of explained variance of latter variables is large. The R 2 value of variable perceived value is 68 per cent, thus indicating that the amount of explained variance of latter variable is large as well. The R 2 values of variables expectations and perceived quality are respectively 50 per cent and 39 per cent, hence indicating that the amount of explained variance of latter variables is moderate. Finally, The R 2 value of variable complaints is 25.4 per cent, nevertheless proportion of variance explained by the fit regarding this variable is sufficient. Table 3 Coefficient of determination Variable R2 Expectations Perceived quality Perceived value Satisfaction Loyalty Complaints All cross-validated redundancy values (Stone- Geissers Q 2 ) for endogenous latent variables are above zero. Consequently, model exhibits predictive relevance. Predictors variables variance inflation factor (VIF) is lower than 5; therefore, there is no multicollinearity in the model. The exogenous variable s effect size on the endogenous variables range from small to large, thus exogenous variable is meaningful. Path coefficients represent the hypothesized relationships among the constructs (see Table 4). As it can be seen, customer expectations have direct, positive and statistically significant influence on perceived quality; nevertheless, expectations have direct, negative and statistically significant influence on perceived value. Image has direct, positive and statistically significant influence on customer expectations and loyalty. Perceived quality has direct, positive and statistically significant influence on customer satisfaction and perceived value. Customer satisfaction has direct, positive and statistically significant influence on customer loyalty and complaints, implying that with the growing satisfaction of customers, their loyalty and perceived complaint handling level also grow. 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 333

5 Perceived value has direct, positive and statistically significant influence on customer satisfaction. Nevertheless, complaints have no statistically significant effect on customer loyalty; expectations and image have no statistically significant direct effect on customer satisfaction. Table 4 Variables Path coefficient Path coefficients Standard Deviation Confidence Interval (2.5 %) Confidence Interval (97.5 %) p value Complaints -> Loyalty Expectations -> Quality Expectations -> Satisfaction Expectations -> Value Image -> Expectations Image -> Loyalty Image -> Satisfaction Quality -> Satisfaction Quality -> Value Satisfaction -> Complaints Satisfaction -> Loyalty Value -> Satisfaction Variables Total Effect Total Effects Standard Deviation Confidence Interval (2.5 %) Confidence Interval (97.5 %) Table 5 p value Complaints -> Loyalty Expectations -> Complaints Expectations -> Loyalty Expectations -> Quality Expectations -> Satisfaction Expectations -> Value Image -> Complaints Image -> Expectations Image -> Loyalty Image -> Quality Image -> Satisfaction Image -> Value Quality -> Complaints Quality -> Loyalty Quality -> Satisfaction Quality -> Value Satisfaction -> Complaints Satisfaction -> Loyalty Value -> Complaints Value -> Loyalty Value -> Satisfaction Despite the fact that expectations and image have no statistically significant direct effect on customer satisfaction, the analysis of the total effects (the sum of direct and indirect effects) 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 334

6 reveals that the only one statistically nonsignificant relationship is the effect of complaints on customer loyalty (Table 5), implying that perceived complaint handling level does not affect customer loyalty. All of the remaining total effects are positive and statistically significant. Hence, customer expectations have positive and statistically significant influence on customer perceived quality, perceived value, satisfaction, loyalty, and complaints. Image of mobile operators has positive and statistically significant influence on customer expectations, perceived quality, perceived value, satisfaction, loyalty, and complaints. Perceived service quality has positive and statistically significant influence on customer perceived value, satisfaction, loyalty, and complaints. Perceived service value has positive and statistically significant influence on customer satisfaction, loyalty, and complaints. Customer satisfaction has positive and statistically significant influence on customer loyalty and complaints. Hair J.F. et al. (2013) suggest that the total effect for the specific endogenous construct represents the importance of the variable, while the average values of the latent variable scores rescaled to a range of zero and 100 represents the performance of the variable. Hence, the performance values of the variables are presented in Table 6 below. Table 6 Performance of variables Variable Performance Image Expectations Perceived quality Perceived value Satisfaction Loyalty Complaints As it can be seen, the level of customer Lithuanian rural areas is moderate. When analysing the performance of the variables that influence customer satisfaction, it could be stated that the level of customer expectations and image of mobile operators are relatively high, while the performance of customer perceived quality and perceived value are relatively low. When compared to the total effects, it could be stated that the most important variable for customer satisfaction perceived quality, has the lowest performance, hence latter variable is the one to concentrate to in order to enhance customer satisfaction with mobile operators services in Lithuanian rural areas. Moreover, perceived value has moderate influence on customer satisfaction (lower than perceived quality) and relatively low performance (higher than perceived quality), thus latter variable becomes the second one that must be managed in order to enhance customer Lithuanian rural areas. Nevertheless, customer expectations have moderate influence on customer satisfaction and relatively high performance, hence it could be stated that customer expectations are managed properly and do not need improvement in order to enhance customer satisfaction with mobile operators services in Lithuanian rural areas. Despite this, image of the mobile operators has relatively high performance, but low influence on customer satisfaction, thus it does not mean that investments are not required, but they should constitute smaller percentage of total investments allocated to satisfy customers. The level of customer loyalty with mobile operators services in Lithuanian rural areas is high. When analysing the performance of the variables that influence customer loyalty, it could be stated that customer satisfaction and perceived quality are the variables that have the highest influence on customer loyalty, but again perceived quality has relatively low performance, hence latter variable is the one to concentrate to in order to enhance customer loyalty. Moreover, 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 335

7 improving perceived quality would result in enhanced satisfaction as well, thus latter variable becomes of the first importance in order to enhance customer satisfaction and loyalty with areas. Customer expectations have low influence on customer loyalty and relatively high performance, hence it substantiates the recommendation that customer expectations are managed properly and do not need improvement in order to enhance customer satisfaction and loyalty with mobile operators services in Lithuanian rural areas. Image of the mobile operators has moderate influence on customer loyalty and relatively high performance. Bearing in mind that when seeking to improve customer satisfaction image should constitute smaller percentage of total investments allocated to satisfy customers as it has low influence on satisfaction, it could be stated that when the level of satisfaction will be improved, then image of mobile operators can constitute a bigger percentage of total investments allocated to enhance customer loyalty. As the research results reveal, improving image is not of the first priority. Perceived value has moderate influence on customer loyalty and relatively low performance, thus latter variable must be managed in order to enhance customer loyalty with mobile operators services in Lithuanian rural areas. Finally, as complaints (perceived complaint handling level) do not influence customer loyalty, the lowest performance of latter variable makes no difference; hence complaints do not require improvement in order to enhance customer loyalty. Conclusions, proposals, recommendations 1) In the information age, new information and communication technologies in everyday life, customers require best services especially regarding communication technologies. Consequently, mobile operators services are expected to be of the best quality. Despite this, the analysis of the research results revealed that the level of customer Lithuanian rural areas is moderate. 2) In order to enhance customer satisfaction with areas, perceived quality is the most important and the first variable to concentrate to in order to enhance customer satisfaction with mobile operators services. Perceived value is the second variable that must be managed in order to enhance customer satisfaction with areas. Customer expectations are managed properly and do not need improvement in order to enhance customer satisfaction with areas. Despite this, investments into the image of mobile operators should constitute smaller percentage of total investments allocated to satisfy customers. 3) The level of customer loyalty with mobile operators services in Lithuanian rural areas is high. Nevertheless, improving perceived quality would result in enhanced satisfaction and loyalty, thus latter variable is substantiated to be of the first importance in order to enhance customer satisfaction as well as loyalty with mobile operators services in Lithuanian rural areas. Perceived value is the second variable that must be managed in order to enhance customer loyalty with mobile operators services in Lithuanian rural areas. Regarding customer loyalty, customer expectations are again substantiated to be managed properly and do not need improvement in order to enhance customer satisfaction and / or loyalty with mobile operators services in Lithuanian rural areas. As the research results reveal, improving image is not of the first priority, hence when the level of satisfaction will be improved, then a bigger percentage of total investments 1 Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 336

8 allocated to enhance customer loyalty can be spend on improving image of mobile operators. Finally, complaints do not require improvement in order to enhance customer loyalty. 4) By following the provided recommendations mobile operators can improve customer satisfaction and loyalty with mobile operators services in Lithuanian rural areas by investing into variables that are important, but of low Bibliography performance, instead of allocating investments into variables that do not affect customer satisfaction and loyalty or are already of high performance. Hence, these recommendations may result in the balance between the importance and performance of factors affecting customer satisfaction and loyalty with mobile operators services in Lithuanian rural areas. 1. Bayol, M.-P., Foye, A., Tellier, C., Tenenhaus, M. (2000). Use of PLS Path Modelling to estimate the European Consumer Satisfaction Index (ECSI) model. Statistica Applicata, Volume 12, No. 3, pp Communications Regulatory Authority of the Republic of Lithuania (2016). Retrieved: 3. Chu-Mei, L., Chien-Jung, H., Mei-Liang, C. (2014). Relational Benefits, Customer Satisfaction, and Customer Loyalty in Chain Store Restaurants. International Journal Of Organizational Innovation, Volume 7, No. 1, pp Hair, J. F., Ringle, C. M., Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, Volume 46, No. 1-2, pp Johnson, M. D., Gustafsson, A., Andreassen, T. W., Lervik, L., Cha, J. (2001). The Evolution and Future of National Customer Satisfaction Index Models. Journal of Economic Psychology, Volume 22, pp Puras G. (2016). Development of Lithuania's communications sector : pres-accession challenges and current achievements. Retrieved: 7. Ringle, C.M., Wende, S., Becker, J.-M. (2015). SmartPLS 3. Bonningstedt: SmartPLS. Retrieved: Access: Ruiz Díaz, G. (2017). The influence of satisfaction on customer retention in mobile phone market. Journal of Retailing and Consumer Services, Volume 36, pp Corresponding author. Tel.: ; fax: address: lina.pileliene@vdu.lt. 337