Kasetsart Journal of Social Sciences
journal homepage: http://kjss.kasetsart.org
The mediating role of cooperate image between service quality, perceived value and customer loyalty: A study of telecommunication industry in eastern region of Thailand
Fei Lu
Marketing, Business Administration, Business & Technology, Stamford International University, Bangkok 10250, Thailand
Abstract
This research proposed an innovative conceptual paradigm and aims to investigate cooperate image’s mediating role in the path association between service quality, perceived value and customer loyalty in Thai telecommunication industry by applying SOR theory. It will elucidate the advantage of examining the organismic component that affects the loyalty antecedents from holistic viewpoint. From 391 collected usable questionnaires, statistical results obtained from adopted research methodology such as structural equation modeling and path analysis have confirmed cooperate image’s mediating role in the relationship between perceived functional mobile service quality and customers’
loyalty in Thai context. These findings will deliver valuable information for management of telecom companies regarding how to create sustainable marketing strategies that can promote customer loyalty in Thai cultural context.
© 2024 Kasetsart University.
Article Info
Article history:
Received 8 February 2023 Revised 12 May 2023 Accepted 21 May 2023
Available online 15 December 2023
Keywords:
loyalty, mediating role, perceived value, service quality,
telecommunication industry
E-mail address: [email protected].
https://doi.org/10.34044/j.kjss.2024.45.1.34 2452–3151/© 2024 Kasetsart University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction
In service marketing, customer loyalty is an imperative aspect of study for both academician and practitioners alike as faithful customers are significant intangible assets that can convey various forms of tangible benefits to companies. These benefits range from short term financial performances such as increased revenue and profit margin, to long term sustainable advantages that includes customers’ willingness to pay premium prices, resistance to offering from competitive firms and positive word of mouth effect (Agha et al., 2021). Thus, the
significance of studying customer loyalty is evident, particularly in more saturated markets and industries where it is obligatory for companies to place emphasis on preserving their existing customers to survive fierce business competition. The customer loyalty concept is nevertheless a complex notion, and scholars have presented various explanations emphasizing different aspects and dimensions over past decades. Academics also proposed various approaches to measure customer loyalty and explore its determinants in different settings such as the food industry, airline industry, and hotel industry (Casidy & Wymer, 2016; Hapsari et al., 2020).
To date, loyalty antecedents identified through prior studies include customer satisfaction, trust, switching costs, commitment, to name a few (Hapsari et al., 2020;
Kaur & Soch, 2018). Since the comprehensive study of all loyalty antecedents will be beyond the scope of any single research, this paper aims to investigate three most acknowledged customer loyalty antecedents advocated in literature, perceived service quality, perceived value, and corporate image. Each of these three factors is an intricate concept and can be examined through various model constructs, and often there are underlying relationships exhibited among them in different contexts (Hasan, et al., 2020). In loyalty literature, although the individual impact on loyalty of these three antecedents has been discussed and investigated, their intertwined effects on loyalty have not been adequately addressed and among a limited number of research studies exploring this issue, conclusions remain equivocal (Agha et al., 2021).
Mobile telecommunication industry has received greater worldwide attention recently as it is replacing traditional landline with its high portability, innovative hardware devices and software applications (Chigwende, 2021). In Thailand, to conform to WTO requirements, the government approved its “Master Plan for Telecommunications Development” in late 1997, which allows issues of licenses to private enterprises and market competition through the regulation of an impartial government body. This signals that the Thai telecom market has shifted from a government monopoly into a synchronized open market (Mesher & Jittrapanun, 2004). Growth and changes have resulted in intense competition, where the Thai mobile service market has reached 120.85 million subscribers, with 129.7 percent penetration rate owing to multiple SIM ownership (Telecommunication Policy and Resource Management Bureau [TPRMB], 2022). Moreover, as switching costs are falling, the Thai telecom market is experiencing unprecedented customer turnover. For instance, Advanced Info Service (AIS), the leading service provider in Thailand, reported an astonishing 2.8 percent monthly blended churn rate in the third quarter in 2021 (TPRMB, 2022). Considering each 2.5 percent customer turnover can lead to approximate 13–43 percent profit loss for a company (Heskett et al., 1994), it is crucial for companies in such competitive and saturated industry as Thai telecom to understand customers and maintain their loyalty.
In customer loyalty literature, classical stimulus- response consumer behavior models emphasize adopting positive reinforcement as the foundation of operant conditioning to induce customers’ repeated consumption or procurements of the products and services, thus, regarding consumer behavior as spontaneous and subjective to the conditioning stimulus factors (Tan,
2019). However, as companies begin to understand the importance of learning cognitive structures underlying customer’s feelings of involvement, another alternative functionalist orientation approach in experimental psychology named Stimulus-Organism-Response (S-O-R) theory was adopted in marketing context to investigate customer loyalty (Yu et al., 2021). This theory is suggestive of cognitive learning that focuses on the how of consumption phenomenon is learned rather than only outcome of learning, which can offer deeper understanding of customer loyalty formation process (Tan, 2019). Therefore, S-O-R theory has been adopted in this proposed research study to attempt to examine the loyalty of mobile phone service subscribers in Thailand.
The eastern region of Thailand has become a key economic driver of the country since the Thai government launched its Eastern Seaboard Development Program under the 5th National Economic and Social Development Plan in 1982, which aimed to progress Thailand’s manufacturing sector from light to a heavy export- directed commercialized industry. The Gross Regional Product of the eastern region accounted for 17 percent of national GDP in 2018 (National Economic and Social Development Board, 2018), which ranked highest among all other regions in Thailand (except for Bangkok).
Moreover, under the 12th National Economic and Social Development Plan, Thailand launched its “Eastern Economic Corridor” project in 2016 aiming to further develop its economy by focusing on high-value-added industries such as electronics, automation, developing digital economy (Tontisirin & Anantsuksomsri, 2021) and connecting citizens to digital services, which necessitates the advance of telecommunication infrastructure in the region. Such recent approaches undertaken by Thai governments as lengthening telecommunications infrastructure coverage in the region by means of ‘Net Pracharat’ and border internet projects, providing mobile phone networks to nearly 30,000 households in the region and building 600 community digital centers will jointly spur demand for telecommunication services due to user base enlargement (Ninkitsaranont, 2019). With steady growth for service, mobile phone service providers are also facing fierce competition on price, operating costs, and customer loyalty, which conjointly will affect their profitability and survival. However, to date, no study in existing literature could be found investigating mobile phone service customers’ loyalty in the eastern region of Thailand;
therefore, considering the significant role that the eastern region of Thailand’ plays in the country’s economy, it was chosen as the experiment site of this proposed study.
The scope of current studies regarding customer loyalty in Thai telecom industry is scant, and among the limited prior studies, primary focus was to investigate direct impact of antecedent factors on customer loyalty (Dhasan & Kowathanakul, 2021). To date, there has not been a single empirical study examining the mediating effect of corporate image in the relationship between customer loyalty and its factors in the Thai telecom market by applying a functionalist orientation approach such as S-O-R theory. Therefore, this study aims to fill such gap in marketing literature with an empirical method in Thai context using an unconventional approach. The objective of this research is twofold: First, to investigate the impact of three chosen antecedents on customer loyalty in the Thai telecom market; second, to investigate the mediating effect of corporate image in the path association between perceived service quality, service value and loyalty in the Thai telecom market.
Literature Review Loyalty
Customer loyalty concept is intricate in nature and has been defined by marketing scholars from attitudinal perspective or behavioral perspective based on their respective research objectives and various contexts (Hapsari et al., 2020). Attitudinal loyalty construct represents customers’ psychological or emotional preference towards a given product or service and is advocated as positive affective emotional state towards brand, while behavioral loyalty refers to customers’ behavior and activity aspects such as purchase intentions or repetitive buying behavior (Casidy & Wymer, 2016). A third construct of defining customer loyalty is the composite approach, which implies that loyalty should be viewed as a mixture of both affirmative attitudes and behavior intentions. This approach is advocated to offer better understanding of customer’s loyalty, minimize model complexities, identify and measure loyalty more effectively, and augment its prognostic power (Kaura et al., 2015).
Considering these advantages, integrated loyalty construct was applied in various business fields, such as retailing, e-commerce, and telecommunication (Hapsari et al., 2020).
Perceived Service Quality
Perceived service quality is consumers’ perceptions of overall service performance, the assessment of superiority of brand when compared to other alternatives,
and it is considered an essential component in building customer loyalty (Lacap et al., 2021). By augmenting service quality, companies can encourage the consumer to re-patronize, be insensitive to price variation, and indorse services to others, and thus establish customer loyalty (Aydin & Ozer, 2005). The evaluation of perceived quality is subjective as it is based on consumers’ own perception of benefits and need realization. Marketing scholars attempted to describe quality construct from various aspects in literature, and among existing quality measurement constructs, the SERVQUAL model based on the disconfirmation concept is considered most influential although the performance-based only model (SERVPERF) has also been found superior in generating valid and reliable model fit statistics in literature (Lacap et al., 2021). In the telecommunication industry, studies showed that strong associations were not found between perceived quality and loyalty due to the absence of crucial related service quality dimensions such as network coverage, signal strength, and internet speed in SERVQUAL model (Dhasan & Kowathanakul, 2021). In this regard, perceived quality measure in this research adopted industry specific construct suggested in similar empirical research (Qayyum et al., 2013).
Perceived Value
Perceived value is stated as practical worth or economic benefit related with procuring a product is buyers’ judgment of benefits acquired from acquisition centered on the perception of obtained gains and incurred expenses (Nguyen & LeBlanc, 2001). Kuo et al. (2009) concluded and outlined customer perceived value from diverse viewpoints such as financial, qualitative and other social psychological aspects. Financial aspect explains benefit gained with relatively less monetary cost, equivalently, perceived value is denoted as the calculated difference between money consumers are willing to pay and benefits received (Kuo et al., 2009). Quality aspect refers to using minimum amount of cost to acquire relative high-quality product, and social psychological aspect explains that customers’ perceived value can be viewed as the significant effects of purchasing product or service for the collective social group they reside in, such as social economic position, social culture impact or social self-concept (Nguyen & LeBlanc, 2001). In the telecom industry, the perceived value can be described as the assessment of the usefulness of mobile service by subscribers, on the basis of their overall costs and prior experiences when patronizing mobile communication services (Hasan et al., 2020).
Corporate Image
Corporate image can be defined as idiosyncratic understanding, attitude, or a combination of both towards a company and its offering, or the overall perception exists on consumers’ minds and is formed through a process integrating concepts, emotions, and preceding experiences with a company and converted into meaning based on stored categories (Hasan et al., 2020). It is also suggested that corporate image is customer’s overall accumulated impressions stored in their minds during their time spent with that company (Le, 2022), thus, corporate image of a company is largely determined by individual customer’s evaluation about the services they acquire (Grönroos, 1984). From studies in literature, it becomes evident that corporate image represents the impressions and links the opinions and attitudes that are deeply held in customers’
mental recollection towards a company, and can act as a sift that impacts the perception of company operations (Hasan, et al., 2020). Moreover, it was found that corporate image can also be associated with tangible aspects of the firm such as company’s name, physical structure, product or service range, post-purchase services, and employee’s expertise (Nguyen & Leblanc, 2001). The overall image of a company can be influenced by both service quality and perceived value; therefore, from resource-based perspective, corporate image can be conceptually perceived as firm’s intangible resource that can strengthen customer attitudes and behavioral intentions such as customer satisfaction, buying intention, and consumer loyalty in service industries such as telecommunication, retailing, and education (Hasan et al., 2020; Le, 2022).
Stimulus-Organism-Response (SOR) Theory
SOR theory was first put forward by Russell and Mehrabian (1974) as guiding paradigm in marketing literature. In SOR theory, stimulus are factors that influence the internal psychological status of consumers (organism) and provoke consumers’ behavioral reactions or response.
The proposed loyalty construct in this study can be explained by SOR theory where perceived service quality and perceived value are stimulus, corporate image acts as organism, and customer loyalty is the resulting response of customers, and this relationship has been supported by relevant early studies. For instance, Hasan et al. (2020) claimed in their studies that perceived service quality and perceived value could be recognized as the environmental cues that prompt customers’ emotional response such as re-purchase intention and attitudinal commitment, and research conducted by Alam and Noor (2020); Özkan
(2019) showed that corporate image could be recognized as the mediator (organism) that linked perceived service quality, perceived value and customer loyalty in Bangladesh retailing industry and Turkey banking industry. Therefore, it can be anticipated that customers’ perceived service quality and perceived value of telecommunication service will assist them form affirmative image towards mobile service companies and thus enhance customer loyalty.
Hypotheses Development
Perceived service quality and customer loyalty This study proposes that perceived quality of the mobile service Thai participants received can positively influence their loyalty to mobile service providers. This conception evident as perceived service quality is regarded as a vital antecedent of customer loyalty. For example, Gandhi and Bhattacharya (2021) concluded that service quality distributors in retail industry directly influence customer satisfaction and loyalty. Perceived service quality thus can be considered as an environmental stimulus according to SOR theory, and it is supported by a plethora of early studies which also includes the telecommunication industry. For instance, Lacap et al. (2021) concluded the statistical significance of perceived service quality in determining the customers’ attitudinal and behavior attachment to mobile service companies. Thus, we propose the first hypothesis as follows:
Hypothesis 1: Thai customers’ perceived service quality of telecommunication service has a positive effect on their loyalty to mobile service companies.
Perceived value and customer loyalty
This study posits that the perceived value of acquired mobile service by Thai respondents positively affects their loyalty to service operators. In literature, it is evident that perceived value can be considered as environmental stimuli under SOR theory. For instance, Hasan et al.
(2020) explained that perceived value is recognized as a superordinate objective while loyalty is a subordinate goal, hence, consumer’s value paradigm regulates their behavioral intentions of loyalty. In the telecommunication industry, Svotwa et al. (2020) applied SOR theory to explain that perceived value is recognized as a stimulus as it allows customers to achieve their composite objectives such as financial benefit of cost saving, social needs of connecting with others, and so on. Furthermore, Hasan et al. (2020) also suggested that perceived value significantly influences customer satisfaction and loyalty in Bangladesh telecommunication industry. Therefore, we propose the second hypothesis as follows:
Hypothesis 2: Thai customers’ perceived value of telecommunication service has a positive effect on their loyalty to mobile service companies.
Corporate image and customer loyalty
This study also postulates that the effect of perceived service quality and value on loyalty can be mediated by corporate image. Under SOR theory, corporate image represents the internal psychological status links stimuli and responses (Chang, 2020), thus, when affirmative stimuli such as excellent service quality and great perceived value occurs, customers’ optimistic interior psychological condition will lead to positive perceived corporate image then commitment or loyalty, thus, considering image as an organism is justifiable (Alam & Noor, 2020). In telecom industry, Kaur and Soch (2018) confirmed corporate image’s mediating role between satisfaction and attitudinal loyalty in Indian context, and their findings were also supported by research conducted in Indonesia telecom industry (Rachmawati & Mohaidin, 2019). Thus, we propose another two hypotheses:
Hypothesis 3: Mobile service companies’ corporate image has a mediating effect on the relationship between Thai customers’ perceived service quality and loyalty of mobile service.
Hypothesis 4: Mobile service companies’ corporate image has mediating effect on the relationship between Thai customers’ perceived service value and loyalty of mobile service.
The proposed loyalty framework incorporates all hypothesis is illustrated in Figure 1.
Office, Ministry of Digital Economy and Society [NSO], 2019), and its gender ratio (male/female = 1.28) was the closest to the average of the eastern region (1.27).
Moreover, Chonburi province has the highest average monthly income (32,355.77 Baht), highest average monthly household expenditure (28,001.46 Baht), and highest mobile phone ownership (30%) in the eastern region of Thailand (National Statistical Office, Ministry of Digital Economy and Society [NSO], 2021). Therefore, Chonburi province can be considered as a suitable sampling frame for our proposed study. The population in this study was customers of mobile service subscription (prepaid or postpaid) to any five major Thai mobile service carriers (AIS, DTAC, TRUE, CAT, TOT) which was 63,750,740 (NSO, 2021), thus a purposive sampling was adopted to collect questionnaires from these five Thai mobile service providers’ customers at various locations of the chosen data collection sites until the 400 suggested sample size were obtained (Yamane, 1973).
After testing 50 collected questionnaires in the pilot study, psychometrics parameters of proposed research instrument were all above the threshold levels, indicating the feasibility of a larger scale application.
The participants were drawn from two largest shopping malls in Chonburi, namely, Central Chonburi and Central Sriracha. The two shopping malls were chosen to acquire research sample because of their high daily visiting rate, and major Thai mobile service operators have designated service offices in these two locations, thus mobile subscribers of different mobile companies can be recruited. In total, 500 self-administered questionnaires were distributed to respondents by researcher assistants at experimental sites near Thai mobile service operators’ offices. The questionnaires with cover letters were dispersed with clear explanations of the research purpose and participants were informed that their responses would be anonymous. Upon completing questionnaires, customers were given a complementary gift as their participation reward. Data collection process took three weeks to complete and included 416 completed questionnaires, which yields an 83.2 percent response rate.
Measures and Data Analysis
Scales and items used in this study were adopted from existing literature. Perceived quality scale adopted from Qayyum et al. (2013) contained five items. Sample question was “My mobile operator provides sufficient geographical coverage”. Perceived value scale adopted from Lai (2004) comprised five questions. Sample item was “By using services of this mobile operator at this price, Figure 1 Proposed loyalty framework
Perceived Service Quality
Perceived Value
Corporate Image Customer
Loyalty H1
H2 H3
H4
H3
H4
Methodology
Participants and Data Collection
The sampling frame of this research was chosen from the population of Chonburi province in Thailand.
Chonburi province’s reported population was 1,535,445 and accounting for 30 percent of the total population in the eastern region of Thailand (National Statistical
I am getting my money’s worth”. Corporate image scale adopted from Aydin and Ozer (2005) included five items.
Sample question was “My mobile operator is innovative and forward looking”. Finally, composite brand loyalty scale adopted from Qayyum et al. (2013) contains four items. Sample question was “I will continue using service of this mobile operator”. All questions were assessed by a 5-point Likert scale to measure the sample respondents’
level of the agreement, which ranges from 1 (strongly disagree) to 5 (strongly agree). The original questions were translated into Thai language for Thai participants.
The back-translation was then performed by bilingual specialists to ensure the validity of translated documents.
After data cleaning process, 26 unusable questionnaires were identified and excluded, thus yielded a total sample size of 391. The demographic profile of respondents’
is illustrated in Table 1. The majority of the participants were female (61.9%) and customers age between 23–27 years old was the largest group (39.9%). Table 1 also shows that the majority respondents in this study had an income level less than 15,000 Baht (61.1%).
Statistical analysis began with normality test, then testing proposed construct’s validity and reliability by confirmatory factor analysis and common method bias methods, followed by multicollinearity test. Structural equation modeling (SEM) and path analysis were conducted to test hypotheses through statistical analysis packages SPSS and AMOS (version 21). A sample size of 200 was recommended by literature for statistical analysis (Hair et al., 2012), thus 390 sample collected in this study was appropriate for the required statistical analysis.
behaviors are the results of physical, mental progresses and accrued life experiences. Moreover, Kalia et al. (2021) concluded that the path association between service quality dimensions and customer loyalty was shown more significant among female customers than the male customers, and such dissimilarities could be the outcome of distinct behavior and characteristic patterns associated with gender. The third controlled variable was customer’s income as it was advocated in literature that higher income is to a certain extent associated with better education, more exposure to developed cultures and sustainable lifestyle.
Thus, they concluded that higher income customers were more loyal to service providers with better quality in the telecommunication sector (Klopotan et al., 2016).
Results
The normality test was conducted using SPSS prior to statistical analysis, and the respective smallest skewness and kurtosis values were -0.68 and -0.373, both of which fell between the acceptable ranges suggested in literature (Brown, 2006), indicating that the data in this proposed study were normally distributed and suitable for structural equation modeling analysis. The convergent validity was assessed by evaluating the factor loadings of all the variables in the proposed model and items with factor loadings below threshold 0.5 were omitted (Hair et al., 2012). The remaining items are shown in Table 2, indicating that the convergence validity passes the recommended criteria. Two sub-dimensions were further factored out from the perceived quality dimensions, and were named functional quality and technical quality, thus the proposed construct was modified in Figure 2, and the composite reliability values for all dimensions were above the acceptable threshold (0.7) suggested in literature (Hair et al., 2017), except for technical quality dimension.
Discriminant validity was evaluated by using square root of the average variance calculated from latent variables to compare with correlations among latent variables, and the AVE values also indicated that customer’s perceived technical quality (TQ) dimension did not meet the validity test criteria (0.5) suggested in literature (Hair et al., 2017). However, it was not excluded from our model due to the limited items in the dimension and needed to be further analyzed. The model reliability was assessed by Cronbach’s alphas and composite reliability coefficients, both of which were suggested to be higher than 0.6 (Cronbach, 1951; Hajjar, 2018).
The results of these tests are shown in Table 2 and Table 3, as all Cronbach’s alphas’ values were above 0.6, which indicated that the reliability of proposed research instruments Table 1 Participant demographic summary
Frequency Percentage Gender
Male 149 38.1
Female 242 38.1
Age
18–22 112 28.6
23–27 156 39.9
>27 123 31.5
Income Level
0–9,000 baht 61 15.6
9,001–15,000 baht 178 45.5
> 15,000 baht 152 38.9
Control Variables
The control variables of this study were participants’
age, gender, and income because they were found to affect customer loyalty by earlier research studies. For instance, Jahan et al. (2019) concluded in their research age-linked dissimilarities had significant moderating effect on customer satisfaction and loyalty as it was advocated that consumer
Table 2 Validity and Reliability Summary
Component Eigenvalues Cronbach’s α CR
1 2 3 4 5
PV1 .587 5.15 .802 .802
PV2 .776
PV3 .723
PV4 .642
B1 .739 1.743 .790 .772
B2 .757
B3 .614
B4 .651
CI2 .661 1.513 .790 .791
CI3 .705
CI4 .671
CI5 .695
PQ1 .650 1.120 .656 .381
PQ2 .627
PQ4 .715 1.079 .645 .947
PQ5 .631
Table 3 Discriminant Validity Variable Name AVE Perceived
Value Technical
Quality Functional Quality Perceived Value .436 .660
Technical Quality .359 .580 .599
Functional Quality .888 .838 .775 .942
Figure 2 Modified loyalty construct
PQ 1
PQ 4 PQ 2
PQ 5
PV 1
PV 3 PV 2
PV 4
TQ
FQ
PV
C I
CL
CI 2 CI 3 CI 4 CI 5
CL 1
CL 3 CL 2
CL 4 .42
.49
.49 .48
.66 .73 .74
.70
.18 .04
.14
.38
.60 .66 .49 .56
.46 .09
.21
.59 .67
.69
.65
were acceptable. Multicollinearity was assessed by variance inflation factor (VIF) values for all predictor variables, which were perceived value (1.547), functional quality (1.334), and corporate image (1.636), thus, it indicated that there was no multicollinearity concern for proposed model as suggested VIF value is below 3.3 (Petter et al., 2007). The measurement scale was also examined for common method bias (CMB) by Harman’s one-factor test. The factor analysis result revealed that the highest loading of a single factor was less than 33 percent, which confirmed CMB was not a concern for this study.
The results of hypotheses testing are summarized in Table 4. Model 1 measured the direct effects between predictor variables (antecedents) and dependent variable (customer loyalty), while Model 2 measured the indirect relationships between predictor variables through the mediating variable (corporate image). Lastly, all control variables (customer’s demographic characteristics) were included in both models to assess their effects in both relationships. Results of the SEM analysis are shown in Table 4, which revealed that chi-square statistic (182.211) of the model was significant and chi-square to degrees of freedom ratio (2.047) was also below threshold value of 3 (Kline, 2005) indicating good fitting of the modified model. The other model fit indices included comparative fit index (CFI), Goodness-of-Fit Index (GFI); normed fit index (NFI); Tucker-Lewis index (TLI); and root mean square error of approximation (RMSEA), which all were
within acceptable range indicating good fit of the modified loyalty model.
The hypothesis testing results and mediating effect of corporate image are also summarized in Table 4, where Model 1 illustrates direct effects of independent variables (technical quality, functional quality and perceived value) on customer loyalty, while Model 2 shows indirect effects of above mentioned independent variables on customer loyalty.
Moreover, Hypothesis 1 stated that perceived service quality has a positive effect on loyalty, and the correlation coefficients estimates shown in Model 1 revealed that both technical quality (.180) and functional quality (.091) positively related to loyalty. Therefore, Hypothesis 1 was supported. Hypothesis 2 stated that perceived value has a positive effect on loyalty, and the correlation coefficients (.462) shown in Model 1 also confirmed such positive relationship. Therefore, Hypothesis 2 was supported.
Hypothesis 3 stated that corporate image mediated the relationship between perceived service quality and loyalty, and this mediating effect was evaluated by measuring predicted variable’s correlation coefficient variation after introducing mediating variable. The results in Model 2 showed that Technical Quality’s correlation coefficient did not change
in significance, which indicated there was no mediation effect. In contrast, Functional Quality’s coefficient estimates dropped in significance, which signified that there was significant mediation effect. Therefore, Hypothesis 3 was partially supported. Hypothesis 4 stated that corporate image mediated the relationship between perceived service value and loyalty. The results in Model 2 revealed that the perceived value’s correlation coefficient did not change in significance, which meant that there was no significant mediation effect. Thus, Hypothesis 4 was not supported.
Lastly, only two control variables (gender and income) were found weakly correlated with loyalty in Table 4.
Discussion
General Discussion of the Findings
The statistical analyses results of this research revealed that under SOR theory, perceived service quality and value served as environmental stimuli which were found to be significant antecedents of customer loyalty. This finding was consistent with some prior studies which concluded that service quality (Yuan, 2019) and perceived value of (Alam &
Noor, 2020) can both influence customer perception and behaviors, whereas it was not fully in line with other research studies (Dhasan & Kowathanakul, 2021), which claimed that functional quality of the telecom services such as employee’s enthusiasm, manners and prompt response did not significantly influence Thai customer’s loyalty.
Under SOR theory, this study’s findings also offers new evidence showing the degree to which corporate image served the role of organism that processes the stimuli and enables customers to response in loyalty model. Hypotheses testing results confirmed that corporate image had significant mediating effect between functional service quality and customer loyalty, which is consistent with previous
studies that concluded that customer’s feeling of service quality and value did not automatically build loyalty, but an indirect influence over corporate image is needed (Alam & Noor, 2020; Özkan et al., 2020). This implies that Thai subscribers who perceived receiving higher functional service quality such as professional and customized services will form positive images of a company then subsequently remain loyal. Furthermore, this study concluded that corporate image did not mediate the relationship between technical service qualities, perceived value and customer loyalty, which was in line with prior research (Rachmawati &
Mohaidin, 2019). It infers that technical quality and perceived value can retain Thai customers’ loyalty regardless of their perceived corporate images.
Theoretical and Managerial Implications
This research extends the body of knowledge regarding corporate image’s mediating role in customer loyalty’s relationship with its antecedents in Thai telecom industry context. The theoretical contributions include, first, that it broadens loyalty literature that mainly focused on direct relationships between loyalty and its antecedents in western cultural settings or non-ASEAN countries, by exploring interaction effects of its influential antecedents in sequentially logical order and confirming corporate image was crucial in maintaining Thai mobile subscribers’ loyalty. Second, by applying SOR theory knowledge, this study elucidated the advantage of examining the organismic component that affects the loyalty antecedents from holistic viewpoint.
The mediating role of corporate image broadens prevailing knowledge by offering more insights for marketing academics to enhance their understanding on the circumstances that can augment or attenuate Thai mobile subscribers’ behavioral loyalty response. This study suggests loyalty factors may have various effects on loyalty through different organismic components that are explained by SOR theory.
Table 4 Hypotheses Testing Results
Variables Model 1 Model 2
Estimate SE p Estimate SE p
Main independent variable
TQ --> CL .180 .041 .000*** .042 .042 .003***
FQ --> CL .091 .042 .047** .136 .041 .366
PV --> CL .462 .050 .000*** .381 .054 .000***
Mediating variable
CI --> CL N/A .211 .057 .000***
Control variable
Age .002 .054 .980 .006 .053 .922
Gender -.057 .058 .157* -.056 .057 .152*
Income .094 .060 .115* .092 .059 .152*
R-square .395 .421
R-square (adjusted) .386 .410
Model fit indices: χ2 = 182.211 (p < .00), df = 89, χ2/df = 2.047, CFI = .954, GFI = .944, NFI = .916, TLI = .939, RMSEA = .052.
Notes: CL= Customer Loyalty, TQ = Technical Quality, FQ = Functional Quality, PV = Perceived Value, CI = Corporate image.
* p ˂ .1, ** p ˂ .05; *** p ˂ .001.
This study also provides suggestions for management of telecom companies regarding service design and management that can promote customer loyalty in Thai cultural context. Findings of this study showed that Thai telecom companies must incorporate measures to improve quality and value in their marketing strategies to retain customer loyalty, such as conducting regular equipment updates to provide better signal strength and data transferring speed, expanding regional network coverage to provide wider geographic range of optimal connection, providing training to improve employee’s technical knowledge and professional manner, and upgrade service packages values in the form of longer free inter-network phone calls, higher internet usage limit, lower monthly or annual subscription fee, and so on. Considering corporate image’s mediating effect, telecom companies must recognize the significance of functional service quality including employee’s proficient knowledge and etiquette, complaint handling techniques, and mental inclination to serve customers with enthusiasm and dedication and improve corporate image.
As technical quality in mobile service is diminishing due to technology advancements and service package price is becoming similar thanks to market competition, functional service quality will play a more significant role in maintaining long-term customer loyalty and should be continuously incorporated into the sustainable marketing strategies for all service providers in the telecom industry.
Conclusion and Recommendation
Despite its significant findings, this research has a few limitations worthy of note. First, research outcomes are based on a small sample of Thai customers visiting two local shopping malls at the time of this study in one single province of Thailand, thus, the scope and representativeness of participants and research findings have limited generalizability. Second, the research instrument adopted in this study was structured questionnaire surveys, thus, the collected responses may contain subjective bias. Third, this study measured service quality with the second-order reflective–formative construct format to predict loyalty as suggested by literature, though the same loyalty framework can be attempted with further higher order construct.
Fourth, this study was cross-sectional in design, thus, causal relationships among variables necessitate confirmation through further longitudinal researches due to the dynamic telecommunication industry environment. Finally, to make the research model more robust, future studies can incorporate other predictors of customer loyalty, such as customer satisfaction, trust, switching costs, and experiment mediating effects of variables apart from corporate image.
Conflict of Interest
The author declares that there is no conflict of interest.
Fundings
This research received financial support from Stamford International University.
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