LOYALTY AND PERCEIVED VALUE IN MOBILE TELECOMMUNICATIONS SERVICES IN COLOMBIA
FIDELIDAD Y VALOR PERCIBIDO EN LOS SERVICIOS DE TELECOMUNICACIONES MÓVILES EN COLOMBIA
Ckayrom Alfonso Valencia Cifuentes*
Sandra Patricia Rojas-Berrio**
*Administración de Empresas, Universidad Nacional de Colombia. Bogotá, Colombia. Magíster en Administración, Universidad Nacional de Colombia. Bogotá, Colombia. Investigador Grupo de Investigación Management and Marketing. Correo electrónico: ckavalenciaci@unal.edu.co
**Administración de Empresas, Universidad Nacional de Colombia. Bogotá, Colombia. Maestría en Administración, Universidad Nacional de Colombia. Bogotá, Colombia. Doctorado en Ciencias Administrativas, Instituto Politécnico Nacional. Ciudad de México, México. Profesora Asociada, Universidad Nacional de Colombia. Bogotá, Colombia. ORCID: https://orcid.org/0000-0002-1148-3779 Correo electrónico: sprojasb@unal.edu.co
Citar este artículo así:
Valencia, C. A. y Rojas, S. P. (2024). Loyalty and perceived value in mobile telecommunications services in Colombia. Revista Criterio Libre, 22(41).
Fecha de recepción: 21 de abril de 2024 Fecha de aprobación: 14 de mayo de 2024
Abstract
It is essential for telecommunications operators to implement strategies aimed at building customer loyalty in order to improve their financial performance. One key factor that positively influences customer loyalty is perceived value, which should be considered in marketing strategies to strengthen this relationship. This study aims to analyze the connection between loyalty and perceived value among mobile telecommunications customers in Colombia. The hypothesis proposes a relationship between the dimensions of value and loyalty, based on a previously developed scale. Using a methodological approach that includes 406 surveys of mobile service users in Colombia, the relationship between these constructs is verified. The results indicate a positive and direct relationship between loyalty and perceived value across the four proposed dimensions.
Keywords: Loyalty, perceived value, value dimensions, mobile telecommunications.
JEL Code Article: L96, M38, M30, D46, L96, M31
Resumen
Es fundamental que los operadores de telecomunicaciones implementen estrategias encaminadas a fidelizar a sus clientes para mejorar su desempeño financiero. Un factor clave que influye positivamente en la fidelidad del cliente es el valor percibido, que debe considerarse en las estrategias de marketing para fortalecer esta relación. Este estudio tiene como objetivo analizar la conexión entre la lealtad y el valor percibido entre los clientes de telecomunicaciones móviles en Colombia. La hipótesis propone una relación entre las dimensiones de valor y lealtad, a partir de una escala previamente desarrollada. Utilizando un enfoque metodológico que incluye 406 encuestas a usuarios de servicios móviles en Colombia, se verifica la relación entre estos constructos. Los resultados indican una relación positiva y directa entre la lealtad y el valor percibido en las cuatro dimensiones propuestas.
Palabras clave: Lealtad, valor percibido, dimensiones de valor, telecomunicaciones móviles.
Clasificación JEL: L96, M38, M30, D46, L96, M31
1. Introduction
According to IWS (2020), internet usage has steadily increased in recent years. By June 2019, global internet penetration reached 58.7%, representing a growth of 1167% since 2000. This highlights the growing significance of telecommunications adoption. In the case of mobile networks, mobile telephony has continued its expansion over the past decade. In South America, internet penetration stands at 71.8%, largely driven by the use of mobile devices to access the internet.
In the Latin American context, countries that have implemented number portability have experienced shifts in market dynamics, with an increasing number of customers taking advantage of this option. According to regulatory authorities, mobile operators are responsible for managing these transactions and covering the associated costs to ensure service provision and uphold consumers' right to portability.
In recent years, smartphone penetration in Colombia has reached approximately 75% of the population, with mobile data consumption increasing by 40% since 2018, driven by the rising demand for digital services (MinTIC, 2023). Additionally, Colombian consumers have demonstrated a preference for bundled telecom service packages. The ease of switching operators has also affected customer loyalty, with 25% of users changing providers in the last two years (CRC, 2022; GSMA Intelligence, 2023).
In countries such as Germany, the ability to retain one's phone number is closely tied to customer loyalty in mobile telephony, as it creates a switching barrier, or cost, for the customer. Gerpott, Rams, and Schindler (2001) argue that this barrier reduces the likelihood of customers changing their mobile operator, even when their satisfaction or loyalty is low.
Similarly, in France, Lee (2001) found that switching costs mediate the relationship between customer satisfaction and loyalty. When such costs, including contractual and portability barriers, are eliminated, other strategies for enhancing customer loyalty become more important. In Korea, Shin and Kim (2007) observed that mobile operators need to adopt new strategies to retain loyal customers when barriers like number portability are removed from the market landscape.
In this context, Chen et al. (2012, p. 807) argue that "raising customer loyalty is one of the most important tasks for telecom companies confronting the circumstance of a gradually saturated market." Consequently, as Morgan and Govender (2017) point out, analyzing customer loyalty becomes crucial, as it serves as a tool for retaining customers, which, in turn, enhances a company's profitability. Oliver (1999) further emphasizes that loyalty has become a key strategy for many companies, given the importance of maintaining a loyal customer base.
In this regard, Fornell and Wernerfelt (1987) argue that in competitive markets, companies must adopt defensive strategies to maintain a loyal customer base, as retaining existing customers is more cost-effective than acquiring new ones. Additionally, according to Gupta et al. (2006), marketing actions that influence customer perceptions, such as loyalty or purchase intent, are directly linked to higher financial profitability.
For this reason, loyalty has been extensively studied in various countries as a critical construct in the context of mobile telecommunications services. Researchers aim to identify the factors most strongly associated with loyalty and explore the managerial implications of leveraging loyalty through other constructs (Aydin & Ozer, 2005; Chen et al., 2012; Deng et al., 2009; Edward, 2011; Kim, Chan, & Gupta, 2007; Lee, 2001; Morgan et al., 2017).
In line with Chen and Cheng (2012), and based on the existing literature on the relationship between perceived value and loyalty, perceived value is considered one of the key factors influencing customer loyalty in mobile telecommunications. Companies should prioritize this aspect to improve their operational outcomes. This study examines the relationship between loyalty and perceived value in mobile telecommunications services, using Colombia as a case study.
2. Literature Review
Zeithaml (1988) defines perceived value in the literature through four distinct perspectives from the consumer's viewpoint: low price, fulfillment of desired attributes in a product, the quality received relative to the price paid, and the overall exchange of what is gained versus what is given.
It is important to recognize that value is entirely dependent on the customer's perception, and only the customer can determine what is truly valuable in a brand or product.
Kotler and Armstrong (2013, p. 13) argue that creating value for the customer is fundamental to building lasting relationships, as the purchasing decision is often linked to the company that generates a higher perceived value for the customer. It is important to note that customers do not objectively assess the value and costs of products and services; instead, they rely on perceived value.
This concept is defined as "the customer's evaluation of the difference between all the benefits and all the costs of a market offer, compared to the offers of the competition." By generating greater value for the customer, companies foster higher satisfaction and loyalty, leading to increased purchases. Similarly, Holbrook (2002) emphasizes that value is a central concept that underpins and rationalizes marketing, as it gives meaning to the transaction, where something is exchanged with the expectation of receiving something of greater value in return.
Woodruff (1997) suggests that value is based on the customer's perception rather than a characteristic determined by the seller. It is the result of a transaction in which the customer weighs what they give or sacrifice against what they receive in return. Additionally, Sheth, Newman, and Gross (1991) propose five dimensions of value that guide consumer behavior in making choices. These dimensions are independent of each other and differentially influence the consumer's purchase decision, as one or more may have an effect:
Sweeney and Soutar (2001) identify four dimensions of value, which are defined in Table 1.
Kim, Chan, and Gupta (2007) examine the adoption of mobile internet from a value-based perspective, focusing on the perceived value at the initial stages of mobile internet usage, when its market penetration was significantly lower than it is today. The findings of the study highlight the importance of perceived value as a critical factor influencing customer adoption of mobile internet.
Perceived value has been shown to have a positive relationship with several marketing constructs, including customer satisfaction (Ali et al., 2015; Lei & Li, 2013; Lin & Chiang, 2011; Liu, 2008; Negi, 2010; Yen, 2012) and intention to adopt (Sharma & Xiaoming, 2012).
According to Oliver (1997, p. 392), loyalty is defined as a "deeply held commitment to consistently repurchase a good or service in the future, leading to repeated purchases of the same brand despite efforts by the market to encourage switching behavior." Dick and Basu (1994) conceptualize loyalty as a relationship between the pattern of repeat purchases and a customer's relative attitude, which is shaped by cognitive, affective, and conative antecedents.
Cunningham (1956) defines loyalty as a behavioral pattern observed in families that repeatedly purchase the same brand within a product category. He associates the concept of loyalty with the questions "what?", "when?", and "how much?", emphasizing that these characteristics are essential to understanding loyalty through the lens of customer purchasing habits. In line with Dick and Basu (1994), Wong and Zhou (2006) categorize loyalty into two key elements. The first relates to purchasing habits, specifically the frequency with which a customer repeatedly buys a good or service. The second focuses on the consumer's purchase attitudes, referring to positive attitudes toward a product, service, or company.
In the study of loyalty, several marketing dimensions have been identified that directly or indirectly influence customer behavior or attitudes toward their mobile telecommunications service providers. According to Gupta and Sahu (2015), elements such as brand image, loyalty programs, customer satisfaction, service quality, technology orientation, long-term relationships, and trust have been linked to this relationship. However, as shown in Table 2, the relationship between loyalty and other constructs has also been examined, considering the country in which each study was conducted. This is important because factors such as satisfaction and loyalty are influenced by the cultural context and socio-economic characteristics of the market (Aksoy et al., 2013; Jahanzeb, Fatima, & Khan, 2011).
In the South African mobile telecommunications market, there is evidence of a highly competitive environment where customer retention is driven by fostering customer loyalty. This study aimed to evaluate the relationship between perceived value and customer loyalty within this context. According to Morgan et al. (2017), a positive relationship between perceived value and loyalty was identified in this market, though it was mediated by customer satisfaction. Additionally, Chen and Cheng (2012) conducted research exploring the relationships between service quality, perceived value, satisfaction, and loyalty. Surveys were administered in public spaces to mobile phone users over the age of 18, and a structural equation model was generated to analyze the relationships between the variables. Similarly, Lai, Griffin, and Babin (2009) conducted a study in China to understand how quality, value, image, and satisfaction contribute to loyalty in a Chinese telecommunications company. Their research found that both customer satisfaction and perceived value have a direct influence on loyalty. Overall, loyalty behaviors in China appear to align closely with those found in Western studies.
In Ghana, Boateng et al. (2020) investigated the relationship between experiential value, brand attachment, and brand loyalty in the mobile telecommunications sector. Their findings revealed a direct relationship between experiential value and loyalty, mediated by brand attachment. It was determined that experiential value can foster brand attachment in customers, which ultimately leads to brand loyalty. Conversely, in Kuwait, Rizomyliotis et al. (2020) examined the effect of perceived quality, perceived value, and brand value on customer loyalty. Using surveys and structural equation models, they concluded that both perceived service value and perceived quality are positively associated with customer loyalty.
Additionally, in Taiwan, Chen (2019) investigated the indirect relationship between perceived value, perceived service quality, and loyalty. The study found that perceived value positively impacts customer satisfaction, which in turn enhances loyalty, and that perceived value is more influential than service quality and brand image in this regard. However, perceived value alone does not have a stronger effect than brand image or service quality unless it is accompanied by high levels of satisfaction.
In general, multiple studies have identified relationships between loyalty and perceived value, with varying results depending on the research.
In several cases, a significant direct positive relationship has been found between perceived value and behavioral intentions related to loyalty (Al-Refaie, Jalham, & Li, 2012; D'Alessandro et al., 2015; Giovanis, Athanasopoulou, & Tsoukatos, 2016; Giovanis & Tsoukatos, 2017; Huan, Xu, & Li, 2005; Lin et al., 2011; Martins, Hor-Meyll, & Ferreira, 2013; Qayyum et al., 2011; Segarra-Moliner & Moliner-Tena, 2016; Tarus & Rabach, 2013; Yee, Ling, & Leong, 2015; Yen, 2012). In other instances, the relationship has been found to be mediated by constructs such as trust (Karjaluoto et al., 2012) and satisfaction (Chung et al., 2016; Hernandez-Ortega et al., 2017; Shafei et al., 2016). However, some studies have found insufficient evidence of this relationship (Leong et al., 2012).
In Pakistan, Aslam et al. (2018) aimed to identify the relationship between customer trust, service quality, and value dimensions in relation to customer satisfaction and loyalty in telecommunications services. To achieve this, a survey was conducted with 406 service users.
Regarding the value dimensions, it was found that the monetary and emotional dimensions positively impact satisfaction, which in turn positively influences loyalty, while the social and functional dimensions show no significant relationship. In contrast, Deng et al. (2009, 2010) indicate that the functional and emotional dimensions in Chinese mobile telecommunications services have a positive relationship with satisfaction and loyalty, whereas the social and monetary dimensions do not show a significant relationship. However, Wang and Lo (2004) had previously found that in the Chinese market, none of the value dimensions demonstrated a significant relationship with loyalty. Similarly, the findings by Senić and Marinkovic (2014) suggest a positive relationship between functional, emotional, monetary, and social value and loyalty. In their study, emotional and social value were related to attitudinal loyalty, while functional and monetary value were linked to behavioral intentions associated with loyalty.
Consequently, the literature review leads to the following hypotheses:
A sagittal diagram (Figure 1) illustrates the relationships proposed in this research, where the variables-functional value (with its components associated with quality/performance and price), emotional value, and social value, as described by Sweeney and Soutar (2001)-are identified as independent variables. Loyalty, in turn, is the dependent variable in these relationships.
3. Method
To test the hypotheses, a quantitative methodological design was selected, building on approaches used in previous research. In the literature, surveys of mobile telecommunications customers are commonly used to evaluate the constructs of perceived value and customer loyalty. Thus, the same strategy was adopted, utilizing multi-item scales that had been previously validated in earlier studies (Chen et al., 2012; Deng et al., 2010; Lai et al., 2009; Miao et al., 2014; Morgan et al., 2017; Ortegón et al., 2016). Following Saunders, Lewis, and Thornhill (2016), a snowball sampling technique was employed, with voluntary participation and aggregated data management.
The scales identified in the systematic literature review were considered, with the scale proposed by Sweeney and Soutar (2001) selected for evaluating perceived customer value. This choice was based on Gil and Gonzalez's (2008) assertion that this multidimensional scale is the most robust and rigorous available. Additionally, it is the same scale used by Ortegón et al. (2016), whose work was the only one identified in the literature review that evaluated value in the context of mobile telecommunications in Colombia.
This scale is characterized by evaluating functional value in two dimensions: one associated with price and the other with quality or performance, as well as emotional value and social value. For the evaluation of loyalty, the scale adapted for the telecommunications sector by Morgan and Govender (2017) was used, as it has been validated and meets reliability standards for measuring loyalty.
In addition, a control question regarding the use of mobile telecommunications services in the country was included, along with two demographic variables-age and gender-which have similarly been incorporated in other studies of this nature, such as those conducted by Deng et al. (2010). Consequently, the proposed measurement instrument is outlined as follows (Table 3):
A pilot test was conducted with 15 mobile telecommunications service users in Colombia, during which feedback was gathered regarding the clarity of the proposed questions. No changes were made to the initially proposed instrument based on the feedback. Similar pilot tests have been carried out in studies such as those by Boateng et al. (2020), Rizomyliotis et al. (2020), and Yew and Rahman (2019).
The systematic literature review revealed that the convenience sampling technique is the most commonly used in this type of research. Additionally, based on the most recent studies included in the review, the average sample size is 475 respondents, with a standard deviation of 222 surveys, as calculated from the data presented in Table 4.
The survey was distributed using an online form via Google Forms, which offers the advantage of easy replication and the ability to make responses mandatory, thereby reducing the likelihood of invalid questionnaires. The survey was available for one week, during which 420 responses were collected. The selection criterion for participants was that they had been using the same mobile operator for over one year.
The analysis of the results was conducted using SmartPLS, a software tool commonly employed in studies evaluating constructs such as loyalty, perceived value, perceived quality, and satisfaction, as referenced by Fornell and Larcker (1981). Additionally, the partial least squares (PLS) technique offers evidence on the quality of the measures and their suitability for assessing the theoretical relationships between the studied constructs (Babin, Hair, & Boles, 2008; Hair et al., 2012).
It is important to note that, of the 420 completed surveys, 14 respondents indicated that they did not use mobile telecommunications services in Colombia. As a result, these records were excluded from the analysis, leaving a total of 406 surveys for this research. Additionally, it should be noted that the FV4 variable corresponds to an inverse scale and was evaluated accordingly. The results will be described and analyzed in the following section.
4. Results
The gender and age range of the respondents were considered. In terms of gender, 57.4% of respondents were female and 42.6% were male. This contrasts with the national population distribution, where, according to DANE (2018), although the majority of the population is female, the gender difference is 2.8 percentage points, compared to the 14.8 percentage points observed in this study. The age distribution of the sample showed a predominance of respondents between 16 and 60 years of age, with the population under 15 years being the least represented (Figure 2).
As part of the validation of the statistical model presented and evaluated using the SmartPLS software, the results of key indicators and their respective interpretations are presented below.
This measure assesses convergent validity, which indicates the degree to which variables are positively correlated with other related variables. The higher the external loadings of a construct, the stronger the relationship between the variables, and thus, this measure should be statistically significant. The square of this relationship is used as an indicator of how much of the variation in an item is explained by the construct. As Hair et al. (2017, p. 113) suggest, the general rule is that the value must exceed the square of 0.5, i.e., greater than 0.708, to be considered acceptable.
It was observed that, with the exception of item FV4, all items demonstrate reliability, as their values exceed 0.708. Item FV4 was removed from the model due to its low score on this indicator (Table 5).
The average variance extracted (AVE) is used to assess the convergent validity of the constructs. It is calculated as the average of the squared loadings of each item that composes a construct, and its result indicates the proportion of variance in the indicators explained by the construct. According to Hair et al. (2017, p. 114), an AVE value greater than 0.5 suggests that more than half of the variance in the indicators is explained by the construct.
The AVE results are acceptable, as they exceed 0.5, taking into account the exclusion of item FV4. In general, all values are above 0.6, with some constructs reaching values greater than 0.8. The results for this indicator are presented in Table 6.
This process helps identify reliability based on the inter-correlation of observed variables. For this purpose, Hair et al. (2017, p. 112) reference Cronbach's alpha as the indicator used to estimate these correlations. Another option for measuring reliability is the composite reliability indicator, although Cronbach's alpha typically produces lower values than composite reliability.
This difference arises because composite reliability accounts for individual item loadings, making the constructs appear more reliable. Consequently, Cronbach's alpha is considered a more conservative indicator, as it tends to yield lower reliability values, as noted by Hair et al. (2018).
For the analysis of both indicators, the same acceptance parameters are typically applied. Values between 0.6 and 0.7 are considered acceptable in the exploratory phase of research, while values between 0.7 and 0.9 are expected in more advanced stages. However, Hair et al. (2018) caution that values above 0.95 can be problematic, as they may indicate redundancy between items, thereby reducing construct validity.
In this case, most constructs show indicators greater than 0.9; however, for Cronbach's alpha, none exceed 0.95. On the other hand, in the case of composite reliability, it is observed that emotional value and social value exceed 0.95, suggesting redundancy in the items (Table 7).
Given the redundancy identified in the internal consistency reliability, collinearity was analyzed to identify the items contributing to it. This was done using the variance inflation factor (VIF) of the external model, which assesses the collinearity of the items. According to Hair et al. (2017), VIF values close to 3 are acceptable, while values greater than 5 indicate a potential collinearity issue.
Given the redundancy identified in the internal consistency reliability, collinearity was analyzed to identify the items contributing to it. This was done using the variance inflation factor (VIF) of the external model, which assesses the collinearity of the items. VIF values close to 3 are considered acceptable, while values exceeding 5 indicate potential collinearity issues (Table 8).
With the adjusted VIF values, improvements are observed in the values of other items within the same construct. Similarly, the internal consistency reliability was re-evaluated, showing that the composite reliability for emotional value and social value decreased to below 0.95 (Table 9).
The AVE and external loadings were re-evaluated to assess the impactofremovingthe three itemsfrom the model and to ensure that the values remained within the acceptable ranges for each indicator. No significant changes were observed in the external loadings, which remained within acceptable limits (Table 10). Similarly, the AVE values also remained within acceptable ranges (Table 11).
Finally, the VIF values of the structural model were evaluated to assess collinearity between the constructs. This was done using the same approach as for the VIF in the external model.
The current values for this indicator were found to be within acceptable ranges for all constructs (Table 12).
Discriminant validity refers to the degree to which a construct is empirically distinct from others. In this case, it is measured using three different methodologies. The first method, proposed by Fornell et al. (1981), suggests comparing the AVE of each construct with the squared correlations between the constructs. The acceptance criterion is that the shared variance between each pair of constructs should not exceed the AVE of the construct itself.
In this case, based on the criteria of Fornell et al. (1981), it is evident that the constructs in the model for this research remain within acceptable parameters, as the shared variance between any two constructs is never greater than the AVE of each construct (Table 13).
Another method for assessing discriminant validity is through cross-loadings, as Hair et al. (2017) suggest. In this approach, the external loadings of each indicator should be greater than any of their cross-loadings. The analysis shows that discriminant validity is acceptable according to this criterion, as no external loadings are found to be lower than the cross-loadings between the items (Table 14).
Lastly, Henseler, Ringle, and Sarstedt (2015) propose the "Heterotrait-Monotrait ratio" (HTMT) as a criterion for assessing discriminant validity. This method evaluates the ratio between correlations that measure the same construct and correlations that measure different constructs, with discriminant validity assumed when the former are higher. For discriminant validity to be confirmed, the resulting HTMT values must be less than 0.9. In this case, all the values meet this criterion, indicating that discriminant validity is confirmed for the model (Table 15).
According to Hair et al. (2017), path coefficients represent the hypothesized relationships between the model constructs after running the PLS-SEM algorithm. These coefficients typically range between -1 and 1, where values close to 1 indicate a strong positive relationship, and values close to -1 indicate a strong negative relationship. Coefficients near 0 suggest weak or non-significant relationships. However, determining whether a relationship is truly non-significant requires considering the value of the external loadings; if the external loading is low, the relationship can be deemed non-significant and discarded. Table 16 presents the path coefficient values observed in this research.
The significance of a coefficient ultimately depends on its standard error, which is calculated through bootstrapping. The p-value is used to indicate the probability of erroneously rejecting the null hypothesis. In this research, bootstrapping was performed with a significance level of 5%, which is commonly used in marketing-related research, as affirmed by Hair et al. (2017, p. 196). Table 17 presents the p-values for each relationship, from which it can be concluded that the coefficients are significant at the 5% level.
The confidence intervals resulting from bootstrapping can demonstrate whether a path coefficient is significantly different from 0. If 0 is not included within these intervals, the hypothesis that the path coefficient equals 0 is rejected, thereby indicating a significant effect, as clarified by Hair et al. (2017, p. 196). In this study, none of the confidence intervals include 0, allowing us to assume a significant effect on the path coefficients (Table 18).
Even if a path shows a significant effect, this does not necessarily imply that it is relevant. Therefore, it is important to evaluate the effect of one construct on another to assess the overall impact they have on each other. The total effect includes both direct and indirect effects. In this research, no model accounts for indirect effects, so the impact is determined solely by direct effects (Table 19). As a result, emotional value is identified as having the greatest effect on loyalty, while functional value associated with price has the least effect.
According to Hair et al. (2017, p. 198), the most commonly used measure to evaluate a structural model is the coefficient of determination (R2). This coefficient measures the variance explained in each endogenous construct, indicating the explanatory power of the model. The higher the R2 value, the greater the model's explanatory power, with values between 0 and 1. Specifically, R2 values of 0.75, 0.5, and 0.25 can be interpreted as substantial, moderate, or weak explanatory power, respectively. However, as Hair et al. (2018) point out, R2 should always be interpreted in context, as even a value such as 0.1 may be considered satisfactory depending on the research.
Given that perceptions such as value and its effect on loyalty are being measured, an R2 value greater than 0.5 (Table 20) is considered satisfactory. This indicates that more than 50% of loyalty is explained by emotional value, social value, and functional value in both its price and quality-performance dimensions.
Hair et al. (2018) explain that the impact of removing any construct from the model on the R2 value is measured by f2. This effect size is interpreted based on Cohen's (1988) guidelines, with values of 0.02, 0.15, and 0.35 representing small, medium, and large effects, respectively.
The effect size of the constructs is interpreted as small (less than 0.1 in all cases), as it falls within the previously defined parameters (Table 21). This indicates that removing any of the constructs does not significantly impact the R2 value, meaning that loyalty will continue to be explained in the same manner.
Q2 is a measure used to evaluate the predictive accuracy of the model. This procedure involves eliminating individual data points from the sample, replacing them with the mean, and recalculating the model parameters. Using the blindfolding procedure, the model predicts the eliminated data points for all variables, and the resulting differences are reflected in the Q2 value, as explained by Hair et al. (2018).
Hair et al. (2018) suggest that Q2 values of 0, 0.25, and 0.5 serve as reference points, indicating low, medium, and high predictive relevance, respectively. In this case, a medium predictive relevance is observed, with a Q2 value of 0.337 resulting from the calculation (Figure 3).
The hypotheses presented at the conclusion of the theoretical framework are evaluated to compare them with the evidence gathered from the fieldwork.
H1: There is a relationship between the functional value (quality/performance) of mobile telecommunications services and customer loyalty.
Functional value, understood as the value associated with the perceived quality or performance of mobile telecommunications services in Colombia, is found to have a significant positive relationship with customer loyalty. This conclusion is based on the positive path coefficient (0.213) and its confidence interval.
While a positive relationship is evident, the path coefficient does not support the claim that the relationship is strong. Nonetheless, the model indicates that this construct has a total effect of 21.3% on loyalty, which can be interpreted as having medium-low relevance.
H2: There is a relationship between the functional value (price) of mobile telecommunications services and customer loyalty.
Functional value, understood as the monetary value perceived by customers of mobile telecommunications services in Colombia, shows a significant positive relationship with customer loyalty. This is based on the positive path coefficient (0.148) and its confidence interval.
Although a positive relationship is evident, the path coefficient does not support the assertion that this is a strong relationship between the constructs. Moreover, the relationship between functional value (price) and loyalty yielded the lowest result in terms of relevance, with the lower limit of the confidence interval being very close to 0. Despite this, the model indicates that this construct has a total effect of 14.8% on loyalty, which can be considered of low relevance.
H3: There is a relationship between the emotional value of mobile telecommunications services and customer loyalty.
Based on the statistical results of this research, it can be affirmed that the emotional value perceived by customers of mobile telecommunications services in Colombia has a significant positive relationship with customer loyalty. This is supported by the positive path coefficient (0.289) and its confidence interval.
While this construct showed the highest relevance in its relationship with loyalty, there is no evidence to suggest a strong relationship. Nevertheless, the model indicates that this construct has a total effect of 28.9% on loyalty, placing it between low and medium relevance.
H4: There is a relationship between the social value of mobile telecommunication services and customer loyalty.
Based on the statistical results of this research, it can be affirmed that the social value perceived by customers of mobile telecommunications services in Colombia has a significant positive relationship with customer loyalty. This is supported by the positive path coefficient (0.221) and its confidence interval.
Although a positive relationship is evident, the path coefficient does not provide enough support to assert that this is a strong relationship between the constructs. Nonetheless, the model indicates that this construct has a total effect of 22.1% on loyalty, which can be considered of medium-low relevance.
4. Discusión
Based on the results presented in the previous section and in line with the objective of this research, which aims to identify the relationship between loyalty and perceived value among mobile telecommunications customers in Colombia, it is observed that, according to the model proposed by Sweeney et al. (2001), there is a direct positive relationship between all dimensions of the value scale and loyalty in the country. The model evaluates perceived value across four dimensions, as applied in this study. In contrast to Aslam et al. (2018) in Pakistan, where functional value related to quality and performance, as well as social value, were found to be associated with loyalty, similar results were observed in Colombia. However, both studies conclude that emotional value and functional value associated with price have a positive relationship with customer loyalty in mobile telecommunications services in both countries. This finding contrasts with Deng et al. (2010, 2009), who found no evidence to support the relationship between functional value associated with price and loyalty, as well as Wang et al. (2004), who found no such relationship between these constructs in the Chinese market.
The differences in the results observed across studies from various countries may align with the conclusions of Aksoy et al. (2013) and Jahanzeb et al. (2011), who suggest that the relationship between marketing constructs-such as value and loyalty-evaluated in mobile telecommunications services can be influenced by cultural or socioeconomic factors. This has important implications for marketing management, as these variables should be considered when defining specific strategies in each country's context. Consequently, by understanding the relationship between perceived value and loyalty, companies could allocate marketing resources to enhance customer-perceived value, thereby positively influencing loyalty. This approach could involve focusing efforts on improving a particular dimension of value that the telecommunications company identifies as weak in a target group or across its entire customer base.
This research provides an empirical foundation on which mobile telecommunications operators can build to better understand customer loyalty behavior. However, it is crucial for companies to identify the factors that enhance perceived value, ensuring that strategies aimed at increasing this perception are well-defined.
A key managerial implication, based on the order of impact on loyalty-emotional value being the most significant, followed by social value, performance, and finally price-is that mobile telecommunications services should focus on offering packages that, regardless of price, enable customers to feel connected with themselves and those who are important to them. This directly affects performance, as meeting these emotional and social needs is not feasible without reliable service quality. From the users' perspective, price alone does not necessarily make a telecommunications company more competitive.
This research revealed certain limitations, which can serve as recommendations for future studies in the field of perceived value, loyalty, and telecommunications services. First, economic and time constraints prevented the selection of a random sample statistically representative of the Colombian population, potentially introducing bias due to the use of convenience sampling. Additionally, demographic and sociocultural factors may have influenced the results, making it worthwhile to explore these aspects further by focusing on specific population groups of interest in future research.
Additionally, future research could consider mediating variables between perceived value and loyalty, such as satisfaction. Chen (2019) identified a positive relationship between perceived value and loyalty with satisfaction as a mediating variable, and this relationship could be tested within the context of mobile telecommunications in Colombia. Furthermore, it would be valuable to evaluate loyalty in terms of both behavioral intentions and attitudinal loyalty, as suggested by Senić et al. (2014), to better understand the relationship between the dimensions of value and loyalty.
Finally, incorporating other constructs, such as trust, satisfaction, word-of-mouth, and perceived quality, could provide a more comprehensive explanation of customer loyalty, as these variables have been identified in the systematic literature review as closely related to loyalty.
As a concluding remark, a promising future direction for this line of research is to examine customer loyalty not only in terms of emotional value, social value, performance, and price, but also by considering different moments and stages of the customer journey. Additionally, it would be beneficial to explore generational differences in relation to the various services offered by telecommunications companies. Future studies could also compare these results across different market segments, such as B2B, to gain a more comprehensive understanding of customer loyalty in diverse contexts.
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