The Influence of Market Dynamics on Switching Intention in the Mobile Telecommunication Industry in Ghana: Exploring the Mediating Role of Competitive Intensity ()
1. Introduction
The telecommunications market, particularly in Ghana, is characterised by a dynamic landscape influenced by various factors such as switching costs, perceived value, customer loyalty, alternative attractiveness, and competitive intensity. Switching costs, which encompass both monetary and non-monetary barriers, play a significant role in limiting customers’ ability to switch service providers, thereby impacting market competition. While traditional economic theory suggests that high switching costs can lead to monopolistic behaviour by dominant operators, the reality is more nuanced, with implications for market regulation and consumer welfare. Moreover, the presence of incumbent operators and the possibility of price discrimination further shape market dynamics, influencing the strategies adopted by both existing and new entrant operators.
Customer loyalty emerges as a critical factor in the telecommunications industry, where firms strive to retain customers amidst intense competition and rapid technological advancements. Loyal customers contribute to revenue stability and positive word-of-mouth advertising, underscoring the importance of fostering strong relationships with customers (Chandramouli, 2020). However, the emergence of new technologies and the deregulation of the industry have empowered consumers, making them more value-oriented and prone to switching behaviors. Consequently, firms must understand and anticipate customer needs to mitigate the risk of customer defection and maintain long-term relationships (Shahzad, Yaqub, Di Vaio, & Hassan, 2021).
The mediating role of competitive intensity adds another layer of complexity to the relationship between market dynamics and consumer behavior. Competitive intensity influences how factors such as switching costs, perceived value, and alternative attractiveness interact to shape switching intention. In highly competitive markets like Ghana’s mobile telecommunications industry, where numerous players vie for market share, understanding the dynamics of competitive intensity becomes imperative for firms seeking to differentiate themselves and retain customers (Ribeiro, Barbosa, Moreira, & Rodrigues, 2023). Moreover, the cultural context of Ghana and other developing countries introduces unique considerations that may influence consumer evaluations of service quality and switching behaviour, highlighting the need for localised research and tailored strategies (Adzinyo, Dzansi, & Strydom, 2024).
This study aims to contribute to a deeper understanding of the intricate relationship between switching costs, perceived value, customer loyalty, alternative attractiveness, and competitive intensity in the context of Ghana’s mobile telecommunications industry. By exploring these factors and their interplay, the study seeks to provide insights that can inform strategic decision-making, regulatory policies, and further research in the fields of consumer behaviour and telecommunications economics.
2. Literature Review
2.1. Empirical Evidence of Existing Literature
2.1.1. Development of Telecommunications in Ghana
The telecommunications history of Ghana traces back to the colonial era, primarily serving the British administration’s control and exploitation of the colony. Initially structured as state-run monopoly systems, telecommunications underwent significant changes in the early 1990s, influenced by global trends towards restructuring, deregulation, and privatization. Motivated by various factors including pressure from international finance agencies, commitments to the World Trade Organization (WTO), inefficiencies in the existing system, revenue generation needs, and the goal of promoting universal service, Ghana embarked on a liberalization process. This process, initiated with the Accelerated Development Programme 1994-2000 (Bandim, 2022), aimed to foster competition within the telecommunications industry, marking a shift towards a more open and competitive market structure.
2.1.2. Market Dynamics Influence
Market Dynamics Influence (MDI), comprising switching costs, perceived value, customer loyalty, and alternative attractiveness, constitutes a multifaceted framework that profoundly shapes consumer behavior and market outcomes. Switching costs, encompassing both monetary and non-monetary barriers to switching service providers, play a pivotal role in consumer decision-making processes (Guan, Shi, Ying, Xue, & Qiao, 2024). Perceived value, representing consumers’ subjective assessments of the benefits relative to sacrifices associated with a service offering, influences their propensity to switch providers (Garrouch & Ghali, 2023). Customer loyalty, characterized by the strength of the bond between consumers and service providers, acts as a deterrent to switching behavior, fostering long-term relationships (Nortey & Twih, 2023). Moreover, alternative attractiveness, defined as the perceived desirability of competing service providers relative to the current provider, exerts a significant influence on consumer switching intentions (Li, Long, Huang, Duan, Hu, & Cui, 2022). Together, these components of MDI form a comprehensive framework for understanding the intricate interplay between consumer behavior and market dynamics in various industries.
2.1.3. Competitive Intensity
Competitive intensity, a crucial aspect of market dynamics, has garnered significant attention in academic literature across various disciplines. Competitive intensity refers to the degree of rivalry and contestation within a market, influenced by factors such as the number of competitors, their market share, and the aggressiveness of their strategies (Lawton & Kock, 2023). Research on competitive intensity has explored its impact on firm behavior, market structure, and ultimately, performance outcomes. Scholars have investigated how competitive intensity shapes firms’ pricing strategies, product differentiation efforts, and innovation endeavors (Costa Climent, Haftor, & Chowdhury, 2021). Moreover, competitive intensity has been found to influence market concentration levels and barriers to entry, with higher levels of competition often associated with lower barriers and increased market dynamism (Adomako et al., 2021). Furthermore, the influence of competitive intensity extends beyond firm-level strategies to consumer behavior, as it affects customers’ perceptions of value, brand loyalty, and switching behavior (Lin, 2023). Overall, understanding competitive intensity is essential for firms seeking to navigate dynamic market environments and formulate effective strategies to achieve sustainable competitive advantage.
2.1.4. Customer Switching Intention
Customer switching intention refers to the action of replacing one service provider with another, which can have detrimental effects on the original provider, including loss of market share, revenue, and loyal customers (Senanu & Narteh, 2022). Acquiring new customers is typically more costly than retaining existing ones, as it involves increased operational expenses associated with understanding the needs of new customers. Extensive literature reveals numerous reasons why customers switch providers, ranging from dissatisfaction with current services to external factors like relocation or involuntary circumstances beyond their or the provider’s control (Bhattacharya, Morgan, Rego, & Hewett, 2024). While previous research has examined various switching antecedents, this study delves deeper into three specific factors: switching costs, perceived value, and alternative attractiveness. Additionally, involuntary or unavoidable switches, triggered by factors like relocation or branch closures, represent common switching behaviors, highlighting the complex interplay of voluntary and involuntary factors in customer decision-making processes (Munyai, 2020).
2.2. Hypotheses Development
2.2.1. Market Dynamics and Switching Intention
Market dynamics encompass a spectrum of factors shaping consumer behavior in various industries, notably including switching costs, perceived value, customer loyalty, and alternative attractiveness, which collectively influence switching intention. Switching costs represent a critical barrier to consumer mobility between service providers, often involving monetary expenses or non-monetary sacrifices associated with transitioning to a new provider (Panama, Ugiagbe, & Aguwamba, 2023). As highlighted by Gremler & Gwinner (2015), these costs may include procedural costs like evaluation and setup costs, financial costs such as benefit and monetary loss costs, and relational costs pertaining to personal and brand relationship loss. Moreover, perceived value, defined as the perceived benefits relative to sacrifices, plays a pivotal role in consumer decision-making (Swart, 2021). Higher perceived value typically leads to greater customer satisfaction and loyalty, reducing the propensity to switch providers (Paulose & Shakeel, 2021). Conversely, alternative attractiveness, indicating the viability of competitors as substitutes, influences switching intentions (Agu, 2022). In essence, market dynamics intertwine these variables, reflecting a complex interplay that shapes consumer behavior and switching intentions within competitive landscapes.
The interrelationship of these factors underscores the multifaceted nature of consumer decision-making and market dynamics in determining switching intentions. Customer loyalty, often cultivated through positive service experiences and emotional bonds with a brand, serves as a protective mechanism against switching (Ahlawat, 2023). Additionally, alternative attractiveness influences consumers’ perceptions of the value proposition offered by competitors, prompting them to consider switching providers (Nikbin, Aramo, Iranmanesh, & Ghobakhloo, 2022). Furthermore, competitive intensity, as a mediating factor, moderates the relationship between market dynamics and switching intentions (Wang, Wang, Wang, & Zhao, 2021). While switching costs and perceived value directly impact switching intentions, the competitive landscape, characterized by alternative attractiveness and customer loyalty, mediates this relationship, shaping consumer responses to market dynamics. Thus, understanding these interconnected variables within market dynamics provides valuable insights into consumer behavior and decision-making processes in competitive environments. Therefore, the hypothesis is:
H1: Switching costs has a positive effect on switching intention.
H2: Perceived value has a positive effect on switching intention.
H3: Customer loyalty has a positive effect on switching intention.
H4: Alternative attractiveness has a positive effect on switching intention.
2.2.2. Market Dynamics and Competitive Intensity
Market dynamics, including switching costs, perceived value, customer loyalty, and alternative attractiveness, exert a profound influence on competitive intensity within markets. Switching costs, as identified by Vøllestad (2023), create barriers to consumer mobility, effectively shaping the competitive landscape. High switching costs often deter consumers from switching providers, thereby reducing competitive pressure on incumbent firms. Perceived value, on the other hand, significantly impacts consumers’ assessment of competitive offerings (Zietsman, Mostert, & Svensson, 2020). When consumers perceive high value in a particular service provider, their propensity to explore alternatives diminishes, consolidating the competitive position of the chosen provider. Similarly, customer loyalty, fostered through positive experiences and emotional connections, contributes to competitive intensity by fortifying the market position of incumbent firms (Dabula, 2017). Loyal customers are less likely to defect to competitors, thereby reducing the intensity of competition within the market.
Moreover, alternative attractiveness plays a pivotal role in shaping competitive intensity by influencing consumer perceptions of substitute offerings (Ribeiro, Barbosa, Moreira, & Rodrigues, 2023). In markets where alternative providers offer compelling value propositions, competitive intensity heightens as incumbent firms strive to retain their market share. The interplay of these market dynamics contributes to fluctuations in competitive intensity, reflecting the dynamic nature of consumer preferences and competitive landscapes. Furthermore, the mediating effect of competitive intensity moderates the relationship between market dynamics and the competitive landscape (Costa Climent, Haftor, & Chowdhury, 2021). While switching costs, perceived value, customer loyalty, and alternative attractiveness directly influence market dynamics, competitive intensity acts as a mediator, amplifying or attenuating the impact of these factors on market competitiveness. Thus, understanding the intricate relationship between market dynamics and competitive intensity is essential for firms seeking to navigate and thrive in competitive market environments. Thus, the hypothesis is:
H5: Switching costs has a positive effect on competitive intensity.
H6: Perceived value has a positive effect on competitive intensity.
H7: Customer loyalty has a positive effect on competitive intensity.
H8: Alternative attractiveness has a positive effect on competitive intensity.
2.2.3. Competitive Intensity and Switching Intention
Competitive intensity, a crucial dimension of market dynamics, exerts a profound influence on consumers’ switching intentions. As competition escalates within industries, consumers are presented with a multitude of choices, prompting them to reevaluate their existing service providers (Meter, 2021). The heightened competitive environment often leads to intensified marketing efforts, price wars, and innovation, all of which can impact consumers’ perceptions of value and alternative attractiveness (Magalhães-Timotio, Barbosa, & Ferreira, 2022). Additionally, competitive intensity may moderate the relationship between other market dynamics factors, such as switching costs and perceived value, and switching intention (Ghamry & Shamma, 2020). Thus, understanding the influence of competitive intensity on switching intention is essential for both firms and policymakers seeking to navigate the complexities of dynamic markets and consumer behavior. Thus, the hypothesis is:
H9: Competitive intensity has a positive effect on switching intention.
2.2.4. The Mediating Effect of Competitive Intensity
The mediating effect of competitive intensity on the relationship between Market Dynamics Influence (MDI), encompassing switching costs, perceived value, customer loyalty, and alternative attractiveness, and switching behavior is crucial for understanding market dynamics. Competitive intensity serves as a key mediator, shaping the strength and direction of the relationship between MDI and switching behavior. Highly competitive intensity amplifies the impact of MDI factors on switching behavior, fostering a dynamic market environment where consumer choices are influenced by a myriad of factors (Tate, 2022). For instance, in fiercely competitive markets where alternative attractiveness is high, consumers are more inclined to switch providers, driven by the allure of superior offerings from competitors. In such scenarios, competitive intensity acts as a catalyst, magnifying the influence of MDI factors on consumer decision-making processes.
Conversely, in markets characterized by low competitive intensity, the mediating effect of competitive intensity may attenuate the relationship between MDI and switching behavior. When incumbent firms dominate the market and face minimal competitive pressure, the impact of MDI factors on switching behavior may be mitigated (Jeong, 2022). For example, in monopolistic or oligopolistic markets where switching costs are high and perceived value is relatively uniform across providers, consumer propensity to switch may be subdued. In these instances, competitive intensity acts as a stabilizing force, tempering the influence of MDI factors on switching behavior and contributing to market inertia. Overall, understanding the mediating role of competitive intensity provides valuable insights into the complex interplay between market dynamics and consumer behavior, facilitating strategic decision-making for firms operating in competitive market environments. Based on the studies described above, the following is presented:
H10: Switching costs and switching intention through competitive intensity.
H11: Perceived value and switching intention through competitive intensity.
H12: Customer loyalty and switching intention through competitive intensity.
H13: Alternative attractiveness and switching intention through competitive intensity.
2.3. Conceptual Framework
Figure 1. Conceptual framework for the study.
The proposed conceptual framework examines relationship switching costs, perceived value, customer loyalty, and alternative attractiveness on switching intention, with competitive intensityacting as a mediator. The model is shown in Figure 1.
3. Materials and Methods
To evaluate the study’s hypotheses, we created a questionnaire and conducted a self-administered survey among three university employees in the Ashanti Region of Ghana who are mobile phone subscribers. After a thorough review of the literature, the questionnaire was prepared and distributed to university employees who are mobile subscribers on the predictors of customer switching behavior. Using a convenience sampling technique, 250 questionnaires were distributed to university staff, leaving 200 questionnaires usable, corresponding to a response rate of 80%. This study adopted the single methods of data collection where the quantitative method (survey) was used to increase the validity of the research by ensuring that there were no gaps in the information or data collected (Saunders, Lewis, & Thornhill, 2009).
3.1. Measurement of Variables
The survey questionnaire consisted of 25 items adapted based on established scales from previous literature to measure the exogenous and endogenous variables of the present study. Participants were given questionnaires with a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Four items from (Caruana, 2003) were used to specify Switching cost, five items from Feng, Cai, Wang, & Zhang (2016) were chosen for competitive intensity. In addition, five customer loyalty items were chosen from Alam et al. (2023), Alternative attractiveness was measured using a five-item scale adapted from Chuang (2011), five Perceived Value items were taken from Yang & Peterson (2004) and Switching intentions were measured using a five-item scale adapted from Bölen (2020).
3.2. Demographic Profile of Respondents
The demographic profile of the respondents who participated in the study. The results showed that of the 200 (100%) respondents, 82 (41%) were men and 118 (59%) were women. This shows that the majority of respondents were female compared to men. Furthermore, the results showed that 21 (10.5%) of the respondents were between 16 and 20 years old, 94 (47%) were between 21 and 30 years old, 62 (31%) of the respondents were between 31 and 40 years old and 23 (11.5%) of respondents were 41 - 50 years old. The majority of respondents were between 21 and 30 years old. Regarding the educational level of the respondents, 81 (40.5%) were those with MSLC/JHS as their highest level of education. The SHS education level was 11 (5.5%). 41 (20.5%) of the respondents had a graduate degree, 59 (29.5%) had a university degree and 5 (2.5%) had a postgraduate degree. and other 3 (1.5%).
4. Analysis and Results of Structural Equation Modelling
Structural Equation Modelling (SEM) was used in testing the hypothesis arising from the theoretical model. The two-stage approach endorsed by Anderson & Gerbing (1988) was adopted in this study, given that the accurate representation of the reliability of each construct is best conducted in two stages to avoid any interaction between the measurement and structural models (Hair et al., 2013).
4.1. Validity and Reliability Results
Reliability test was conducted to ensure the level of consistency in measuring the intended latent construct. Cronbach’s alpha was used to assess the reliability and the results exceeded the satisfactory level > 0.70, which shows a high reliability for the instruments adopted (Nunnally, 1978).
The Composite Reliability (CR) values were also greater than 0.80, and the Average Variance Extracted > 0.50, demonstrating convergent validity where the multiple items measuring a single concept are in agreement, and indicating adequate internal consistency of the constructs (Fornell & Larcker, 1981). Table 1 shows the details of the reliability, factor loadings, and average variance extracted of the constructs.
Table 1. Validity and reliability results.
Research constructs |
Cronbach’s alpha |
Rho _A |
CR |
AVE |
Loadings |
Alternative Attractiveness |
0.780 |
0.801 |
0.860 |
0.609 |
|
DC1 |
|
|
|
|
0.794 |
DC2 |
|
|
|
|
0.896 |
DC3 |
|
|
|
|
0.753 |
DC4 |
|
|
|
|
0.658 |
Competitive Intensity |
0.901 |
0.918 |
0.939 |
0.838 |
|
CI1 |
|
|
|
|
0.826 |
CI2 |
|
|
|
|
0.962 |
CI3 |
|
|
|
|
0.952 |
Customer Loyalty |
0.953 |
0.954 |
0.966 |
0.876 |
|
CL1 |
|
|
|
|
0.882 |
CL2 |
|
|
|
|
0.954 |
CL3 |
|
|
|
|
0.959 |
CL4 |
|
|
|
|
0.947 |
Perceived Value |
0.992 |
0.992 |
0.993 |
0.967 |
|
PV1 |
|
|
|
|
0.990 |
PV2 |
|
|
|
|
0.977 |
PV3 |
|
|
|
|
0.980 |
PV4 |
|
|
|
|
0.988 |
PV5 |
|
|
|
|
0.984 |
Switching Costs |
0.922 |
0.928 |
0.945 |
0.813 |
|
SC1 |
|
|
|
|
0.858 |
SC2 |
|
|
|
|
0.880 |
SC3 |
|
|
|
|
0.961 |
SC4 |
|
|
|
|
0.903 |
Switching Intention |
0.961 |
0.970 |
0.971 |
0.871 |
|
SI1 |
|
|
|
|
0.977 |
SI2 |
|
|
|
|
0.977 |
SI3 |
|
|
|
|
0.966 |
SI4 |
|
|
|
|
0.961 |
SI5 |
|
|
|
|
0.769 |
Source: field data by the authors, 2024.
Discriminant validity was also used to measure the extent to which the constructs differed and to ascertain the freedom of measurement model of a construct from redundant items. Specifically, if the items in a construct were more strongly associated with each other than with items measuring other constructs, the measure was regarded as having discriminant validity. In this light, a scale should not be highly correlated with the measure of a different construct (Zikmund et al., 2016). Scholars like Fornell and Lacker (1981) postulate that the square root of the AVE should be greater than the correlation between the construct and the other constructs and this study is in line with this argument because all the constructs met the discriminant validity with the AVE for each construct greater than the squared correlation with the other constructs as illustrated in the table above. This is the inter-construct correlation matrix, as shown in the below Table 2 and Table 3. Table 4 and Table 5 as well as Figure 2 provide an explanation of the hypothesis test results pertaining to the connections between these components.
Table 2. Discriminant validity.
|
AA |
CI |
CL |
PV |
SC |
SI |
Alternative Attractiveness |
0.780 |
|
|
|
|
|
Competitive Intensity |
0.673 |
0.915 |
|
|
|
|
Customer Loyalty |
0.430 |
0.818 |
0.936 |
|
|
|
Perceived Value |
0.544 |
0.936 |
0.888 |
0.984 |
|
|
Switching Costs |
0.554 |
0.929 |
0.864 |
0.959 |
0.901 |
|
Switching Intention |
0.590 |
0.971 |
0.876 |
0.985 |
0.968 |
0.933 |
Source: field data by the authors, 2024.
Table 3. Total effects.
|
AA |
CI |
CL |
PV |
SC |
SI |
Alternative Attractiveness |
|
0.217 |
|
|
|
0.067 |
Competitive Intensity |
|
|
|
|
|
0.390 |
Customer Loyalty |
|
−0.025 |
|
|
|
0.011 |
Perceived Value |
|
0.539 |
|
|
|
0.687 |
Switching Costs |
|
0.314 |
|
|
|
0.262 |
Switching Intention |
|
|
|
|
|
|
Source: field data by the authors, 2024.
Figure 2. Structural model. Source: Authors own construction, 2024.
Table 4. Structural analysis.
Study’s hypothesis |
Hypothesis |
Path coefficients |
T-Statistics |
P-Values |
Supported/Rejected |
SC -> SI |
H1 |
0.140 |
3.360 |
0.001 |
supported |
PV -> SI |
H2 |
0.478 |
10.222 |
0.000 |
supported |
C L -> SI |
H3 |
0.020 |
1.347 |
0.178 |
Rejected |
AA -> SI |
H4 |
−0.018 |
1.670 |
0.096 |
Rejected |
S C -> CI |
H5 |
0.314 |
2.041 |
0.042 |
supported |
P V -> CI |
H6 |
0.539 |
3.612 |
0.000 |
supported |
CL -> CI |
H7 |
−0.025 |
0.323 |
0.746 |
Rejected |
AA -> CI |
H8 |
0.217 |
6.448 |
0.000 |
supported |
CI -> SI |
H9 |
0.390 |
10.057 |
0.000 |
supported |
Source: field data by the authors, 2024.
Table 5. Indirect effects.
Study’s hypothesis |
Hypothesis |
Path
Coefficient |
T Statistics |
P Values |
Supported/
Rejected |
SC -> CI -> SI |
H10 |
0.193 |
1.888 |
0.059 |
supported |
PV -> CI -> SI |
H11 |
0.852 |
21.476 |
0.000 |
supported |
CL -> CI -> SI |
H12 |
0.446 |
5.366 |
0.000 |
supported |
AA -> CI -> SI |
H13 |
0.082 |
1.042 |
0.297 |
supported |
Source: field data by the authors, 2024. Note: SC = Switching Costs; PV = Perceived Value; CL = Customer Loyalty; AA = Alternative Attractiveness; CI = Competitive Intensity; SI = Switching Intention. *Significance at p < 0.05; **Significance at p < 0.01; ***Significance at p < 0.001.
4.2. Discussion of the Results
The study evaluates the relationship between market dynamics and switching intention in the mobile telecommunications industry. This leads to four hypotheses: (H1) Switching costs has a positive effect on switching intention, (H2) Perceived value has a positive effect on switching intention, (H3) Customer loyalty has a positive effect on switching intention and (H4) Alternative attractiveness has a positive effect on switching intention. The results of the structural model showed that switching costs (β = 0.140, t = 3.360, p = 0.001), perceived value (β = 0.478, t = 10.222, p = 0.000), customer loyalty (β = 0.020, t = 0.323, p = 0.178), and alternative attractiveness (β = −0.018, t = 1.670, p = 0.096) each played a significant role in determining switching intention. The findings regarding the factors influencing switching intention in the mobile telecommunications industry align with existing research in the field. Studies have examined the impact of various factors on switching intentions in the context of mobile services. For instance, Abina & Ajayi (2023) investigated the influence of switching costs on customer switching intention in the telecommunications sector, highlighting the significant role of switching costs in determining switching behavior. Additionally, Zhong & Chen (2023) explored the effect of perceived value on customer switching intention in the mobile industry, further supporting the notion that perceived value plays a crucial role in shaping switching intentions. Moreover, Mandal (2024) examined the relationship between customer loyalty and switching intention in the telecommunications industry, emphasizing the importance of customer loyalty as a determinant of switching behavior. These studies collectively provide robust evidence supporting the significant impact of switching costs, perceived value, and customer loyalty on switching intention in the mobile telecommunications industry.
Furthermore, the study evaluates the relationship between market dynamics and competitive intensity in the mobile telecommunications industry. This leads to four hypotheses: (H5) switching costs has a positive effect on competitive intensity; (H6) perceived value has a positive effect on competitive intensity; (H7) customer loyalty has a positive effect on competitive intensity; and (H8) alternative attractiveness has a positive effect on competitive intensity. The results of the structural model showed that switching costs (β = 0.314, t = 2.041, p = 0.042), perceived value (β = 0.539, t = 3.612, p = 0.000), customer loyalty (β = −0.025, t = 0.323, p = 0.746), and alternative attractiveness (β = 0.217, t = 6.448, p = 0.000) each played a significant role in determining competitive intensity except customer loyalty. Relevant literature supports the significant role of switching costs, perceived value, and alternative attractiveness in determining competitive intensity in the telecommunications industry. Studies have explored the impact of these factors on competitive dynamics in various industries. For example, Ribeiro, Barbosa, Moreira, & Rodrigues (2023) examined the influence of switching costs on competitive intensity in the telecommunications sector, highlighting the significant role of switching costs in shaping competitive dynamics. Negash, Jyun, Tarhini and Rehman (2024) also investigated the effect of perceived value on competitive intensity in the mobile industry, further supporting the idea that perceived value is a key determinant of competitive dynamics. Furthermore, Xue & Jo (2023) explored the relationship between alternative attractiveness and competitive intensity, emphasising the importance of alternative attractiveness in influencing competitive dynamics. These studies collectively provide strong evidence supporting the significant roles of switching costs, perceived value, and alternative attractiveness in determining competitive intensity in the telecommunications industry.
In addition, the study explores the relationship between competitive intensity and switching intention in the mobile telecommunications industry. The result shows that competitive intensity has a positive and significant relationship with switching intention (H9). The results of the structural model showed that competitive intensity (β = 0.390, t = 10.057, p = 0.000) plays a significant role in determining switching intention in the mobile telecommunications industry. Existing research in the field supports the importance of competitive intensity in determining switching intention in the mobile telecommunications industry. Studies have investigated the impact of competitive intensity on customer behaviour and switching intentions. For example, Jo (2024) explored the influence of competitive intensity on customer switching intention in the telecommunications sector, highlighting the significant role of competitive intensity in shaping customer decisions to switch providers. Additionally, Panama, Ugiagbe and Aguwamba (2023) examined the effect of competitive intensity on customer churn behaviour in the mobile industry, further supporting the notion that competitive intensity plays a crucial role in determining switching intentions. These studies provide strong evidence that competitive intensity has a significant impact on switching intention in the mobile telecommunications industry.
Finally, the study examines the mediating effect of competitive intensity on market dynamics and switching intentions in the mobile telecommunications industry. The mediation test suggests that competitive intensity mediates the relationship between market dynamics and switching intention. The results of the structural model showed switching costs (β = 0.193, t = 1.888, p = 0.059), perceived value (β = 0.852, t = 21.476, p = 0.000), customer loyalty (β = 0.446, t = 5.366, p = 0.000), and product innovation (β = 0.082, t = 1.042, p = 0.297). This suggests that competitive intensity is a partial mediator in the relationship between market dynamics and switching intention in the mobile telecommunications industry. Scholarly research in the field backs up the idea that switching costs, perceived value, and customer loyalty have big impacts on competitive intensity, as well as the finding that switching intensity is a mediator between market dynamics and switching intention. Studies have delved into the mediating role of competitive intensity and the impact of various factors on competitive dynamics and customer behavior. For example, Ali Qalati, Li, Ahmed, Ali Mirani and Khan (2020) investigated the mediating effect of competitive intensity in the relationship between market dynamics, influence, and switching intention in the telecommunications industry, supporting the mediating role of competitive intensity. Additionally, Pant, Saxena, Gupta, Yadav, AD and Pant (2024) explored the influence of perceived value on competitive intensity, further emphasising the significant impact of perceived value on competitive dynamics. Moreover, Rane, Achari and Choudhary (2023) examined the relationship between customer loyalty and competitive intensity, providing further support for the significant role of customer loyalty in shaping competitive dynamics. These studies collectively align with the findings of your study regarding the mediating role of competitive intensity and the effects of switching costs, perceived value, customer loyalty, and product innovation on competitive intensity in the context of switching intention in the mobile telecommunications industry. Therefore, H10, 11, 12, and 13 were supported in this study.
5. Conclusion
The study has provided valuable insights into the factors influencing switching intention in the mobile telecommunication industry, as well as the mediating role of competitive intensity. The findings underscore the importance of considering market dynamics influence, including switching costs, perceived value, customer loyalty, and alternative attractiveness, in understanding customer behavior and decision-making processes. Specifically, switching costs and perceived value were found to have a significant positive effect on switching intention, highlighting the need for telecommunication companies to carefully manage these factors to mitigate customer churn. Moreover, the study revealed the pivotal role of competitive intensity in mediating the relationship between market dynamics influence and switching intention, emphasizing the importance of market competitiveness in shaping customer behavior.
Overall, this research contributes to both theoretical understanding and practical implications for stakeholders in the mobile telecommunication industry. By identifying key determinants of switching intention and elucidating the mediating role of competitive intensity, the study offers valuable insights for marketers, policymakers, and industry practitioners. Moving forward, addressing the recommendations and overcoming the limitations outlined in this study can further enhance our understanding of customer behavior in the telecommunications sector, ultimately leading to the development of more effective strategies for customer retention and market competitiveness.
This study significantly contributes to the policymaking process in the mobile telecommunication industry by offering valuable insights into the factors influencing customer behavior and competitive dynamics. Policymakers can leverage these findings to design evidence-based regulations and initiatives that foster a more competitive and consumer-friendly market environment, such as measures to reduce switching costs, enhance perceived value, and promote customer loyalty. The identification of competitive intensity as a key determinant of both switching intention and market dynamics underscores the importance of policies that encourage fair competition, innovation, and investment in infrastructure and services. Additionally, this study serves as a foundational resource for students, consultants, marketing professionals, and other stakeholders interested in exploring similar topics. It provides a comprehensive analysis of the relationship between market dynamics, competitive intensity, and switching intention, offering a valuable reference point for further research and professional development in the mobile telecommunication sector.
Based on the findings of this study, several recommendations can be made to stakeholders in the mobile telecommunication industry to enhance competitiveness and customer satisfaction. Telecom operators should prioritize reducing switching costs by simplifying contract terms, offering incentives for switching, and streamlining the service transfer process, thus mitigating financial and procedural barriers. Additionally, operators should focus on enhancing perceived value by investing in infrastructure, technology, and customer support, delivering high-quality services, personalized experiences, and innovative offerings to boost customer satisfaction and loyalty. Policymakers should promote competitive intensity by encouraging new market entrants, innovation, and infrastructure investment, reducing barriers to entry through spectrum allocation and licensing regulations, and ensuring fair competition practices and consumer protection standards. This will create a competitive market landscape, incentivizing operators to improve offerings, innovate, and prioritize customer satisfaction, benefiting consumers and the industry as a whole.
Despite the valuable insights gained from this study, several limitations should be acknowledged. The research was conducted within the specific context of the mobile telecommunication industry in a particular region, which may limit the generalizability of the findings to other industries or areas. The study relied on self-reported data, which might be subject to recall or social desirability bias, potentially affecting the accuracy of the results. The cross-sectional design of the study prevents establishing causal relationships between variables, highlighting the need for longitudinal studies to provide more robust evidence. Additionally, while the analysis included a comprehensive set of variables, other factors influencing switching intention, such as network quality, pricing strategies, and brand reputation, were not explicitly examined. Future research could address these limitations by using diverse methodologies, expanding the scope of variables considered, and replicating the study in different contexts to enhance the validity and reliability of the findings.