Antecedents of consumers' brand switching behavior in mobile service provider

1.1 Research gap

The presented literature provides valuable insights into consumer behavior, brand switching and factors influencing brand loyalty in the context of the rapidly evolving Indian mobile market, particularly in response to the disruptive entry of Reliance Jio. However, despite the comprehensive coverage of relevant concepts and prior research, several research gaps remain that merit further exploration:

While the literature acknowledges the evolving consumer demands and increasing brand switching tendencies, there is a potential gap in understanding the underlying dynamics of these shifts. The discussion also focuses on the decline of brand loyalty and the impact of brand switching on firms (Gray et al., 2017). However, there is a research gap in investigating the multi-dimensional nature of brand loyalty (Zhao and Hassan, 2023). While the literature references customer satisfaction as a key factor influencing brand switching, it does not delve deeply into the various dimensions of customer satisfaction that matter most in the mobile industry context. Investigating specific aspects like network quality, customer service and pricing could shed light on which factors drive or hinder customer satisfaction and subsequent brand switching.

The case of Reliance Jio underscores the impact of technological innovation on consumer behavior. However, there is an opportunity to delve deeper into the mechanisms through which innovation influences brand switching (Cronin, 2000; Liu et al., 2011). The discussion largely centers on the mobile industry, but there might be insights to glean from other industries that have faced similar disruptive forces. Exploring case studies from other sectors could provide a broader perspective on how innovation, brand loyalty and switching behavior interplay.

The presented literature primarily focuses on technological and market-driven aspects, possibly neglecting the influence of cultural and socioeconomic factors on brand switching. While the immediate effects of brand switching are discussed, there is a research gap in exploring the long-term implications for both consumers and brands. Investigating the consequences of frequent brand switching on consumer satisfaction, loyalty and brand reputation could provide valuable insights into the sustainability of this trend.

Addressing these research gaps through empirical studies, quantitative analyses, or cross-disciplinary investigations could contribute to a more comprehensive and nuanced understanding of the complexities surrounding brand switching in the evolving landscape of the Indian mobile market.

The paper's structure unfolds as follows: Section 2 provides a literature review and hypothesis formulation, Section 3 details the research methods, Section 4 presents analysis results and findings and Section 5 delves into discussions and implications.

2.1 Switching behavior

The seminal work of Keaveney (1995) explains the unique factors affecting switching behaviors like negative customer experience, ethical problems, competition, personal reasons and involuntary switching. This study conveys that consumers have no dissatisfaction with the current brand even though they switch the brand (Bogomolova and Grudinina, 2011). Customers switch the brand for various reasons: customer-driven, personal and other. These uncontrollable reasons cause customer brand switching (Reichheld, 1993). Many customers switch the brands regardless of whether they are delighted (Colgate and Lang, 2001); other studies found that dissatisfied customers continue using it (Ranganathan, 2006).

Customer switching means customers change the existing service provider to another (Temerak and El-Manstrly, 2019; Singh and Rosengren, 2020; Zhao and Hassan, 2023) The belief is that satisfaction leads to loyalty and dissatisfaction leads to switching, but many researchers found it reverse (Chuah, 2017; Ou et al., 2017).

Keaveney's model was based on 45 various services, and to generalize the same on one service may not be appropriate, so specific variable impact on mobile service providers required further specification and testing of proposed variable is desirable (Keaveney, 1995; An and Noh, 2009; Levesque and McDougall, 1993; East et al., 2012; Mittal and Lasser, 1998). For this study, the researcher identified factors specific to mobile user switching behavior by doing exploratory research through interviews of industry experts and added SC (Lee et al., 2001) and changes in Technology (Lee and Murphy, 2005). The variable, employee responsiveness to a service failure, is merged with service encounter failure (SEF) from the input received from an expert through exploratory research.

2.2 Service inconvenience

As per the research findings of Keaveney (1995), 21.6% of consumers switched the service providers because of service inconvenience. Service inconvenience (SI) is defined as long waiting time for consumers, shorter operation time and location of service providers (Keaveney, 1995; Suleiman Awwad and Awad Neimat, 2010; Aslam and Frooghi, 2018). Kassim and Ramayah (2016) suggested that the ease of use and customer convenience can change customer attitudes towards the brand. Levesque and McDougall (1993) found that the inconvenience of location is an essential factor for consumer brand switching behavior. Colgate and Hedge (2001) also investigated that long service times and inconvenience of location are the primary influencers for the customer to switch the brand. The impact of SI on consumer switching behavior is shown in Figure 1 and the hypothesis is as under.

2.3 Core service failure

The customer feels dissatisfied with services due to core service failure. Zhao and Hassan (2023). Several studies have identified a strong relationship between customer dissatisfaction due to core service failure and switching behavior (Wong et al., 2019; Lee et al., 2001; Bolton and Bronkhorst, 1995). Core service failure means the service fails to meet the expectation of customers (Tesform et al., 2016; Michel, 2001). The core service failure is also defined as customer issues and services (Ahmad, 2002). The quality of interaction with service staff has an indirect impact on brand switching through service quality and satisfaction (Valenzuela, 2014; Zolfagharian et al., 2017). The impact of service failure on consumer switching behavior is shown in Figure 1 and the hypothesis is as under. Service failure is responsible for switching the service provider, but it also spreads negative word-of-mouth and affects other existing customers or potential customers (Malhotra and Malhotra, 2013; Aslam and Frooghi, 2018).

2.4 Service encounter failure

The second most important factor responsible for brand switching is SEF (Tesform et al., 2016; Keaveney, 1995). When service providers do not entertain the customer's complaints or treat them in a manner that hurts them, service encounter failure. SEFs are related to the human factor in the firm. When employee treats the customers in an impolite, uncaring and unresponsive manner might cause dissatisfaction amongst customers. The primary reason for these human errors are an employee's incompetence and lack of knowledge, which cause customers were switching the brand (Tax et al., 1998). Building a strong customer orientation in employees increases their job satisfaction and commitment and helps build customer loyalty (Todd Donavan et al., 2004). On average, 40% of the customers who suffer through a terrible service experience stop using the service without complaining (Martilla and James, 1977). The impact of SEF on consumer switching behavior is shown in Figure 1 and the hypothesis is as follows.

2.5 Technology and edge on competition

The competitors identify and create the factors that encourage the customer to switch the brand (Yani-de-Soriano and Slater, 2009) and increase their market share (Colgate and Lang, 2001; Ahmed and Al-Kwifi, 2014; Shukla, 2004). Product involvement impacts the readiness to switch, which means consumers would like to experience the new product brands and find out if they match their products (Sidhu, 2005). Mobile service providers offer more and more services and benefits, making the market more competitive and increasing brand switching. New technology evolution in the mobile service provider industry makes competiton more rigorous, makes their offers more attractive and increases brand switching (Al-kwifi and Yammout, 2013; Sidhu, 2005). Brand switching is highly dominated by high technology product features that have capabilities to achieve competitive advantage (Suleiman Awwad and Awad Neimat, 2010; Pookulangara et al., 2011). New and latest technology products will attract and retain customers (Sidhu, 2005). The companies that cannot maintain the pace of technological change lose the market share, and their customer base switches to competitors (Malhotra and Malhotra, 2013). The service provider offers the latest technology products, which customers value to reduce brand switching and increase loyalty.

Brand leadership and differentiation have been achieved through product and process innovation (Malhotra and Malhotra, 2013). The Jio is a live example of infrastructure innovation in its impact on services with the latest, innovative, and next-generation services like 4G and VoLTE. The impact of Technology and edge on competition on consumer switching behavior is shown in Figure 1 and the hypothesis is as under. We try to understand customers' perceptions of the innovativeness of their mobile service providers and its impact on their switching behavior.

2.6 Price

One of the most crucial determinants for brand switching is price (Zhao and Asiaei, 2022). Customers usually are price conscious in their purchasing behavior (Levesque and McDogall, 1993; Beckett et al., 2000; Mortomer and Weeks, 2019). The finding of Lee and Murphy (2005) indicates that price is the topmost factor for brand switching compared to service and loyalty programs (Aslam and Frooghi, 2018). Anderson (1996) found the importance of price tolerance in addition to satisfaction when predicting customer switching.

Varki and Colgate (2001) suggest that price plays an essential role as an attribute of performance and may impact customer satisfaction and behavioral intentions. The customer who gets price competitively competitive from an alternative brand triggers the brand switching, particularly from a private brand (Govender, 2017). According to Dick et al. (1996), a low price strategy can increase switching between brands, and thus, a low price store stagey can influence this kind of behavior. Colgate and Hedge (2001) also found that price is one of the essential factors in brand switching behaviors in New Zealand and Australia. The impact of price on consumer switching behavior is shown in Figure 1 and the hypothesis is as follows.

2.7 Switching cost

SC is a cost that customers incur if they change their mobile service provider. First-time SC has been considered by Fornell (1992) in Marketing (Tesform et al., 2016; Jones and Sasser, 1995; Chuah et al., 2017; An and Noh, 2009; Bansal et al., 2005; Steven et al., 2015). SC is one of the vital factors to determine the competitiveness of the market. High SCs make it difficult for the consumer to change service providers irrespective of whether they are satisfied or dissatisfied (Temerak and El-Manstrly, 2019; Zolfagharian et al., 2017). High SCs make customers insensitive to satisfaction (Chang et al., 2017; Klemperer, 1987; Farah, 2017; Valenzuela, 2014). The SC is divided into three types: transactional cost, learning cost and artificial cost. Transactional cost is the financial loss incurred due to the change of the present service provider to a new service provider. Learning cost incurred to learn the usage of the newly acquired service. The last one is called artificial or contractual cost, which a service provider deliberately creates. The impact of SC on consumer switching behavior is shown in Figure 1 and the hypothesis is as under.

3.1 Data collection

First, exploratory research was conducted to address the contemporary issues related to consumer brand switching in the mobile market. The marketing and brand managers of mobile companies were interviewed. Then the depth literature review was conducted to develop theoretical background and methodology, exploring the research in the area and concepts to validate the variables of primary research. Keaveney has done pioneering work in service switching behavior (1995), which is the foundation of this study. The survey method was adopted for this research, which was widely used and accepted as an efficient tool for this kind of consumer study (Andreassen, 2000; Lee et al., 2001; Malhotra, 2009).

The primary research was conducted using a self-administered questionnaire based on a five-point Likert scale. A pilot questionnaire was designed and administered to 28 young adults. Also shared with few experts from industry and academia, it helped outline many potential areas, and the same has been filtered. The data were collected in-person, and an online survey was conducted via email and social media. More than 561 questionnaires were administered. Forty-seven responses were removed due to incompleteness and outliers, resulting in 514 valid responses with an overall 91.62% response rate. The correlation of independent variables was checked, and there was no multicollinearity found as the variance inflation factor (VIF) value was less than the threshold limit in coefficient analysis.

3.2 The sample

The sample was selected randomly chosen from the consumer who had changed their mobile service provider within the last six months, and no other bias was kept for the selection of sample. The sample frame was maintained that the consumer must have subscribed to a new mobile service within the last six months. Total 514 valid samples were collected. As per the requirement of structural equation modeling (SEM), the minimum sample value is 400 for analysis (Bagozzi and Yi, 1988). Out of the total sample, 54.6% male respondents and 43.4% female respondents participated in the study. The majority of the respondents were from 18 to 25 years (62.7%), while the 26 to 35 age group, 18.2% and 35 to 45, were 11.9%. A total of 80.6% of respondents had graduate or higher qualifications.

3.3 Construct validation

Exploratory factor analysis (EFA) was applied to check the common method bias (CMB) in the dataset, and the variance was less than 50% of the threshold limit, so there is no evidence of CMB (Jarvis et al., 2003). In any field research, the most critical part of knowledge development is to draw causal conclusions (Viglia and Dolnicar, 2020).

Confirmatory factor analysis (CFA) was used to establish the validity of the construct with the help of AMOS 22.0. A two-stage approach was used; in the first part, a standardized regression coefficient was obtained by estimating the second part's measurement model for estimating the structural model (Gerbing and Anderson, 1988). CFA will help to check the construct validity through the maximum likelihood method. The model is called fit if we get a normed chi-square value that is chi-square/degree of freedom (DF), between 2 and 5, the comparative fit index (CFI) should be above 0.9, the normed fit index (NFI) should be above 0.9, and a root mean square error of approximation (RMSEA) value should be less than 0.08 was considered an indication of proper fit and values above 0.10 indicated poor fit (Pallant, 2007; Pradeep et al., 2021; MacCallum et al., 1996).

It is observed that the aforesaid criteria such as chi-square (CMIN) = 540.591, DF = 254, CMIN/DF = 2.327, goodness-of fit-index (GFI) = 0.924, adjusted goodness-of-fitness index (AGFI) = 0.902, NFI = 0.908, CFI = 0.948 and RMSEA = 0.047 are very close to the recommended level. Many authors raised their doubts about applying only chi-square to test the goodness of fit, so it is recommended to use a comparative model fit. As shown above, the values of GFI and RMSEA are satisfactory and represent absolute fit to the model (Bagozzi and Yi, 1988).

Cronbach's Alpha (α) was computed in SPSS 22 for testing the reliability of scales of each latent variable as shown in Table 1. An alpha value of 0.6 was recommended for the survey-based research threshold (Hair et al., 2009). The vales of Cronbach Alpha reveal that all the alpha values ranging from 0.71 to 0.86 were higher than the cutt-offs. These statistics signal scales' internal consistency.

As shown in Table 2, standardized estimates of all the items for their respective constructs are significant and are above 0.5. All the values of average variance extracted (AVE) were above 0.5, which indicated that convergent validity was achieved (Bagozzi and Yi, 1988). All the squares of interconstruct correlations were lower than their respective square roots, proving discriminant validity.

To determine how distinct the various constructs or variables in a study are from one another, a discriminant validity table is used. The square roots of the AVELong, 1983; Pradeep et al., 2021) for each construct are commonly shown on the diagonal, along with the correlation coefficients between each pair of variables. A construct's degree of differentiation from other constructs in the study is shown by the amount of variance in a construct that is explained by its indicators, or AVE.

The diagonal values in this table relate to the square roots of the AVE for each construct. The table demonstrates that there is little correlation between any two constructs, demonstrating that they are distinct from one another. For instance, ST AND RSF and SI have the highest correlation coefficient of 0.69, which is lower than the square root of the AVE for both constructs. Based on this table, it indicates that the discriminant validity of these constructs is sufficient overall.

5.1 Policy implication

Consumer switching behavior carries significant policy implications for both marketers and regulators, impacting market growth and brand management. From a business perspective, such behavior often signals customer dissatisfaction with a particular product or service. Consequently, companies should take proactive measures to address the root causes prompting customers to switch. These measures might encompass enhancing product quality, optimizing customer service, or revising pricing strategies.

This study's policy implications primarily revolve around the consumer switching behavior within the mobile service provider sector. Furthermore, such behavior can also influence competition policy. The degree to which consumers switch between brands can serve as an indicator of the competitiveness within the market. A healthy rate of switching may suggest a robust competition, which is generally beneficial for consumers. Conversely, limited or absent switching behavior might indicate barriers to entry for new businesses, potentially hindering fair competition.

Previous scholars have extensively investigated consumer switching behavior (Kumar and Chaarlas, 2011; Bogomolova, 2011; Keaveney, 1995) and have identified various factors that drive consumers to switch. Notably, technology and pricing have emerged as critical factors influencing brand retention and mitigating brand switching. Consequently, this study offers valuable insights into consumer behavior, market competition dynamics and the effectiveness of regulatory measures.

5.2 Theoretical contributions

This study represents a significant theoretical advancement in our comprehension of consumer behavior, particularly in the context of brand switching. It augments the existing body of knowledge within the domain of consumer behavior with several noteworthy contributions:

The study discerns and explicates the factors that wield substantial influence over consumer switching behavior. Notably, it elucidates that SCs and SEFs have a discernible impact on consumer decisions to switch brands, aligning with the works of Narteh (2013), Vyas and Raitani (2014) and Pradeep et al. (2021). By recognizing the intricate interplay of various factors, including technology and pricing, in retaining consumers within a brand, this research underscores the complexity of consumer behavior. This underscores the need to consider a multifaceted array of determinants when analyzing brand loyalty and switching. The study emphasizes the significance of timely complaint resolution mechanisms provided by companies. It reveals that the failure to address consumer complaints promptly can be a primary driver for consumers to switch service providers, corroborating the findings of Colgate and Hedge (2001).

The research sheds light on the phenomenon of silent churn, where a significant proportion of dissatisfied customers switches brands without formally voicing their grievances. This unvoiced discontent underscores the paramount importance of effective customer service and the potential impact of unaddressed service issues. Contrary to conventional wisdom, the study challenges the prevailing notion that price competition is the primary driver of brand switching. It underscores the growing significance of value-added services and innovation as key determinants in retaining customer loyalty, in line with the findings of Colgate and Hedge (2001) and Pradeep et al. (2021). In a rapidly evolving mobile service market, the study highlights the essential role of technology and maintaining a competitive edge in retaining consumers. This underscores the dynamic nature of the industry and the need for service providers to stay ahead in technological innovations. The research elucidates that core service failures are central catalysts for consumers switching service providers. Furthermore, it points out that such failures can have a cascading effect by generating negative word-of-mouth, thereby impacting both existing and potential customers. This finding aligns with the work of Malhotra and Malhotra (2013).

In summary, this study contributes significantly to the theoretical understanding of consumer behavior, particularly regarding brand switching. It not only identifies and explains influential factors but also challenges existing assumptions about the drivers of brand switching in a dynamic market. This research enriches our theoretical knowledge, providing valuable insights for academia and industry practitioners alike.

5.3 Managerial implications

This study provides valuable insights for mobile service providers and stakeholders across various domains. It is especially relevant for marketers seeking to refine their branding and marketing strategies. Additionally, educators, practitioners and researchers keen on delving into the intricate dynamics of mobile user behavior can benefit from these findings.

One central takeaway is that consumers are prone to switch brands when they perceive more cost-effective alternatives or experience service failures from their current providers. This resonates with the observations of Bogomolova and Grudinina (2011), who emphasized that brand switches can occur irrespective of customer satisfaction levels. Consequently, it becomes paramount for mobile service providers to not only maintain competitive pricing but also excel in service quality to retain their customer base. Importantly, this study emphasizes that the mobile market has transcended the traditional price-driven competition. The concept of value pricing, as illuminated by the findings of Malhotra and Malhotra (2013), is now more pertinent. Therefore, mobile service providers should prioritize the delivery of value-added services and innovations that align with their users' preferences. SCs emerge as a significant factor and encouraging customers to commit to extended subscription plans and higher investments can help reduce brand switching. This aligns with insights from Ehrenberg (1988) and Shukla (2009), which suggest that brand switching is driven by an array of factors beyond loyalty programs and customer satisfaction. Factors such as employee empathy, response time and addressing customer inconveniences play pivotal roles in influencing brand switching.

Echoing the findings of Lee et al. (2001), this research underscores that service failures can trigger brand switching among mobile service providers and lead to negative word-of-mouth. Hence, organizations are advised to actively measure the voice of the customer to gain insights into what satisfies or dissatisfies consumers, allowing them to proactively prevent brand switching. Furthermore, staying current with new and cutting-edge technological products is vital for retaining customers. Organizations that fail to keep pace with technological advancements risk losing market share. This underscores the necessity for organizations to remain technologically competitive. It is important to note that these findings challenge previous research that heavily emphasized the influence of price on brand switching. In today's mobile market, consumers consider a broader spectrum of factors, including value-added services and cost advantages, when making their choices. Consequently, mobile service providers should tailor their strategies to address this evolving consumer landscape effectively.

5.4 Limitation and future research

This study offers valuable insights into the factors influencing the switching behavior of mobile users, making a substantial contribution to the existing knowledge base on consumer behavior. While the research has provided significant findings, particularly within the context of the mobile sector, it lays the foundation for further exploration and collaboration among various stakeholders. Nevertheless, several limitations have been identified, which pave the way for potential avenues of future research:

Although the mobile industry is inherently global, it operates within diverse cultural contexts. Future studies could benefit from a cross-cultural approach to better understand switching behavior variations across different regions and cultures. Including data from multiple countries would provide a more comprehensive perspective for future research endeavors. Conducting longitudinal studies to examine the factors affecting switching behavior over time can be insightful. This would allow for the confirmation of existing findings and the identification of evolving trends or shifts in consumer behavior patterns within the dynamic mobile industry. While this study focuses on the mobile sector, it would be valuable to test the developed framework on other services and products. This cross-industry analysis could help researchers gain a deeper understanding of the complexities of consumer behavior and identify commonalities or differences in switching determinants.

Access to big data from companies or agencies could significantly enhance research capabilities and provide genuine insights into consumer behavior. Analyzing extensive datasets could uncover nuanced patterns and correlations that may not be apparent through traditional research methods. The emerging trend of dual SIM card usage in the mobile industry presents an intriguing area for further investigation. Exploring how consumers manage and make decisions regarding multiple service providers within a single device could yield valuable insights into contemporary mobile user behavior.

In conclusion, while this study offers substantial contributions to our understanding of switching behavior in the mobile sector, it also identifies important directions for future research. By addressing these limitations and exploring these avenues, researchers can continue to advance our knowledge of consumer behavior and its implications for businesses and industries.