Abstract
This study examined the influence of trust dimensions on customer engagement, and the resultant impact of customer engagement on customer loyalty in the context of life insurance. Furthermore, it investigated the mediating role of customer engagement in the relationships between trust dimensions and customer loyalty. A total of 452 valid responses from life insurance customers in Ghana were examined using structural equation modeling (SEM). The results revealed that trust in service provider, trust in the regulator, economy-based trust, and information-based trust significantly influence customer engagement, with trust in service provider and trust in the regulator driving a higher level of customer engagement. The results also uncovered that customer engagement significantly enriches customer loyalty and mediated the relationships between the trust dimensions and customer loyalty. The findings highlight the importance of building convincing customer trust to advance customer engagement and customer loyalty.
Introduction
In the present-day global environment, understanding of customers is imperative in shaping future decisions and strategies. Also, to survive and grow by attracting new customers in today’s fast-moving financial services sector, insurance firms must be able to engage their customers at all imaginable touch points to align their strategies with the customer needs. Customer engagement (CE) is depicted as a way to create, build, and improve customer relationships (Abbas et al., 2018; Brodie et al., 2013) and is regarded as a strategic imperative to build a sustained competitive advantage (Hollebeek, 2012; Islam et al., 2019; Kumar & Pansari, 2016; Van Tonder & Petzer, 2018). Similarly, CE is a concept that is being examined as a tool to facilitate predictive power of customer behavior which includes loyalty and referrals (Brodie et al., 2011; Islam et al., 2017; Khan et al., 2016; Thakur, 2016; Zhang et al., 2017). The extant literature indicates that CE makes a strategic contribution to firms and have the potentiality to impact customer loyalty (Hollebeek, 2011a; Islam et al., 2019), firm reputation (Dijkmans et al., 2015), firm performance (Rather et al., 2019; So et al., 2014; Van Doorn et al., 2010), and firm value (Vivek et al., 2014; Zhang et al., 2017). Moreover, CE contributes to novel product development (Hoyer et al., 2010), thus becoming the topic of much discussion within academia, business, and the media in recent years (Dessart et al., 2015; Thakur, 2016). According to Brodie et al. (2011), engaged customers usually participate in online marketing by suggesting brands to others as well as putting in good words for products and services to others. Given the significance of CE, many researchers (e.g., Brodie et al., 2013; Kosiba et al., 2018) have called for more studies to be conducted to understand the drivers of CE. Moreover, while Marketing Science Institute (MSI) acknowledged CE as a key research priority in their research priorities list of 2010–2012 and 2014–2016 (MSI, 2010, 2014), authors, such as Kumar (2015), further propose it as an emerging research space that requires scholarly consideration.
Past studies have highlighted the significant influence of trust on engagement (Johnson & Grayson, 2005; Putnam, 1993) and most recently by Kosiba et al. (2018). Van Tonder and Petzer (2018) assert that trust is crucial in relational exchanges among stakeholders. Moreover, trust is established to be key in buyer–seller interactions (MacMillan et al., 2005; Morgan & Hunt, 1994; Verma et al., 2016). Also, trust is considered to minimize perceived risks, thus improving consumers’ commitment to service providers (Van Tonder & Petzer, 2018), and as a result of that committed consumers become engaged with service organizations and show improved customer loyalty (Brodie et al., 2013; So et al., 2014). This is indicative that trust can have a positive influence on CE. Again, previous research confirms that trust is among the six most frequently mentioned defining constructs in engaging relations in the insurance, banking, and health care sector (Agariya & Singh, 2011; Kosiba et al., 2018), and it is argued to be the most defining construct for the insurance and banking sector (Agariya & Singh, 2011). For example, empirical study by Kosiba et al. (2018) revealed that trust significantly drives CE in the retail banking sector.
Despite the growing research interest in CE and the recognition that trust plays an important role in enhancing engagement among customers and service providers, little empirical investigation has been done on the topic so far (Kosiba et al., 2018; Van Tonder & Petzer, 2018). More specifically, it is difficult to find empirical proof in support of how different dimensions of trust drive CE in life insurance context since earlier studies have mainly focused on service quality assessment (Bala et al., 2011), cost efficiency (Ansah-Adu et al., 2012), performance (Akotey et al., 2013), growth (Alhassan & Fiador, 2014), and customer satisfaction (Abaidoo & Nwosu, 2016). Furthermore, it is unclear how trust can bring about increased CE in the life insurance sector as some authors have argued that differences in cultural and institutional milieus may affect relationship conduct (Bianchi & Saleh, 2010; Yang & Gabrielsson, 2017). Therefore, this study endeavors to fill this gap in the trust–CE literature by developing a conceptual framework, and then examining it empirically. Thus, the main aims of this study are as follows: first, to explore and provide empirical evidence of the influence of trust on CE. In addition to the nexus between trust and CE, the extant literature further suggests a direct link between CE and customer loyalty (Bowden, 2009; Brodie et al., 2013; Islam et al., 2018; Islam & Rahman, 2017; So et al., 2014; Thakur, 2016; Vivek et al., 2012). CE is said to enrich loyalty and buy decisions (Hollebeek, 2009; So et al., 2014) via a strong, continuing psychological connection along with shared brand experiences that go beyond purchase (Brodie et al., 2011; Islam et al., 2019; So et al., 2016). Yet, most of the available empirical evidence establishing this relationship have come from online brand communities (Islam et al., 2018; Islam & Rahman, 2017), tourism brands (Rather et al., 2019; So et al., 2014), online banking (Khan et al., 2016), and bank customers (Abbas et al., 2018). Hence, a need exists to test this relationship in a different context, as undertaken in this research. So, the second aim of this study is to examine the follow-on effect of CE on customer loyalty. Third, the study also investigates whether CE plays key mediating role in the link between dimensions of trust and customer loyalty.
According to prior studies, trust has multiple dimensions (Chai & Kim, 2010; Gefen et al., 2003; Hsu et al., 2007; Kosiba et al., 2018; Ratnasingam, 2005). So, to achieve the aims of this study and to comprehensively understand the nexus between the two concepts, we conceptualized trust from four dimensions: trust in service provider, trust in regulator, economy-based trust, and information-based trust, and examined their impacts on CE. This conceptualization of trust from four dimensions is very important as customer uncertainty regarding insurance products and services is very high, particularly in developing economy like Ghana, where settlement of claims is sometimes challenging (Abaidoo & Nwosu, 2016; Kosiba et al., 2018). Furthermore, the insurance industry in Ghana has a huge potential; however, insurance penetration is still very low in the country (Abaidoo & Nwosu, 2016; Akotey et al., 2013; Alhassan & Fiador, 2014). Insurance penetration, which is explained as the contribution of total insurance premiums to gross domestic product (GDP) is still below 2% (Abaidoo & Nwosu, 2016; National Insurance Commission [NIC], 2016). This situation has been attributed to the low level of education regarding insurance, which has not been able to alter the perceptions and mind-set of the people about their services. One of the main reasons that has been attributed to the challenge in altering perceptions and the mind-set about the industry is “hidden conditions” in insurance contracts, which are only disclosed to customers during claim settlement (“Financial and insurance contributions to GDP slump in 2017,” 2018). This phenomenon appears to create and raise trust issues and therefore a study on how trust influences CE in the life insurance context is worthwhile.
This study seeks to make several contributions to the evolving literature on trust–CE relationship by examining how different dimensions of trust impact CE and the ensuing effect of CE on customer loyalty. Second, our study is one of the first studies to investigate the mediating role of CE in the relationship between multiple dimensions of trust and customer loyalty. Furthermore, the results will aid life insurance marketers to build and enrich customer relations as well as develop effective marketing strategies that will motivate and drive customers to engage their services or brands for the growth and sustainability of the sector.
The rest of the study is arranged as follows. Next section presents the theoretical background and discusses CE, trust, dimensions of trust as conceptualized in this study, and customer loyalty. Next, the research model and hypotheses formulation are presented, followed by research methodology. Next, data analysis and results, discussion, and implications are presented. The last section highlights the conclusions, limitations, and future research.
Theoretical Background
Different theoretical views have been applied to study CE, involving organizational psychology (Dwivedi, 2015), regulatory engagement theory (Hollebeek & Chen, 2014), relationship marketing (Bowden, 2009; Brodie et al., 2013; Hollebeek, 2011b; Vivek et al., 2014), service-dominant logic (Hollebeek, 2011b; Rather et al., 2019), social exchange theory (Hollebeek, 2011b), congruity theory (Islam et al., 2018), theory of planned behavior (Bitter et al., 2014), stimulus-organism response theory (Islam & Rahman, 2017), among others. Following Rather et al. (2019), this study adopts service-dominant logic to frame CE given its wide applicability and emphasis on customer/brand interactivity and value, which is important for life insurance firms as strongly engaged customers are most likely to be brand enthusiasts (KBM Group & 1to1 Media, 2013). S-D logic suggests that service is the unit of exchange and customers are always cocreators in the value creation process (Rather et al., 2019; Vargo & Lusch, 2008). It focuses on close customer–company relationships for creation of value (Abbas et al., 2018). Based on this logic, previous studies argue that CE is necessary for value creation and customer life value is better represented by CE (Abbas et al., 2018; Kumar et al., 2010). In the remainder of this part, we discuss CE, trust and its dimensions, and customer loyalty.
CE
Firms are acknowledging the pressing need to focus on building personal two-way relationships with customers who further interactions (Ángeles Oviedo-García et al., 2014; Kumar et al., 2010). CE has been acknowledged as an emotional link between a firm and its customers focused on contact with customers and their involvement. CE is considered as an expansion to the relationship marketing domain (Brodie et al., 2013; So et al., 2016; Van Tonder & Petzer, 2018; Vivek et al., 2012). It is depicted as a method to create, build, and improve customer relationships (Brodie et al., 2013) and may lead to the development of strong bonds between a firm and its customers (Habibi et al., 2014). It is seen as a strategic necessity to build a sustainable competitive advantage (Brodie et al., 2013; Islam et al., 2019; Van Doorn et al., 2010). In addition, as stated earlier, CE has the potential to contribute to customer satisfaction, customer loyalty, and firm performance (Brodie et al., 2013; Hollebeek, 2011a; Islam & Rahman, 2017; So et al., 2014). CE denotes the thorough procedure in which members encourage and help other members to participate, letting every member to realize his or her own objectives (Nambisan & Baron, 2007). According to Van Doorn et al. (2010), CE can similarly be seen to be a customer-to-firm relationship that centers on behavioral aspects of the relationship. They also maintained that CE includes customer cocreation and customers may decide to use voice (communication behaviors aimed at expressing their experience) or exit (behaviors planned to limit or grow their relationship with the brand).
Generally, the CE concept has been looked at from diverse perspectives in the literature. Mollen and Wilson (2010) define it as “the cognitive and affective commitment to an active relationship with the brand.” However, Bowden (2009) grasped CE as a “psychological process” that models the essential tools by which customer loyalty toward a service brand is shaped in new customers, as well as the tools by which that loyalty may be sustained for repeat purchase customers of a service brand. Moreover, Brodie et al. (2011) express CE as an interaction with a brand articulated via their emotional, behavioral, and cognitive interactive experience with the brand. While some scholars have described CE from a unidimensional perspective (Ángeles Oviedo-García et al., 2014; Jaakkola & Alexander, 2014; Sprott et al., 2009; Van Doorn et al., 2010), many others have looked at it from multidimensional perspectives including cognitive, emotional, and behavioral (Brodie et al., 2013; Dwivedi, 2015; Hollebeek, 2011b; Islam et al., 2019). Following Hollebeek (2011b), Brodie et al. (2011), and Islam et al. (2019), CE in this study reflects customers’ interaction with a brand articulated via their cognitive, emotional, and behavioral interactive experience with a service brand.
Trust
Trust is key in interpersonal and business interactions (Hsu et al., 2007; Van Tonder & Petzer, 2018). This is evinced by the many research efforts in other academic disciplines such as sociology, social psychology, economics, and marketing (Hsu et al., 2007). Trust is crucial in interactive exchanges between stakeholders because customers are anticipated to pay for services they have not yet received or experienced (Morgan & Hunt, 1994). Moorman et al. (1993) defined trust as the willingness to rely on an exchange partner in whom one has confidence. On that account, trust also relates to the perceived credibility and benevolence of the firm rendering the service. Credibility indicates a customer’s perception that the words and promises of a service firm can be trusted, whereas benevolence indicates a customer’s belief that a service firm’s motives and intents are beneficial to its customers (Cater & Zabkar, 2009; Doney & Cannon, 1997; Fullerton, 2011; Tabrani et al., 2018). Similarly, trust is explained as the belief that the other party will act or perform in a socially responsible way and thus will meet the trusting party’s expectations devoid of taking any advantage of its vulnerabilities (Gefen, 2000). Accordingly, trust enables customers to share personal information based on a belief that the information will stay confidential, pay for goods and services, and act on advice (Ponder et al., 2016). When the service provider is regarded as trusty by customers, there is a higher chance that they will share germane information to see to it that the relationship continues to grow and develop (Cazier et al., 2007; Chai & Kim, 2010).
Trust has been identified as a catalyst in consumer–marketer relationships as it provides expectations for the dearth of trust has been talked up as one of the major reasons for customers’ nonengagement (Kosiba et al., 2018; Pavlou, 2003). CE is viewed as trust driven as all interactions or exchanges necessitate an element of trust. Therefore, when trust is established in a relationship, individuals in it are better willing to partake in cooperative interactions or exchanges (Chai & Kim, 2010). Based on this, trust can be considered as a driver of CE as it engenders cooperation and interactions, which is vital in nurturing ongoing relationship with customers. A lot of studies have acknowledged that trust is a multidimensional construct (Abrams et al., 2003; Cazier et al., 2007; Chai & Kim, 2010; Gefen et al., 2003; Hsu et al., 2007; Kosiba et al., 2018; McAllister, 1995; Parkhe, 1998; Ratnasingam, 2005). Drawing from the literature related to trust development, we conceptualize trust from four dimensions, including trust in service provider, trust in the regulator, economy-based trust, and information-based trust, and examine their effect on CE. We proceed to discuss these four dimensions of trust below.
Trust in the service provider
According to Berry (1995), since services are high involvement, most customers favor continuity in service providers. Therefore, this makes trust in a service provider very vital for sustained engagement in interactive exchanges. For that reason, Chaudhuri and Holbrook (2001) posit that trust in a service provider is the willingness of the average customer to trust in the ability of the service provider to deliver its specified function. Therefore, trust in the service provider denotes that the customer believes in and is willing to rely on the service provider (Chai & Kim, 2010; McKnight et al., 1998). Consequently, the customer is most likely to engage in the relationship when the service provider fulfills its promise. Naturally, the level of trust improves when customers trust a service provider (Morgan & Hunt, 1994; Tabrani et al., 2018). Thus, trust in the service provider, in the context of this study, refers to customers’ trust toward life insurance service providers (e.g., I believe my life insurance provider is very reliable and does the right thing, etc.).
Trust in regulator
Trust in the regulator is the creation of trust via a third party effect. This can be a government agency or some other institution that guarantees the trustworthiness of the target firm (Cazier et al., 2007). In other words, this dimension of trust arises due to customers’ sense of security from guarantees, legal recourse, and regulations that exist within a specific context (Gefen et al., 2003; McKnight et al., 1998; Shapiro, 1987). In this view, trust originates from the security that one feels about the situation as a result of such guarantees, safety nets, regulations, supervisory roles, or other structures (Gefen et al., 2003; McKnight et al., 2000). Example of this sort of trust within the context of this study is the NIC. It puts assurance on insurance companies by giving them certification, regulating, monitoring, and supervising the activities of all the insurers they have duly given approval to do business. Having such a third party like the reputable NIC vouch for the licenses of insurers should possibly create trust in that such guarantees have normally been one of the main ways of building trust in the insurance industry. In this sense, customers’ engagement with the service providers is likely to be high when the supposed trust in the regulator is high. This dimension of trust is defined in this study as customers’ trust in NIC, the regulator of insurance business in Ghana. For example, I feel safe doing business with my life insurance provider because the regulator (NIC) will protect me.
Economy-based trust
Economy-based trust, in the view of Hsu et al. (2007), is what attracts a member or customer into engaging in a relationship. New members or customers, from the onset of most interactions, may have slight experience with or knowledge about the service provider; nonetheless, they have an idea of the benefits they could get from the relationship or interaction. As the relationship advances via interactions over time, the new members or customers gain more information about the service provider through their experience (Hsu et al., 2007; Panteli & Sockalingam, 2005), and can calculate or evaluate the costs and benefits arising from the relationship, and that creates an economy-based trust. In this explanation, it is argued that economy-based trust is built on the benefit and fear of penalty for being untrustworthy or for the violation of trust (Chai & Kim, 2010; Gefen et al., 2003; Hsu et al., 2007; Panteli & Sockalingam, 2005; Shapiro et al., 1992). In other words, trust in this way is derived from an economic evaluation occurring in an ongoing relationships or interactions (Doney et al., 1998). Consequently, mutual trust will be molded when there is the existence of economy-based trust. So, when economy-based trust is assumed to be high, it is most likely to impact the degree of engagement as high economic benefits are gained and the possibility of defaulting is diminished because of consequent penalties. In this study, economy-based trust is defined as customers’ trust toward economic benefits gained by patronizing life insurance products and services (e.g., I save time and cost doing business with my life insurance provider).
Information-based trust
Information-based trust is also known as knowledge-based trust (Hsu et al., 2007; Lander et al., 2004). According to Ba (2001), information-based trust is established on the familiarity of the other party that their conduct is expectable and the sense of insecurity and risk is lessened. It trusts on information rather than dread of penalty or rewards of being dependable (Hsu et al., 2007; Lander et al., 2004). It arises among trading partners as a result of the adherence to technical standards, security procedures, and protection mechanisms (Ratnasingam & Pavlou, 2002). Similarly, information-based trust suggests the subjective likelihood that the fundamental technological infrastructure and control mechanisms are efficient in facilitating transactions in line with its confident expectations (Hsu et al., 2007; Ratnasingam, 2005). Based on this, information-based trust is described in this study as customers’ trust toward life insurers owing to sound privacy protection policies. Customers, in their interactions with insurance companies, provide all sorts of personal information to them and thus are expected to have high level of engagement with these firms if they can satisfy themselves that their private information will be protected and not be given out to unauthorized sources without their prior knowledge.
Customer Loyalty
Loyalty may be described as a customer’s intent or predisposition to buy from the same seller or the same brand again (Edvardsson et al., 2000; Thakur, 2016) and is a consequence of the belief that the value received from the said seller or brand is greater than the value obtainable from other substitutes (Hallowell, 1996). Consequently, customer loyalty has been articulated to be a major factor in realizing success and sustainability for a seller or brand over time (Flavián et al., 2006; Keating et al., 2003; Thakur, 2016). Customer loyalty has been defined in two ways, namely behavioral and attitudinal loyalty (Amin et al., 2013; Baumann et al., 2012; Chai et al., 2015). The behavioral loyalty denotes customers’ behavior to repurchase since they enjoy a particular brand or service (Jiang et al., 2015; Rather et al., 2019; Zeithaml et al., 1996). However, Høst and Knie-Andersen (2004) contend that this definition does not offer any accurate account of the existence of loyalty as a result of relatively objective measurement of customer loyalty. On the contrary, attitudinal loyalty suggests the emotional and psychological longing of the customer to repurchase and to recommend to other people (Baumann et al., 2012; Rather et al., 2019). This concept involves customers’ commitment and advocacy to re-patronize and willingness to pay more for a desired service or product constantly in the future (Chai et al., 2015; Islam & Rahman, 2017; Ladhari, 2009; Oliver, 1999), though the situational effects and marketing efforts have the potential to trigger switching behavior (Oliver, 1999). Consistent with this concept, Agustin and Singh (2005) explain that loyalty intents are shown by an inclination to act and to improve an ongoing relationship with the service provider, including repeat purchasing and bigger share of wallet. Following prior studies, this study defines customer loyalty as customers’ commitment to repeatedly select a particular life insurance provider constantly in the future.
Research Model and Hypotheses Formulation
The conceptual model for the study is presented in Figure 1. Consistent with the existing literature, we posit that trust drives CE, and then CE leads to customer loyalty. Our assertion that CE is driven by trust is in line with a similar assertion put forward by Putnam (1993). The author argues that trust drives civic engagement. For the purpose of this study, trust is conceptualized from four dimensions, involving trust in service provider, trust in the regulator, economy-based trust, and information based trust. As mentioned earlier, CE reflects customers’ interaction with a brand articulated via their cognitive, emotional, and behavioral interactive experience with a service brand (Brodie et al., 2011; Hollebeek, 2011b; Islam et al., 2019), and customer loyalty reflects customers’ commitment to repeatedly select a particular life insurance provider constantly in the future.
Conceptual model.
Trust in Service Provider and CE
Trust, explained as a readiness to place reliance on an exchange partner in whom one has confidence (Moorman et al., 1992), is a fundamental feature that separates economic exchange from relational exchange (Ponder et al., 2016). In this definition, trust is defined as the readiness of one to be vulnerable to another (Cazier et al., 2007). Trust involves two parties, the provider and the user of the service. The trust is created or built on the assumption that the service provider represented by the brand is trustworthy and responsible for the interest and welfare of the user (Lee et al., 2015). When users recognize that their interests and welfare are considered, they are likely to remain in the relationship, triggering a continuous interaction with the service provider. Thus, when customers consider the service provider trusty, the possibility of sharing germane information to foster the sustained progress of the relationship is high. In line with this, research has found that trust promotes interactions between customers and service providers (Ballantyne, 2006; Ponder et al., 2016). It has also been established that a positive relation exists between trust and information sharing (Chai & Kim, 2010; McLeod, 2008). Moreover, Johnson and Grayson (2005) suggest that trust in a service provider positively affect customers’ expectation of future interactions. Similarly, Kosiba et al. (2018) identified that trust in service provider contributes positively to CE. Consistent with these explanations and arguments, we propose the following hypothesis:
Trust in the Regulator and CE
This dimension of trust emanates from a third party effect (Cazier et al., 2007). For example, a government agency or some other organization that guarantees the reliability and credibility of the target firm (life insurance providers). This form of trust is built due to customers’ sense of security arising from guarantees, legal recourse, and most importantly regulations that exist within a specific context (Gefen et al., 2003; McKnight et al., 1998). In this regard, trust originates from the security that one feels about the situation due to the presence of such guarantees, safety nets, regulations, supervisory roles, or other structures (Gefen et al., 2003; McKnight et al., 2000). In the context of this study, the NIC is a third party that provides guarantees and assurances to customers because of its regulatory powers and supervisory role and should build or create trust in customers in their interactions with life insurance providers. The NIC has the complete mandate to ensure effective administration, supervision, regulation, and control the business of insurance in Ghana. The NIC is also mandated to perform a wide range of functions including licensing of entities (insurance firms), setting of standards, and facilitating the setting of codes for practitioners. Moreover, it supervises insurance firms on an ongoing basis to certify that they obey regulatory requirements for running insurance business in Ghana. Therefore, we argue that when customers have trust in the regulator to perform its regulatory and supervisory roles effectively, it will most likely enhance the interactions between them and the life insurers. Thus, in the insurance industry, trust in the regulator is very crucial and is anticipated to impact CE. Thus, we suggest the following hypothesis:
Economy-Based Trust and CE
Economy-based trust, according to Hsu et al. (2007), is what entices a member or a customer into engaging in a relationship, and it is focused on economic benefit and fear of punishment for the violation of trust (Panteli & Sockalingam, 2005). It is also termed as calculative-based trust. Calculative trust enlightens expectations by calculatingly and rationally evaluating forward-looking situations (Poppo et al., 2016). It involves calculations of benefits and costs and centers on the comparative values of cheating (e.g., net costs of termination) and cooperation (Bromiley & Harris, 2006; Gefen et al., 2003; Lewicki et al., 2006). When it is high, parties deem that cooperation and performance objectives will be realized since falling short of them invites penalties, such as exchange termination (Parkhe, 1993). Therefore, sanctions, the anticipated payoffs of rewards over the penalties, reduces opportunistic behavior, control exchanges, and maintain cooperation. In this view, trust is based on the premise that while other people may not be certainly good, they are rational, calculative, and act in their own best self-interest, and as result, will desist from perpetrating harm on themselves. So, Shapiro et al. (1992) posited that calculative trust is deterrence-based trust in that people will not participate in opportunistic behavior out of panic of facing the adverse effects of being unreliable. In the e-commerce context, this has been confirmed by Awad and Ragowsky (2008). Likewise, Chai and Kim (2010) identified a positive relationship between users’ trust regarding economic benefits of using blogs and their share of knowledge and information. Hence, we propose that economy-based trust will facilitate CE and formally hypothesized the following hypothesis:
Information-Based Trust and CE
According to Ba (2001), information-based trust is founded on the knowledge and understanding of the other party that their conduct is unsurprising and the sense of self-doubt and risk is lowered. Information-based trust is also referred to as knowledge-based trust (Hsu et al., 2007; Lander et al., 2004; Panteli & Sockalingam, 2005). It is trust developed via repeated interactions that allow an individual or a firm to gather information concerning the other and develop an anticipation that the other’s conduct is predictable. Information-based trust relies on information rather than dread of punishment or rewards of being trusty. Thus, we argue that knowledge of or familiarity with what a particular life insurer does will build trust in customers and improve their interactive relationships with such a life insurer. Furthermore, we contend that sharing or exchanging personal information can place customers in a potentially vulnerable position. So, having a knowledge that the other party has security procedures and sound privacy policies in place creates assurance and trust, thereby increasing the chance of a person’s willingness to be put in this vulnerable position through exchanging personal information. Accordingly, we propose that the knowledge and understanding of the parties (i.e., the insurer and the insured) involved in an exchange relationship builds trust and enhances engagement. Therefore, we propose the following hypothesis:
CE and Customer Loyalty
The direct association that exists between CE and customer loyalty is well known in the literature (Brodie et al., 2013; Islam et al., 2018; Islam & Rahman, 2017; Rather et al., 2019; So et al., 2014; Thakur, 2016). Customer loyalty denotes a customer’s commitment to repurchase or rebuy a preferred product and service consistently in the future (Amin et al., 2013; Oliver, 1999; So et al., 2014). CE, on the contrary, reflects customers’ beyond-purchase connections with the brand (Vivek et al., 2012). CE is said to enhance loyalty and buy decisions (Hollebeek, 2009; So et al., 2014) via a strong, continuing psychological connection along with shared brand experiences that go beyond purchase (Brodie et al., 2011; So et al., 2016). CE with a brand, service, or product impacts customer consequences, for example, brand recognitions and brand attitudes, and consequently impacts brand loyalty (Sprott et al., 2009). In addition, engaged customers are likely to foster more positive attitudes about a service, firm, or brand, resulting in loyalty toward the firm (So et al., 2014; Vivek et al., 2012). According to studies, customers often cultivate an attitude toward buying behavior based on previous experiences (Amin et al., 2013; Caruana, 2002), which either results in loyalty or a longing to switch (Anthanassopoulos et al., 2001). Therefore, when customers are highly engaged, they will most likely continue to deal with a particular life insurance provider and offer positive word of mouth thereby influencing those they know. Hence, we posit the following hypothesis:
Mediating Role of Consumer Engagement
As previously indicated, CE is seen as trust driven since all interactions or exchanges necessitate an element of trust. Consequently, when trust is established in a relationship, persons in it are better willing to partake in cooperative interactions or exchanges (Chai & Kim, 2010; Kosiba et al., 2018; Van Tonder & Petzer, 2018). Moreover, CE with a brand, product, or service impacts customer consequences (e.g., brand recognitions and attitudes) and thus influences brand loyalty (Abbas et al., 2018; Islam et al., 2018; Rather et al., 2019; Sprott et al., 2009). Beside the direct effect of trust on CE (Kosiba et al., 2018), studies have shown that CE plays mediating role between its antecedents and outcomes (Abbas et al., 2018; Rather et al., 2019). For instance, Rather et al. (2019) identified CE’s mediating role in the influence of place attachment and place authenticity on customer trust, loyalty, and cocreation. Again, the theoretical framework of Van Doorn et al. (2010) suggested that precursors based on firm, customer, and context can benefit consumers or firms via CE behavior. Considering the central role of CE, and the fact that there is scant research on the mediating role of CE in the relationship between trust dimensions and loyalty, we propose the following hypotheses:
Method
Questionnaire and Scales
A review of existing theories, conceptualization, and measures indicated that the measurement of trust dimensions, CE, and customer loyalty could reliably be attained via the adoption of measures adapted from existing literature. The four items for gauging trust in the service provider were adapted from the works of Verhoef et al. (2002) and Hsu et al. (2007); the four items gauging trust in the regulator were taken from Gefen et al. (2003); the four items gauging economy-based trust were sourced from Hsu et al. (2007), which in turn, were largely based on the earlier works of Gefen et al. (2003) and Ratnasingam (2005); and the four items measuring information-based trust were also adapted from Hsu et al. (2007) and McKnight et al. (2002). In addition, the six items measuring CE were adapted from Rich et al. (2010) and Islam and Rahman (2017). Also, customer loyalty was measured using scales adapted from Islam and Rahman (2017). All the measures in the questionnaire were somewhat modified to suit the life insurance context. Every item was gauged by a Likert-type scale, which is a psychometric-scale, normally utilized in questionnaires (Lu & Lee, 2010). A 7-point Likert-type scale ranging from “strongly disagree” (1) to “strongly agree” (7) was utilized to measure each construct, which is similar to other prior studies (Khan et al., 2016; Rather et al., 2019). The scale development for each dimension is listed in Table 1.
Measurement Scale.
Note. NIC = National Insurance Commission.
Sample and Data Collection
To examine the efficacy of the model presented in Figure 1, survey research design was employed. The population of our study comprises all clients of all life insurance providers in Ghana. We focused on life insurance providers because the sector has become very competitive in recent years and prospects in life businesses are yet to be completely tapped. Moreover, the sector offers a number of benefits to persons, firms, and the nation, involving the usage of life insurance policies as collateral for short-term loans such as bank overdrafts, and mortgage in addition to provision for retirement. The life insurance sector has a total asset of GHC 2,241 million and a total investment of GHC 1,939 million (NIC, 2016). A convenience non-probability sampling method was employed in the current study, which is similar to other studies (e.g., Van Tonder & Petzer, 2018). Data were gathered via a developed questionnaire and customers were chosen from branches of 10 life insurance providers selected based on popularity and size in three regional capitals in Ghana, including Accra, Kumasi, and Takoradi. These cities were selected since most life insurance providers in Ghana operate or have most of their offices located in these cities. Accra is the capital city of Ghana where most of these life insurance companies are headquartered. It is Greater Accra Region’s administrative and economic hub, and economic activities there include commercial and financial sectors, fishing, textiles, among others. Kumasi is the industrial, commercial, and cultural capital of Asanteman and Takoradi is the Western region’s biggest city and commercial and industrial hub of the Western region of Ghana. The respondents were politely approached, and the purpose of the study was made clear to them and were assured of confidentiality. Data were collected from February 3, 2017 to April 3, 2017 and was conducted by three trained senior students and two researchers.
Prior to the final distribution of the questionnaires to the participants, a pilot study was conducted with 30 life insurance clients who have at least 5 years’ experience with an insurance provider. The results of the pilot study were used to enhance and improve the validity and reliability of the measurement. Based on the feedback from the pilot study, the wording of some of the items were carefully modified and then refined. A total of 485 questionnaires were distributed. However, 452 responses were found to be utilizable following the initial screening (Malhotra & Birks, 2007). The sample showed an equal distribution across the cities: Accra, 38.1%; Kumasi, 31.6%; and Takoradi, 30.3%. There were 258 males, constituting 57.1% and 194 females, representing 42.9%. The descriptive statistics of the respondents are provided in Table 2.
Demographic Information of Respondents.
Note. HND = Higher National Diploma.
Testing for Common Method Bias
The most often cited problem in any self-report survey is the issue of exposure to common method variance (Malhotra et al., 2017; Tabrani et al., 2018) as it threatens the validity of the results (Kosiba et al., 2018; Reio, 2010). Accordingly, we examined this concern in this study employing the Harman’s single factor test. It establishes if majority of the variance can be expounded by a single factor (Podsakoff et al., 2003). This was done by entering all the variables into a principal component factor analysis excluding a rotation (Podsakoff & Organ, 1986). The results show that all the factors are extracted with the first factor explaining 29% of the total variance, confirming that common method variance is not a concern in this data set.
Data Analysis and Results
A two-step approach of structural equation modeling (SEM) suggested by Anderson and Gerbing (1992) was utilized to test the research hypotheses. First, a confirmatory factor analysis (CFA) was carried out to evaluate the validity of all the construct measures employed in the study. This was conducted employing the Analysis of Moment Structures (AMOS).
Tests of Measurement Model
The results of the CFA indicate the model gives an acceptable fit: χ2/df = 3.110, goodness-of-fit index (GFI) = 0.940, root mean square error of approximation (RMSEA) = 0.06, and comparative fit index (CFI) = 0.953 (Hu & Bentler, 1999). To evaluate internal consistency of the constructs, Cronbach’s alpha (α) was employed. As suggested by Bagozzi and Yi (1988), the value for each construct should be above .60 to be considered reliable. In this study, Cronbach’s alpha (α) values ranged from .786 to .861, demonstrating support for internal consistency (see Table 3). Furthermore, the composite reliability (CR) scores ranged from .847 to .899, surpassing the minimum critical recommended value of .70, thus signifying adequate constructs’ internal consistency (Dillon & Goldstein, 1984). In addition, the validity analysis included both convergent and discriminant validity. As shown in Table 3, the standardized factor loadings of all the items measured ranged from .723 to .827, exceeding the threshold value of .60 suggested by Hair et al. (2010). Again, the average variance extracted (AVE) of the constructs (see Table 3) ranged from .582 to .625, which were all greater than the recommended value of .50 (Hair et al., 2010). Table 4 revealed the discriminant validity of the construct, since the square root of the AVE between each pair of factors was higher than the correlation estimated between factors, hence confirming its discriminant validity (Hair et al., 2012). In summary, the results of the measurement test showed satisfactory reliability, convergent validity, and discriminant validity.
Measurement Model.
Note. CR = composite reliability; AVE = average variance extracted.
Descriptive Statistics, Correlation, and Discriminant Validity.
Bold value signifies that P < 0.001.
Tests of Structural Model
After confirming the measurement model, the structural model was run to test the relationship between exogenous variables and endogenous variable. The fit indices of the structural model were assessed and established to be within their acceptable thresholds (χ2/df = 2.139; GFI = 0.936; CFI= 0.922; RMSEA= 0.063), indicating that the structural model shows a good fit to the data (Anderson & Gerbing, 1988; Hair et al., 2010). The results of the tested hypotheses are indicated in Figure 2 and Table 5.
Results of the model.
Direct Path of Hypothesized Model.
Note. TSP = trust in service provider; CE = customer engagement; TR = trust in the regulator; EBT = economy-based trust; IBT = information-based trust; CL = customer loyalty.
Analysis of the Structural Model
The examination of the structural model should explore the direction and significance of causal relations between latent variables (Lou et al., 2000; Lu & Lee, 2010). As disclosed in Figure 2 and Table 5, the relationship between trust in service provider and CE was buttressed by the significant path coefficient estimated (H1: β = .483, t = 6.512, p < .05). The results also indicate that CE is significantly influenced by trust in the regulator, economy-based trust, and information-based trust with path coefficients estimated (H2: β = .464, t = 4.765, p < .05; H3: β = .357, t = 4.635, p < .05; H4: β = .313, t = 5.081, p < .05). Therefore, the results provide support for H2, H3, and H4. Furthermore, the results show that CE has a positive relationship with customer loyalty (H5: β = .418, t = 4.663, p < .05), confirming H5.
To test the mediating paths, we used the biased-corrected (BC) confidence interval (CI). We employed the bootstrapping approach which is regarded as a powerful method for mediation analysis (Abbas et al., 2018; Elahi et al., 2019; Rather et al., 2019; Shrout & Bolger, 2002). In this study, with bootstrapping of 1000, a 95% CI was computed. In H6, it was hypothesized that CE mediates the link between trust in service provider and customer loyalty. The findings revealed that the indirect impact of trust in service provider on customer loyalty was significant (β = .201, p = .000, BC, CI = .050–.152), thus providing support for H6. Furthermore, we proposed in H7 that CE mediates the relationship between trust in the regulator and customer loyalty. The results showed a significant and positive indirect impact of trust in the regulator on customer loyalty (β = .193, p = .000, BC, CI = .071–.186), thus confirming H7. Moreover, our results show that the indirect effect of economy-based trust on customer loyalty was positive and significant (β = .149, p = .000, BC, CI = .020–.161), thereby supporting our hypothesis that CE mediates the relationship between economy-based trust and customer loyalty. Finally, our results also revealed that the link between information-based trust and customer loyalty is mediated by CE (β = .130, p = .000, BC, CI = .020–.147). Hence, H9 was also supported (see Table 6).
Indirect Path of Hypothesized Model.
Note. TSP = trust in service provider; CL = customer loyalty; TR = trust in the regulator; EBT = economy-based trust; IBT = information based-trust; BC CI = biased-corrected confidence interval.
Discussion and Implications
This study examined the influence of trust dimensions on CE, and the consequent effect of CE on customer loyalty. To achieve this, we conceptualized trust from four dimensions: trust in service provider, trust in the regulator, economy-based trust, and information-based trust, and examined their influence on CE. This study makes significant contribution to the existing literature as it is among the first to examine the indirect influence of multiple dimensions of trust on customer loyalty through CE. Overall, the results indicated that CE is significantly driven by trust, and that CE leads to customer loyalty, thereby validating the suggested research hypotheses. These findings support the assertion that trust is crucial in relational exchanges among stakeholders (Van Tonder & Petzer, 2018), and promotes interactions between customers and services providers (Ballantyne, 2006; Ponder et al., 2016), which in turn, produces customer loyalty (Islam et al., 2018; Islam & Rahman, 2017).
More specifically, for H1, the results uncovered that trust in service provider contributes to CE. Thus, when customers of life insurance providers trust that their insurers can be relied upon to keep their promises and to do the right things, it fosters firm credibility which in turn motivate these customers to engage them. That is, customers’ trust in service providers, in this case, life insurers, impact their behavior and consequently become engaged with these service brands. This result is in line with prior study by Kosiba et al. (2018) in the retail banking sector and also concurs with a similar result found by Chai and Kim (2010) that trust in blog service providers impacts bloggers knowledge sharing behavior. Similarly, it was found in H2 that trust in the regulator significantly influences CE, supporting the argument that the presence of guarantees, safety nets, regulations, supervisory roles, or other structures help build trust (Gefen et al., 2003; McKnight et al., 2000). Thus, customers engage these life insurance providers because of guarantees from the regulator which includes licensing and certification of the insurers and the assurance that doing business with them is safe and secure. In other words, the findings suggest that customers build trust and engage with life insurers since a government agency, in this context, the NIC, guarantees the reliability and credibility of these insurers, and also trust that the regulator has effective regulations to promote the safety and soundness of the insurance companies.
Furthermore, for H3 the results showed that economy-based trust has a significant influence in increasing CE in the life insurance sector. If customers trust in economic benefits including low premium charges, savings in time, and believe that a particular life insurer has nothing to gain by being dishonest in its interactions with them, they would engage the life insurer. The result supports the existing literature (Awad & Ragowsky, 2008; Kosiba et al., 2018). Again, the result is analogous to Chai and Kim (2010) who identified that economic-based trust impacts individuals’ decisions to engage in knowledge sharing. By implication, all things being equal, the customers are more likely to engage a particular life insurer when it is perceived to be economical and chance of defaulting is reduced because of consequent penalties. Moreover, according to the results, information-based trust has a positive effect on CE. Thus, customers’ knowledge that life insurers have security procedures and sound privacy policies in place creates assurance and trust, thereby driving such customers to engage the insurers. The results confirm the significant role of information-based trust in influencing customers’ decisions to engage life insurance providers. That is, the knowledge and understanding of the other party (life insurers providers) that their conduct is unsurprising lowers customers’ sense of self-doubt and drives CE (Ba, 2001). The result also agrees with the argument that as customers’ satisfaction with information behaviors upsurges, engagement increases (KBM Group & 1to1 Media, 2013).
Finally, the findings also confirmed that CE has a positive effect on customer loyalty, thus providing support for H5 and the conceptual work carried out by Van Doorn et al. (2010) where loyalty was suggested as an outcome of CE behavior. The results further suggest that when life insurance customers are highly engaged, they will most likely continue to deal with a particular life insurer and offer positive word of mouth thereby influencing those they know. This result is consistent with the suggestion that CE boosts customers’ loyal relationship with a particular brand (Abbas et al., 2018; Hollebeek, 2009; Islam et al., 2018; Islam & Rahman, 2017; Rather et al., 2019; So et al., 2014; Thakur, 2016). For H6–H9, the results of the mediating test show that CE mediates the link between trust dimensions and customer loyalty, supporting Van Door et al. (2010) suggestions that precursors based on firm, customer, and context can benefit consumers or firms through CE behavior.
Implications for Theory
From theoretical perspective, this study contributes to the present literature by offering deeper insights into the effect of trust dimensions on CE, and the ensuing effect of CE on customer loyalty in the life insurance domain. Past studies have identified trust as a relevant construct for the insurance and banking sector, which can foster ongoing relationships with customers (Agariya & Singh, 2011; Kosiba et al., 2018; Van Tonder & Petzer, 2018). Given that less research has investigated how different dimensions of trust drive CE, this study tries to fill this void by conceptualizing trust from four dimensions, including trust in service provider, trust in the regulator, economy-based trust, and information-based trust, and examined how each further CE. The results prove empirically that the relationships between these trust dimensions and CE are all statistically significant, and that every dimension of trust has a different level of influence in enhancing CE. Among the relationships, the results uncovered that trust in service provider and trust in the regulator have more dominant influence in driving customers to engage with life insurance companies. Thus, customers who have trust in their service providers (life insurers) and the regulator of these service providers will more likely increase their engagement with them.
Second, the study reinforces the importance of trust in driving and enhancing CE and has enriched the insurance marketing literature by exploring deeper into the influence that these different dimensions of trust have on CE in the life insurance context, an area which is under-researched (Kosiba et al., 2018). More significantly, while previous studies in other contexts have established that CE is a germane construct that furthers customer or brand loyalty (Abbas et al., 2018; Islam et al., 2018; Islam & Rahman, 2017; Thakur, 2016), this study offers support for the mediating role of CE in the link between multiple dimensions of trust and customer loyalty in a new context (life insurance) and extends the literature on CE.
Finally, though some researchers argue that CE could produce customer loyalty in a particular brand (Hollebeek, 2009; Islam et al., 2018), a few studies have investigated this relation (Islam & Rahman, 2017; So et al., 2014; Thakur, 2016). Studies in the life insurance context focused on growth, performance, and customer satisfaction in the sector (Abaidoo & Nwosu, 2016; Akotey et al., 2013; Alhassan & Fiador, 2014). CE and its outcomes such as customer or brand loyalty remained under explored in the insurance marketing literature. Hence, our empirical results confirm the positive effect of CE on customer loyalty in a life insurance setting and from a developing country perspective, thereby advancing the current literature. When customers are highly engaged, they will most likely continue to deal with a particular life insurer or brand and offer positive word of mouth, thus influencing those they know. Furthermore, this finding implies that customer loyalty to a particular brand or service can be increased not solely via an excellent experience in the real service encounter but likewise via CE. The findings offer a significant synthesis of the customer loyalty literature and the evolving CE literature.
Implications for Practice
From a practical side, the knowledge and understanding engendered from this study reinforces the significance of trust in driving and enhancing CE. The results indicate that building trust and assurance in customers ought to be important for life insurance companies since customers do not just build trust, but rather concerted efforts must be created to foster customers’ trust. The more customers trust and believe in life insurers, the more they may engage with them. The results also suggest that customers build trust across a range of issues regarding their interactions with life insurance companies so issues that may create distrust in the minds of customers such as needless delay in settling claims and hidden charges in contracts ought to be eliminated by life insurers to reinforce their trustworthiness toward their customers.
More importantly, customers’ trust in service provider and trust in the regulator strongly drive CE. Therefore, life insurance providers should adhere to standing behind their promises and treat their customers as valued customers so as to improve company credibility and enrich customer trust. Thus, life insurers must show great interest in the welfare of their customers, and when customers recognize that their interest and welfare are considered, they are likely to remain in the relationship which will trigger a continuous interaction. Similarly, the regulator of insurers, in this regard, the NIC, should play its supervisory roles effectively and efficiently and must be seen as supervising and monitoring the activities of insurance companies in an ongoing basis to certify that they obey regulatory requirements at all time as such third party assurances and guarantees build and develop customers’ trust, resulting in increased engagement.
Furthermore, the findings show that economic-based trust plays an indispensable role in the engagement process. As such, to earn customers’ trust and entice them into engaging relationships, life insurers should provide affordable and competitive premium policies and time-saving services to their customers and eliminate all possible hidden costs. Thus, their services must be seen and trusted to be economical. Moreover, the findings suggest that customers’ confidentiality concerns play an important role in their trust in the life insurance providers. Accordingly, insurers should safeguard their customers’ personal and financial information, as the results exhibit that customers’ knowledge of the existence of sound privacy protection policies to safeguard the information they divulge to their insurers promotes and sustains engagement.
Although prior study establishes the significance of purchase-related loyalty precursors, for example, satisfaction (Clemes et al., 2010), this study indicates that CE beyond purchase can similarly enrich customer loyalty in a certain brand. Consequently, the findings of this research deepen managerial knowledge and understanding of CE by highlighting the importance of building convincing customer interactions with their service and product brands. The strong impact of CE on customer loyalty as evinced in this study provides a sound motivation for life insurers, insurance agents, and marketing managers to place more emphasis on formulating marketing strategies and actions that are likely to enhance CE, which will in turn impact their loyalty.
Conclusions, Limitations, and Future Research
This study has made a substantial effort by exploring the influence of trust on CE and the consequent effect of CE on customer loyalty in a life insurance milieu. In addition, the study examined the mediating role of CE in the relationship between multiple dimensions of trust and customer loyalty. Our findings reveal that all four dimensions of trust—trust in service provider, trust in the regulator, economy-based trust, and information-based trust—conceptualized in this study positively and significantly impact CE, with trust in service provider and trust in the regulator driving a higher level of CE. Furthermore, we found that CE enriches customer loyalty and also mediates the relationship between the various trust dimensions and customer loyalty. While the study discloses some interesting findings, there are still some limitations that are worth mentioning.
First, the implications are based on life insurance customers’ viewpoint from three regional capitals in Ghana, and the findings cannot be generalized. Future study can improve on the conclusion reached in this study by expanding the sample size to include more regional capitals in Ghana. Second, since the total sample is limited to only respondents in Ghana, there is a chance to assess the model in diverse cultures. Culture impacts individual behavior and has consequences for trust development (Doney et al., 1998; Ponder et al., 2016). Gwinner et al. (1998) argued that relational benefits accrued in the customer-service provider relationship differ considerably across cultures; therefore, it is possible that trust will manifest itself differently in diverse cultures. Third, the findings of this study are limited and constrained by the measures adopted to measure trust dimensions, CE, and customer loyalty. Although the measures utilized are established as consistent and valid and their selection is defendable, further understanding of the relationship may be gotten by adopting measures of trust, CE, and loyalty which reflect different perspectives. Finally, this study did not examine how gender difference would affect the trust–engagement relationship. Therefore, future studies should take this into consideration.
Footnotes
Acknowledgements
We thank the anonymous reviewers for their invaluable comments.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
