Influence of Trust on Customer Engagement: Empirical Evidence From the Insurance Industry in Ghana

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.

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.

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:

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.

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Table 1.

Measurement Scale.

Construct	Item code	Source
Trust in service provider		Verhoef et al. (2002) and Hsu et al. (2007)
I believe my life insurance provider puts me first	TSP1
I believe my life insurance provider is very reliable	TSP2
I trust my life insurance provider to keep its promises to me	TSP3
I trust my life insurance provider to do the right thing	TSP4
Trust in the regulator		Gefen et al. (2003)
I feel safe doing business with my life insurance provider because the regulator (NIC) will protect me	TR1
I feel safe doing business with my life insurance provider because NIC has approved its license	TR2
I feel safe doing business with my life insurance provider because NIC supervises its activities	TR3
I feel safe doing business with my life insurance provider because NIC has effective regulations to promote the safety and soundness of life insurance providers	TR4
Economy-based trust		Gefen et al. (2003), Ratnasingam (2005), and Hsu et al. (2007)
I save time doing business with my life insurance provider	EBT1
I save cost using my life insurance provider’s services	EBT2
Using my life insurance provider’s services is economical	EBT3
My life insurance provider has nothing to gain by being dishonest in its interactions with me	EBT4
Information-based trust		McKnight et al. (2002) and Hsu et al. (2007)
I believe that my life insurance provider has enough safeguards to make me feel comfortable to divulge personal information	IBT1
I trust my life insurance provider will not use my personal information for any purpose without my consent	IBT2
I trust my life insurance provider to protect my personal information from unauthorized persons	IBT3
I believe my life insurance provider will not sell my personal information	IBT4
Customer engagement		Rich et al. (2010) and Islam and Rahman (2017)
I am enthusiastic as regards to using my life insurance provider’s services	CE1
I feel very positive about my life insurance provider	CE2
My mind is focused on my life insurance provider when it comes to insurance services	CE3
I become absorbed by my life insurance provider and its services	CE4
I do my best to do all my life insurance business with my life insurance provider	CE5
I feel very active in relation to using my life insurance provider’s services	CE6
Customer loyalty		Islam and Rahman (2017)
I say positive things about my life insurance provider to others	CL1
I recommend my life insurance provider to someone who seeks advice	CL2
I am willing to recommend family and friends to do business with my life insurance provider	CL3
I will continue to do more business with my life insurance provider	CL4

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.

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Table 2.

Demographic Information of Respondents.

Measures	Items	Frequency (n = 452)	%
Gender	Male	258	57.1
	Female	194	42.9
Age (in years)	Below 25	64	14.2
	26–35	142	31.4
	36–45	113	25.0
	46–56	91	20.1
	57 and above	42	9.3
Education	Vocational/high school	36	8.0
	HND	67	14.8
	University degree	264	58.4
	Postgraduate degree	85	18.8
Occupation	Student	78	17.3
	Employed	240	53.1
	Other	134	29.6
City	Accra	172	38.1
	Kumasi	143	31.6
	Takoradi	137	30.3
Policy length (in years)	Below 2	75	16.6
	3–5	197	43.6
	6–8	91	20.1
	9 and above	89	19.7

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.

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.

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Table 3.

Measurement Model.

Construct/dimensions	Item code	Loadings	Cronbach’s alpha	CR	AVE
Trust in service provider	TSP1TSP2TSP3TSP4	.790.741.800.760	.823	.855	.597
Trust in the regulator	TR1TR2TR3TR4	.745.723.827.753	.820	.847	.582
Economy-based trust	EBT1EBT2EBT3EBT4	.766.798.757.802	.846	.862	.609
Information-based trust	IBT1IBT2IBT3IBT4	.785.810.780.788	.798	.869	.625
Customer engagement	CE1CE2CE3CE4CE5CE6	.769.766.810.798.752.745	.861	.899	.598
Customer loyalty	CL1CL2CL3CL4	.768.782.822.775	.786	.866	.619

Note. CR = composite reliability; AVE = average variance extracted.

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Table 4.

Descriptive Statistics, Correlation, and Discriminant Validity.

Construct	Item	M	SD	1	2	3	4	5	6
1. Trust in service provider	4	4.514	1.461	.772
2. Trust in the regulator	4	4.230	1.215	.542	.762
3. Economy-based trust	4	4.162	1.160	.468	.369	.780
4. Information-based trust	4	4.341	1.334	.493	.457	.431	.790
5. Customer engagement	6	4.101	1.192	.379	.512	.361	.448	.773
6. Customer loyalty	4	4.450	1.341	.394	.383	.368	.451	.396	.786

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.

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Figure 2.

Results of the model.

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Table 5.

Direct Path of Hypothesized Model.

Hypothesis	Relationship	Estimates	p value	Expected	Results
H1	TSP → CE	.483	.000	+	Accepted
H2	TR → CE	.464	.000	+	Accepted
H3	EBT → CE	.357	.000	+	Accepted
H4	IBT → CE	.313	.000	+	Accepted
H5	CE → CL	.418	.000	+	Accepted

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).

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Table 6.

Indirect Path of Hypothesized Model.

Hypothesis	Relationship	Estimates	p value	95% BC CI
H6	TSP → CL	0.201	.000	[.050, .152]
H7	TR → CL	0.193	.000	[.071, .186]
H8	EBT → CL	0.149	.000	[.020, .161]
H9	IBT → CL	0.130	.000	[.020, .147]

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.

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.