1. Introduction
Digitization has become part of our everyday schedules. It is molding the customary manners by which purchasers and organizations cooperate. Digitization, mainly social media, has been professed to change shopper conduct [1,2], with significant ramifications for firms and brands [3,4]. Customers are progressively investing their energy on the web and utilizing social media [5,6,7]. They use online organizations for perusing, putting away and playing music, to email, to get to Facebook, Twitter, and applications with different associated gadgets, for example, advanced mobile phones, tablets, and PCs, and that is changing how the web is being utilized [8,9]. The proverb, “if an organization can’t be found in Google, it doesn’t exist,” seems to exemplify shopper conduct today. It ought to be evident that the use of advanced digital channels is significant for brands. It ought to be a movement that organizations should follow if they need to remain severe and develop. Digital marketing became increasingly advanced in 2010 when gadgets’ expansion to access digital media prompted unexpected development. Insights created in 2012 and 2013 indicated that advanced promoting was all the while developing. With the improvement of digital marketing during the 2000s, for example, LinkedIn, Facebook, YouTube, and Twitter, customers have become exceptionally reliant on web-based networking media in their day-by-day lives.
Digital marketing is the most encouraging setting for arriving at any age [10,11]. Digital marketing is the act of advancing items and administrations utilizing computerized conveyance channels through PCs, cell phones, PDAs, or other automated gadgets [12,13]. PCs and cell phones are essential devices for human beings, even viewed as fundamental. While there have been various investigations about advertising online, minimal scholastic exploration concentrated on what people favor sorts of automated promoting procedures and which ones impact their conduct [12]. There is potential for future development and incentive in digital marketing; however, advertising procedures must be engaging the purchaser.
In the worldwide economy, the financial service sectors like insurance are profoundly competitive [14]. Digitalization patterns, such as the development of web accessibility, have made new instruments for improving innovative advertising methodologies for the insurance business. The Internet is the most cost-effective method for selling insurances at any point in time. It is significant since shoppers purchase essential commodity-type insurance items on value considerations in most cases [15]. Insurance agencies and makers are exploiting the business capability of the Internet by setting up sites. The insurance business changed itself, considering new issues and new advancements. Retail insurance marketing contrasts from physical items marketing, just as other financial services marketing only [16]. Online insurance shopping is a packed space that gives tough competition to every insurance company [17]. As web-based business turns out to be wholly incorporated into insurers’ strategic policies, it will furnish analysts with chances to quantify the degree to which web-based business influences insurance expenses and the benefit towards insurers [18].
India’s life insurance sector had an excellent development in most of the financial year 2020 until the pandemic struck in March 2020. The effect of COVID-19 proceeds in the financial year 2021 memorably. Individuals’ dispensable livelihoods are seeing a significant disintegration and subsequently affect the division’s new business. The economic imbalance created by COVID-19 will make individuals defer their choices to take extra security. Usually, new organizations stay on the level. The unfriendly impact will be on renewal premiums regardless of the spare time to pay premiums that the industry has provided. The business’s effect will rely on the extent to which the lockdown proceeds and the economy can return to commonality. The first quarter will see a more significant impact, and things might change slowly as the year advances. There could be more surrenders as individuals might want to improve their liquidity circumstances [19]. The prevailing conditions throughout the world are gloomy due to the attack of COVID-19. Due to the increasing populace affected by the coronavirus, nations worldwide have started lockdowns. Along with some other sectors, the insurance industry is also facing massive trouble attending to customer queries physically, solving their issues, etc. The whole system has now turned into virtual mode. All the work done by the managers with clients has to be conducted virtually as physical contact being stopped by the governments.
The Crux of the Study
As the title suggests, the authors wanted to explore the prevailing digital marketing practices adopted by life insurers to enhance customer satisfaction and influence purchase intention. Five primary methods were found to be practiced by Indian insurers in digital mode. Our focus was on assessing these strategies, when the customers were forced to stay at home due to a pandemic, and physical contact was forbidden. The study was conducted in the COVID-19 period, which is also unique. No review previously mentioned the effect of the pandemic in digital marketing in the life insurance context. Measuring the effectiveness of each of these strategies in attracting eyeballs and final purchase holds enormous implications for the Indian life insurance sector’s existing and future marketing practices. Due to this pandemic, the big question is: has digital marketing practices adopted by life insurers to enhance customer satisfaction and influence purchase intention affected the clients? The lack of research on adopting digital marketing strategies helped the researchers to frame the research objective. The study aimed to measure customer satisfaction and purchase intention towards life insurance companies in India during the COVID-19 pandemic. In a nutshell, researchers intended to address three primary research questions: RQ1—How do the various digital marketing strategies affect consumers’ satisfaction and intention to purchase life insurance policies during the COVID-19 pandemic? RQ2—Does customer involvement moderate the associations between digital marketing strategies and purchase intention? RQ3—Does customer satisfaction mediate the relationship between digital marketing strategies and purchase intention?
The study also made two novel contributions to research. First, several studies related to the adoption of life insurance policies have been conducted previously. Still, no analysis has been available on how several digital marketing strategies affect satisfaction and purchase intention. Second, the moderation effect of customer involvement and the mediation effect of satisfaction has also been shown in the study.
2. Review of Existing Literature and Hypotheses Development
Although there are numerous digital marketing practices, we have broadly divided the methods into five classes that almost cover all the prevailing digital marketing (DM) practices. The five digital marketing components have been identified from various sources: search engine optimization and search engine marketing (SEO and SEM; S), display advertising (D), E-CRM (EC), content marketing (CM), and communication (C) [12,20,21,22]. Our study used customer satisfaction (CS) as the mediating factor between these five DM and purchase intention (PI) dimensions. Customer involvement (CI) was used as the moderator between these five DM and purchase intention dimensions and between CS and PI.
2.1. SEO and SEM, Customer Satisfaction, and Purchase Intention
Web search engines have changed the procedure by which people scan for data. Web searches have become very popular nowadays wherever consumers think about purchasing a product or service [23,24]. Thus, website improvement has risen as an effective technique for gaining and holding shoppers for organizations, all things considered [21,25]. SEO is an essential criterion to increase customer satisfaction [26]. Companies usually invest around 47% of their promotional spending in SEO [27]. Google Search, Google Inc’s internet searcher, stands apart as the predominant web crawler, representing just about 13 billion pursuits each month [28] and a worldwide ordering portion of about 75% of all inquiries all in 2017 [22]. Even though utilizing a web search engine has become a customary online action, we conceptualized client steadfastness expectation towards a web search engine based on ongoing conduct [29]. Since purchaser conduct is objectively coordinated, we contended that utilizing a specific search engine for surfing the web is a prudent decision that prompts a particular customer experience [30]. This experience produces tremendous results as a relative increase or loss in search quality, influencing future practices like client loyalty [31]. SEM has become one of the most critical techniques for online destination marketing, and it is influencing customers to purchase the services [32]. SEM is an important source from where the consumers can gather the information, and SEO practices will try to seduce customers into buying a service [33]. Accordingly, we suggested the relationship between SEO and SEM practices, customer satisfaction, and purchase intention as:
SEO and SEM practices have a positive and significant impact on customer satisfaction.
SEO and SEM practices have a positive and significant impact on purchase intention.
2.2. Display, Customer Satisfaction, and Purchase Intention
The wide assorted variety of online communication directions in the market allows advertisers to tweak their communication messages [34,35] to arrive at the client’s consideration and premiums [36]. Li and Kannan [37] indicated that email and display ads significantly affect shopper choice in the short run. Nonetheless, the objective of show-publicizing efforts ought to be particular and not arbitrarily doled out to all site guests after a tick [38]. The classifications of media received by advertisers to expand advanced battle results are paid, possessed, and earned media [39,40]. Paid media is the best way to draw in with clients because the promotion position is bought for various channels [39]. Each channel affects the shopper venture alternately, so it is principal to see how to be increasingly influential in the assets portion as indicated by every media channel and how buyers see the brand’s substance [39,40]. Content advertising is a vital methodology that underscores the creation and conveyance of applicable and steady drift through online stages. It is planned to pull in and hold an intended interest group and drive gainful client sales [41]. It has been observed that the display positively impacts the customers’ purchase intention [42]. The video display’s effect increases the customers’ awareness of the product, increasing the purchase intention [43]. Accordingly, we suggest the relationship between display marketing practices, customer satisfaction, and purchase intention as:
Display marketing practices have a positive and significant impact on customer satisfaction.
Display marketing practices have a positive and significant impact on purchase intention.
2.3. E-CRM, Customer Satisfaction, and Purchase Intention
E-CRM has been considered a feature of computerized showcasing, like regular CRM devices; however, it utilizes electronic channels with e-business usage to shape institutional CRM systems [44]. The more clients, who use electronic media, the more they make their data accessible to organizations to break down and comprehend their conduct [45,46]. E-CRM is intended for individuals of all degrees of business who need to create associations with clients electronically [47,48]. E-CRM has empowered associations to pull in new clients, increment client worth and administration, hold clients, give expository client inclinations and practices, and utilize the correct techniques to energize clients’ loyalty [49,50]. CRM is a significant thing to make business progress. The E-CRM process’s motivation is to create unique assets for productivity, client appraisals, client maintenance, and clients’ accomplishment [51]. The essential focal point of exploration has concentrated on the effect of E-CRM execution from the client’s viewpoint [51]. The past examinations found a few positive impacts of E-CRM on consumer loyalty and clients’ satisfaction [52,53,54]. The pre- and post-purchase of E-CRM significantly impact customer satisfaction [55,56,57]. Accordingly, we suggest the relationship between E-CRM practices, customer satisfaction, and purchase intention as:
E-CRM practices have a positive and significant impact on customer satisfaction.
E-CRM practices have a positive and significant impact on purchase intention.
2.4. Content Marketing, Customer Satisfaction, and Purchase Intention
The content is the propensity of an ad to contain exceptional data and access effectiveness [58]. An ad should incorporate appealing thoughts as a worth expansion to stand out enough to be noticed [59]. Chen and Hsieh [60] expressed that there were three primary components in SMS publicizing. First, advertisers needed to fabricate a brief message and contain essential data to the clients by utilizing primary and reasonable language. Second, the notification must be engaging or appealing, using funniness or shock. Third, the message must be customized to guarantee pertinence to the client’s needs and inclinations. Additionally, Van der Waldt et al. [61] demonstrated that appealing substance had a beneficial SMS outcome promoting its beneficiaries’ mentality. The significance of interactive publicizing is the capacity to cause the client to get the passed on and customized messages [62]. Each client’s words should be one of a kind because every individual wishes to get different content [63]. A customized message does not just permit the client to contact the news depending on inclinations [62] and get it at the perfect time. This message incorporates unique item offers or item proposals that depend on the client’s inclinations and individual data. Lee et al. [64] concurred that clients who were given modified promotions would be wise to observations, better perspectives, and higher aim to visit the store. The past research has also observed that content marketing practices are much more helpful than traditional marketing, and it creates the purchase intention towards the customers [65,66,67]. Accordingly, we suggest the relationship between content marketing practices, customer satisfaction, and purchase intention:
Content marketing practices have a positive and significant impact on customer satisfaction.
Content marketing practices have a positive and significant impact on purchase intention.
2.5. Communication, Customer Satisfaction, and Purchase Intention
Communication stimuli can create a beneficial outcome in purchasers, and their impression of the communication influences their mindfulness and picture of a brand [68]. Furthermore, highlighting communication is identified with brand value. As long as the communication is given, it can positively respond to the item, contrasted with other non-marked items in a similar classification [69]. The presence of social media itself, in the end, diminishes the organization’s job as the primary wellspring of brand communication [70]. Moreover, social media channels are considered financially well-informed and easy to operate to procure customer data to shopper communication [71]. Internet-based communication had a positive impact on faithfulness and quality. Accordingly, we suggest the relationship between communication practices, customer satisfaction, and purchase intention as:
Digital communication practices have a positive and significant impact on customer satisfaction.
Digital communication practices have a positive and significant impact on purchase intention.
2.6. Customer Satisfaction (CS) and Purchase Intention (PI) and the Mediating Role of CS
CS plays a massive role in influencing purchase intention regarding any type of product or service. There are numerous measures of its ultimate goal: to assess the customers’ satisfaction level in the pre-purchase, transaction, and post-purchase stages. It is a handy measure in the marketing domain [72]. It is usually used as a predictor as well as a mediator for PI. As our five predictors of DM adopt various strategies to enhance CS to influence their PI, the mediating role of it is crucial [73]. Three components usually measure customer satisfaction: ‘attitude towards the brand’ (Sat1), ‘attitude towards the service’ (Sat2), and ‘attitude towards the contact person’ (Sat3) [74,75,76,77]. PI does have three components too. Willingness to buy, the capability to purchase, and future intentions to buy [74,78,79] are used in this study to measure PI. If the customers correctly decode the marketers’ message, the CS can be enhanced, leading to a positive PI. However, perception plays a crucial role in assessing its value, and therefore, DM practices must provide a better perception [80,81,82]. As CS is a predictor and mediator for PI, the DM practices must enhance it to influence PI.
Further, the DM components’ direct and indirect effects can be assessed only by considering the mediating role of CS. Along with the normal relationship between CS and PI, the mediating part of CS between the five dimensions of DM and PI should be verified [83]. The antecedent behavior of DM must be measured to find the same. The mentioned DM practices are considered good indicators for both CS and PI. Indirect effects of CS are in the relationship between DM dimensions and PI [84,85]. Consequently, we suggest two hypotheses between customer satisfaction and purchase intention as follows:
Customer satisfaction has a substantial and positive impact on purchase intention.
Customer satisfaction mediates the association between digital marketing and purchase intention.
2.7. Digital Marketing, Customer Satisfaction, Purchase Intention, and the Moderating Role of CI
Customers’ intentions to follow or use goods and services are influenced by electronic word of mouth differently depending on their involvement with the product or service [86]. The perceived personal importance of a product or service based on customers’ desires, wishes, and values is referred to as involvement in the product or service [87]. Since they are inspired to evaluate feedback critically, individuals with high participation are more likely to consider the contents of reviews relevant [88]. As a result, they process the thoughts using three core route elements: claim efficiency, review accuracy, and review valence [89]. When consumers are less engaged, they rely on peripheral cues to process information [90]. It is also worth noting that consumers’ interest in a product varies according to their degree of engagement with it [91]. Customers who have a high level of involvement in a product or service are more likely to trust it than those with a low level of involvement [92].
Customers’ assessments of digital marketing component triggers and initial confidence differ depending on their level of involvement in the product or service, as seen in the preceding discussion [93]. High-involved consumers are more likely than low-involved consumers to scrutinize the reviews in depth for validity, timeliness, accuracy, and comprehensiveness; thus, high-involved consumers would have more confidence in digital marketing strategies, resulting in its adoption [94]. Individuals with a high degree of interest, on the other hand, look at the accuracy of feedback across channels [95]. Consequently, we propose:
Customer involvement moderates the association between customer satisfaction and purchase intention.
Customer involvement moderates the association between digital marketing and purchase intention.
Based on the Theory of Planned Behavior and the SERVQUAL model, we developed an integrated conceptual framework that combined all the hypotheses and provided a holistic view of our work (see Figure 1).
3. Research Methodology
A detailed and structured questionnaire was developed with all the 28 variables mentioned in the following section. Keeping the life insurance sector in mind, the statements were created from the finalized items. Regarding the development of a suitable scale, we adopted the paradigm widely used to get valid and better measures [96]. To make things simple, the structured questionnaire was bifurcated into two divisions. Various demographic and socio-economic information was solicited in the first section. In the next segment, the intended respondents were questioned to assess multiple parameters on digital marketing practices adapted for the life insurance industry (on a 5-point Likert Summation Scale with two extremes at ‘strongly disagree’ and ‘strongly agree’). This part has 18 (reduced from 25) statements for performance scores regarding the five dimensions of digital marketing and three statements each for three dimensions of customer satisfaction and three dimensions of purchase intention, respectively. Customer involvement (moderator) has four statements. Based on the type of dimension and the statements, due recoding and reverse coding were done.
3.1. Data Collection
We began with a well-drafted cover letter explaining our research questions and a statement of protection of privacy. It was distributed online due to the COVID-19-induced restrictions. Few could be collected offline too. Data collection was done in the last six months of 2020. The data collection area included five northern states of India. The respondents were primarily millennials and acquainted with life insurance products’ various digital marketing practices (Table 1). After discarding a few incomplete questionnaires out of the total 590 received from the respondents, we settled at 535 responses that satisfied all the criteria for a filled-in questionnaire. The normality of the data was confirmed through skewness and kurtosis tests. Further, no specific pattern was found in the collected data. These included five dimensions of digital marketing collated from the available literature.
The constructs and the items (questions) are given below. The first factor is SEO and SEM practices, and it has three things: clarity, guidance, and sorting (s1, s2, s3) [21,23,31]. The second factor is display practices, and it has three items: visualization, retargeting, and stimulation (d1, d2, d3) [36,39,40]. The third construct is E-CRM, and it has four items: additional customer services, personalization, interaction, and information clarity (ec1, ec2, ec3, ec4) [97,98]. Further, the fourth dimension is content marketing, and it has four items: brand awareness, attention-seeking, detailed information about features, and reaching the naysayers to commercials (cm1, cm2, cm3, cm4) [58,62,63]. The last factor is communication, and it has four items: brand image, latest updates, feedback, and integration of other practices (c1, c2, c3, c4) [68,70,99]. In this proposed model, ‘customer satisfaction’ has been measured through three components: ‘attitude towards the brand’ (Sat1), ‘attitude towards the service’ (Sat2), and ‘attitude towards the contact person’ (Sat3) [74,75,76,77]. Further, purchase intention consisted of three items: willingness to buy, the capability to buy, and future intentions to buy [74,78,79] (Int1, Int2, and Int3). Finally, customer involvement has four items: ‘important to me, ‘of no concern to me’ (reverse coded), ‘relevant for me’, and ‘very meaningful for me’ [74,90,100] (ci1, ci2, ci3, and ci4). The three aspects of user data, e.g., normality, reliability, and validity, were checked to make the data fit for further analysis.
3.2. Data Analysis
3.2.1. Content and Construct Validity
After a thorough literature review, the final items considered for further analysis were gathered, ensuring content validity. A pilot study with a sample of 60 respondents was carried out to check the measurement tool’s validity. We conducted an exploratory factor analysis to validate the constructs and to take them further. It was done along with the three items of purchase intention too. The questionnaire’s temporal stability was verified as all the constructs generated significant correlations taking the rotation route.
3.2.2. Reliability, EFA, and CFA
Reliability was also undertaken after checking normality (skewness and kurtosis) and validity (content and construct) for the data. Cronbach’s alpha for the overall construct was above 0.7, indicating good reliability. Further, 0.77 was the lowest alpha for any individual factors [96,101] (see Table 2). These eight factors with 28 variables explained more than 78% of the total variance, which amounted to 22% of other unaccounted variables. Ultimately, eight factors, namely SEM/SEO (S), display (D), E-CRM (EC), content marketing (CM), and communication (C) with 18 variables as well as customer satisfaction (CS; three), purchase intention (PI; three), and customer involvement (CI; four) that revalidate the adopted approach to this study (Table 2). All the items under the respective constructs showed a factor loading of more than 0.7, which was higher than the recommended level (Table 2).
Further, to confirm the results from EFA, confirmatory factor analysis (CFA) was used. To re-affirm the findings or groupings, confirmatory factor analysis (CFA) was conducted. It provided enhanced control and more accurate validation of the scales adopted in this study. Various goodness of fit measures was explored. Normally, G-o-F measures are divided into three categories: absolute, incremental, and parsimonious. It explains whether the various dimensions developed are represented by the constructs. The CFA result showed that all the values for the mentioned fitness indices exceed the threshold levels set by widely accepted literature [102,103,104,105,106,107]. Table 2 depicts a good model fit (CMIN/DF: 2.817, goodness-of-fit index (GFI): 0.922, adjusted goodness-of-fit index (AGFI): 0.891, Root Mean Square Residual (RMSR): 0.048, Root Mean Square Error of Approximation (RMSEA): 0.058, Tucker–Lewis index (TLI): 0.941, normed fit index (NFI): 0.925, comparative fit index (CFI): 0.950).
3.3. Evaluation of the Measurement Model (Fornell and Larcker)
All seven measurable constructs were evaluated for the measurement model fit as per the guidelines (see Table 2) [108]. AVE (average variance extracted) for all the constructs was found to be more than 0.5. Further, Cronbach’s alpha for all the constructs was more than 0.7. Similarly, MSV for all the constructs was less than the corresponding AVE. Hence, considering all these findings, it can be said that there are no validity concerns in the measurement model.
3.3.1. Testing of Hypotheses with SEM
After the factors were established and the resultant relationship was proved, the scores were explored. Customer satisfaction and purchase intention were linked to the various dimensions of digital marketing. Two sides of the model were connected through a path analysis with structural equation modeling (SEM). It was a stepwise analysis involving five steps. In the first stage, the relationship between the items and the respective dimensions was explored. The causal relationship between the DM dimensions and customer satisfaction and purchase intention was explored in the second stage. In the third stage, the relationship between customer satisfaction and purchase intention was assessed. The fourth stage talked about the mediation effect of customer satisfaction between the DM dimensions and purchase intention. The final stage explored the moderation effect of customer involvement on the DM practices and CS on purchase intention. The path diagram constructed by Smart PLS [109] depicted the series of relationships between the various constructs. The path diagram was the foundation for any SEM. After, the structural equations, as well as the final measurement model, were estimated.
3.3.2. Validity Analysis (SEM)
As none of the construct loadings’ estimates was found to be wrong, various goodness-of-fit criteria have been assessed. The SEM model’s overall fit was evaluated first to ensure that the developed path model represents the relationships between endogenous and exogenous constructs. GFI was found to be 0.945, AGFI was 0.918. Further, RMSR was 0.032, and RMSEA was found to be 0.063. Additionally, for incremental measures, all three criteria, comparative fit index (CFI) at 0.939, Tucker–Lewis index (TLI) at 0.926, and normed fit index (NFI) at 0.913, were found to be on the upper side of the threshold limit. After going through all these measures, it could be concluded that the proposed model passed all the tests of goodness-of-fit indices that led us to study the outcomes of this research.
3.4. Relationships/Findings
After assessing various reliability, validity, and other goodness of fit measures, the relationships between all the observed and unobserved variables were analyzed. Smart PLS is used to visualize and construct the path diagram. It was done by checking the respective loadings as well as the path coefficients. As discussed earlier, it was diagnosed in five stages (see Figure 2).
3.4.1. Stage 1
In the first stage, the relationship between the items and the respective dimensions was explored. Almost all the items were found to be having a significant impact on individual dimensions. Hence, the CFA of the items was found to be valid. The factors were named SEM/SEO (S), display (D), E-CRM (EC), content marketing (CM), and communication (C). All the 18 items significantly impacted their respective dimensions, demonstrated by the path diagram’s left-hand side (see Figure 2).
3.4.2. Stage 2: Impact of DM on CS and PI
The causal relationship between the DM dimensions and customer satisfaction and purchase intention was determined in the second stage. It was found that SEM/SEO practices, display practices, and E-CRM practices were having a significant impact on CS. The display had the maximum impact (0.21), followed by SEM/SEO (0.14) and E-CRM (0.13). The other two dimensions did not have a significant impact on it. Further, three predictors significantly affected the purchase intention: display (0.14), SEM/SEO (0.08), and E-CRM (0.15). The other two dimensions did not have any impact on it (see Figure 2 and Table 3).
3.4.3. Stage 3: Relationship between CS and PI
Then, we measured the impact of CS on PI and found it to be positive and significant (0.2; see Figure 2 and Table 3).
3.4.4. Stage 4: CS as the Mediator
In this stage, the authors tried to assess the mediation effect of CS between the five dimensions of DM and PI. H7 tested this relationship (Table 4). The mediation effects can be measured by evaluating two things: the relationship between the predictor and mediator and between the mediator and dependent variable [110]. If both relationships are significant, then we can infer that the mediation effect is present. Further, the mediation effect can be full, partial, and zero. It is considered complete if the direct effect is insignificant and the indirect effect is significant. If both are significant, then it is partial. However, if the indirect effect is negligible, it is zero mediation [111]. To assess the significance of our model’s indirect effects, we used bootstrapping (2000) at a 95% level.
Table 5 provides us with three instances of the CS mediation effect between the associations of SEM/SEO, display, and E-CRM with purchase intention. All three mediation effects were partial. However, for content marketing and communication, there was no mediation effect from CS.
3.4.5. Stage 5: CI as a Moderator
The moderation effect of CI on various digital marketing practices and customer satisfaction is shown in Figure 2 and Table 5. H8 tested different relationships, where H8(a) showed the moderation effect of CI on the CS and PI relationship. Although the moderation effect was relatively high, it was not significant though. H8(b), H8(c), and H8(d) showed that there was a non-significant moderation effect of CI between the SEM/SEO, display, and E-CRM dimensions of digital marketing and PI. H8(e) and H8(f) showed a significant moderation effect of CI on the content marketing and communication dimensions of digital marketing and PI. To be specific, CI dampened the positive relationship between content marketing and PI (Figure 3), and CI dampened the negative relationship between communication and PI (Figure 4).
4. Discussions, Implications, and Suggestions for Future Research
The reason behind undertaking this research was to understand the factors influencing purchase intentions and the specific case of millennials as the target audience was discussed. Understanding the customers’ preferences for a particular digital marketing strategy and developing products can close the gaps in perceptions and expectations in the life insurance industry. This study tried to assess the digital marketing (DM) dimensions and their particular order in influencing millennials’ purchase intentions. The result was quite clear from the path analysis.
This pandemic constrained work environment activity to go virtual, and numerous organizations made the required changes, effectively, in a brief period [112]. This paper investigated how the pandemic affected the insurance industry and how the digital marketing adaption could save them in the current situation. We urge supervisors to create inventive marketing strategies to get ready for the digitized changes of the market. The insurance industry managers now need to convert this problem into an opportunity and focus on innovative digital marketing strategies [113].
The researchers have identified five components of digital marketing. Their effect on the consumers’ purchase intention and satisfaction as SEM/SEO are among the most critical factors. It came from the study because this digital component of marketing largely influences millennials. The managers have primarily attracted new customers [114]. Search Engine Result Pages (SERP) rankings were phenomenal for the insurers during the period mentioned. Managers tapped the search engine providers better to integrate marketing tools on their platforms [30,115]. Customized targeting with behavioral pattern analysis made this strategy successful [116]. They further optimized their SEM practices to be cost-effective.
E-CRM is another critical factor, which most millennials liked. Customer experience is the way to winning the stiff competition and acting as the estimation of separation from different brands [117]. All three aspects of E-CRM, identify, acquire, and retain, were successfully used by the insurers operating digital platforms [53,54]. The providers deployed a well-trained team that managed customers, knowledge, specific cases, and various processes. The display also had a positive and significant impact on the purchase intention of insurance services from life insurance companies. The use of display ads such as banners, texts, audio, image or video, etc., on websites, apps, or social media was done successfully by the insurers [118]. These practices affected customer satisfaction and enhanced purchase intention [39,40]. We found that all three factors had a positive and significant relationship with customer satisfaction and purchase intention.
Talking about the negligible influence of the other two factors rose a few questions. Content marketing and communication strategies had a non-significant impact on customer satisfaction as well as purchase intention. These practices took a back seat in the insurance sector because they felt that the message was not appealing. This study found that customers were not able to decode the messages sent by the insurers. Two takeaways were evident here. Customers did not want to go into the product’s technicalities, and the insurers’ communication was full of jargon and misleading. Regarding content, we expected the outcome because Indian customers are still in love with mainstream commercials and yet not ready for content-based marketing strategies.
Customer satisfaction acted as a mediator in this study, and it mediated (partially) the relationship between SEM/SEO, E-CRM, display, and purchase intention. The researchers understood that if the customers are happy with these initiatives conducted by the company professionals, then the satisfaction level will be high, and the intention to purchase insurance services will increase. Regarding the other two factors, both direct and indirect effects (through CS) were negligible. This can be a topic to be explored in the future with some other service industries like education, transportation, etc. In the end, the researchers could expand the number of independent variables, which affected the purchase intention towards the insurance industry. The researchers could also add few moderating variables like age, gender, and income to see the purchase intention in the future. A successful encounter was bound to make a positive, passionate incentive for the client. In this way, clients would be progressively faithful, ready to repurchase and prescribe the brand to their companions or family. The digital mode of CRM strategies must involve the RATER approach to enhance quality and customer satisfaction. If the managers can stimulate their unconscious needs through attractive display ads on digital media, the chance to purchase that particular service will increase. It can be assured that digital marketing will be the dominant option soon, especially in sectors like insurance, banking, etc. The managers also understand from the findings that they need to understand the various components of digital marketing and be very specific about which elements successfully influence customer satisfaction leading to purchase intention. The Insurance industry should adopt collaborative and co-creation marketing in this digital environment. A contact-free society/economy will be the new normal. Operators and agents must be digitally empowered to provide the best possible solutions to the customers. Claim assessments would present complexities because of troubles in physical checks. Both insurers and assessors must utilize digital marketing tools for loss assessments. Deferrals in claims intimations happen quickly with digital marketing tools, particularly during the lockdown period. Lockdown and longer social distancing/physical distancing rules require a change in workplace strategy. On the web, video conferencing, computerized gatherings with various online platforms will become the new standard. Customer involvement can be accelerated in all these collaborative practices.
5. Conclusions
One of the key drivers of the widening distance between markets and firms [119] and one of the most crucial experiments for marketing [113] is the internet. Digital marketing has revolutionized how businesses manage and engage with their consumers and society on a global scale. It is becoming a critical and required tool for addressing the issues in the marketing area. Simultaneously, since the dawn of the twenty-first century, sustainability has become one of the most significant concerns facing businesses, notably marketers [120]. Researchers have extensively studied the relationship between marketing and sustainability [121,122], culminating in the conclusion that both concepts have something to offer each other. Sustainability supports acquiring improvements in the supply chain, product differentiation, access to knowledgeable investors, or a higher level of staff commitment [121]. As a result, marketing gives for a better understanding of client behavior and a tool to alter society’s attitudes and values [121].
This study comprised five components of digital marketing, and it showed a path to the insurers to deliver success inside this pandemic situation. First, we began with the five components of digital marketing practices adopted by the insurers. It has become mandatory due to the pandemic. We tried to assess the impact of these components on customer satisfaction and purchase intention. Further, we assessed the mediating role of customer satisfaction in the relationship between the five components and purchase intention. Finally, we used customer involvement as an influencer for the relationship between the five components and digital marketing, customer satisfaction, and purchase intention. After a detailed analysis, we found that SEM/SEO, display, and E-CRM practices significantly impacted customer satisfaction and purchase intention. Further, a mediation-cum-moderation approach was undertaken. Customer satisfaction significantly affected purchase intention and played a good mediator between digital marketing practices and purchase intention. Additionally, customer involvement moderated the relationship between content marketing and communication with purchase intention.
The COVID-19 flare-up is a bad dream turned reality. None would have anticipated such a circumstance. However, humanity has perseveringly adjusted to rising realities and produced new arrangements that address new difficulties. It is to be trusted that there is a bright finish to the present course of action and that the passage is short. This study is quite evident because the millennials are practical and less emotional compared to previous generation customers. With a robust digital marketing strategy in place, customer satisfaction and customer involvement can play a huge role in influencing purchase intention. Hence, the life insurers must improvise on the service/product development and marketing strategies rather than wasting a huge sum of money on an unnecessary strategy that cannot grab eyeballs. They must turn millennials to cater to the millennials in true spirit and nature.
Author Contributions
Conceptualization, G.D. and D.C.; methodology, G.D.; software, G.D.; validation, G.D. and D.C.; formal analysis, G.D.; writing—original draft preparation, G.D. and D.C.; writing—review and editing, G.D. and D.C.; visualization, G.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality and privacy issues.
Conflicts of Interest
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figures and Tables
Enlarge this image.Figure 1. The conceptual framework. Note: H7 (a), H7 (b), H7 (c), H7 (d), and H7 (e) are five sub-hypotheses that assess the mediation role of CS between the five dimensions of DM and PI. H8 (a) is a sub-hypothesis which assesses the moderation role of customer involvement (CI) between CS and PI; H8 (b), H8 (c), H8 (d), H8 (e), and H8 (f) are five sub-hypotheses that assess the moderation role of CI between the five dimensions of DM and PI (conceptualized by the authors).
Demographic profile of the survey respondents.
| Gender | ||||
|---|---|---|---|---|
| Male | Female | Total | ||
| Age (years) | <40 | 245 | 150 | 395 |
| 41–60 | 79 | 34 | 113 | |
| >60 | 15 | 12 | 27 | |
| Total | 339 | 196 | 535 | |
| Education | Secondary | 61 | 25 | 86 |
| Higher Secondary | 56 | 55 | 111 | |
| Graduate | 186 | 109 | 295 | |
| PG | 36 | 7 | 43 | |
| Total | 339 | 196 | 535 | |
| Marital Status | Married | 269 | 164 | 433 |
| Unmarried | 54 | 23 | 77 | |
| Divorcee | 16 | 0 | 16 | |
| Widower | 0 | 9 | 9 | |
| Total | 339 | 196 | 535 | |
Measurement model summary.
| Factor/Construct | Scale Items | Factor Loading (EFA) | Contributions | |
|---|---|---|---|---|
| SEM/SEO (S) |
1 | s1 | 0.938 | Vuylsteke et al., 2010; Ratchford, 2015; Lafley and Martin, 2017 |
| 2 | s2 | 0.890 | ||
| 3 | s3 | 0.887 | ||
| Display (D) |
1 | d1 | 0.841 | Rajeev and Keller, 2016; Strauss and Frost, 2014; Stern, 2017 |
| 2 | d2 | 0.872 | ||
| 3 | d3 | 0.916 | ||
| E-CRM (EC) |
1 | ec1 | 0.861 | Romano and Fjermested, 2003; Goldberg, 2001 |
| 2 | ec2 | 0.867 | ||
| 3 | ec3 | 0.866 | ||
| 4 | ec4 | 0.835 | ||
| Content Marketing (CM) |
1 | cm1 | 0.900 | Humbani et al., 2015; Bright, 2014; Li, 2016 |
| 2 | cm2 | 0.875 | ||
| 3 | cm3 | 0.893 | ||
| 4 | cm4 | 0.844 | ||
| Communication (C) |
1 | c1 | 0.949 | Eysenck and Keane, 2010; Li and Bernoff, 2011; Schivinski and Dabrowski, 2013 |
| 2 | c2 | 0.921 | ||
| 3 | c3 | 0.884 | ||
| 4 | c4 | 0.927 | ||
| Customer Satisfaction (CS) |
1 | Sat1 | 0.802 | Dash et al., 2021; Bagozzi et al., 1979; Ostrom, 1969; Eagly and Chaiken, 1993 |
| 2 | Sat2 | 0.848 | ||
| 3 | Sat3 | 0.782 | ||
| Purchase Intention (PI) |
1 | Int1 | 0.862 | Dash et al., 2021; Shao et al., 2004; Eagly and Chaiken, 1993; Blackwell et al., 2001 |
| 2 | Int2 | 0.856 | ||
| 3 | Int3 | 0.861 | ||
| Customer Involvement |
1 | ci1 | 0.813 | Premazzi et al., 2010; Zhang et al., 2016; Dash et al., 2021 |
| 2 | ci2 | 0.877 | ||
| 3 | ci3 | 0.722 | ||
| 4 | ci4 | 0.774 | ||
Notes: α: Cronbach’s α; CR: construct reliability; AVE: average variance extracted; FL: factor loading. Model fit summary: CMIN/DF: 2.817, goodness-of-fit index (GFI): 0.922, adjusted goodness-of-fit index (AGFI): 0.891, Root Mean Square Residual (RMSR): 0.048, Root Mean Square Error of Approximation (RMSEA): 0.058, Tucker–Lewis index (TLI): 0.941, normed fit index (NFI): 0.925, comparative fit index (CFI): 0.950.
Table 3Standardized regression weights/testing of hypotheses (H1–H6).
| Hypothesis | Hypothesized Relationship | Estimate | Accepted/Rejected | ||
|---|---|---|---|---|---|
| H1 (a) | SEM/SEO | → | CS | 0.14 ** | Accepted |
| H2 (a) | Display | → | CS | 0.21 ** | Accepted |
| H3 (a) | E-CRM | → | CS | 0.13 ** | Accepted |
| H4 (a) | Content Marketing | → | CS | 0.04 | Rejected |
| H5 (a) | Communication | → | CS | 0.00 | Rejected |
| H1 (b) | SEM/SEO | → | PI | 0.08 * | Accepted |
| H2 (b) | Display | → | PI | 0.14 ** | Accepted |
| H3 (b) | E-CRM | → | PI | 0.15 ** | Accepted |
| H4 (b) | Content Marketing | → | PI | 0.01 | Rejected |
| H5 (b) | Communication | → | PI | −0.06 | Rejected |
| H6 | CS | → | PI | 0.20 ** | Accepted |
* significant at 5%; ** significant at 1%.
Table 4Summary of the mediation effects (H7).
| Relationship/H7 | Direct Effect | Indirect Effect | Result | Accepted/Rejected |
|---|---|---|---|---|
| H7 (a): SEM/SEO→CS→PI | 0.08 * | 0.03 * | Partial | Accepted |
| H7 (b): Display→CS→PI | 0.14 ** | 0.05 ** | Partial | Accepted |
| H7 (c): E-CRM→CS→PI | 0.15 ** | 0.03 * | Partial | Accepted |
| H7 (d): Content Marketing →CS→PI | 0.01 | 0.01 | No | Rejected |
| H7 (e): Communication→CS→PI | −0.04 | 0.00 | No | Rejected |
* significant at 5%; ** significant at 1%.
Table 5Summary of the moderation effects (H8).
| Hypothesis | Estimate | P | Result of Moderation |
|---|---|---|---|
| H8 (a): CI * CS | 0.16 | 0.19 | Rejected |
| H8 (b): CI * SEM/SEO | −0.05 | 0.28 | Rejected |
| H8 (c): CI * D | −0.05 | 0.52 | Rejected |
| H8 (d): CI * EC | 0.09 | 0.27 | Rejected |
| H8 (e): CI * CM | −0.1 * | 0.03 | Accepted |
| H8 (f): CI * C | 0.15 ** | 0.00 | Accepted |
* significant at 5%; ** significant at 1%.
© 2021 by the authors.
Details
; Chakraborty, Debarun 2 1 College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
2 Symbiosis Institute of Business Management, Nagpur, Constituent of Symbiosis International (Deemed University), Pune 440008, India;