Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2021 Vol: 20 Issue: 5

The Influence of Relationship Marketing on the Loyalty of Generation Y and Generation Z Customers for Online Retail Businesses during the Covid-19 Crisis

Kanokwan Thaipradit, Walailak University

Phattarawan Tantong, Walailak University

Abstract

This study extends the relationship marketing framework to the domain of online retailing to identify what strategies help build relationships with online generation Y and generation Z customers for online retail businesses during the COVID-19 crisis. Specifically, the objectives of this study were 1) to develop a causal model of the influence of relationship marketing, and to validate a causal model of the relationship marketing. The research found the development of a model for examining the influence of relationship marketing on the loyalty of Generation Y and Generation Z customers for online retail businesses during the COVID-19 crisis that all components were consistent with the empirical data. Finally, the analysis of the influence of direct and indirect effects among variables revealed that shared value and relationship benefit directly influenced satisfaction and indirectly influenced trust, commitment, expectation of continuity, word of mouth, and customer loyalty with statistical significance. Satisfaction was found to have a direct influence on trust and indirect influence on commitment, expectation of continuity, word of mouth, and customer loyalty at a statistically significant level. Trust had a direct influence on commitment, whereas indirect influence on expectation of continuity, word of mouth, and customer loyalty at a statistically significant level. Commitment had a statistically significant direct influence on expectation of continuity, word of mouth, and customer loyalty.

Keywords

Relationship Marketing, Generation Y, Generation Z, Online Retail Businesses, COVID-19 Crisis.

Introduction

Online retailing has been growing continually for the last ten years. Its circulation all over the world increased by 17% to $236 billion in 2007 (Verma et al., 2016). In Thailand, the value of the e-commerce market in 2019 was 163,300 million baht. Due to the situation of pandemic COVID 19 in the country, the consumers have turned to buy the products via online channels increasingly, which makes a result that the numbers of Thailand e-commerce may be high up to 220,000 million baht. In 2020, the growth rate is higher to 35% from the previous year (TE TECHSAUCE Team, 2020). Consumers today are more complicated since they search and analyze as well as compare the qualities of products, price, payment channels, delivery information, and return policy before deciding to buy products online (Song et al., 2012). Hence, it enhances the online retailers to realize the necessity of presentations and offers online that influence and interest these potential consumers. Moreover, many retailers are trying to use online social networks to make some types of relationships with the consumers as another channel (McWilliam, 2012). Additionally, perceiving the customers’ behaviors of each generation is considered essential towards marketing. The Generation Y and Generation Z customers is the groups of people who were born in different ages have different experiences of social environments, political views, history, and economy. Hence, the styles of e-commerce become unique and more complicated (Sladek & Grabinger, 2014). For this research, it was about the study of what strategies help build relationships with online generation Y and generation Z customers for online retail businesses during the COVID-19 crisis. The objectives of this study were 1) to develop a causal model of the influence of relationship marketing, and to validate a causal model of the relationship marketing.

Literature Review

For this research on relationship marketing according to the literature reviews and research, it is found that there are few studies about such issue. Most of them are about the studies in terms of the relationship marketing activities on the loyalty of generation Y and generation Z customers for online retail businesses during the COVID-19 crisis, which include: (1) communication, (2) shared value, (3) seller expertise, (4) relationship benefits, (5) buyer-seller relationship marketing, and (5) customer loyalty, however, in this study, it will be about the study of an online retailing industry context, we could not find any study that has examined how the relationship marketing on the loyalty of Generation Y and Generation Z customers for online retail businesses during the COVID-19 crisis. Thus, this study explains antecedents of relationship marketing to pass to buyer-seller relationship marketing to predict consequences of relationship marketing in online retail businesses (Figure 1).

Figure 1 Proposed Framework of the Study Shows the Influence of Relationship Marketing on the Loyalty

Antecedents of Relationship Marketing

Communication is both formal and informal exchange that makes the information exchange between the buyers and sellers meaningful and prompt (Zephaniah et al., 2020). For this study, there is a measurement of communication from various factors such as the information which is timely and reliable, the information provided on new services, and information promised and precise (Jesri et al., 2013), as well as the shared value of data. According to the shared value, Sivades & Kashyap (2012) indicated that there is a development of the shared value over relationship time when the relevant parties determine each other and co-create value mutually. And for the seller expertise is regarded as knowledge, experiences, and efficiency of the sellers as a whole. Wan et al. (2012) mentioned the relationship with the sellers that it is about the sellers who try to build confidence by taking care and treating their customers to make friendships. The sellers who are experts more can feel the power and credit on responding between the buyers-sellers than the sellers whose expertise is less (Carter et al., 2014). The relationship benefits that they are the benefits got. Besides, it includes time-saving, convenience, friendship, and better decisions. These are from the study of use, society return, and received rewards. The variables formed into groups under the structures of relationship benefits consist of convenience motivation, information quality, price consciousness, and website design. Relationship marketing has an effort to enhance loyalty to customers by providing typical products and services (Kozlenkova et al., 2015). The Internet makes various brands able to follow, keep, analyze, and use varied information about customers easier (Ruswanti & Lestari, 2016).

Buyer–seller Relationship in Online Marketing

Relationship marketing is a paradigm that has just occurred recently, attracted by marketing research (Scheer et al., 2015). The relationship quality means an overall evaluation of strength for the relationship between both parties. The relationship quality is crucial in the e-commerce context. The relationship quality is a group of values immeasurable that will increase the services and products, and it originates the expected exchange between the buyers-sellers (Verma et al., 2016).

In this research, we refer to relationship quality is a high order construct and has two distinct yet related components: trust and satisfaction (Bojei & Alwie, 2016). Therefore, a high relationship quality indicates that the customer trusts the intermediary and has confidence in the intermediary’s future performance because its past performance has been consistently satisfactory. First, trust has been considered an important dimension of relationship quality. Only when a seller trusts the intermediary does he or she perceive there is a good relationship between the intermediary and him or her. Second, satisfaction with the relationship is defined as a positive emotional state resulting from the assessment of the intermediary’s relationship with sellers.

Marketing research does not usually consider the technological antecedents of relationship quality. In this research, we refer to three factors to evaluate an e-commerce intermediary: information quality, website design, and convenience motivation.

Customer Loyalty

Loyalty is the real intention for products or services purchase again in the future even though there are effects from situations. Also, marketing efforts make an opportunity through changing behaviors (Rungsrisawat et al., 2019). Customers’ loyalty will help promote companies by word-of-mouth of loyalty people for building and passing on the businesses, including references and recommendations to others, or giving advice (Bojei & Alwie, 2016). Furthermore, the customers’ loyalty helps increase circulation through purchases more widely, and this causes having the purchase more frequently. According to word-of-mouth (WOM), it is regarded as informal communication emphasizing other consumers about their ownership of usage or the unique characteristics of products and services, and/or their sellers. For the expectation of continuity, it is the intention of customers to keep the future relationship and possibility to buy products from the sellers again. These are the concepts investigated seriously. The empirical researchers use similar structures such as the intention to either purchases or spends and the changed intention (reflection) to observe this concept (Sriyakul et al., 2019).

We identify 13 constructs in our RM framework for online retailing, four antecedents—communication, shared value, seller expertise, relationship benefits, Further, as in the original model, the context of online retailing also we identify satisfaction, trust, and commitment as relationship quality in online marketing relational mediators. Commitment is defined as Ban enduring desire to maintain a valued relationship (Tung & Carlson, 2015). Trust is defined as confidence in an exchange partner’s reliability and integrity (Bojei & Alwie, 2016). Trust and commitment are the most frequently studied constructs. However, they have been studied as antecedents, mediators, and even consequences. Trust has often been identified as an antecedent to commitment. Relationship quality is the overall assessment of the strength of a relationship (Bojei & Alwie, 2016). Satisfaction is defined as the satisfaction of the consumer from the overall relationship (Saengchai & Jermsittiparsert, 2020). Finally, we identify three consequences of RM as expectation of continuity, word of mouth, and customer loyalty.

Therefore, the following hypotheses are tested:

H1 The influence of relationship marketing on the loyalty of Generation Y and Generation Z customers for online retail businesses during the COVID-19 crisis is consistent with empirical data.

Research Methodology

This research method was descriptive research to assess the construct validity of relationship marketing on the loyalty of generation Y and generation Z customers for online retail businesses during the COVID-19 crisis. The research tools included online questionnaires. All variables measurement carried out using five-level of Likert-type Scale. The result of Cronbach’s Alpha value equaled .897 that passed the criteria since its value should be higher than 0.6 (Hair et al., 2010). Unit of analysis or in other words the population of this study was customers who purchased online products via Lazada application or website during the COVID-19 crisis in Thailand. They are Generation Y and Generation Z groups. The researcher selected the Structural Equation Model (SEM) technique and used the method of determining the sample size of Hair (2010), which recommended between 5-10 times of the observed variables in each research. For this study, there were 354 samples regarding the examination of validity in this study. Concerning the sampling, the convenience sampling was used by considering the statistical data of customers to construct the categorized tendency of the samples. The data were collected by giving out the questionnaires to 354 customers who purchased online products via Lazada application or website during the COVID-19 crisis.

Research Results

The questionnaires data collection in this study, the researcher kept 354 questionnaires after having examined the information. The respondents using the service from online purchases were mostly female whose age was lower than 20 years old. They had bachelor’s degree with the monthly income was lower than 10,000 baht, having careers as students/university students, single marital status, using online purchase about 1-3 times/month, product bought was electronic equipment. They mostly bought online products because the Lazada website was the easiest for usage.

Measurement Models: Validity and Reliability of the Scales

The measurement validity and reliability were assessed with a CFA carried out on the 57 items. In the process of assessing convergent validity, items with factor loadings less than 0.40 were deleted. Also, items contributing to low reliability were deleted. The Partial fit for the measurement model, without any modifications, was fair based on fit indices. The convergent validity of the measures is evidenced by the large and significant factor loadings as shown in Table 1, and discrimination validity is indicated by the relatively high correlations between the dimension of the same construct (Table1).

Table 1 Measurement Model
Construct Factor loading (λi) t S.E. R2
COMMU1 0.80** 17.57 0.03 0.65
COMMU2 0.80** 17.44 0.03 0.64
COMMU3 0.74** 15.68 0.03 0.55
COMMU4 0.81** 17.65 0.04 0.65
COMMU5 0.79** 17.11 0.03 0.62
Construct Reliability: CR = 0.89
Average Variance Extracted: AVE = 0.62
SV1 0.82** 17.63 0.03 0.67
SV2 0.83** 17.95 0.03 0.68
SV3 0.77** 16.21 0.04 0.59
SV4 0.74** 15.46 0.03 0.55
Construct Reliability: CR = 0.87
Average Variance Extracted: AVE = 0.63
SE1 0.78** 16.25 0.03 0.6
SE2 0.77** 16.47 0.03 0.6
SE3 0.80** 16.84 0.03 0.63
SE4 0.79** 16.1 0.03 0.63
SE5 0.78** 16.52 0.03 0.6
Construct Reliability: CR = 0.89
Average Variance Extracted: AVE = 0.62
WD1 0.78** 17.19 0.03 0.61
WD2 0.76** 16.57 0.04 0.59
WD3 0.74** 15.93 0.04 0.55
WD4 0.78** 17.19 0.04 0.61
INFOQ1 0.78** 17 0.03 0.61
INFOQ2 0.78** 17.18 0.03 0.61
INFOQ3 0.76** 16.29 0.03 0.57
PRI1 0.72** 15.42 0.03 0.52
PRI2 0.71** 15.09 0.04 0.51
PRI3 0.74** 15.84 0.04 0.54
PRI4 0.71** 15.11 0.04 0.51
PRI5 0.73** 15.45 0.04 0.53
CONVE1 0.69** 14.51 0.04 0.48
CONVE2 0.81** 18.08 0.04 0.65
CONVE3 0.79** 17.4 0.03 0.62
CONVE4 0.78** 17.13 0.03 0.61
Construct Reliability: CR = 0.96
Average Variance Extracted: AVE = 0.57
SAT1 0.86** 19.87 0.03 0.74
SAT2 0.87** 20.06 0.03 0.75
SAT3 0.83** 18.67 0.03 0.69
SAT4 0.83** 18.66 0.03 0.68
SAT5 0.81** 17.89 0.03 0.65
SAT6 0.78** 17.02 0.03 0.61
Construct Reliability: CR = 0.93
Average Variance Extracted: AVE = 0.69
TRU1 0.87** 19.95 0.03 0.75
TRU2 0.81** 18.05 0.03 0.66
TRU3 0.88** 20.51 0.03 0.78
TRU4 0.81** 17.79 0.03 0.65
TRU5 0.79** 17.41 0.04 0.63
Construct Reliability: CR = 0.92
Average Variance Extracted: AVE = 0.69
COM1 0.74** 14.92 0.04 0.55
COM2 0.91** 21.3 0.04 0.82
COM3 0.88** 20.64 0.04 0.77
COM4 0.83** 18.47 0.03 0.69
Construct Reliability: CR = 0.91
Average Variance Extracted: AVE = 0.71
EXPC1 0.74** 15.35 0.04 0.55
EXPC2 0.83** 18.33 0.04 0.7
EXPC3 0.81** 17.78 0.04 0.66
EXPC4 0.89** 20.42 0.03 0.8
Construct Reliability: CR = 0.89
Average Variance Extracted: AVE = 0.67
WOM1 0.88** 20.39 0.03 0.78
WOM2 0.86** 19.78 0.03 0.75
WOM3 0.81** 17.77 0.03 0.65
WOM4 0.81** 17.81 0.04 0.65
Construct Reliability: CR = 0.91
Average Variance Extracted: AVE = 0.71
LOY1 0.80** 17.58 0.03 0.65
LOY2 0.79** 17.22 0.03 0.63
LOY3 0.85** 19.19 0.03 0.73
LOY4 0.86** 19.47 0.03 0.74
Construct Reliability: CR = 0.90
Average Variance Extracted: AVE = 0.68
= 2492.90, RMSEA = 0.046, NFI = 0.99, GFI = 0.80, CFI = 0.99, AGFI = 0.77, RMR = 0.021.
Notes ** P<0.01 or | t | > 2.58 and * P<0.05 or | t | > 1.96

The results of validity and reliability consideration by confirming factor analysis had the validity as follows: According to the convergent validity and discriminant validity, it was considered from the factor loading. For considering delete indicator from the factor loading which its value should be > |0.5| and have cross loading. Table 1 shows all values of the factor loading which all items value greater than > |0.5|.

From Figure 2, the result of path analysis between latent endogenous variable (Eta) (Yvariables) and latent exogenous variable (X-variables) showed that COMMU had a direct effect on BUSE, SV had a direct effect on BUSE, and SE had a direct effect on BUSE (Table 2). Moreover, COMMU had an indirect effect on LOY, SE had an indirect effect on BUSE, SE had an indirect effect on LOY, and BUSE had a direct effect on LOY (Table 3).

Figure 2 Path Model

Table 2 Data Summarization and Correlations Among Constructs
Construct SAT TRU COM EXPC WOM LOY COMMU SV SE RB
SAT 1                  
TRU 0.97 1                
COM 0.93 0.96 1              
EXPC 0.91 0.94 0.97 1            
WOM 0.88 0.91 0.95 0.92 1          
LOY 0.88 0.91 0.95 0.92 0.9 1        
COMMU 0.82 0.79 0.76 0.74 0.72 0.72 1      
SV 0.88 0.85 0.82 0.79 0.77 0.77 0.92 1    
SE 0.84 0.81 0.78 0.76 0.74 0.74 0.86 0.89 1  
RB 0.89 0.86 0.83 0.81 0.79 0.79 0.88 0.92 0.92 1
Table 3 Hypothesis Result
Hypothesis Testing Result
H1: COMMU to SAT Not Supported
H2: SV to SAT Supported
H3: SE to SAT Not Supported
H4: RB to SAT Supported
H5: SAT to TRU Supported
H6: TRU to COM Supported
H7: COM to EXPC Supported
H8: COM to WOM Supported
H9: COM to LOY Supported

Discussion and Conclusion

This study is the research contribution of antecedents on relationship marketing, buyerseller relationship in online marketing, and the consequences of relationship marketing. The research examination followed the hypothesis. The research result suggests that all components were consistent with the empirical data. The opinions of online customers towards the influence of relationship marketing revealed that they were at a high level as a whole, which included communication, relationship benefits, shared value, and seller’s expertise. It follows the research contribution of Hänninen & Karjaluoto (2017) about the consumers' relationship that it is concordant with the intimacy theory that communication is a crucial factor for potential relationship start. It makes the customers feel intimate with the sellers and being a part of products since there is the listening to the customers' opinions. They just make the customers feel that their sayings are essential to the sellers' brands, and there are some people who accept and perceive what they said. These are considered the first crucial factors the customers focus on, for starting a good relationship. According to the customers' opinions toward the online purchase for the buyers-sellers marketing relationship, it indicated that the online customers' opinions toward all three factors were at a high level, which included satisfaction, trust, and commitment, respectively. It is concordant with the commitment-trust theory of relationship marketing (Morgan & Hunt 1994), a theory cited the most in the research articles related to relationship marketing (Ruswanti & Lestari, 2016). When considering the commitment, it is the maintenance of valuable relationship and trust. Using the commitment together with trust can build effective and efficient results through the relationship with the retailers-producers (Figure 3). Additionally, it conforms to the research results of some academics, which revealed that the customers’ satisfaction had a positive result to the customers’ loyalty. The customers’ opinions on the online purchase towards the customers’ loyalty revealed that the influences toward all three factors were at a high level, which included loyalty, the expectation of a continuous relationship, and word-of-mouth communication. This is in accordance with the concept which indicates that today the service providers have to select new strategies relevant to marketing innovation to establish the trust and loyalty to the existing customers so that they can motivate the customers to share positive talking toward the brand when they have to encounter the increasing competition today. Hence, various companies realize the essence of making a relationship with existing customers more. The Word-of-Mouth (WOM) communication that is such an incredibly powerful communication for business today. The entrepreneurs take an interest, especially the service business. Because of the modernity of technology, it makes the information widespread quickly and continuously, which makes the consumers who recognize the news and information share information with each other easily.

Figure 3 The Result of Model Analysis

Some results in this study that should be addressed are as follows: Relationship marketing takes a crucial and outstanding role in the field of business strategies. The researcher synthesized the empirical research relevant to relationship marketing in the frame of meta-analysis. Even though the primary research evidence which appeared about relationship marketing has positive effects and gets support well. Regarding the synthesis of dependent research, it also identifies that the relationship marketing is more effective when the relationship is important to the customers (such as service presentation, exchange of business marketing channels), and building the relationship with the individuals more than the selling from companies (some parts describe the mixed effects between relationship marketing and the report of efficiency from the previous studies). When considering each aspect, it was found that every variable has a relationship as statistical significance except the communication variables and sellers expertise. This may be because of the crisis of Covid 19 that makes the consumers decide to buy products urgently. Therefore, they did not focus on the sellers’ communication and expertise much because they only need the necessary things as quickly as possible.

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