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Abstract

In the online to offline (O2O) model, online marketing and online purchases drive offline (non-Internet) operations and offline consumption. E-commerce must use more effective methods in combination with offline physical stores for consumers to obtain better online services. However, if there are positive effects from offline from customers on the re-purchase, it can generate a positive power return to online. In addition, could online to offline moves on a path that have either stronger or weaker influence of re-purchase on chain stores with customers’ electronic word-of-mouth and online involvement? To understand the influence of O2O on chain stores, this study investigates the relationships between chain store brand images, perceived value, customer satisfaction and re-purchase intention on offline considerations in a Taiwan telecom chain store (N = 1,602). The research results find that customer satisfaction plays a fully mediating role. In addition, electronic word-of-mouth and online involvement play two moderating roles on the relationships between chain store brand image, perceived value, customer satisfaction, and re-purchase intention respectively. Finally, theoretical and practical implications, conclusion, limitations and future research are presented.

Plain Language Summary

This study presents a theoretical model to investigate how the Taiwan Chunghwa Telecom chain stores can meet this challenge of connecting online to offline consumer activity. The research findings may be an example for other chain store operators to extend business models based on our theoretical and practical implications. Finally, online to offline is a broad issue, both academically and in practice, and it is a topic of customers’ purchase and re-purchase, both online and offline. It might be that it is a cycle of two-way from offline to online and from online to offline on business models. Thus, future research topics in this area can provide more valuable evidence and cases for retailing and e-commerce.

Keywords

re-purchase intention chain store brand image perceived value customer satisfaction online involvement electronic word-of-mouth

Introduction

The online to offline (O2O) business model was proposed by TrialPay founder Alex Rampell in 2010. He pointed out that e-commerce must use more effective methods in combination with offline physical stores for consumers to obtain better online services. In the O2O model, online marketing and online purchases drive offline (non-Internet) operations and offline consumption (Kusuda, 2022). This model serves reservations, through promotions, information provision, discounts, etc., and transmits the information of the physical store to network users, converting them into customers of the physical store. This model is especially suitable for products and services that must be consumed in stores, such as catering, leisure facilities, movie theaters, beauty salons, fitness, photography, retail instant delivery, and department stores (J. Chen et al., 2022; Jin & Huang, 2021; Liao et al., 2018). Types of O2O development include using online promotion to gather users and shift them offline; or upgrading to a service e-commerce model, such as Foodpanda’s meal delivery. O2O is now becoming an inseparable part of life in various fields, such as the express logistics provided by Express, and the high-end restaurant rankings of Delicious (Liao & Yang, 2020; Pu et al., 2021; Zhang & Demirkan, 2021). In terms of e-commerce, the main purpose for the interactive relationship between online and offline is consumers’ intention to re-purchase in the physical channel (H. Y. Choi & Kim, 2022). Thus, the influence of online to offline on a chain store for re-purchase intention is not only a critical issue for retailing but also e-commerce business model development.

A chain store is a group of retailers under the same brand, usually managed by a central group with standardized business methods and practices. Such stores may be owned by the company or may be operated by individuals or small and medium enterprises (SMEs) under a franchise contract with the parent company (Jung et al., 2019). Usually, chain stores include central marketing and transactions, and they can obtain higher profits due to their lower costs (Lin et al., 2020). In addition, chain stores can include wholesale and retail, as well as catering and service industries. Recent research on retail chain stores have considered the relationship between using online social media information and the behavior of physical channels, for the online and offline development of chain stores. The influence of e-commerce business model has increased significantly, showing trend of online to offline that can combine physical channels with online network power to develop a new business model for e-commerce (Chai et al., 2021; E. Y. Choi, 2022; Hou et al., 2021).

Regarding chain store re-purchase intention, Liao et al. (2022) stated that customer re-purchase intention refers to the customer’s intention to re-purchase from the company that originally sold the product or service, which represents the customer’s commitment to consume the same product or service again. From the perspective of consumers to chain stores, Bolton et al. (2022) stated that consumer knowledge is composed of familiarity and expertise. The brand image of chain stores can be measured by the number of times a consumer recalls contact with a brand. The so-called brand contact experience includes contact through advertising, seeing it in chain stores when shopping, learning of it from others, and having purchased or used the brand’s products. When consumers are more exposed to a brand, the more successfully and clearly the brand can enter the minds of consumers, so they become familiar with the brand image (L. Wang et al., 2021). Consumers’ attitude toward a chain store brand image can be evaluated by a product or brand, and brand image can be generated through individual subjective evaluation (Badrinarayanan & Becerra, 2019). Perceived value is regarded as a product between of the trade-off between the “Give” and “Get” of a product or service by consumers (W. Li et al., 2023); and the evaluation of purchase intention through perceived value of the brand image of chain stores is based on the likelihood that consumers intend to purchase the product again (Tu et al., 2022). For retail chain store operators, re-purchase intention is a critical factor for their survival. If the re-purchase intention of chain stores declines, the area and scope of sales will be affected more seriously than individual stores would be, and the losses will also be greater.

In the online influence on offline, studies have shown that the quality of electronic word-of-mouth (EWOM) directly affects consumers re-purchase intention and attitude toward products and service (Lee et al., 2022; Liao et al., 2021). Sardar et al. (2021) also confirmed that high-quality EWOM, compared with low-quality EWOM, can make message receivers feel that the information is more credible, and leads to a stronger response to the purchase intention. Social media has become another marketing battleground for chain store operators, and EWOM also affects business opportunities that firms develop on social networks. Therefore, companies should pay more attention to the impact of positive and negative EWOM. On the other hand, involvement is generally believed to be an individual state of mind, whose intensity is affected by the degree to which something is related to personal needs, values, and goals in a given context. When the degree of involvement deepens, there can be a series of follow-up behaviors due to care about the thing (Krugman, 1966). Consumers are involved in the online environment as they search for information, experience products or services, share their experiences, and then generate consumption decisions. This is known as online involvement (Knox & Walker, 2003; Xu et al., 2022), and one of the most frequently discussed research issues (Liao et al., 2021). There is also empirical evidence that online involvement is an influential factor in the formation of online voice, and an important motivation for consumers offline re-purchase intention (Trivedi & Sama, 2020).

Chunghwa Telecom Co., Ltd. (CHT) is the largest telecommunications service firm of chain stores in the Republic of China (Taiwan). It is also a multi-party communication business and products chain store including local calls, long-distance calls, public telephones, dedicated circuits, ADSL, smart networks, MOD, enterprise integration service, mobile phones, mobile phone peripherals and network data services. In 2021, Chunghwa Telecom had 239 chain stores in Taiwan, with about 6,000 employees. To understand the influence of online to offline on chain stores, this study investigates the relationships between chain store brand images, perceived value and re-purchase intention on offline considerations. Customer satisfaction plays a mediating role in this relationship. EWOM and online involvement play a moderating role on the relationships between chain store brand image, customer satisfaction, perceived value and respectively. Thus, this study proposes the initial conceptual model.

Proposing the Initial Conceptual Model

This study indicates that influence of online to offline on a chain store is a valuable research issue regarding marketing research, indicating that it is an appropriate context and conceptual model to investigate the three research questions in this study (Figure 1).

Research questions one (RQ1): Do chain store brand image and perceived value affect re-purchase intention through customer satisfaction, along with the perceived value of store service enhance Taiwan consumers re-purchase intention in the process of telecommunications service?

Research question two (RQ2): As a result of customer satisfaction, how will individual customer behavior be determined by an individual’s perception of their store brand image and perceived value affect telecommunication product and service re-purchase? That is, how can those attributes of store image and service perception for consumer behavior be influenced by CHT’ chain store brand image, relevance, and perceived high service value?

Research question three (RQ3): What is the moderating role for online involvement and EWOM in exploring the moderating effect in the research model? That is, whether the indirect effect of chain store brand image and perceived value on re-purchase intention through customer satisfaction is stronger at low or high degrees of online involvement and electronic word-of-mouth input.

Research question three (RQ4): In addition, if there are positive effects from offline from customers on the re-purchase, it can generate a positive power return to online. In addition, could online to offline moves on a path that have either stronger or weaker influence of re-purchase on chain stores with customers’ electronic word-of-mouth and online involvement?

Figure 1.

Conceptual model.

Therefore, this study incorporates four online involvement and electronic word-of-mouth motivations into the research model to investigate the two moderation effects in Taiwan telecommunications chain store (N = 1,602).

The rest of this study is organized as follows. In Section 2, we present literature review and hypotheses on the theoretical model development. Section 3 describes the methodology, including the subjects, data collection, and measurement. Section 4 presents the research results. Section 5 presents a discussion including theoretical and practical implications. Finally, conclusion, limitations, and future research are presented.

Theoretical Model and Hypotheses Development

Chain Store Brand Image and Customer Satisfaction

Martineau (1957) first study presented that retail chain store brand image has become a research topic in the retail industry. The brand image of a chain store is considered as some multiple attributes, including physical characteristics associated to the service or store, and emotional responses to the brand and store for customers (Liao et al., 2022). Since brand image affects consumer satisfaction and purchase intentions, thus retail operators invest in building chain store brand images (C. Y. Li & Fang, 2019). Chain store brand image is the sum of customers’ perceptions and associations of brand image elements, customer satisfaction is consumers’ perception of retail brand performance meeting their expectations, and customer loyalty occurs when the psychological approval of a retail brand is put into practice (Dey et al., 2020). This study thus proposes the hypothesis H1:

Hypothesis 1: Chain store brand image has a positive impact on customer satisfaction.

Perceived Value and Customer Satisfaction

Regarding cause-and-effect relationship from perceived value to customer satisfaction, Jeong and Kim (2020) developed a comprehensive view of specific transactions, in which perceived value is the premise of satisfaction, so that perceived value is the self-determination factor of customer satisfaction. Uzir et al. (2021) found that service quality and perceived value are important factors affecting customer satisfaction. Slack et al. (2021) argued that customer satisfaction and perceived value can be viewed as overall levels, since customer satisfaction is generally considered to be broader than service or product quality. Accordingly, this study proposes the hypothesis H2:

Hypothesis 2: Perceived value has a positive impact on customer satisfaction.

Customer Satisfaction and Re-Purchase Intention

In researching consumers re-purchase intention, Sohaib (2022) found that customer satisfaction is a critical construct for re-purchase intention. Goel et al. (2022) defined re-purchase intention as a customer’s judgment to purchase the same service from the same firm again after considering their future purchase situation. After customers use products or services, they will evaluate the products and services, and if satisfied, they are more likely to re-patronize the store and support it with WOM publicity. Customers’ experience with products and services will affect customers’ their re-purchase intention through a physical channel (Hui-Wen Chuah et al., 2022). Therefore, this study proposes the hypothesis H3:

Hypothesis 3: Customer satisfaction has a positive impact on re-purchase intention.

Chain Store Brand Image, Customer Satisfaction and Re-Purchase Intention

Mishra and Banerjee (2019) took snack products of a retail brand as an example and found that consumer brand experience has a significant mediating effect between customer satisfaction and perceived value. Han et al. (2019) took chain steakhouse brand image as an example and found that brand image has a significant and positive relationship between customer satisfaction and perceived value. Law et al. (2022) took Laotian air passengers as an example and found that air transport business brand image has a significant positive impact on customer satisfaction in terms of enhancing the level of service quality. Accordingly, this study proposes the hypothesis H4:

Hypothesis 4: Customer satisfaction plays a mediating role between chain store brand image and re-purchase intention.

Perceived Value, Customer Satisfaction and Re-Purchase Intention

Jan et al. (2020) considered e-logistics services as an example, their empirical study found that perceived value is a pre-determined variable of satisfaction, which in turn has a significant impact on satisfaction and re-purchase intention. Hasan (2021) took the Norwegian EV market as an example to explore the impact of service quality and perceived value on customer satisfaction and re-purchase and confirmed that perceived value has a significant positive impact on re-purchase intention. Gómez-Carmona et al. (2022) found through COVID-19 prevention. Perceived value has a significant positive impact on customers to re-purchase intentions. Based on the above literature, this study proposes the hypothesis H5:

Hypothesis 5: Customer satisfaction plays a mediating role between perceived value and re-purchase intention.

Moderated Mediating Role of Online Involvement on Chain Store Brand Image, Customer Satisfaction and Re-Purchase Intention

Im and Ha (2011) explored the relationship between buyers and sellers; finding that perceived value is an independent variable of brand image, and brand image has a mediating role that can enhance customers’ purchase intention. Therefore, online and offline operators have modified the final marketing model, resulting in the brand image of products and services mediating the effect between perceived value and consumers’ intention to purchase in an online apparel shopping context. In that study, enjoyment played a key role in purchase intention and cognitive effort by mediating online involvement effects and persistent engagement effects. Based on consumers’ extensive participation or recognition of a firm’s brand image, different types of consumers may have different perceptions of service and product value when purchasing goods, as well as their participation in brand perception. With different product brands, better satisfaction will give consumers a positive purchase intention. Involvement can strengthen the relationship between satisfaction and purchase through brand power (Y.-C. Chen et al., 2023). Levy and Gvili (2020) pointed out that perceived service value affects image and purchase intention. To study the online shopper engagement in price negotiation, in terms of brand image and purchase intention; they regarded involvement as a moderating role between product brand and price. Accordingly, this study proposes that chain store brand image of re-purchase intention through customer satisfaction is stronger at low degrees of online involvement than at higher degrees of online involvement, and hypothesis is proposed as follows H6:

Hypothesis 6: The indirect effect of chain store brand image on re-purchase intention through customer satisfaction is stronger at low degrees of online involvement than at high degrees of online involvement.

Moderated Mediating Role of Electronic Word-of-Mouth on Perceived Value, Customer Satisfaction and Re-Purchase Intention

Goraya et al. (2021) believed that there is an effect of emotion on post-purchase behavior is more important for active users of social media than passive users because EWOM can affect their reconsideration plans, and EWOM also moderates the relationship between satisfaction and purchase intention. These results show EWOM to be a strong driver of change in customer satisfaction with the brand, while overall customer satisfaction contributed to increased purchase intention. This implies that the consumer’s EWOM for the Internet will affect the satisfaction level of the customer’s satisfaction. This study argues that IWOM has different considerations under different levels of perceived risk and different EWOMs, which in turn affect purchase intention. Therefore, the hypothesis 7 are proposed:

Hypothesis 7: The indirect effect of perceived value on re-purchase intention through customer satisfaction is stronger at low degrees of electronic word-of-mouth than at high degrees of electronic word-of-mouth.

Research Method

Theoretical Model

Following the literature review, this study proposes a theoretical model based on chain store brand image, perceived value, customer satisfaction, re-purchase intention, online involvement and EWOM. Chain store brand image and perceived value are independent variables, re-purchase intention is a dependent variable, customer satisfaction is a mediating variable, and the online involvement and EWOM are moderating variables. The theoretical model is presented in Figure 2.

Figure 2.

Theoretical model.

Subjects and Data Collection Procedure

This research uses multistage cluster sampling is an extension of cluster sampling. sampling, which can be used when the sample size is large to save labor and it is a phased selection of samples. It is a phased selection of samples, where the list and sampling are divided into two or more cycles or stages. The sampling is conducted in two or more rounds or stages, that is, the sampling is conducted from the samples (Gordis, 2008). In Taiwan, Chunghwa Telecom has 400 physical stores, 86 of which are in the sampling area of Taipei and New Taipei City areas. The sampling period was from June 23, 2021, to October 10, 2021. The sampling method is a random sample of one person from each physical store on each business day during the sampling period. The total number of samples sampled was 1,980, after deducting 188 invalid samples, the final valid samples were 1,792, with a recovery rate of about 91%. In this study, the Surveycake system was used in physical stores as a systematic tool for questionnaire filling, recovery, and subsequent Excel data processing.

Measures

Chain store brand image has three items, which are modified from Kremer and Viot (2012). This study developed a measure scale specifically for the chain store brand image that was based on retailer brand image in the literature review. The construct of perceived value was developed by six items (modified from Apaolaza et al., 2020). This section of the questionnaire measured respondents’ experiential value, including escapism and service excellence. The customer satisfaction scale consisted of three items adapted from Othman et al. (2020), which were appropriately reworded to fit the retailing customer context. Re-purchase intention was measured with three items modified from Herjanto and Amin (2020). From the measures of C. Y. Wang (2019), three items were modified to measure online involvement. To measure electronic of word-of-mouth, this research used six items which modified from Zhang et al. (2021). All items were rated on five-point Likert-type scales, ranging from strongly disagree = 1 to strongly agree = 5. To prevent measurement errors of common method variance, there were precautions in the design and delivery of the questionnaire, including hidden respondent information, randomized items, reverse questions, and modified wording (Liao et al., 2021). (Appendix—the questionnaire)

Results

Estimation Method

Table 1 shows the values of skewness and kurtosis of all observed variables are less than 2, indicating that the observed variables in this study have good normality so the most likely estimation method is suitable for various parameter estimation and fitness testing of the estimation model.

Table 1.

Estimation Model Results.

Construct	Dimension	Mean	Standard deviation	Skewness	Kurtosis
Chain store brand image (CSBI)	Single	11.21	2.29	−0.552	0.843
Customer satisfaction (CS)	Single	11.09	2.32	−0.573	0.916
Perceived value (PV)	Single	11.16	2.31	−0.679	1.088
Re-purchase intention (RI)	Single	10.89	2.41	−0.416	0.448
Online involvement (OI)	Single	11.21	2.20	−0.547	0.922
Electronic word-of-mouth (EWOM)	Single	10.97	2.33	−0.470	0.771

Measurement Model

Reliability Analysis

Table 2 shows Cronbach’s α coefficient for customer satisfaction is 0.929, for chain store brand image is 0.807, for EWOM is 0.904, for online involvement is 0.852, for perceived value is 0.931, for re-purchase intention is 0.941. Thus, it indicates that the measurement constructs in this study had good internal consistency reliability.

Table 2.

Reliability Analysis Results.

Construct	Dimension	Item no.	Cronbach’s α
Chain store brand image (CSBI)	Single	3	.916
Customer satisfaction (CS)	Single	3	.929
Electronic word-of-mouth (EWOM)	Single	6	.904
Online involvement (OI)	Single	3	.852
Perceived value (PV)	Single	6	.931
Re-purchase intention (RI)	Single	3	.941

Construct Reliability (CR)

Construct reliability is a reliability index of latent variables, which can be used to measure the internal consistency of index (items) of latent variables. Higher CR values indicate higher consistency between items. The combined reliability of latent variables should be greater than 0.6 (Anderson & Gerbing, 1988). The CR value of each latent variable in this study is greater than 0.6, indicating that each latent variable in this study has good construct reliability (Table 3).

Table 3.

Construct Reliability Results.

Construct	Dimension	Construct reliability
Chain store brand image (CSBI)	Single	0.917
Online involvement (OI)	Single	0.853
Electronic word-of-mouth (EWOM)	Single	0.872
Customer satisfaction (CS)	Single	0.929
Re-purchase intention (RI)	Single	0.907
Perceived value (PV)	Single	0.910

Convergent Validity (CV)

Table 4 shows that the standardized factor loadings of potential variables to observed variables in this study are all greater than 0.7. In addition, the t value, which is the criteria for evaluating convergent validity, must reach a significant level. As shown in Table 4, the t-values of each observed variable have reached a significant level (p < .001), so that the model has good convergent validity.

Table 4.

Convergent Validity Results.

Construct	Dimension	Item no.	Standardized factor loading	Standard error	t value
Perceived value (PV)	Single	PV1	0.876	0.093	36.899***
		PV2	0.843	0.098	34.783***
		PV3	0.862	0.095	36.005***
		PV4	0.871	0.080	37.063***
		PV5	0.911	0.082	39.969***
		PV6	0.904	0.081	39.437***
Chain store brand image (CSBI)	Single	CSBI16	0.882	0.085	37.999***
		CSBI17	0.896	0.084	38.959***
		CSBI18	0.882	0.088	38.005***
Customer satisfaction (CS)	Single	CS19	0.902	0.084	39.650***
		CS20	0.907	0.084	39.904***
		CS21	0.896	0.084	39.146***
Online involvement (OI)	Single	OI7	0.823	0.102	31.908***
		OI8	0.827	0.091	32.190***
		OI9	0.787	0.104	30.079***
Re-purchase intention (RI)	Single	RI10	0.840	0.088	35.013***
		RI11	0.876	0.085	37.316***
		RI12	0.782	0.113	31.472***
		RI13	0.906	0.088	39.854***
		RI14	0.934	0.083	42.075***
		RI15	0.916	0.089	40.671***
Electronic word-of-mouth (EWOM)	Single	EWOM22	0.838	0.092	33.53***
		EWOM23	0.836	0.090	33.48***
		EWOM34	0.824	0.095	32.60**

Note: ***indicates that the statistical significance level is reached when α = 0.001

Confirmatory Factor Analysis (CFA) is to examine whether specific indicators (questions) fall under each of the theoretical expectations, primarily for theoretical validation. As can be seen from Table 5, all the fitness index values of the model are within the standard range of values. The chi-square values are susceptible to the size of the sample and the effect of the effect. Because of the appropriate sample size (N = 1792) in this study, the chi-squared values are likely to reach significant levels. Furthermore, the rest of the fit indicators are in line with CFA’s measurement standards., indicating that the model fit in this study is still within good standards.

Table 5.

Fit Statistics of the Confirmatory Factor Analysis (CFA).

Fit statistics		Standard value	Model computed value	Results
Model absolute fit measures	χ²/df	<3	497.36/168 = 2.960	Good standard
	SRMR	<0.05	0.019	Good standard
	RMSEA	<0.08	0.042	Good standard
Incremental fit measure	NFI	>0.92	0.980	Good standard
	NNFI	>0.94	0.983	Good standard
	CFI	>0.91	0.986	Good standard
	RFI	>0.90	0.975	Good standard
	IFI	>0.95	0.986	Good standard
Parsimonious goodness-fit-index	PNFI	>0.59	0.784	Good standard
	PCFI	>0.54	0.789	Good standard
	CN	>200	449	Good standard

Correlation Analysis

Table 6 shows that the correlation coefficients between perceived value and chain store brand image, re-purchase intention, customer satisfaction, internet involvement and EWOM are all significantly positively correlated. There was a significant positive correlation between chain store brand image and re-purchase intention. EWOM was significantly positively correlated with chain store brand image and re-purchase intention. There was a significant positive correlation between customer satisfaction and re-purchase intention. Online involvement, customer satisfaction and re-purchase intentions were all significantly positively correlated respectively. The research hypotheses reached a statistically significant level, so further hypothesis testing was performed. In addition, the correlation coefficient between two different variables should be less than the square root of the average variation extraction (AVE) of each variable. Two different variables are measured, and the results are correlated. If the correlation is low, it means that the two concepts have discriminant validity. It can be seen from Table 6 that the square root values of the average variation extraction of all variables in this study are greater than the correlation coefficient between the two variables, indicating that all variables have good discriminant validity.

Table 6.

Correlation Analysis.

Construct	1	2	3	4	5	6
1. CSBI	1
2. PV	.822**	1
3. OI	.502**	.473**	1
4. EWOM	.822**	.749**	.538**	1
5. CS	.884**	.826**	.491**	.688**	1
6. RI	.869**	.822**	.480**	.879**	.822**	1

Note. The number in the diagonal brackets is the square root of the average variable extraction (AVE), which should be greater than the absolute value of the correlation coefficient between other variables.

p < .05. **p < .01. ***p<.001.

Hypotheses Testing

For RQ1, Table 7 indicates that the standardized regression coefficient of perceived value on customer satisfaction is 0.437, it indicates that perceived value positively predicts customer satisfaction (B = 0.437, p < .001). On the other hand, the standardized regression coefficient of chain store brand image to customer satisfaction is 0.850, it indicates that chain store brand image can positively predict customer satisfaction (B = 0.850, p < .001). The standardized regression coefficient between satisfaction and re-purchase intention is 0.975, it indicates that customer satisfaction can positively affect re-purchase intention (B = 0.975, p < .001). Accordingly, it can be confirmed that H1, H2, and H3 of the second model are all supported in this study.

Table 7.

Hypotheses Testing.

Hypothesis	Path	Relationship	Path coefficient	t value	Result
H1	PV → CS	Positive	0.437	14.344^***	Support
H2	CSBI → CS	Positive	0.850	26.102^***	Support
H3	CS → RI	Positive	0.975	50.854^***	Support

Note: ***indicates that the statistical significance level is reached when α = 0.001

Mediation Effect Test Results

Analysis results for mediating effect are shown in Table 8. The indirect effect of perceived value on re-purchase intention through customer satisfaction is 0.426, indicating that the fully mediating effect of customer satisfaction between perceived value and re-purchase intention exists, and is 426, Z ≥ 3.29). The indirect effect of chain store brand image on re-purchase intention through customer satisfaction is 0.828, indicating that the fully mediating effect of customer satisfaction between chain store brand image and re-purchase intention exists (effect = 0.828, Z ≥ 3.29), so both H4 and H5, for one of the models in this study, are supported.

Table 8.

Mediation Effect Test Results.

Hypothesis	Path	In-direct effect	Z value	Results
H4	PV → CS → RI	0.426	13.60^***	Support
H5	SCBI → CS → RI	0.828	23.40^***	Support

Note.∣z value ∣ ≥ 1.96, the significant level of 0.05 is marked with *; ∣z value ∣ ≥ 2.58, reaching the 0.01 significant level, marked with **; ∣z value ∣ ≥ 3.29, reaching the 0.001 significant level, marked with ***.

Moderated Mediation Test Results

Online Involvement Has a Moderating Effect Between Chain Store Brand Image and Customer Satisfaction to Re-Purchase Intention

PROCESS 4.0 is used to evaluate the influence of online involvement on the mediating effect of the independent variables of chain store brand image through customer satisfaction to re-purchase intention (Hayes, 2013). It was found that the influence of plus or minus one standard deviation of online involvement on the mediating effect exists. When the online involvement was high, the indirect effect of customer satisfaction on re-purchase intention through chain store image was 0.061 (effect = 0.061, 95% CI [0.0247, 0.0988]), but with low online involvement, the indirect effect of customer satisfaction on re-purchase intention through chain store brand image increased to 0.100 (effect = 0.100, 95% CI [0.0570,0.1471]), indicating that when customers refer to online involvement to a high degree, the influence of customer satisfaction on re-purchase intention through chain store brand image will be weaker. This study further tests the influence index of online involvement on the two mediation models, finding that the 95% confidence interval significantly does not contain 0 (index = −0.0092, 95% CI [−0.0158,−0.0035]; index = −0.0046, 95% CI [−0.0092,−0.0006]). These results show that H6 of this study is supported (Table 9 and Figure 3).

Table 9.

Moderated Mediation Effect Test (Independent Variable = Chain Store Brand Image).

CHBI	In-direct effect value	Standard error	LLCI	ULCI
-1SD	0.969	0.060	0.8567	1.0896
MEAN	0.982	0.059	0.8717	1.1020
+1SD	0.994	0.061	0.8789	1.1185
	Index	Standard error	LLCI	ULCI
OI	0.0055	0.0052	−0.0046	0.0159

Figure 3.

The moderated mediation of online involvement moderating effect on high and low level—chain store brand image.

EWOM Has a Moderating Effect Between Chain Store Brand Image and Customer Satisfaction to Re-Purchase Intention

The influence of EWOM on the mediating effect of the independent variables of perceived value through customer satisfaction to re-purchase intention was evaluated. It was found that the influence of plus or minus one standard deviation of high EWOM on the mediating effect exists. The indirect effect amount is 0.131 (effect = 0.131, 95% CI [0.0750,0.1843]), but with low EWOM, the indirect effect of chain store brand image on the re-purchase intention through the perceived value increases by 0.208 (effect = 0.208, 95% CI [0.1450,0.2754]), indicating that when customers refer to EWOM to a high degree, the influence of perceived value on customers’ re-purchase intention will be weaker. Conversely, when the level of customers’ reference to EWOM is low, the influence of the perceived value on the customer’s re-purchase intention will also increase. Thus, H7 is supported (Table 10 and Figure 4).

Table 10.

Moderated Mediation Effect Test (Independent Variable = Perceived Value).

PV	In-direct effect value	Standard error	LLCI	ULCI
-1SD	0.613	0.027	0.5794	0.6829
MEAN	0.628	0.026	0.5756	0.6787
+1SD	0.624	0.028	0.5701	0.6780
	Index	Standard error	LLCI	ULCI
EWOM	−.0017	0.0030	−0.0077	0.0043

Figure 4.

The moderated mediation of online involvement moderating effect on high and low level—perceived value.

Discussions

Theoretical Implications

For RQ2, regarding mediating roles, CS plays a fully mediating role between CSBI, as does PV on RI, which means that CSBI and PV must have a positive effect on RI through CS, so that CS plays a critical mediating role on the influence on RI. Thus, it is important to work on building a stronger CSBI and higher PV to further generate CS, thereby obtaining a positive effect on RI. CSBI pertains to retailing brand, which means it is an overall brand image for a chain of stores instead for an individual store. Thus, from a macro perspective, CSBI is an overall picture for operators to develop so they can gain a positive brand image from all customers to generate brand satisfaction. On the other hand, from a micro perspective, PV derives from an individual perception on product and service delivered by operators. In this regard, it is important to meticulously provide all customers with good sales and overall service, so they can perceive a high value, and satisfaction with products and services. Retail operators must pay attention to this process for offline operating, and this is the first empirical study to demonstrate these mediating relationships.

For RQ3, regarding moderating roles, OI and EWOM play moderating roles on the mediating effects. OI and EWOM act on the Internet and have a tacit influence online. These relationships point out that online influence has the power to shape or change an attitude or behavior of customers on CSBI and PV through CS to RI. Thus, these two moderated mediation models verify that online activity has both direct and indirect impacts on offline success of chain stores. This is also the first empirical study to demonstrate these moderated mediating relationships.

For the proposed theoretical model, this study finds the influence of online to offline on chain stores. Both mediating and moderating roles indicate that chain store operators must cultivate both offline and online operations as an overall management strategy (He et al., 2020; Wang et al., 2018; L. Wang et al., 2021). The proposed theoretical model in this study can be the basis for an empirical example for how to balance both offline and online operations. This is also the first empirical study to investigate the influences of online to offline relationships on Telecom chain stores. In addition, for RQ4, this study found that there are positive effects from offline from customers on the re-purchase, it can generate a positive power return to online. In addition, could online to offline moves on a path that have either stronger or weaker influence of re-purchase on chain stores with customers’ electronic word-of-mouth and online involvement.

Practical Implications

The telecommunications industry is entering the new era of 5G. It is estimated that in 2022, there will be more than 170 telecommunications companies’ worldwide, providing equipment, products and other services related to the deployment of 5G networks (Parcu et al., 2022). With the recent prevalence of e-commerce and intense industrial competition, various home appliance merchants have launched new types of sales methods for business development. These substantial changes mean that the telecommunications industry must, more than ever, go beyond offline physical stores to serve the volume and activities of Internet consumers to maintain the sales of physical stores. Many telecommunications stores operate as chain brands, offering telecommunications services and peripheral merchandise. Using established chain brands can deepen consumers’ image of communication services and commodity sales. Under the trend of “big to get bigger,” due to the wide service area covered by chain stores and their economic scale, chain brands should be a long-term development strategy for telecom operators. In this study, the chain of Chunghwa Telecom currently has 239 physical stores, making it the largest Telecom chain in Taiwan. Linking the physical stores with Internet commerce so that consumers can have better interaction with goods and services is important for the development of Chunghwa Telecom’s business model.

In this empirical study on Taiwan consumers, we found that both chain store brand image and perceived value have push power to satisfy customer, and through this satisfaction can increase re-purchase intention. This study investigates how the process of offline-online operations of a Telecom chain can support customer re-purchase in its physical stores. In addition, both online involvement and EWOM from consumers are critical for providing greater influence on chain store brand image and perceived value to re-purchase intention through customer satisfaction. These findings are the first evidence that appropriate online activities of consumers can contribute positive credit on customer re-purchase intention. This kind of positive influence is also an example of online to offline activity by a Telecom chain store operator.

The main revenue stream for the 5G telecommunications industry is no longer just communication activity. In addition to basic products, rates and marketing methods, innovative retail development must be used to allow consumers to interact with merchants through O2O and physical chain stores to improve added value of goods and service quality to attract customers. If operators rely on either online or offline operations, their revenue growth will be limited. Only by strengthening the dual power of offline and online in the O2O model can Telecom businesses provide consumers with better perceived value, satisfy consumers with service and consumption, shape chain store brand image, and maintain high customer loyalty in terms of competitive advantage through increased re-purchase intention.

Conclusion, Limitations, and Future Research

Influence of the online-offline relationship on chain stores illustrates how to integrate strategies and work in both offline and online retail operations. By developing this relationship, customers can have expanded access to evaluating specific brands or products to fit their purchase goal. From both micro and macro perspectives, a chain store operator should both develop an overall picture for the chain store brand image and use this as the basis for high individual perceived value to all customers. This study presents a theoretical model to investigate how the Taiwan Chunghwa Telecom chain stores can meet this challenge of connecting online to offline consumer activity. The research findings may be an example for other chain store operators to extend business models based on our theoretical and practical implications. Finally, online to offline is a broad issue, both academically and in practice, and it is a topic of customers’ purchase and re-purchase, both online and offline. It might be that it is a cycle of two-way from offline to online and from online to offline on business models. Thus, future research topics in this area can provide more valuable evidence and cases for retailing and e-commerce.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This research was funded by the National Science and Technology Council,Taiwan,Republic of China (MOST 111-2410-H-032-017-MY2).

ORCID iD

Shu-Hsien Liao

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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