1 Introduction

Airline companies are exposed to various challenges: (1) low-cost carriers (2) economic crises and (3) government regulations. Although the airline industry is a growing and competitive market, airline companies should not allow their customer to switch to any other airline industry is a growing and competitive market, airline companies should not allow their customer to switch to any other airline if they want to survive in this highly competitive environment. In this regard, it is important to determine the factors that can affect customer choice and make this choice repetitive (Calisir et al. 2016). While today there is less trust in organizations and advertising methods, Word of mouth (WOM) plays a more prominent role in convincing customers to use a product or service. This can provide a competitive advantage for the organizations in terms of those products or services that need a higher degree of qualitative assurance (Sweeney et al. 2007). Researchers and practitioners both acknowledge that service innovation is a crucial factor especially in the financial performance of service organizations (e.g., Ordanini and Parasuraman 2011). Service innovation is the employability of a superior version of service concept. It can be a new channel for interacting with consumers and is beneficial to service providers as well as customers (Nanda et al. 2013). Furthermore, service quality allows companies to stay competitive (Khajeheian 2016).

According to Shao-Chang (2013), satisfaction is associated with loyalty and WOM in private higher education in Taiwan. A study by Nanda et al. (2013) on Indian Retail Sector reveals that customer loyalty is influenced by innovation factors. The findings of a study conducted by Alwi and Kitchen (2014) show that corporate brand image has a direct effect on loyalty in business schools. The effect of service quality (pre-flight, in-flight and post-flight) on customer satisfaction and loyalty was investigated in Ugandan and Turkish airline industries (Namukasa 2013; Calisir et al. 2016). Research also shows that an airline company’s image is a strong indicator of passenger loyalty (Calisir et al. 2016). The present study investigates the effect of variables like service quality, customer satisfaction, and brand image on loyalty and word of mouth among passengers of a travel agency. Given that there are a few researches concerning the effect of innovation on customer variables, this study makes a contribution to the existing literature by investigating the effect of innovation on the variables included in the research model.

2 Theoretical background

This section addresses the variables included in the research model including service quality, customer satisfaction, brand image, customer loyalty, innovation, and WOM.

2.1 Service quality

Service quality is described as “an overall judgment on the level of a service provider’s performance” (Nyadzayo and Khajehzadeh 2016, p. 264). It highlights the ability of a firm to determine correctly customer expectations. Customers are unlikely to return or recommend a service firm that falls short of their expectations of service quality (Nyadzayo and Khajehzadeh 2016). Service quality model (SERVQUAL) has taken the center stage in hospitality and tourism marketing research. It has been productively utilized to measure service quality in many proprietary studies. It has also served as the basis for measurement approaches employed in published studies that have examined service quality in a variety of contexts e.g., real estate brokers, physicians in private practice (Parasuraman et al. 1994). Service quality dimensions include: “(1) Assurance: knowledge and courtesy of staff and their ability to convey trust and confidence; (2) Empathy: caring, individualized attention the organization provides to its customers (3) Responsiveness: willingness to help customers and provide prompt services; (4) Reliability: ability to perform the promised service dependably and accurately; and (5) Tangibles: facilities, equipment, and staff’s appearance” (Shao-Chang 2013, p. 377). Researchers have found that high service quality and high value correlate with relatively high customer satisfaction (e.g., Cronin et al. 2000). Several service scape studies have found that specific atmospheric attributes have a direct impact on customers’ emotions and satisfaction (e.g., Mehrabian and Russell 1974; Bitner 1992; Turley and Milliman 2009; Lin 2010; Manhas and Tukamushaba 2015). Also if firms utilize these quality dimensions, they will attain the benefits of customer loyalty (Nyadzayo and Khajehzadeh 2016). “Polk’s analysis of the automotive industry suggests service quality as a crucial driver of customer loyalty” (Nyadzayo and Khajehzadeh 2016). Furthermore, According to Rhoades and Waguespack (2008), in service industries such as the aviation industry, the quality of services refers not only to transporting passengers to their destinations, but also services must be rendered along with meeting their needs and wants in a manner consistent with the set standards. If the quality of services offered by an airline company is poor and passengers receive rude services from the same airline company continuously, they will be dissatisfied with the services eventually (Calisir et al. 2016). Therefore, we propose that the following hypotheses:

  • H1: Service quality influences customer satisfaction.

  • H2: Service quality influences brand image.

  • H3: Service quality influences customer loyalty.

2.2 Customer satisfaction

Customer satisfaction refers to “good or bad feelings or attitudes by customers after consuming products or receiving service” (Shao-Chang 2013, p. 377). It is, in effect, an entire evaluation from customers for the whole service process or product attributes. A smart company always measures customer satisfaction in order to retain long-standing customers. Customer satisfaction can ensure a good long lasting relationship between the customer and the company as it creates customer loyalty to the company and then makes the company more competitive in the market (Shao-Chang 2013). In addition, researchers generally agree that service quality precedes customer satisfaction, and satisfied customers tend to stay loyal to the service provider. Nevertheless, some hold different views and indicate that customer loyalty, depending on the study context, is not necessarily preceded by customer satisfaction (Shi et al. 2014). On the basis of the discussion, we hypothesize that:

H4: Customer satisfaction influences customer loyalty.

2.3 Brand image

By definition, brand image is “how a brand is perceived by consumers and it relates to the set of brand associations in consumers’ memories and such associations are influenced by the benefits/consequences of using a brand, product attributes and brand personality” (Nyadzayo and Khajehzadeh 2016, p. 265). Brand image plays a significant role in helping customers to decide whether or not to buy a brand and thereby influencing their repurchase behavior. It can also serve as a defensive marketing tool for retaining customers and hence driving loyalty, particularly in the context of services where the service brand/firm are deemed synonymous. Corporate/brand image can also affect customer loyalty (Nyadzayo and Khajehzadeh 2016). Andreassen and Lindestad (1998) examined the role of corporate image in the formation of customer loyalty in the service sector and found both an indirect and direct influence of brand image on loyalty. Mikulić and Prebežac (2011) suggest that the image of an airline company is a strong indicator of passenger loyalty. If customers have favorable feelings and thoughts for an airline company, they will tend to reuse that company for future flights (Calisir et al. 2016). In the airline industry, an image of the airline company is commonly formed by customer reviews as well as print and television advertising (Calisir et al. 2016). Based on the discussion above, we propose the following hypotheses:

H5: Brand image influences customer loyalty.

2.4 Customer loyalty

Development, maintenance, and enhancement of customer loyalty remain a central focus of the majority of firms’ marketing activities. Loyalty has been considered as the degree to which a customer shows repetitive purchasing behavior from a service provider, exhibiting a positive attitudinal disposition toward the provider, and thereby considering using the services of the provider when a need arises (Nyadzayo and Khajehzadeh 2016). Then we assume that:

H6: Customer Loyalty influences WOM.

2.5 Innovation

There are several definitions for the concept of service innovation and they all relate to performance improvement and capacity strengthening of the firm to compete with other firms. According to the third edition of the Oslo Manual (OECD/Eurostat, 2005; as cited in OECD 2009, p. 11), innovation is “the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations”. Concerning innovation and its impact on customer services, it can be suggested that when companies look at their operations from a customer perspective, it will lead to innovation in their services and thus value creation for customers (Kandampully 2002). While many studies have explored the relationship between service quality, customer satisfaction, and customer loyalty, limited research effort has been made into understanding the relationship between service quality, innovation, and customer loyalty (Nanda et al. 2013). For instance, Khajeheian (2016) revealed that innovation leads to the creation of new venture. According to Nazemi and Saadatyar (2013), the impact of reputation and innovation variables on customer loyalty through the mediating role of perceived value and satisfaction is significant. The findings of Nanda et al. (2013) study in Indian retail study demonstrated that innovations have a direct effect on customer loyalty. Based on the above discussion, we assume that:

H7: Innovation influences customer loyalty.

2.6 Wom

Word-of-Mouth (WOM) is characterized as “oral, person-to-person communication between a receiver and a communicator whom the receiver perceives as noncommercial, regarding a brand, product or service” (Shao-Chang 2013, p. 377). The concept of WOM is constructed differently, such as the informal transfer of positive or negative purchases and behaviors associated with consumption among consumers (De Bruyn and Lilien, 2008). Sheth (1971) concluded that WOM was more important than advertising in raising awareness of an innovation and in securing the decision to try the product (Buttle 1998). According to Suryani and Hendryadi (2015), customers who are satisfied with the quality of service will create loyalty to generate WOM behavior and loyal consumers will act as an agent of word-of-mouth marketing. Loyalty is positively and significantly related to WOM. Accordingly, we assume that:

  • H8: Customer satisfaction has a mediating role between service quality and customer loyalty.

  • H9: Brand image has a mediating role between service quality and customer loyalty.

  • H10: Customer loyalty has a mediating role between service quality and WOM.

  • H11: Customer loyalty has a mediation role between innovation and WOM.

2.7 Conceptual model

Based on theoretical framework and purpose of this study, the conceptual model of the study is illustrated in Fig. 1:

Fig. 1
figure 1

(Lai et al. 2009; Shao-Chang 2013; Nanda et al. 2013; Alwi and Kitchen 2014)

Conceptual model : developed based on

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3 Methodology

3.1 Data collection and measuring instrument

A survey methodology was employed to gather data in this study. The research population included customers of one of the agencies of ticket representative. The mean reason for selecting this agency is that there are few active agencies acting as a ticket representative, and that this agency of ticket representative was identified as one of the best options for offering continuous flight services that correctly measure the concept of loyalty. Moreover, the majority of airline ticket sales are carried out by this agency (accounting for almost 60% of total ticket sales). Thus, we did not focus on other agencies of ticket representative. In this study, people who were considered in the study as loyal customers were those who had used the services offered by the agency for at least three times. The data collection was done according to the researchers’ restrictions over a period of 3 months.

The questionnaire used in this study is made of two parts: The first part contained demographic questions including respondents’ age, gender, education, and occupation. The respondents’ demographic information is summarized in Table 1. The second part contained the previously tested scales used for measuring all constructs under study. The research variables were measured on a five point Liker-type scale (1 = strongly disagree to 5 = strongly agree).

Table 1 Demographic information
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Most of passengers in the airport received services from this agency of ticket representative and their number did not exceed about 400–500 passengers per month. In a 3-month-period, about 300 passengers were estimated as the maximum number of respondents that could be optimally included into the research sample through convenience random sampling. Finally, 300 questionnaires were distributed among the respondents. Among the returned questionnaires, 180 questionnaires met the requirement of using the agency services for at least three times and were accepted for data analysis (It should be noted that based on the request of the companies, the respondents’ personal data and those of the agency under analysis were kept confidential).

Perception of service quality was measured using a five-dimensional measurement scale proposed by Parasuraman et al. (1994) with 22 items. To assess customer satisfaction, we adopted the measurement items from Nyadzayo and Khajehzadeh (2016), with four items. Innovation as another research variable was measured using seven items adopted from Nanda et al. (2013). To measure WOM, we applied a five-item scale developed by Krystallis and Chrysochou (2014). In addition, to measure brand image construct, we adopted a seven-item scale used by Salinas and Pérez (2009). Customer loyalty was assessed using a four-item scale developed by Nyadzayo and Khajehzadeh (2016). The items for the constructs under study are shown in “Sect. 8”. To ensure the internal reliability of our measurement, the questionnaire items were assessed using Cronbach’s alpha for each construct. The value of the Cronbach’s alpha should be ≥0.7 (Nunnally 1978). The results shown in Table 3 indicate that all variables have the acceptable internal reliability. To calculate the validity of the questionnaire, the opinions of 10 professors and administrators were solicited. CVR index for the number is obtained as 80% and the content validity of the research instrument is also acceptable. In addition, CVI index for each of the questions were examined. The results demonstrate the fitness of their research instruments.

3.2 Data analysis

3.2.1 KMO

Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test are convenient options for measuring sampling adequacy. KMO value is (KMO >0.7). Moreover, Bartlett’s should be significant value (i.e., a significant value of less than 0.05). Test result is shown in Table 2.

Table 2 KMO and Bartlett’s test
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In the study, we adopted partial least square (PLS) method for data analyses. PLS is a structural equation modeling technique which uses a component-based approach to evaluate the relationship within, and variance explained by a structural equation model (SEM). According to Anderson and Gerbing (1988), the data analysis process of SEM is divided into two steps: (1) measurement model analysis, which involves following the initial analysis with a confirmatory factor analysis (CFA) to measure the reliability and validity of the latent variables, and (2) structural model analysis, in which hypotheses are tested by examining path coefficients and their significance.

3.2.2 Measurement model analysis

Two steps were taken to test the measurement model. In the first step, the internal reliability of our measurement items was assessed using the Cronbach’s alpha coefficient for each construct in the research model. The value Cronbach’s alpha should be >0.7 (Nunnally 1978). As it is shown in Table 3, all the variables are reliable. In the second step, a confirmatory factor analysis was run to check the convergent validity and discriminant validity of measurement items. In order to validate the measurement model; (1) Internal reliability was examined using CRFootnote 1 (0.77 < CR < 0.96) by Fornell and Larcker (1981); and (2) the convergent validity was assessed using two criteria (Fornell and Larcker 1981; Hair et al. 2010): (a) all factor loadings should be significant and greater than 0.5 (Wixom and Watson 2001); (b) (AVE > 0.5). As shown in Table 4, in order to confirm discriminant validity, the square root of the AVEs of a construct should be greater than the correlations between the construct and other constructs in the model (Fornell and Larcker 1981). Overall, our analysis showed that the study scales possessed good convergent and discriminant validity.

Table 3 Summary of reliability and validity for measures
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Table 4 Correlation among constructs and the square root of AVEs
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3.2.3 Structural model analysis

The test of the structural model consisted of path coefficients (β: strength of the relationships between constructs) (Fig. 2) and the coefficients of determination (the significance of all paths) (Fig. 3).

Fig. 2
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Structural model in path coefficients estimation

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Fig. 3
figure 3

Structural model in T-value estimation

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R-square values (R2): R2 was used as an indicator of the overall predictive strength of the model. The results of our analysis indicate good values for latent variables as shown in Table 3.

Scale of R2: 0.19 (Weak), 0.33 (Normal), and 0.67 (Acceptable).

3.2.4 GOF

It is a global criterion of goodness-of-fit (GoF) as proposed by Tenenhouse et al (2005) and is calculated as follows:

$$GOF = \sqrt {\overline{communality} \times \bar{R}^{2} } \to \sqrt {0.567 \times 0.653} = 0.61$$

4 Testing the hypotheses

As shown in Figs. 2 and 3, all relationships are positive and significant. Accordingly, all research hypotheses (H1–H11) were confirmed. For instance, Hypothesis 1 which states that there is a positive significant relationship between service quality and customer satisfaction (β = 0.799 and t value = 23.61) and (p = 99%) was confirmed. Hypothesis 2 which shows the effect of service quality on brand image (β = 0.865 and t value = 35.06) was also confirmed (p = 99%). The effect of service quality on customer loyalty in H3 was confirmed by (β = 0.269 and t value = 2.11) at confidence level of 0.95 (p = 95%). In addition, H4 (β = 0182 and t value = 3.37) and (p = %95), H5 (β = 0.222 and t value = 2.3) and (p = 95%). H6 (β = 0.695 and t value = 15.88) and (p = 99%), H7 (β = 0.251 and t value = 2.94) and (p = 99%) were also confirmed.

Mediator analysis: According to hypothesis 8 (H8) which explains the role of mediator customer satisfaction on relationship of service quality and customer loyalty and given the positive and meaningful assumptions of H1, H3 and H4, it can be concluded that this construct plays a partial mediator role.

Moreover, to identify the mediating role of brand image (H9), as stated in H2, H3 and H5, brand image plays a partial mediator role in the relationship between service quality and customer loyalty.

Generally, according to the results presented in Table 5, it can be concluded that there is a partial relationship between loyalty and service quality (0.337) between brand image and customer satisfaction as mediator variables.

Table 5 Direct and indirect effects
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As it can be seen in Table 5, loyalty as a mediator variable shows an indirect relationship (0.421) between service quality and WOM (H10). By the same way, loyalty as a mediator variable demonstrates an indirect relationship (0.174) between innovation and WOM (H11).

5 Results

The model used in this study explains 64% of variance for customer satisfaction, 75% of variance for brand image, 74% of variance for loyalty, 48% of variance for WOM, indicating the use of good constructs for modeling latent variables.

The results of this study are in line with the findings of previous studies concerning the impact of satisfaction on loyalty Shao-Chang's (2013) study in university campus; Lai et al.’s (2009) study in Chinese mobile communication company, Alwi and Kitchen’s (2014) study in the context of business schools; Shao-Chang’s (2013) study on customer loyalty on WOM; Suryani and Hendryadi’s (2015) study in the Islamic banking sector; Nanda et al. (2013) on service innovation on customer loyalty in Indian retailers).

It should be considered that in most cases satisfied customers will be loyal to the company and such customers through positive word of mouth will contribute to the formation of a favorable image of the company. This brings about significant benefits to airlines.

Nowadays, airlines offer new services and try to gain more of market share and attract more and more passengers to use their services by using a variety of promotional tools. But some of these tools fail to encourage consumers to use their services, especially in the final stages of their decision-making. In this context, word of mouth is a way to achieve a competitive advantage.

Basically passengers require information about an airline company and its services to choose that airline. They collect such information through communication tools such as television channels, satellite, and the Internet. Although these sources provide valuable information for passengers, they prefer to get a substantial part of the information they need from informal sources such as relatives, friends, and other people. Given the reduced reliance on commercial advertising, such people are more trusted by passengers as good sources of information as they are not beneficiaries to airline companies. Addressing the most important factors in creating customer loyalty and their impact on word of mouth, this study considers innovation as an important factor in today’s world of business and as a basic tool to create competitive advantage and augment shopping. As such, the findings of this study can help managers understand the key role of service innovations and adopt better innovation-oriented strategies into their actions.

6 Conclusion

While many studies have explored the relationship between service quality, customer satisfaction, and customer loyalty, by including innovation as a variable, this study has had a greater contribution to the existing literature.

According to research results and the literature, it can be concluded that due to the speed and growth of technology and communication as well as an increase in customer knowledge today, factors like service quality alone cannot guarantee the survival and growth of the firm's executive operations. In this study, factors like brand image and customer satisfaction were considered. It is suggested that in addition to improving the quality as the primary and necessary condition for the growth and activity of companies, it is necessary to consider factors that improve customer satisfaction and enhance the corporate image to create customer loyalty and which in turn leads to word of mouth for the company. Furthermore, in today’s competitive world, factors such as innovation and its relationship with loyalty as established in previous researches and shown in the present study should receive high priority.

Since the research population was not large (due to some limitations), this study can be replicated with other cases and even larger samples to achieve more conclusive results so that it will be possible to generalize the survey results to other agencies as well as in agencies in other countries.

This study, like many studies was conducted with some limitations. A sole model cannot explain all factors influencing a variable in the organization. So to obtain better results and identify other factors affecting WOM as an important factor, developing a model using the concepts with a significant impact on the competitiveness of businesses in the world today such as personal and organizational factors, cultural factors which affect relations should definitely be taken into account by future research. In addition, other models can be addressed by similar studies: 1) Exploring the model with other forms of advertising can also reveal the importance of the model and variables explained in the model can become more obvious. 2) Since the implementation of a plan is limited in terms of the spatial dimension, testing the model at a greater scale with other agencies and in big cities is necessary.

In our survey, the effect of demographic factors such as gender, education etc. was not analyzed, so these factors can be taken into account in future researches.