Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2020 Vol: 24 Issue: 2

Enhancing Popularity of a Tourist Destination: Why Relationship Matters?

Bidyut Jyoti Gogoi, Indian Institute of Management Shillong

Abstract

Tourism is a strategic sector which has a large contribution to the Indian GDP. There are lots of opportunities of revenue generation from this sector. The change in information technology has facilitated the proliferation of information which has benefited the tourism industry at large. It has facilitated the ease to know about any tourist location, compare and make travel plans. Any shortcoming of a location is easily identified and can be spread out within minutes through the social media. The popularity of a tourist location is dependent on the level of satisfaction a customer gets from visiting the location. The level of satisfaction comes from the exposure to various touchpoints associated with the location. The service quality associated with the touchpoints plays a great role in developing competitive edge of the location and in defining the factors of differentiation. Since popularity of tourism depends on the ecosystem on which it operates, it is important to understand the dimensions which leads to popularity of a destination. In this paper the researcher tries to understand the influence of service innovation, commitment, trust, behavioral intention and affective intention on increasing the popularity of a tourist destination.

Keywords

Service Innovativeness, Commitment, Trust, Behavioral Intention, Affective Intention, Popularity of A Tourist Destination.

Introduction

Tourism is considered as a lucrative sector which contributes around 9.2% to the Indian GDP (WTTC). Tourism has attracted researchers to contribute research in various fields (Cibinskiene, 2012; McKercher, 2016; Panfiluk, 2015; Santos et al., 2016; Jurdana & Frleta, 2017; Montenegro, 2017; Tanford & Jung, 2017; Tiew et al., 2015; Yu¨ru¨k et al., 2017). There is stiff competition among the tourist destinations to attract tourists and hence is a strategic sector of the economy (Abreu et al., 2018; Island & Higgins, 2018; Kelly & Fairley, 2018; Teixeira & Ferreira, 2018). Highlighting a locations distinct features and service distinctions help create a competitive edge and needs more relevant scientific research (Cibinskiene, 2012). The success of a destination is dependent on the overall experience a tourist experiences during the visit to the location. The thrill, excitement, the supporting infrastructure, supporting services all add to the overall experience encountered. Maintaining, innovating and upgrading the services as per the customer requirement is crucial for sustaining the competition (Anderson & Zeithamal, 1984; Babakus & Boller, 1992; Garvin, 1983).

Service innovation and quality improvement helps in increasing the customer satisfaction (Kumar et al., 2008) which helps in building trust and influences the purchase decision (Oliver, 1980; Gremler et al., 2001). Tourists are attracted to a location due to the popularity of a location. The popularity of a location builds up with the experiences the tourists gets visiting the location. The ecosystem surrounding the location viz., quality of services of the enterprises, support systems, infrastructure, efficient supply chain and the natural environment available all adds to making a location famous.

Studies on customer motivation, satisfaction and competitiveness of a tourist destination has been limited to the tourist’s perception about the destination (Enright & Newton, 2004; Chen et al., 2016; Cracolici et al., 2008). There are a few studies conducted on the tourist satisfaction with regional competition (Chen et al., 2016). Some studies are found on organizational quality and satisfaction (Gong & Tung, 2016; Jurdana & Frleta, 2017; Lee et al., 2017; Montenegro, 2017; Oom et al., 2012; Okayasu et al., 2016; Santos et al., 2016; Yu¨ru¨k et al., 2017). There is still a gap in understanding customer satisfaction, its attributes and competition related to tourism (Pansiri, 2014). There is a need to study the relationship between of trust and affective and behavioral intention of consumers. Moreover, understanding the affective intention and behavioral intention of customer will tell us about the customer intention in choosing a destination.

The researcher in this paper tries to find out the factors which has an influence on increasing the popularity of a tourist destination. Service innovation and commitment increases the trust of the customer and trust in fact influences the affective and behavioral intention of the customer. Positive behavioral intention and affective intention will increase the popularity of a tourist location.

Literature Review

Tourism business usually depends on the flow of tourists to a location. People usually wish to travel to locations which are very popular. A tourist destination is an aggregation of products and services and public goods made available to tourists providing an overall integrated experience of the location (Buhalis, 2000; Harris & Leiper, 1995). Tourists travelling to destination experiences the services available in the location and holds positive or negative opinions. A destination is a place interpreted by the consumer’s purpose of the journey and past experience (Davison & Maitland, 1997; Hall, 2000). The consumer interprets the experience based on the travel purpose, schedule and past exposure (Fuchs & Weiermair, 2003). A good experience helps increase the popularity of the destination. A tourist location comprises of several stakeholders like the customers, firms, entities, public sector, the local population and NGOs (Buhalis, 2000). There is lot of conflict amongst the stakeholders in a tourist location (Sheehan et al., 2007) and a tourist destination are considered the most difficult entities to manage and operate (Sautter & Leisen, 1999). The choice of a tourist destination and decision to visit the destination again is dependent on the tourist satisfaction (Yoon & Uysal, 2005). Enhancing the level of customer satisfaction should be one of the priorities of the stakeholders (Morgan & Pritchard, 1998; Pearce, 1997; Seaton, 1997). Proper strategies increasing that locations attractiveness and competitive positioning (Álvarez et al., 2009) will help increase the traffic to the location.

Understanding the factors influencing satisfaction and increasing popularity is of utmost importance (Fuchs & Weiermair, 2003). There is a necessity for benchmarking customer satisfaction (Gomezelj & Mihalic, 2008) at a destination. Popularity of a location is dependent on several factors several factors such as service innovativeness, commitment, trust, behavioral intention and affective intention.

Service Innovativeness

Introducing something new to customers is service innovativeness (Barcet, 2010). The new innovation in services produce high service and value perceptions in customers (Hollebeek et al., 2018; Ordanini & Parasuraman, 2011). Service innovativeness helps firms in introducing innovation in the services provided thus developing competitive edge and superior customer services (Chen et al., 2018). Service innovations in terms of internet-based services from Trip Advisor, Airbnb, MakeMy Trip are enhancing the experiences provided to the customers (Snyder et al., 2016). Innovation drives economic growth both at the micro and macro levels (Gomezelj, 2016). Innovativeness drives growth through increased operational efficiency and value enhancement thereby increasing customer experience and loyalty leading to acquisition of new customers (Walls et al., 2011).

Studies were conducted on service innovativeness in the destination level (Stamboulis & Skayannis, 2003; Gomezelj, 2016), hotel and transportation area (Tsai, 2017), effects on consumer behaviour, satisfaction and loyalty (Hollebeek & Rather, 2019). There is a need to understand the role of customer co-creation, satisfaction, behavioral and affective intention and loyalty in tourism (Buonincontri et al., 2017, Divisekera & Nguyen, 2018; Kim et al., 2018).

There is a difference between service innovation and service innovativeness (Kim et al., 2018). Service innovation is new offering to the firm’s customers (Ordanini & Parasuraman, 2011). Service innovation denotes innovative organizations (Deshpandé et al., 1993) which has a favorable impact on its performance (Hwang et al., 2019). Service innovativeness is the firm’s capability to develop new services or ideas for its customers (Kim et al., 2018; Tajeddini et al., 2018). Service innovativeness is a relative advantage through newness in services (Leckie et al., 2018). Changes in the organization competences levels bring innovation (Menor & Roth, 2007).

Service innovativeness can be viewed form the angles of assimilation approach, demarcation approach and synthesis approaches (Coombs & Miles, 2000). Assimilation approach is a product innovation approach which focus on introduction of new technology (Hollebeek & Rather, 2019). Innovativeness in assimilation approach is seen from the perspective of goods dominant logic (Gallouj & Savona, 2009). There are inherent similarities in goods based and service based contexts (Nijssen et al., 2006). The demarcation approach holds good in services due to its distinctive characteristics of intangibility, variability, inseparability and perishability (Sundbo et al., 2007). This necessitates innovativeness in service integration (Hollebeek & Rather, 2019). The synthesis approach states that innovation posits novel aspects (Gallouj & Savona, 2009; Hsieh et al., 2013). Innovation proliferates by interlinking of the important aspects throwing light to gauge new opportunities (Gallouj & Weinstein, 1997). Innovation is the novel configuration of resources resulting in new service offerings benefiting stakeholders (Chen et al., 2018).

Service innovativeness brings in new services and builds trust in customers. Existing research also shows the impact of service innovativeness on customer involvement, trust, knowledge sharing, collaboration and motivation (Sarmah et al., 2017; Roberts et al., 2013). Base on the discussion the following hypothesis is proposed.

H1: Service Innovativeness has a positive influence on Trust

Commitment

Consumer usually compare their preconceived expectations with the experiences post-delivery. They are delighted if they get more than their expectations, mere satisfaction if the expectations are fulfilled and dissatisfaction if the expectations are not met (Hollebeek & Rather, 2019). There are several researches conducted in the area of commitment in relationship development (Morgan & Hunt, 1994; Gruen et al., 2000) and influence on consumer behavior (Sharma & Patterson, 2000; Verhoef et al., 2002; Bansal et al., 2004). Commitment is a firm inclination towards behavior of resisting any change (Fullerton, 2005; Gilliland & Bello, 2002). Commitment is the affective outcome of understanding the goals, objectives and values of parties in the exchange of relationship (Allen & Meyer, 1990). A firm’s commitment with the other firm indicates the expected relationship in future (Morgan & Hunt, 1994). The firm develops confidence in the stability of future relationship and stays committed to the relationship (Kumar et al., 1995). Commitment is a future status to conform to development and maintenance of a stable relationship (Wilson, 1995). Commitment strengthens the relationship (Liljander & Roos, 2002; Selnes, 1998). Trust and commitment are the foundation to relationship marketing (Bove & Robertson, 2005; Liljander & Roos, 2002; Spekman et al., 2000). Commitment is also an indication for long term stability of the relationship (Anderson & Weitz, 1992).

Commitment increases with the level of trust among partners (Bansal et al., 2004; Crotts & Turner, 1999; Garbarino & Johnson, 1999; Kwon & Suh, 2005; Morgan & Hunt, 1994; Sharma & Patterson, 2000). Consumers commitment is limited to the services they receive (Kandampully, 1997). Organizational commitment refers to the level of interest in its consumers and the effort taken in maintaining brand loyalty in terms of catering to the needs, communication, and services it provides (Álvarez et al., 2009). The consumer will trust the service provider more if she perceives that the firm is more committed towards the service delivery (Bansal et al., 2004; Sharma & Patterson, 2000). Based on the discussion the following hypothesis is proposed.

H2: Commitment has a positive influence on trust

Trust

Customer acquisition costs more (Blattberg & Deighton, 1991; Filiatrault & Lapierre, 1997). Trust helps in increasing profits between 20 to 85 percent (Álvarez et al., 2009) as it helps in developing relationship between the firm and the customer. Trust is the foundation of human interaction or exchange activity (Moorman et al., 1992). Trust is the integrity and reliability between partners (Morgan & Hunt, 1994). This is the reason firms should concentrate on the whole lifecycle rather than onetime transaction (Reichheld, 1996). Increasing successful transactions help the development of relationship over a period of time (Ravald & Grönroos, 1996).

Trust is a combination of cognition and behavior in terms of intentions, motivations, honesty and benevolence (Álvarez et al., 2009). Trust is measured empirically in terms of credibility and benevolence (Bove & Robertson, 2005; Morgan & Hunt, 1994; White, 2005).

Trust builds confidence in customers which leads to advocacy and customer recommendation or positive word-of-mouth and leads to repurchase (Reinartz et al., 2005; Sivadas & Baker-Prewitt, 2000). Positive word-of-mouth is a strong influencer in the buying process (Money, 2000) which is based on the past experience of the customer. Trust leads to building of customer loyalty and leads to a favorable attitude and repeat purchase towards a brand (Dick & Basu’s, 1994; East et al., 2005).

Trust is an important factor in developing positive relationship in tourism (Bejou & Palmer, 1998; Crotts et al., 1999; Kang et al., 2005; Ross, 2004). Trust helps in developing positive affective intention and also positive behavioral intention. Based on the discussion the following hypotheses are proposed.

H3: Trust has a positive influence on Behavioral Intention

H4: Trust has a positive influence on Affective Intention

Behavioral Intent

Behavioral measures are generally used for measurement of customer loyalty (Dekimpe et al., 1997; Kau & Loh, 2006). Consumers are more prone to switching brands if loyalty doesn’t convert to a favorable attitude (Bloemer & Kasper, 1995; Maicas et al., 2006).

Trust helps build positive behavioral intention. Several studies present the positive relationship between customer satisfaction and behavioral intentions (Oliver, 1980, 1997; Reichheld & Sasser, 1990). There are positive relationships between perceived quality, satisfaction and behavioral intentions (Getty & Thompson, 1994). Customer satisfaction has a positive influence on customers’ behavioral intentions (Park et al., 2019) of revisiting or recommending the services (Heung & Gu, 2012). Behavioral loyalty intent refers to the customers desire to revisit the company and recommend it to others (Grissemann & Stokburger Sauer, 2012). Behavioral loyalty intent is the probability of the consumer to repurchase and inclination to further recommend the offering (Oliver, 1997; Hollebeek & Rather, 2019). Behavioral intentions are indications of a person’s future behavior (Fishbein & Ajzen, 1980). Behavioral intent provides an in-depth insight to the consumer behavior (Hollebeek et al., 2014).

Behavioral intent is essential for positive consumer purchase intent in tourism (Hollebeek and Rather, 2019). Thus positive behavioral intent helps in building loyalty thereby increasing the popularity of a location. Based on the discussion the following hypothesis is proposed.

H5: Behavioral Intention has a positive influence on Popularity

Affective Intention

Emotions are a central to the consumption experience and consumer reactions (Babin et al., 1998) which influence customer satisfaction (Oliver, 1993; Oliver & Westbrook, 1993). Favorable consumption experience leads to customer satisfaction (Babin & Griffin, 1998). Several research studies were conducted to understand the relationship of positive emotions and customer satisfaction (Lyubomirsky et al., 2005). Positive affect is directly related to customer satisfaction (Babin & Darden, 1996). The positive emotions while consuming a product or service help consumers decide on the satisfaction (Rust & Oliver, 1994). Positive emotions or affect influences satisfaction (Park et al., 2019) and increases satisfaction level (Clark & Isen, 1982). Positive affective intention thereby has a tendency to increase popularity of a location. Based on the discussion the following hypothesis is proposed.

H6: Affective Intention has a positive influence on Popularity

Based on the premises developed a conceptual framework is developed as depicted in Figure 1.

Marketing-Studies-CONCEPTUAL-MODEL

Figure 1: The Conceptual Model

A primary survey was carried out to measure the responses of the customers. A questionnaire was developed to collect the responses of the respondents. The variables for measurement used were Service innovation, Commitment, Trust, Behavioral intention, Affective intention and popularity of a tourist destination.

A total of 1200 questionnaire were distributed, out of which only 1050 questionnaires contained fully filled data. There are 20 items in the measurement scale. If we consider 10 samples per variable the total sample required will be 200 (Hair et al., 2008). Also, we see that several researchers mentioned that the minimum sample size for testing a SEM is 100 to 200 (Hoogland & Boomsma, 1998; Boomsma & Hoogland, 2001; Kline, 2005). So, a sample size of 1050 will suffice for testing the proposed model.

The research design used is descriptive in nature. A primary survey was carried out to collect data from customers who liked to travel. The research was carried out using a structured questionnaire. The responses were measured on a 5 point scale. 1 being highly disagree to 5 being highly agree.

The survey was done in Guwahati in the State of Assam and Shillong in the State of Meghalaya in India. The sample size of the survey was 1050. Random sampling method adopted was used. The respondents were customers who loved to travel. They were asked to rate on their views about their favorite tourist destination.

Data Analysis and Interpretation

Data was analyzed using SPSS 22 and LISREL 9.2.

Gender

Out of 1050 respondents surveyed 56.2% were male respondents and 43.8% were female respondents.

Income

27.3% of the respondents have an annual household income of less than INR 5 LPA, 39.6% of the respondents have an annual household income INR 5 LPA to less than 10 LPA, 21.2% of the respondents have an annual household income of INR 10 LPA to less than 15 LPA, 8.4% of the respondents have an annual household income of INR 15 LPA to less than 20 LPA and 3.4% of the respondents have an annual household income of INR 20 LPA and above.

Confirmatory Factor analysis

A confirmatory factor analysis was done on the 20 items under the six identified constructs. The KMO test value is 0.868 whish shows sample is adequate. Bartlett’s test of Sphericity shows high significance which indicates that there is correlation among the variables and data is adequate for the analysis. 86.69% of the variance is explained by the 6 factors. The pattern matrix of the 20 items are shown in Table 1.

Table1: Pattern MatrixA
  Component
1 2 3 4 5 6
SI1         0.754  
SI2   0.995  
SI3   0.904  
COM1   0.779
COM2   0.687
COM3   0.774
TRUST1   0.888  
TRUST2   0.902  
TRUST3   0.815  
TRUST4   0.939  
BI1   0.828  
BI2   0.763  
BI3   0.950  
AI1   0.859  
AI2   0.901  
AI3   0.968  
POP1 0.914  
POP2 0.773  
POP3 0.978  
POP4 0.856          

Test of Validity and Reliability

The factor loadings from Table 1 were used to calculate the Average Variance Explained (AVE) and Composite Reliability (CR) as shown in Table 2. Cronbach’s alpha value is also determined as shown in Table 2. Table 3 shows the discriminant validity of the constructs.

Table 2: Test of Validity and Reliability
Construct Codes Statements Factor
Loading
AVE Cronbach's
alpha
CR
Service Innovation
(Leckie et al., 2018)
SI1  This tourist location services are new 0.754 0.884 0.946 0.806
SI2  This tourist location services are unique 0.995
SI3  This tourist location services are different 0.904
Commitment
(Álvarez et al., 2009)
COM1 This tourist location carries out the necessary changes in the performance of its duties to deal with me suitably 0.779 0.747 0.921 0.747
COM2 I observe that the tourist location considers consumers to be the basic target to be satisfied in the performance of its activities 0.687
COM3 I believe this tourist location makes an effort for me to feel like an important consumer 0.774
Trust
(Doney & Cannon,1997; Morgan & Hunt, 1994; Price & Arnould, 1999; Sharma & Patterson, 2000)
TRUST1 The tourist location is prepared to consider all my requests and suggestions 0.888 0.886 0.942 0.847
TRUST2 The tourist location has detailed knowledge about the tourism products and services available in the market 0.902
TRUST3 This tourist location guides me suitably when I ask for its opinion about the best tourism service 0.815
TRUST4 Overall, I consider the location a good provider of tourism services 0.939
Behavioral Intention
(Grissemann & Stokburger Sauer, 2012)
BI1 I program my trips with this tourist location because it is the best alternative available 0.828 0.847 0.834 0.792
BI2 I consider myself very loyal to this tourist location 0.763
BI3 The next time I travel, I will go to this tourist location 0.950
Affective Intention
(Garbarino & Johnson, 1999; Price & Arnould, 1999)
AI1 Overall, I am happy with the relationship I go from the tourist location 0.859 0.909 0.921 0.814
AI2 Overall, I think the tourist location has done a good job 0.901
AI3 Overall, I have obtained value from the relationship from the tourist location 0.968
Popularity of a tourist destination
(Yoo et al., 2018)
POP1 I would prefer to use the services of a tourist location who meets my expectations 0.914 0.880 0.945 0.846
POP2 I would prefer to use the services of a location which I was satisfied even if the prices rise due to peak demand 0.773
POP3 I would prefer to use additional products and services of my preferred location 0.978
POP4 I would recommend my preferred location to others 0.856
Table 3: Discriminant Validity of the Constructs (Fornell & Larcker Criterion)
  SI COM TRUST BI AI POP
SI 0.940
COM 0.610 0.864
TRUST 0.660 0.562 0.941
BI 0.657 0.402 0.694 0.920
AI 0.600 0.566 0.564 0.479 0.954
POP 0.575 0.812 0.490 0.383 0.565 0.938

Table 2 provides factor loadings, Cronbach’s alpha measures, AVE and Composite Reliability values.

Reliability is measured using Cronbach’s alpha (Cronbach, 1951) and Composite reliability gives the internal consistency (Fornell & Larcker, 1981). AVE gives the measure of content validity (Fornell & Larcker, 1981)

Factor loadings of 0.5 and higher is good. CR above 0.7 is good. AVE of above 0.5 is good. From Table 2, Cronbach’s Alpha value of the overall reliability is 0.955, which shows that the data is highly reliable. Cronbach’s Alpha of all the individual constructs are 0.834 and above, which indicates that the data of the individual parameters are reliable. Composite reliability (CR) of the constructs are 0.747 and above which is shows internal consistency.

AVE scores are 0.747 and above for all the constructs which shows convergent validity. In Table 3, the diagonals are the square root of the AVE. Off-diagonals are the correlations of the latent constructs. The diagonals indicate the highest of any column or row. Also in the cross loading in Factor analysis, the items under each construct fall under the same factors. This complies with the discriminant validity requirements.

The measurement model thus meets the reliability requirements. There is also compliance for convergent and discriminant validity.

Path Analysis

The results of the structural equation model analysis of the proposed conceptual model is depicted in Figure 2.

Marketing-Studies-CONCEPTUAL-MODEL

Figure 2

Results of the structural model analysis from Figure 2 is shown in Table 4.

Table 4: Goodness of Fit Indices for Structural Model
Fit Indices Accepted Value Model Value
Absolute Fit Measures
χ2 (Chi-square) 4.14
Df   8
χ2 (Chi-square)/df 3 0.517
GFI (Goodness of Fit Index) > 0.9 0.944
RMSEA (Root Mean Square Error of Approximation) < 0.10 0.0001
Incremental Fit Measures  
AGFI (Adjusted Goodness of Fit Index) > 0.80 0.984
NFI (Normed Fit Index) > 0.90 0.960
CFI (Comparative Fit Index) > 0.90 1.000
IFI (Incremental Fit Index) > 0.90 1.035
RFI (Relative Fit Index) > 0.90 0.925
Parsimony Fit Measures  
PGFI (Parsimony Goodness of Fit Index) > 0.50 0.379
PNFI (Parsimony Normed Fit Index) > 0.50 0.512

The test of the structural model was performed using SEM in order to examine the hypothesized conceptual framework Figure 1 by performing a simultaneous test. Table 4 depicts that the goodness-of-fit for the model was met: χ2 (Chi-square)/df=0.571, CFI=1.000, GFI = 0.944, AGFI=0.984 and NFI=0.960. The overall values provided evidence of a good model fit. All of the model-fit indices exceed the respective common acceptance levels, following the suggested cut-off value, demonstrating that the model exhibited a good fit with the data collected. Thus, it is possible to proceed to examine the path coefficients.

Properties of the causal paths for the structural model (standardized path coefficients (β), standard error, and hypotheses result) are signified in Table 5.

Results of Path Analysis

Table 5: Summary of Hypotheses Testing Results
Path Estimate (β) S.E. p Results
SI à TRUST -0.058 0.122 0.634 Reject H1
COM à TRUST 1.156 0.186 0.00001 Accept H2
TRUST à BI 1.150 0.213 0.00001 Accept H3
TRUST à AI 1.067 0.253 0.00001 Accept H4
BI à POP -1.501 0.618 0.015 Accept H5
AI à POP 2.426 0.377 0.0001 Accept H6

Discussion and Conclusion

The data analysis shows that service innovation does not have an influence on trust. There is saturation in most of the tourist segments (Avci et al., 2011; Grissemann & Stokburger-Sauer, 2012; FitzPatrick et al., 2013). New and innovated services need to be introduced to sustain the competition. Though several researches show that introduction of new services increases the satisfaction level (Hollebeek & Rather, 2019) of the tourists yet it seems consumers are not confident of new service innovations introduced to increase the efficiency of the services delivery. Service innovativeness influences customer advocacy (Hollebeek & Rather, 2019). It is necessary that the service providers of the locations focus on service innovation and make the visitors aware of such services to increase their trust and confidence.

Commitment has a positive influence on trust. Due to information available the tourists are well aware of facilities available in a location and hence becoming more demanding (Sanchez et al., 2006). Tourists are dependent on the service providers of a destination. The commitment from the service providers builds confidence on the tourists and hence builds up the trust.

Trust has a positive influence on the behavioral intention. Building trust is difficult but if the tourist trusts the service provider it helps in positive purchase intentions and building loyalty.

Trust has a positive influence on affective intention. When trust builds up the customer starts developing positive opinions and positive attitude towards the service providers and the location.

Behavioral intention has a positive influence on popularity of a tourist destination. Research shows that overall satisfaction is a better predictor of behavioral intentions as it is closely related to related specific attitudes which is likely to help in prediction of future behavior (Anderson et al., 1994). Overall satisfaction influences repurchase intentions (Anderson et al., 1994; Bitner, 1990; Oliver, 1997; Parasuraman et al., 1994) and brand loyalty. Positive behavioral intention increases customer loyalty by choosing to visit a location again and recommending it to others thereby increasing the popularity of a location.

Affective intention has a positive influence on popularity of a location. The digital media has increased the importance of brand advocacy (Roy, 2013) through more customer engagement and referrals. Consumer trusts more in brand advocacy through family and friends than advertising (Sasser et al., 2014). Customer advocacy drives customer choice knowledge and involvement (Lawer & Knox, 2006). Advocacy affect brand perceptions (Groeger et al., 2016), customer satisfaction and commitment over a period of interactions (Roy, 2013). Positive affect develops an inclination towards the location by spreading positive vibes and feelings towards the location making the location popular.

The conclusion is that commitment influences trust, trust influences behavioral intention and affective intention, behavioral intention and affective intention influences popularity of a tourist destination. From this we can infer that synchronizing and improving the services helps in building trust in tourists which helps in increasing the popularity of a tourist destination.

Limitations

The survey was limited to the cities of Guwahati and Shillong. The study can be extended to other parts of the county.

Bussiness Implications

Business thrives on the number of customers for the sale of products or services. Increasing popularity will increase the traffic flow to a tourist destination. This will not only help business to flourish but also upgrade the local economy. The stakeholders of a tourist destination need to devise proper strategies to build trust through innovation and synchronized service delivery to develop positive relationship with the visitors. This will go a long way to develop a liking for the location and enhance popularity of the location.

References

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