Importance–performance analysis approach to destination management

Abstract

The role of government in destination development is not a new topic; however, the way the government can impact tourism destination development is still under research. This study addresses such issues, specifically, striving to shed some light on the aspects of importance/performance as it relates to destination management. By understanding such relationships, government officials (at local and state levels) will be able to create specific policies/procedures that would facilitate tourism development. This study utilizes importance/performance methodology to analyze three types of regions (mountain, spa, and wine) in the Czech Republic. The results indicate that variations among the regions exist in terms of areas of importance and performance and point out opportunities for further improvements. Thorough discussions and implications for the industry are made along with suggestions for future research.

Keywords

destination management government importance/performance model IPA model planning

Introduction

As globalization continues to increase, tourism has been subjected to continued expansion, becoming one of the largest and fastest growing economic sectors in the world (UNWTO, 2013). This implies that more than ever, perspective travelers have a wide variety of destinations to choose from, causing most destinations to be easily substitutable (Ayikoru, 2015). Thus, competitiveness is evident in the market place as countries are enticed by the potential benefits that tourism has to offer and the ever growing number of destinations, which are trying to have their fair share of the pie (Crouch, 2011; Das and DiRienzo, 2010; Omerzel, 2011). With this in mind, governments can no longer overlook the importance tourism plays in their destinations.

While destinations vary, it is the public sector that creates an environment (positive or negative) that can add to or weaken the value of tourists’ experiences (Dwyer et al., 2016). Governments at national and local levels have been playing a vital role in tourism development via public policy settings, through formal ministries, and other institutions (Hall and Campos, 2014; Nunkoo, 2015). Some governments have focused on developing national tourism strategies, creating destination marketing and establishing special agencies concentrating on tourism.

It can be said that tourism development is neither a short-term project nor something that the private sector alone can accomplish and involves a consistent long-term government policy that is well planned and efficiently implemented by competent bureaucracy (Lee, 2015). Government officials often do not have the understanding or know-how of what is really important for tourists when they visit a destination but also do not have a measure of performance as a destination. Therefore, the main objective of this study is to provide importance–performance analyses (IPAs) for three types of destinations. In doing so, the researchers strive to provide valuable information not only to government officials, allowing them to understand the difference between what is important to visitors and performance as a destination, but also to enterprises involved in tourism. By understanding such a relationship, local government officials will be able to put policies and procedures in place, allowing for destinations to develop and become competitive, leading to increased tourism. In addition, local businesses will be able to improve their operation strategies, having a positive impact on their customers and operations. It is important to acknowledge that the term “government” refers to multilevel institution, where each level has a specific role in the decision-making process (Zuo et al., 2017).

The organization of the article is as follows. The next section offers a brief overview on the role of government in destination management and destination performance. Then, the discussion of the research methodology and data analysis procedures are provided. Lastly, the study’s findings are discussed, ending with conclusions and suggestions for future studies.

Literature review

Role of government in destination management

Destinations strive to achieve a bigger market share while increasing their competitiveness in the tourism industry (Dwyer et al., 2016). Therefore, offering a new destination these days is not enough to guarantee its success and profitability as the number of destinations tourists can choose from continues to increase. With this in mind, the type of experience tourists seek now and in the future is of utmost importance not only to key tourism stakeholders but also to local and national governments. The ability to recognize and deal with change across a wide range of key trends is needed by government officials to implement successful tourism industry in a destination (Dwyer et al., 2016).

In the tourism industry, it is the private and public sectors at the same time that add value to the tourists’ experiences. The private sector creates the experience tourist will obtain during their visit. From the government perspective, the government plays a pioneering role in a country’s tourism growth as huge investments are needed to create the basic infrastructure and facilities necessary to encourage tourism development (Aissa and Goaied, 2017). Government is the one that determines policies and regulations related not only to environmental, political, social, and economic aspects of tourism development but also to production and delivery of products and services (Zuo et al., 2017). From the private sector perspective, businesses can more confidently take risks until confident and full potential environment is created (Aissa and Goaied, 2017). Thus, the need for long-term as well as short-term policies is obvious to attain continuous and responsible destination management while achieving sustainable development.

To achieve such development, government officials (at local and national levels) must be aware of what the current situation is when it comes to tourism and visitors’ expectations. Specifically, understanding what is important for tourists in a destination and how this destination performs relative to others. In other words, how the destination resources/offerings are being utilized, for example, when it comes to managing visitor experience, training, service coordination, or information provision (Pearce, 2015). Therefore, by utilizing the IPA results, government officials (at local/national level) will be able to develop policies and procedures to attract additional visitors, improve services offered, and manage the destination.

Measurements of destination performance

These days, a destination is not a mere product, it is a system composed of multiple stakeholders with complex relationships (Barrado, 2004). In today’s competitive environment, a tourism destination “is at present often no longer seen as a set of district natural, cultural, artistic and environmental resources, but as an overall appealing product available in a certain area: a complex and integrated portfolio of services offered by a destination that supplies a holiday experience, which meets the needs of the tourist” (Cracolici and Nijkamp, 2008: 336). Thus, the more complex the tourism product/services are, the greater the number of service providers in the destination who are participating in the creation of a tourism value chain, even though customers perceive a destination as a whole (Caber et al., 2012).

In order for a destination to achieve social and economic objectives, it must maximize the benefits received from tourism while providing high-quality goods and services to satisfy domestic and international visitors (Caber et al., 2012). Thus, understanding the customers’ needs and quality expectations is of utmost importance not only for business providers but also for governments as well. Therefore, destination authorities and managers have to identify those destination attributes that are the most vital determining factors in customer satisfaction (Caber et al., 2012). For this reason, the main aim is to identify what is important to the visitors and how a destination actually performs.

In this study, the authors utilized existing literature and developed a series of questions comprising micro- and macroeconomic factors, while also addressing generic business factors. Specifically, areas centering on attractions, amenities, accessibility, destination image, price, and perception of the destination were investigated (Buhalis, 2003; Middleton and Clarke, 2001). According to Dortyol et al. (2014), individual factors can be then grouped into categories, such as employee friendliness, food quality, safety, and security. Furthermore, others attributes were added to this list, such as destination information, the quality of transportation provided, and destination cleanliness (Milošević et al., 2016).

Previous studies also suggest that one approach on how to evaluate destination quality is to investigate visitor’s satisfaction. Some propose that service quality and customer satisfaction as being synonymous or have narrow distinction (Crompton and Love, 1995; Otto and Ritchie, 1995). Quality has also been found as a predictor to satisfaction and having direct effect on satisfaction (Campo-Martinez and Garau-Vadell, 2010; De Rojas and Camarero, 2008; Fornell et al., 1996; Kozak and Rimmington, 1999).

Since creating a survey that would incorporate all aspects of a destination performance can be challenging, the researchers’ selected 19 key attributes creating an inclusive overview of a destination performance based on quality management (Buhalis, 2003; Crouch and Ritchie, 1999; Grönroos, 2007). These attributes are presented in Online Appendix A.

Research context

The case of the Czech Republic is being utilized. Czech Republic is considered a developed country, located in the center of Europe, with advanced economy based on manufacturing and service industries. In the late 1990s, the country underwent a transitional process, changing from communist state to a state with European social model. At that time, the service industry was heavily impacted as many enterprises changed from state ownership to private one.

When it comes to tourism industry, according to the Czech Confederation of Commerce and Tourism (SOCR ČR, 2018), tourism is the seventh best-performing industry in the country, contributing 7.8% to the country’s gross domestic product (GDP) and 9.4% to total employment (Knoema, 2019; WTTC, 2019).

In 2017, the number of international tourist arrivals reached 10 million. Specifically, leisure travel spending generated 83% of travel and tourism GDP when compared to business travel spending and domestic travel spending contributed 40.2% to direct travel and tourism GDP when compared to foreign visitors (WTTC, 2019). In 2017, domestic tourism increased by 9% while international tourism increased by 9.2% (SOCR ČR, 2018). In 2018, the Czech Republic ranked 29th in terms of competitiveness, as reported by Global Competitiveness Index, moving up from 38th place in 2011 (WEF, 2019).

In this study, three areas of the Czech Republic are being investigated. Specifically, the mountain region, spa region, and wine region, with each type of region being very distinctive, have their own characteristics and clientele. Mountain region can be described as a hilly region, where the terrain and climate are favorable for winter sports and summer hiking. Spa destination is a natural healing resort with health-enhancing climate, offering special wellness treatments. Finally, a wine destination can be described as one rich in folk architecture, local traditions, customs, products, and cultural landscape (Pásková, 2009).

Methodology

Data collection and measurement

IPA was utilized for data analyses. IPA is a commonly used research technique, which allows researchers and practitioners alike to understand destination performance and to formulate strategies for improvements in products/services (Bi et al., 2019). IPA has been employed not only in tourism research but also in studies centering on meetings and events, leisure and recreation, destination competitiveness, destinations’ attributes, and accommodation among others (Abalo et al., 2007; Bacon, 2003; Bi et al., 2019; Caber et al., 2012; Dwyer et al., 2016; Guizzardi and Stacchini, 2017; Lai and Hitchcock, 2015; Murdy and Pike, 2012). IPAs allow researchers to divide the attribute’s importance–performance dimensions into four quadrants in an effort to ease data interpretation and derive practical suggestions (Dwyer et al., 2016). The four quadrants (Q) range from “keep up the good work” in Q1 to “concentrate here” in Q2, “low priority” in Q3, and concluding with “possible overkill” in Q4.

Specifically, factors representing “keep up the good work” (Q1) are evaluated by clients very positively and have high importance. Therefore, destinations and local businesses need to continue to deliver high-quality product. Factors with performance deficits (concentrate here) are represented in Q2 and are characterized by high importance and low performance. These factors are important to a visitor, however, their present level of performance does not meet visitors’ requirements. Therefore, heighten attention needs to be paid to these factors. Factors representing “low priority” in Q3 quadrant have low importance for visitors, thus having low impact on the performance of a destination. Companies and local governments should not devote a lot of time to improve these as the return on investment will be minimal. Lastly, “possible overkill” in the quadrant Q4 is characterized by low importance and high performance, highlighting the fact that excessive importance is giving to the factor, yet, visitors “do not care.”

The results can be represented by mapping approach (Lai and Htichcock, 2015). Thresholds that divide the data into these quadrants utilized in this study are data-centered (Ramakrishnan and Usha, 2016) and scale-centered (Tonge and Moore, 2007). This has been very controversial in the academic literature due to the placement of the crosshairs within the importance–performance matrix (Oh, 2001). Some suggest, when using scale-centered IPA, to use median values of the scale for the placement of the crosshairs as it is the most transparent way (Boley et al., 2017; Oh, 2001). Others, on the other hand, point out that it tends to “inflate” the results (Oh, 2001). When using data-centered approach, the mean is being utilized for placing the crosshairs (Azzopardi and Nash, 2013; Boley et al., 2017). By utilizing this approach, the data attributes are compared relatively to each other and solving the so-called “ceiling effects” by measuring that salient attributes are graphed according to their relative importance–performance (Boley et al., 2017). In this study, the researchers have utilized both approaches.

Self-administered surveys from visitors to these three types of destinations (mountain area, health spa region, and wine region) in the Czech Republic were collected during peak seasons between fall 2016 and summer 2017. Specifically, for the mountain region, it was during the winter of 2016, for the spa region, data were collected during the spring of 2017, and for the wine region, data were collected during the summer of 2017. The specific allocation of the regions is depicted in Online Appendix B. The questionnaire was developed with the aim of understanding visitors’ evaluation of the destination quality and performance. Specifically, importance questions were formulated as “how important is…to you?” and performance question was formulated as “how satisfied were you with…?,” utilizing factors Q1–19, as outlined in Online Appendix A. For example, a question “how satisfied were you with local transportation?” was asked when analyzing the performance aspect of IPA. When it came to importance, question such as “how important is local transportation to you?” was examined.

A total of 835 surveys covering visitors’ characteristics (such as age, gender, education, and type of employment) and importance/performance characteristics centering on destination management were collected, ranging from 1—“not at all important/satisfied” to 5—“extremely important/satisfied.” A total of 131 failed to appropriately respond and had to be eliminated, leading to a total of 704 usable surveys. Specifically, a total of 269 surveys were collected in the mountain area (38.2%), 228 surveys in the wine region (32.4%), and 207 surveys from the spa region (29.4%).

Given the number of parameters in the measurement mode, the sample yielded a ratio of approximately 18.5 observations per parameter, confirming that the sample size is sufficiently large for the data analyses. Attributes used in this study were derived from previous literature. Online Appendix A provides a brief description of each attribute with adequate references.

Results

Characteristics of respondents

The sample included 47.4% of men, with 18.6% between the ages of 31 and 40 years old. Majority of the respondents had high school education (54.8%). Additional descriptive information is provided in Table 1.

Table 1.

Sample characteristics.

	Percentage (%)	Mountain area (%)	Wine region (%)	Health spa (%)
Gender
Male	47.4	55.4	47.8	36.7
Female	52.6	44.6	52.2	63.3
Age
18–23	15.3	12.3	22.8	11.1
24–30	15.3	16.7	19.7	8.7
31–40	18.6	17.5	20.2	18.4
41–50	17.0	20.1	14.0	16.4
51–60	15.6	16.7	11.4	18.8
61–70	11.9	10.0	7.5	19.3
Over 71	6.9	6.7	4.4	7.2
Education
Trade school	13.8	17.5	13.6	9.2
High school	54.8	56.1	53.9	54.1
College graduate	31.4	26.4	32.5	36.7

MANOVA results

To test the differences among the levels of importance and performance across the three destinations, two separate multivariate analyses of variance (MANOVAs) were performed (one for the importance measures and the other one for the performance measures). MANOVA allows researchers to better control of type 1 error (Hair et al., 2010). Prior to data interpretation, the assumption of the equality of variance–covariance matrices had to be met. Box’s M test was utilized and statistically significant (M_importance = 1066.8, t = 0.001; M_performance = 1346, t = 0.001), violating the assumption. However, Hair et al. (2010) point out that “a violation of this assumption has minimal impact if the groups are of approximately equal size.” Utilizing this assumption, the groups were compared (largest group size/smallest group size = <1.5) and the results indicate that the ration was less than 1.5, specifically, 1.29, thus supporting the assumption that the MANOVA is a robust statistical technique (Boley et al., 2017).

Next, the MANOVA F-test is being computed, utilizing the Wilk’s λ. In our case, we conclude that there is a difference in importance/performance measures based on the destination F_importance (38, 1366) = 12.08, p < 0.001 and F_performance (38, 1366) = 9.55, p < 0.001. Partial η² for importance is equal to Wilks’s Λ = 0.252, thus, 25.2% of the variability in importance is being explained by the destination location. When it comes to partial η² for performance, Wilks’s Λ = 0.21, thus, 21% of the variance is explained by destination location.

Lastly, to analyze the differences between the investigated regions, multiple ANOVAs were run. The results for the “mountain region” and “wine region” are summarized in Table 2.

Table 2.

MANOVA results.

Attributes		Combined				Mountain region				Wine region
N = 704		Importance		Performance		Importance		Performance		Importance		Performance
N = 704		F	Sig	F	Sig	t	Sig	t	Sig	t	Sig	t	Sig
1	Natural attractions	2.19	0.113	13.87	0.001	−1.61	0.106	−4.85	0.001	−1.99	0.047	−0.93	0.350
2	Cultural Sights	2.05	0.130	29.17	0.001	0.74	0.460	−5.19	0.001	1.98	0.048	1.87	0.062
3	Accommodation	24.46	0.001	28.31	0.001	−4.75	0.001	−6.77	0.001	−6.89	0.001	−6.43	0.001
4	Food and beverage	9.21	0.001	21.81	0.001	−3.89	0.001	−6.32	0.001	−3.63	0.001	−5.04	0.001
5	Events	7.79	0.001	9.74	0.001	3.94	0.001	−2.16	0.031	2.24	0.025	2.04	0.041
6	Access	4.45	0.012	6.24	0.002	2.95	0.003	−3.51	0.001	1.97	0.040	−2.18	0.029
7	Local transportation	17.43	0.001	2.27	0.104	5.86	0.001	−2.04	0.041	2.64	0.008	−0.60	0.545
8	Access to information	2.52	0.081	6.09	0.002	1.25	0.209	−3.38	0.001	−0.87	0.380	−2.55	0.011
9	Communication prior to departure	12.51	0.001	24.25	0.001	−1.20	0.227	−5.97	0.001	−4.72	0.001	−6.25	0.001
10	Residents’ friendliness	14.90	0.001	28.79	0.001	4.33	0.001	−4.03	0.001	5.14	0.001	3.19	0.001
11	Destination image	1.21	0.296	17.21	0.001	1.12	0.263	−5.67	0.001	−0.30	0.763	−1.82	0.068
12	Pricing	10.36	0.001	7.27	0.001	−4.39	0.001	−3.59	0.001	−3.40	0.001	−3.03	0.003
13	Employees’ knowledge	30.19	0.001	23.09	0.001	−7.75	0.001	−5.99	0.001	−3.80	0.001	−5.94	0.001
14	Safety and security	5.88	0.003	17.29	0.001	−3.22	0.001	−5.52	0.001	−2.73	0.006	−1.31	0.190
15	Cleanliness	7.53	0.001	16.14	0.001	−3.83	0.001	−5.58	0.001	−1.58	0.113	−3.91	0.001
16	Crowded	2.35	0.095	9.37	0.001	1.43	0.152	−0.28	0.778	−0.58	0.557	−3.77	0.001
17	Uniqueness	3.51	0.030	9.87	0.001	1.64	0.100	−3.16	0.002	−0.85	0.395	0.89	0.372
18	Infrastructure	36.39	0.001	9.92	0.001	4.63	0.001	0.14	0.884	−3.48	0.001	−3.65	0.001
19	Preservation	14.69	0.001	10.46	0.001	4.05	0.001	−4.57	0.001	−0.81	0.414	−2.55	0.011

Note: MANOVA: multivariate analysis of variance.

No results were provided for the “health spa” region due to either very small variation in the random intercept, slope, or in the covariance between intercept and slope. Thus, IPA graphs were utilized to represent the IPAs based on the location while providing industry-specific recommendations.

The MANOVA results indicate that there are significant differences between importance–performance results (Table 2). What is very interesting to observe is that what is important may not necessarily be statistically significant when it comes to performance and vice versa. For example, local transportation is important for visitors in both regions (mountain and wine regions); however, the performance is not statistically significant when it comes to wine region. Similar results were found when it comes to infrastructure in the mountain region or safety and security in the wine region. On the other hand, having small crowds was key to successful performance in the wine region but it was not noted as important in the mountain region. Similar results are seen when it comes to access to information in the wine region or cultural sights in the mountain region.

IPA results

To analyze the differences between the investigated regions, IPA graphs were utilized. In addition to the graphs, means for each variable are presented in Table 3, allowing for further interpretation.

Table 3.

Importance–performance by destination.

	Attributes	Mountain region			Wine region			Spa region
	Attributes	Mean	Standard deviation	N	Mean	Standard deviation	N	Mean	Standard deviation	N
Importance
1	Natural attractions	3.85	1.06	269	3.81	1.11	228	4.01	0.98	207
2	Cultural sights	3.69	1.00	269	3.81	1.03	228	3.62	1.00	207
3	Accommodation	3.89	1.01	269	3.69	0.93	228	4.29	0.88	207
4	Food and beverage	3.89	0.94	269	3.90	0.89	228	4.22	0.89	207
5	Events	3.79	0.95	269	3.63	1.15	228	3.41	1.03	207
6	Access	3.81	0.95	269	3.72	1.12	228	3.52	1.20	207
7	Local transportation	3.60	1.04	269	3.26	1.27	228	2.96	1.26	207
8	Access to information	3.86	0.93	269	3.66	1.1	228	3.74	1.01	207
9	Communication prior to departure	3.87	0.91	269	3.53	1.05	228	3.99	1.05	207
10	Residents’ friendliness	3.82	0.95	269	3.92	1.12	228	3.40	1.12	207
11	Destination image	3.93	0.91	269	3.80	0.99	228	3.83	0.99	207
12	Pricing	3.74	1.02	269	3.82	0.91	228	4.14	0.94	207
13	Employees’ knowledge	3.68	0.98	269	4.02	0.98	228	4.37	0.88	207
14	Safety and security	4.05	1.00	269	4.09	1.04	228	4.35	0.91	207
15	Cleanliness	4.03	0.91	269	4.22	0.99	228	4.36	0.90	207
16	Crowded	3.90	1.03	269	3.71	1.06	228	3.77	0.94	207
17	Uniqueness	3.76	0.94	269	3.53	1.12	228	3.61	0.97	207
18	Infrastructure	3.85	0.96	269	3.06	1.11	228	3.41	1.02	207
19	Preservation	3.82	0.96	269	3.38	1.07	228	3.45	0.87	207
Performance
1	Natural attractions	3.78	0.96	269	4.12	0.94	228	4.20	0.89	207
2	Cultural sights	3.51	0.97	269	4.16	0.90	228	3.99	0.90	207
3	Accommodation	3.65	0.93	269	3.66	0.83	228	4.21	0.88	207
4	Food and beverage	3.63	0.90	269	3.71	0.82	228	4.14	0.86	207
5	Events	3.43	0.82	269	3.77	0.88	228	3.60	0.86	207
6	Access	3.59	0.88	269	3.69	0.90	228	3.88	0.95	207
7	Local transportation	3.23	0.98	269	3.36	0.96	228	3.42	0.91	207
8	Access to information	3.64	0.91	269	3.71	0.90	228	3.93	0.53	207
9	Communication prior to departure	3.59	0.86	269	3.55	0.85	228	4.09	0.96	207
10	Residents’ friendliness	3.41	0.94	269	4.05	0.96	228	3.76	0.93	207
11	Destination image	3.59	0.93	269	3.93	0.97	228	4.09	0.92	207
12	Pricing	3.58	0.93	269	3.61	0.84	228	3.88	1.00	207
13	Employees’ knowledge	3.65	0.94	269	3.64	0.88	228	4.15	0.89	207
14	Safety and security	3.72	0.98	269	4.09	0.89	228	4.21	0.96	207
15	Cleanliness	3.69	1.01	269	3.82	0.94	228	4.19	0.94	207
16	Crowded	3.57	0.89	269	3.24	1.04	228	3.59	0.99	207
17	Uniqueness	3.59	0.85	269	3.94	0.99	228	3.86	0.90	207
18	Infrastructure	3.62	0.90	269	3.29	0.85	228	3.61	0.92	207
19	Preservation	3.46	0.95	269	3.46	0.85	228	3.68	0.86	207

One of the highest ranking attributes when it comes to importance for all three regions is cleanliness (M_Mountain = 4.03, M_Wine = 4.22, M_Spa = 4.36), followed by safety and security (M_Mountain = 4.05, M_Wine = 4.09, M_Spa = 4.35), and employees’ knowledge (M_Wine = 4.37, M_Spa = 4.02). Interestingly, when it comes to the spa region, quality of accommodation (M_Spa = 4.29), food and beverage (M_Spa = 4.22), and pricing (M_Spa = 4.14) was also important. Such results may have been influenced by the visitors’ behaviors as those that visit spa regions tend to stay for an extended period of time (usually 3–4 weeks) due to the length or variety of treatments as opposed to mountain or wine region (average length of stay is less than 1 week).

When it comes to the performance attribute, the mountain region had the lowest scores out of all three regions, with the highest score of 3.78 centering on natural attractions. The area of major concern is the quality of local transportation (M_Mountain = 3.23) and residents’ friendliness (M_Mountain = 3.41). Such results are surprising and point out to number of areas for potential improvements. In terms of spa and wine regions, the two areas of concerns are crowd management (M_Spa = 3.24; M_Wine = 3.59) and infrastructure (M_Spa = 3.29; M_Wine = 3.61).

The IPA graphs are shown in Figures 1 to 3 and allow us to provide further in-depth understanding of each attribute, specifically how it relates to the four quadrants (low priority, concentrate here, keep up the good work, and possible overkill). The data-centered crosshairs are represented by solid lines and scale-centered crosshair by dashed lines.

Figure 1.

Mountain region.

Figure 2.

Wine region.

Figure 3.

Spa region.

Figure 1 shows the IPA results for mountain region. By analyzing the 19 factors, the results indicate that there is only one area that needs major improvement, namely crowd control (Q16). Furthermore, areas of “possible overkill” should be of a concern. This is an area, where firms are overperforming, however, these attributes are not that important to visitors, namely, employees’ knowledge (Q13), uniqueness (Q17), and access (Q6).

When it comes to the wine region (Figure 2), the IPA results reveal that areas that need improvements center on food and beverage (Q4), pricing (Q12), and employees’ knowledge (Q13). Attributes such as destination access (Q6) and crowdedness (Q16) may be potential areas of concern. The aspects of possible over performance relates to events (Q5) and uniqueness (Q17).

Lastly, Figure 3 depicts the results for spa region.

After closer investigation, when it comes to the spa region, it is necessary to focus on the price level at the destination (Q12). Furthermore, areas of possible over performance center on cultural sights (Q2) and access to information (Q8).

Discussion

The results indicate some interesting outcomes when it comes to the IPAs and the differences among the investigated regions. The IPA graphs have shown some factors with similar results while others with more substantially different rankings in the different destination types.

Mountain region

When investigating the four quadrants for the mountain region, the only one area of concern located in the quadrant “concentrate here” centers on overcrowding (Q16). Such results are not surprising when it comes to the Czech Republic as the majority of the population engage in various sport activities, such as skiing during the winter season or hiking in the summer and the mountain regions tend to be busy during those times. Since the surveys were collected during such a busy time, it may be a possible explanation why overcrowding was so important for visitors. A possible solution may be the implementation of crowd management. This can be done by collaboration between local business and government officials (at local or national level).

Implementation of such measures is not a new concept as numerous destinations around the world face similar challenges when it comes to crowd management these days. For example, recently, Venice city council has considered charging visitors a fee to enter St. Mark’s Square to regulate the size of the crowds (Paris, 2017). Similarly, the Slovak Republic has been addressing overcrowding in the mountain region by adapting revenue management to regulate pricing for ski passes and utilizing demand and supply strategy. The main reason for such measures is to ensure that all visitors experience high-quality ski conditions and the destinations have sufficient refreshment facilities and parking capacity (Chovaňák, 2018).

Other attributes worthy of investigation are those located near the crosshair sections of “concentrate here” quadrant, as Dwyer et al. (2016) point out that IPA methodology tends to inflate importance ratings of most attributes. Particularly, attributes are related to communication prior to departure (Q9), destination image (Q11), infrastructure (Q18), residents’ friendliness (Q10), and preservation (Q19).

Specifically, communication prior to departure and destination image should be of high interest to the local lodging providers as those attributes that can be easily influenced by making minor adjustments to internal strategies; for example, ensuring that predeparture emails/letters are sent to all guests. These days, technology allows hotels to customize their communication to the individual customer, thus, having accurate quests’ information and being proactive is a key to success.

Furthermore, it is ensured that effective marketing campaign is developed to create awareness, generate interest, and stimulate a traveler’s desire about that destination. It is significantly important in establishing, maintaining, reinforcing, and changing a destination brand and its position in the market. Marketing is very important as its efforts represent a destination until the actual visitation takes place. This can be done by collaborating with local government officials on promoting the destination and its attractions. For example, in the case of India, the government of India has been successful in marketing famous Indian icons, such as the Taj Mahal, the desert of Rajasthan, and the 1000-year-old yoga pose for eternal youth of body (Medhekar, 2014). Similar practices can be adopted by the local government to promote the mountain region of the Czech Republic.

When it comes to government involvement, the opportunities lie in terms of infrastructure, residents’ friendliness, and destination preservation. Investments in infrastructure improvements are needed to provide comfortable transportation and sufficient parking not only for tourists but also for local residents. Previous studies have pointed out that infrastructure requirements can decide the future of tourism development and destination competitiveness, either hindering or accelerating such progress (Benur and Bramwell, 2015).

In terms of residents’ friendliness, studies have confirmed that residents who display friendly and approachable behavior will have a positive impact on the destination. This is a great opportunity for the local government via marketing campaign or town hall meetings to make residents understand the need for tourism support to sustain destination development and growth. Finally, government involvement is required for destination preservation. Ensuring that the natural resources are preserved for a long period of time is a key to long-term sustainability. This can be done by either passing specific laws and/or implementing new policies addressing such issues.

Wine region

In terms of wine region, the results indicate that the attributes located in the quadrant “concentrate here” represent access to the destination, having food and beverage offerings (Q4), pricing (Q12), knowledgeable employees (Q13), and crowd management (Q16). In addition, destination cleanliness (Q15) should be of a concern due to its closer proximity to crosshair lines.

When it comes to the role of government, similar to the mountain region, local government needs to collaborate with providers by developing a strategy on how to manage attendance during peak times. In addition, government officials can explore the opportunities for food and beverage offerings, possibly by offering mini-grants that would allow local business to open new restaurants in the region. In addition, providing training and/or certification specifically geared toward further development in customer service and product knowledge could improve the overall tourists’ satisfaction with the destination. This is a major concern as previous research has shown that the quality of service received will reflect on the perception of price paid by customers, thus influencing the perception of value received. For example, training on customer service and wine knowledge specifically can enhance the quality of the product provided, hopefully leading to better tourist experience and satisfaction (Caber et al., 2012; Semrad et al., 2014). Another area of concern for government officials is the access to the destination, having an impact on destination competitiveness. Lastly, what is surprising is that wine tourists want knowledgeable employees; however, they do not necessarily seek the information in booklets or on websites. In other words, they are looking for human interaction.

This is where local business comes into play as the issue of quality of service and price paid is reflected in the disparity between importance/performance values. Previous studies have confirmed that price plays a vital role in performance evaluation (Han et al., 2017). Therefore, local businesses must revisit their pricing strategies and evaluate the value customers are receiving through their customer service. Another potential issue that may be influencing the pricing is the overall cleanliness of the establishments, corresponding with findings in previous studies (Meng et al., 2008; Ryglová et al., 2017; Ryglová et al., 2018). Therefore, local service providers need to re-evaluate the quality of the product offered to sustain tourism development.

Spa region

The last destination to assess is the spa region. Looking at the four quadrants “concentrate here,” the data reveal the need for better pricing strategies. Another area of concern is crowd management (Q16), the same as in the mountain and wine regions. A study conducted by Han et al. (2017) confirms that price is a vital aspect of performance evaluation, therefore, it is crucial to pay attention to pricing policies. In the Czech Republic, the price policies in spa and wellness services have been subjected to major changes. After a healthcare reform in 2011, there was a significant drop in subsidies from the government, leading to an increase in prices paid for spa treatments by residents. Such change had a negative impact on local visitors, who could no longer afford some of the treatments. However, this has not stopped the international visitors, who seek spa treatments in the Czech Republic.

Therefore, local businesses need to collaborate with government officials on creating dual pricing strategy (domestic visitor versus international visitor). Similar practices have been made available in other countries, such as Honduras, where local businesses are charging lower prices to domestic visitors. Furthermore, creating different pricing strategy (revenue management) could assist with crowd management (Han et al., 2017). Specifically, creating rate fences could allow customers to segment themselves into appropriate rate categories, based on their willingness to pay for their preference (slow vs. busy time), controlling the crowd levels of a destination (Choi et al., 2014).

Conclusion and limitations

The main objective of this study was to examine the importance/performance factors impacting destination quality. Specifically, how destination resources are being utilized and where the opportunities lie in terms of destination management. The results indicate some fascinating outcomes when it comes to wine, mountain, and spa destinations in the Czech Republic, pointing out individual challenges in each region.

The results strengthen the argument for government involvement at local and national levels. Specifically, from the government perspective, the results provide a number of opportunities when it comes to each region. Since tourism is very unique industry, government support is needed for sustainable development, ranging from improvements to infrastructure, to crowd management, having friendly residents, and knowledgeable employees. Previous research has pointed out that tourism performance largely depends on the success of other businesses and a benefit provided by one company (e.g. hotel) can be reaped by everyone (e.g. restaurant, tour provider). Thus, government needs to create a system for businesses to develop and grow.

Government has the necessary and legitimate power to provider political stability and legal and financial framework to smooth tourism development and only the government can create an environment, which is conducive for industries to compete, creating necessary earnings, employment, and general well-being of its citizens (Devine and Devine, 2011). In addition, businesses will not take any risks until they are fully confident that sufficient environment has been created (Aissa and Goaied, 2017). Thus, cooperation and collaboration between the tourism providers and government is essential in providing superior products/services and to achieve broad-based support for policies permitting to compete (Vernon et al., 2005) in order to sustain long-term development.

From the industry business perspective, the results indicate that improvements to food and beverage, employees’ knowledge, and communication prior to departure are needed. This corresponds with previous studies, which show that the tourism industry’s ability to acquire new knowledge and expertise is a key in achieving their success (Mei et al., 2015). Specifically, training on customer service and wine expertise can enhance the quality of the product, leading to improved visitors’ satisfaction (Caber et al., 2012; Semrad et al., 2014). In addition, businesses must concentrate on improvements in communication with their visitors, especially prior to arrival. Providing strategic marketing communication is essential in influencing visitors’ behavior (Wieler et al., 2017). Specifically, communication that is credible and trustworthy will make visitors’ aware about the products and services offered and it will inform consumers how these offerings meet their needs (Tölkes, 2018). Furthermore, the types of communication channels (ranging from print material to social media) must be taken into consideration.

As with any other studies, the current study has some limitations. Foremost, the results of this study cannot be generalized due to the location of the study and its convenience sample. It would be strongly recommended to conduct similar survey in another location (either mountain, spa, or wine regions) within the same country and/or utilize different destinations outside the Czech Republic. Due to the type of visitors these destinations receive, diverse group of visitors could enhance the generalizability of findings of the study. In addition, it may be very difficult for respondents to evaluate importance and performance at the same time, posing yet another potential limitation. This study mainly concentrates on the role of government in destination performance, omitting some important factors that may impact destination. Therefore, additional factors centering on human capital, tourism superstructure, efficiency, and effectiveness, or technology could be taken into consideration when measuring importance/performance at the destination, giving opportunity for future studies.

Another limitation this study faces has to do with the data analyses utilizing the IPA technique (Dwyer et al., 2016; Oh, 2001). As Dwyer et al. (2016) point out, “there are no agreed upon guidelines for developing a set of attributes…to be used in IPA” (p. 1331). This limitation does not impact the validity of the study, however, more attributes could be added to the destination analyses. Lastly, IPA methodology tends to inflate importance ratings of most attributes, limiting the variation in importance score (Dwyer et al., 2016).

Supplemental Material

Supplement_material - Importance–performance analysis approach to destination management

Supplement_material for Importance–performance analysis approach to destination management by Ida Rašovská, Marketa Kubickova and Kateřina Ryglová in Tourism Economics

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Marketa Kubickova

Supplemental Material

Supplemental material for this article is available online.

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