The Influence of Smart Technologies on Customer Journey in Tourist Attractions within the Smart Tourism Management Framework

Abstract

Nowadays, smartness and smart management of tourism destinations and suppliers are becoming a top priority and big challenge. This article focuses on tourist attractions and aims at exploring how smart technologies influence the customer journey. The main research question is how smart technologies are influencing the tourists’ visit experience. The study takes a consumer behavior perspective with a specific focus on the visit cycle (prospective, active, and reflective phases), based on the theoretical foundations of customer journey process model. First, a research framework was elaborated, encompassing three hypotheses. Then, this model was empirically tested and validated by means of a quantitative research using as a study site the Ningbo Museum, Ningbo, China. This investigation allows us to get insights into consumer behavior, which is useful for tourist attraction to become ‘smarter’. The study’s findings indicate that smart technologies have an influence on the customer journey at all three phases, the most significant being at the prospective and active phases, without neglecting the reflective one. This article extends our knowledge by providing new insights into the influence of smart technologies that have theoretical and marketing implications for tourist attraction.

1. Introduction

Within the general context of digitalization—which is not a trend any more, but an everyday reality—the extensive use of smart technologies constitutes an essential component in the field of tourism [1]. These technologies render tourist consumers more active and exhibiting higher expectations in terms of experience at a destination or a specific tourist attraction. Tourism destinations and attractions have to address this challenge by adopting and implementing smart infrastructure and technologies in their offering to tourists by designing and crafting an attractive and memorable tourism experience [2].

Academic literature [3,4] suggests that smart tourism (ST) is a management framework combining tourism infrastructure with ICT tools to increase destination and business efficiency and tourists’ experiences. Countries and organizations all over the world are working hard to promote the ST development [2,3], and China could not be an exception. Over the last decade, local and national authorities in China have been very active in this field [1]. In 2018, the planning and design of 5A-level scenic spots in all Chinese provinces as “smart tourism scenic spots” were promoted. By this year, all scenic spots rewarded with 4A-level or higher in China should offer free of charge full coverage of Wi-Fi, smart tour guide, online booking, and other smart services [5].

The ST ecosystem is determined by cutting-edge technologies. However, ST is not merely a ‘tourism + technology’ issue, it is about enhancement and facilitation of tourism activities by means of technological media. It constitutes the process of using smart technologies to improve the experience quality through the entire trip/visit or ‘customer journey’. Therefore, ST is essentially an approach and framework for planning, designing, and managing tourism resources, assets, and experiences at destination level, supported by smart technologies [4]. It is believed that smart technologies contribute to the improvement of customer knowledge and innovation [6], and of destination and business competitiveness and sustainability [7].

Scholars suggest that ST is an ecosystem, formed by smart destination (spatial zone), a smart business network, and a smart technologies infrastructure. It is an ecosystem beneficial to the destination and to all actors and stakeholders involved [3,8]. There are three fundamental principles of ST, namely (i) enhancement of tourism experiences, (ii) improvement of asset/resource management, and (iii) attainment of competitiveness with a focus on sustainability [3,8]. ST also helps the design of value propositions offering the potential for co-creation of customized experiences. ST is about contextualizing the experience to visitors/tourists’ needs [2,4,8]. Therefore, enhancing and facilitating tourism experiences by means of smart technologies is a key principle, strategic aim of, and challenge for smart tourism destinations and Smart Tourist Attractions (STAs).

Academic research on ST as a novel management framework is at its initial steps [4,8]. It has mainly explored related issues in the context of tourism destinations and the hotel industry. Tourist attractions are under-researched within the ST management framework. The same is valid for the studies focusing on consumer behavior. Our study is aimed at addressing this gap. It is driven by the motivation to acquire a better knowledge of smart tourists’ consumer behavior in order to apprehend ST, in the sense that they constitute one of the key actors/stakeholders [4,8].

It is the argument of this paper that there is an imperative for tourist attractions to follow the technological advances and incorporate Smart Tourism Technologies (STTs) in their management functions for a series of reasons, mainly: (i) To achieve better efficiency and effectiveness in terms of operations and performance; (ii) to become more attractive in terms of offering value propositions, and (iii) to attain a competitive advantage in the market. Our approach consists of putting a special emphasis and focus on the role of smart tourists as co-creators of experiences at tourist attractions. The main research question is: “Are the smart technologies useful and influential on the customer journey to a tourist attraction?”. In other words, what makes a tourist attraction smart according to consumers’ perceptions, and expectations?

In order to address this question, the study’s aim is to analyze the influence of smart technologies in terms of experience in tourist attractions within the Chinese context. Hence, the study takes a consumer behavior perspective to investigate the perceptions of tourists through their visit experience based on the ‘customer journey’ model/approach: Prospective phase (pre-visit period), active phase (during the active visit experience itself), and reflective phase (the post-visit period).

The remainder of the paper is structured as follows. Section 2 reviews the literature related to main elements of ST framework; i.e., technologies, tourists, and tourism businesses and attractions. Section 3 discusses the suggested framework for use of STTs by tourist attractions and the research hypotheses regarding the influence of these technologies on customer journey. It also presents the research design and methodology of the empirical study implemented in Ningbo Museum, Ningbo, China. Section 4 presents and discusses the results of statistical analyses of data. Then, the theoretical and marketing implications of the findings are discussed in last section. The study’s limitations are outlined, and pathways for future research are suggested.

2. Literature Review

As already highlighted, the combination of smart technologies and tourism has given birth to the ST ecosystem/framework [2,3]. Over the last five years, this field has attracted academic interest, with scholars attempting to explore related elements, issues, and aspects. This section reviews the three main elements of ST ecosystem; namely, smart technologies, smart tourists, and smart tourism businesses.

2.1. Smart Technologies

The concept of ‘smart technologies’ encompasses new forms of cooperation and value creation technologies [2,3,4]. It is worth noticing that ‘smart’ is not the advance of a single technology, but the interconnection and collaborative progress/advance of various technologies simultaneously. Smart technologies include a variety of computing and information technologies, as depicted in

Table 1. Smart Technologies: Form and short description.

Form of Smart Technology	Short Description
Internet of Things (IOT)	A network capable to process identification, location, tracking, monitoring, and management through RFID, infrared sensor, GPS, laser scanning, and other information sensing equipment, and connect the goods with the network for information exchange and communication.
Cloud computing technology	This technology has two meanings: (i) It refers to the system platform used to construct applications, whose status is equivalent to the operating system on a personal computer, (called cloud platform); and (ii) it describes the cloud computing application built on this platform (cloud application).
Artificial Intelligence	Technology allowing use of computer software and hardware to simulate intelligent human behaviors to effectively process and analyze data and information, and to support decision-making and problem-solving. Examples: Driverless cars, virtual assistants.
Mobile communication technology	The technology used for wireless communication allowing wireless real-time connection between systems and remote devices. 5G is the fifth-generation mobile communication technology, much faster and reliable than the previous (4G).
Mobile devices and applications	Electronic equipment, such as mobile phones and tablets, and the technology connected with them. The mobile internet comprises various different devices and platforms; i.e., smartphones, tablets, in-car systems, and wireless home devices. It includes personal and business applications.
Big Data	Big data is a term that describes the large volume of data—both structured and unstructured—that inundates a business on a day-to-day basis. Big data can be analyzed for insights that lead to better decision-making. It is worth noticing that this is exclusively used by businesses, not consumers.
Ubiquitous connection between Wi-Fi and other networks	A technology that allows electronic devices to connect to a wireless local area network.
Virtual Reality	A form of information technology which enables users to navigate in computer-simulated environments. VR is a computer-generated environment in which people can experience places and situations as if they were actually present. Example: Virtual tour
Augmented Reality	An enhanced version of reality by which people see the real world with a digital display superimposed technology. AR enhances people’s current perception of reality and enhances and leverages visitor experience through additional digital content.
Intelligent chat robot	A robot able to understand and talk using human language with users.
Wearable devices	A portable device that can be worn directly on the body or integrated into the user’s clothes or accessories. For example, smart watch, smart bracelet, etc.
Beacon network	Transparent GIF or PNG images that can be hidden in any web element or email are often used to collect data such as online habits of targeted computer users.

Source: Elaborated from various sources [8,9,10,11,12,13,14].

.

These technologies provide real-time connection and advanced analysis of the physical world, helping companies/organizations to optimize business processes and improve their performance [12,13].

STTs are technological media that tourist consumers use at all phases of their stages of decision-making process and customer journey [13,14]. These technologies enable tourism destinations and suppliers to acquire better knowledge and understanding of tourists’ needs and to improve their resource/asset management and performance [2,3]. They also create value for tourist consumers and assist them in making the right decisions through their experience [6,8,14,15]. In this regard, STTs provide a significant potential for co-creation of experiences at both destination and business levels [6,16].

2.2. Smart Tourists

Literature suggests that tourist consumers use smart technologies to interact dynamically with other stakeholders and to create their own experiences [17]. Smart tourists are recipients and users of services provided by STTs. Nowadays, smart tourists are active; they are creating and sharing information, and attempting to influence other users. In this smart context, digital media and platforms—e.g., Web 2.0, social media (SM), and smartphones—have become much more than an information source for tourists, as they are used for various purposes [17,18].

Over the last two decades, the widespread adoption and extensive use of ICTs have resulted in a radical shift of the tourist consumer behavior [14,15,17]. Tourists are now independent and skilled. The main impact comes from the Web 2.0 tools [19], the extensive use of mobile technologies, applications, smart devices [20], and context and location-aware services [16]. STTs help tourists to integrate content and improve the quality of their decision-making [21]. These changes have shaped a smart tourist profiting from cutting-edge technologies [8,14].

It is believed that the key issues and aspects related to tourist consumers—a key element of ST management framework—are under-researched by academic research [2,4,15]. Only one study has focused on smart tourist behavior [22] and found that all stages of tourist/visit experience are important in terms of smart technologies. That is the reason why Femenia-Serra et al. [8] call for a more conceptual and broader empirical research to acquire a better knowledge on and deeper understanding of tourist consumers in the ST management framework. This study was performed in this vein and in the context of tourist attractions.

2.3. Smart Tourism Businesses and Attractions

Smart tourism businesses are the suppliers of tourism services and value propositions/experience opportunities within the ST ecosystem; in simple terms, suppliers that are adopting and making efficient use of smart technologies. Literature suggests that these technologies have the potential to contribute to asset management and business efficiency improvement and value co-creation [2,23,24,25]. Smart technologies help tourism businesses and other stakeholders to break through the limitations of traditional data analysis, process huge amounts of data, and produce meaningful and valuable information [25], expand consumers’ social intelligence, improve the quality of the interpersonal communication, and make SM more intelligent and effective [24].

Literature also indicates that smart technologies promote the resource allocation and cooperation between suppliers/firms and improve the quality of tourism experience [26]. According to these authors [26], the design of smart tourist attraction depends on the integration of these two dimensions. Smart businesses can benefit from big data analysis. When the concept of smartness is applied to the design, management, and operation of tourist attractions, it means that these tourism businesses have moved from the concept to practice [27]. Their strategic aim should be to enhance the co-creation of tourist experiences and to improve the resource management efficiency [26,28]. Only a few studies were performed in this field from a supply/destination perspective, with a specific focus on the interrelation between tourism destinations and smart tourists and the impact of smart services on tourism experiences. The study by Wang et al. [28] investigated tourists’ preferences in a STA context. Findings suggest that tourists apply a series of key evaluation factors for STA. Therefore, the study’s focus was on tourist consumers’ evaluation and preferences [28].

A number of studies explored this topic from the technology perspective. Smart environments are exploited to rejuvenate consumers’ interest in the cultural heritage by guaranteeing really interactive cultural experiences [29]. Hereafter are outlined some studies analyzing projects/initiatives that were designed and are taking place in other countries, especially in Italy. The paper by Ceipidor et al. [30] presents the design of a mobile multimedia guide for the visitors of the Wolfsoniana Museum, Genoa, Italy. Their study is based on the assumption that the visitor experience could become more interactive and engaging through a mobile application implementing along with smart technologies. Authors discuss an application of Usability and User Experience (UX) of Near Field Communication (NFC) technology applied to the cultural tourism field. Another study by Chianese et al. [31] outlines and discusses a location-based application, called ‘Smartweed’, developed within a high technology district for cultural heritage management. The project was aiming at exploiting several location-based services and technologies to craft a smart multimedia guide system able to detect the closest artworks to visitors, make them able to ‘tweet’ and ‘talk’ during their visit and be capable of automatically telling their stories using multimedia facilities. Moreover, the project deployed some sensors that allow the visitors’ mobile devices—by using Wi-Fi technology—to detect the closest artwork in a museum context. The study by Amato et al. [32] presents a project, named Talking Museum and developed within the same technology district (cultural heritage management). The project exploits the IoT technologies in order to make objects of a museum exhibition able to “talk” during the customer’s visit and capable of automatically telling their story using multimedia facilities. As a case study, these authors used an example of a talking museum as a smart guide of sculptures’ art exhibition within the Maschio Angioino Castle, Naples, Italy. The final outcome of both projects should be the facilitation and increased stimulation of visits.

The study by Alletto et al. [29] discusses the design and validation of an indoor location-aware architecture able to enhance the visit experience in a museum. In particular, the proposed system relies on a wearable device that combines image recognition and localization capabilities to automatically provide the visitors with cultural content related to the observed artworks. The smart infrastructure provides localization information, and the system interacts with the cloud to store multimedia content produced/shared by visitors. All the above-mentioned studies illustrate the valuable contribution and utility of smart technologies in making customer journeys more interesting and memorable.

The above discussion highlights the key elements of the ST management framework and points out that there is a need for more conceptual and empirical research in the field of ST management framework to apprehend the tourist consumer behavior in general, and the influence of STTs on consumers’ experience in specific contexts and settings in particular. Our study attempts to address a knowledge gap in this field. The main argument is that tourist attractions must adopt the appropriate approach and make adequate use of STTs in order to meet the customers/visitors’ requirements and render their visit experience more attractive, interesting, and memorable.

3. Materials and Methods

3.1. Materials

3.1.1. Theoretical Background

This study applied the ‘customer journey’ model as a theoretical background. This journey model has its origins in the work on service blueprinting and service mapping by Shostack [33] and further developed by other studies [34,35]. The services provided by tourist attractions constitute experiential services where the focus is on the experience of the visitor/customer when interacting with the business/organization, rather than just the functional benefits following from the products and services delivered. The experiential services should be considered from the perspective of the ‘customer journey’ rather than as a single product or transaction, as suggested by some authors [6,36,37].

This ‘customer journey’ model puts the emphasis on the central role of the visitor/customer. This model implies that a customer experience is built over an extended period of time, starting before and ending after the actual experience to include pre- and post-purchase phases. Hence, the ‘customer journey’ consists of three phases: A prospective pre-trip period phase, an active tourism experience, and a reflective post-trip phase [36,37,38]. The prospective phase (before the actual visit/trip) involves information search, decision-making, and the purchase process [9,39]. In the second phase, the active one, the customer is on-site, at the business/attraction, having on-site interactions. The latter are most intense, and value is co-created through participation and engagement [6,8,36]. Finally, the reflective phase encompasses a recollection of the experience, satisfaction or otherwise, sharing memories and making recommendations. It is argued that all three phases have to be properly managed and marketed [18,23,35].

Therefore, a visitor experience cycle in terms of ‘customer journey’ includes anticipation, arrival, the visit itself, departure, recollecting, and sharing the experience [38]. The journey starts with information search about a tourist attraction, and includes the trip, visiting the attraction, venues and exhibits (experiencing the attraction itself), recollecting, and sharing the experience.

This model has been applied in or suggested by several studies in various settings, such as, for instance, in the context of innovation in experiential services in general [36]; to acquire insights into the responsible tourist experience [37]; for customer knowledge, in the context of learning from customers [6]; and in the context of customer value creation [39]. Other studies opted for the model of consumption behavior purchase (decision-making) of tourists; see [40,41], for instance. It is the argument of this study that the ‘customer journey’ model is more suitable because it refers specifically to experiential services as are those provided by tourist attractions.

Therefore, STAs should have a comprehensive/integrated approach to visitor behavior, considering all three phases of the customer journey. A framework considering the uses of smart technologies in all three phases is suggested in the following section.

3.1.2. Suggested Framework for the Influence of Smart Technologies

STA can be defined as smart scenic spots that are designed on the integration of cutting-edge technologies, as outlined in Section 2. The purpose is to improve the experience quality of tourists and combine innovative service and management concepts. The main benefits should be the efficient and effective management of STA; provision of smart services and value propositions to visitors, and efficient and effective marketing of the tourist attraction. The ultimate outcome would be the integrated, coordinated, and sustainable development of the tourist attraction, local society, and economy [38]. In STAs, visitors can co-create their experience through the digitalization of core business processes, design of services and value propositions, and marketing communications as well [28].

The strategic goal of every STA should be to make the customer journey more attractive, interesting, and memorable. Therefore, the STTs are the tool/medium for achieving the abovementioned goals.

Table 2. Impact/influence of smart technologies on customer journey.

Phase of Journey/Visit Experience	Uses of Smart Technologies	Their Influence on Visitors’ Experience
Prospective phase (Pre-visit)	Collecting information, opinions, and feedback from various sources. Getting information about tourist attractions Searching related tourism products and services Planning the trip/visit	Searching and planning Reducing decision risk Increasing interest in Building an understanding
Active phase (On-site, the visit itself)	Reading online reviews and comments Making short-term decisions Making mobile communication and transactions Collecting and recording moments/memories of the experience in form of video, image, audio, etc.	Facilitating navigation and communication Enhancing convenience and speed. Enhancing experience, flexibility, engagement, and enjoyment Making short-term decisions Recording and storing/collecting memories
Reflective phase (Post-visit)	Sharing videos, images, texts, etc., on SM/SNSs Sharing data and knowledge Posting reviews and giving advice	Recollecting memories Sharing experiences Evaluating (making recommendations and suggestions)

Source: Authors’ own elaboration, based on suggestions by various studies [6,8,14,37,40,41].

summarizes how visitors use smart technologies at different phases of the customer journey and the influence of these technologies on their behavior.

This outline leads us to postulate the research hypotheses regarding the influence of smart technologies on customer journey/visit experience at STAs.

3.1.3. Research Hypotheses: Impact/Influence of Smart Technologies on Visit Experience

As already indicated, over the last years, tourists are making use of various smart technologies throughout their visit cycle; they use them at all phases of the consumption experience, ‘customer journey’ [18,35,36,41]. The services provided by smart systems are influencing tourists in all tourism contexts and settings. This paper suggests the following influence/impact of smart technologies on the visit experience to tourist attractions in terms of phases of customer journey (i.e., prospective, active, and reflective).

Prospective phase (Pre-visit): Before their visit experience, tourists use smart technologies to search for information on relevant tourism services and select the tourist attractions that meet their own requirements from a large number of tourist attractions [26,42,43], and make their reservation and book in advance all tourism products and services needed for their visit or trip [20,35,40]. It is worth mentioning that at this stage of searching, comparing, and planning, tourists trust in the reviews of real experiences and recommendations by their peers and influencers on social networks [29,42], because the tourism product purchase has certain risk. An increasing number of tourists are using ‘shared knowledge of all tourists’ on digital platforms, and tourists depend on other user generated content (UGC) to get input so as to make their decision [43,44]. This knowledge on tourist attractions is the outcome of real experiences shared by other tourists in SM, and is very useful in reducing the risk of decision-making [23,40].

Within the ST context, tourist attractions can propose their offerings and make their services more convenient and faster, and to a certain extent, increase the attractiveness of their business [28]. At the same time, STAs have to use and implement the appropriate integrated marketing communications with the aim to convey a consistent, relevant, and effective message without creating higher/excessive expectations [18]. Furthermore, a segment of tourists will use smart technologies to actively search and learn relevant knowledge about the tourist attractions, the aim being to increase their understanding about the tourist attractions, to fully enjoy and cherish the visit, and make their experience more meaningful [30,31,42]. Based on this discussion, it can be claimed that smart technologies can be influential at the prospective phase of customer journey to a STA. Hence, the following hypothesis is stated:

Hypothesis 1 (H1).

Smart technologies do positively influence the prospective phase (PP) of customer journey by providing updated and reliable information and conveying the right, consistent message to make an Attractive and Memorable Visit Experience (AMVE) in Tourist Attractions.

Active phase (On site, visit itself): Human–computer interaction is the most direct reflection of tourists’ use of smart technologies in tourism activities, mobile tourism guides [45], mobile recommendation systems [46], navigation systems [47], congestion management systems [48], which are some of the most commonly used smart technologies for tourism activities. These smart technologies enhance tourists’ ability to co-create and co-manage their visit experience process, attaining more emotion and action to the tourism experience activity, thus increasing the degree of tourists’ input [49]. When tourists can get a higher degree of involvement in the process of tourism activities, they can give more positive emotional responses during the visit, thus improving the tourists’ sense of pleasure [50]. Electronic tour guides provide explanations for tourists instead of manual guides, which liberates tourists to a certain extent and maximizes tourists’ ability to move freely on the premise of getting the most accurate and timely explanation. Mobile devices (smart phones and tablets) can be used for access to information, communication, and self-entertainment during a trip or visit [20]. These technologies can enhance/help tourists to solve problems, make the visit/journey more flexible [8,17], and immediately provide (in real-time) their comments on their experience.

Therefore, it is argued that smart technologies can be influential in all actions and interactions during the visit itself, on site at tourist attractions. Hence, the following hypothesis is advanced:

Hypothesis 2 (H2).

Smart technologies do positively influence the active phase (AP) of customer journey, the visit itself by enhancing flexibility, providing convenience and speed, and facilitating engagement and enjoyment in making an Attractive and Memorable Visit Experience (AMVE) in Tourist Attractions.

Reflective phase (Post-visit): Likewise, once the visit experience has been completed, smart technologies will be used to comment on and recommend their experiences [4,18,20]. Tourists will post their experiences on social networking sites [23], and use these social networking sites to depict, reconstruct, and revisit their trips [51] in order to form a complete chain of opinions that will influence their peers and potential visitors. The study by Wu and Yan [52] found that writing and publishing post-trip experiences can help tourists strengthen and build tourism experience, and at the same time have an impact on the decision-making behavior of potential tourists. Tourists directly present their experiences on digital platforms in various forms, and this has important reference value for tourists to make future travel plans and to influence their behavior [19,53].

Hence, this study suggests that smart technologies can be influential in helping smart tourists to identify and address issues, to provide their feedback, and make suggestions about their visit experience. Therefore, the following hypothesis is advanced:

Hypothesis 3 (H3).

Smart technologies positively influence the reflective phase (RP) of customer journey by providing the platforms to tourists to share their knowledge and information, as well as to evaluate their experience, with the aim of rendering an Attractive and Memorable Visit Experience (AMVE) in Tourist Attractions.

The research framework/model is composed based on the above three hypotheses (Figure 1) depicting the predictor constructs (the three phases of customer journey) to dependent construct (Attractive and Memorable Visit Experience).

This research framework has been tested by means of an empirical study.

3.2. Empirical Study: Research Design and MethodologY

3.2.1. Study Site

Ningbo Museum is located in Yinzhou District, Ningbo City, Zhejiang Province, China. It covers an area of 10 acres, with a total construction area of 30,000 square meters and a total exhibition hall area of 8000 square meters. It was built in 2006, completed and opened to the public free of charge in 2008, with a volume of visitors more than 1.2 million per year [54]. It is a national first-class museum, and focuses on the Ningbo area history and traditional customs. The collection includes more than 60,000 pieces of precious bronze, porcelain, bamboo, jade, calligraphy and painting, gold and silver, folk customs, and other cultural relics from culture in prehistory to modern times [54,55].

Ningbo Museum currently operates one of the most advanced intelligent management systems in China. A variety of smart technologies are applied, such as AR, VR, Wi-Fi, and Artificial Intelligence. The museum management system is fully smart, from the electronic security inspection system to the electronic navigation system [54]. Ningbo Museum was chosen as the site for the empirical study mainly for the following reasons. It is classified as 5A tourist attraction by the China National Tourism Administration. Following a series of interviews conducted in February 2020 with managers of tourist attractions in Ningbo—to explore the uses of smart technologies by the tourist attraction, i.e., which technologies and for what purposes—the research team realized that the tourist attraction has prioritized smart technologies at the highest level compared to other attractions. Many visitors of Ningbo Museum are local residents; according to the Management of Ningbo Museum, the local residents are around 50 per cent. These reasons justified the option made by the research team.

3.2.2. Instrument Development

This study applied a quantitative research, using the technique of online survey. The initial plan was to conduct the questionnaires with personal interview, face-to-face; however, this plan was abandoned due to the lockdown and restrictions imposed in China because of the Covid-19 outbreak. That is the reason why the research team opted for plan B, the online survey technique, which was the only feasible and realistic one. The research instrument (questionnaire, see Supplementary Materials) encompassed three sections: (i) One section on opinion about smart technologies during the visit experience (6 items); (ii) one section about the research constructs (influence of smart technologies on customer journey, i.e., prospective, active, and reflective phases) with 5 questions; and (iii) one section on demographics (with five items).

As for Section 2 of the questionnaire, the research constructs and items were measured as follows. A total of 12 items were measured on a 5-point Likert scale by rating from not all influence/useful to very strong influence/useful, and strongly disagree to strongly agree. Items used to measure the consumers’ perceptions were derived from previous studies [6,19,37,40,41] and the suggested framework. A pilot test was performed with 10 persons to assure clarity and internal coherence. Following the pilot test, the questionnaire was accordingly finalized.

3.2.3. Data Collection

The technique of online survey was used to collect data. The study used a convenient sampling method on one condition; that is, they have visited Ningbo Museum at least one time over the last 12 months. Sampling procedures included post links to the questionnaire on WeChat platform (the most used among Chinese consumers) and snowballing. The research team considered that this strategy was suitable for attaining the research aim, as suggested by [56].

In total, the research team collected 503 questionnaires from Chinese consumers aged 18 years or older and having had a visit experience to Ningbo Museum over the last 12 months. The questionnaires were conducted over February to March 2020. It is believed that the sample size is suitable in the sense it provides reliability and validity [57]. A summary of the sample/respondents’ profile is shown in

Table 3. Sample profile (n = 503).

Characteristics	Frequency (n)	Percentage (%)
Gender
Male	147	29.2
Female	356	70.8
Age group
18 to 25	257	51.1
26 to 30	103	20.5
31 to 40	78	15.6
41 to 50	55	10.9
51 to 60	9	1.8
60+	1	0.1
Educational level
Junior high school	7	1.4
High school	27	5.4
Undergraduate student	288	57.2
Postgraduate student	165	32.8
Other	16	3.2
Occupation
Student	199	39.5
Teacher	94	18.7
Worker	8	1.6
Freelancer	58	11.5
Civil servant	27	5.4
Admin/Office employee	33	6.6
Enterprise staff	68	13.5
Other	16	3.2
Visits to Ningbo Museum (in numbers)
1	384	76.3
2	104	20.7
3+	15	3

.

The statistical analysis of data was performed on SPSS Version 25.0. The following section discusses the analyses and results.

4. Results: Data Analysis and Discussion

This study used a regression analysis to undertake a correlational analysis [57]. This analysis determines the influence of smart technologies on tourists at the three phases of customer journey to a tourist attraction; in other words, to have an Attractive and Memorable Visit Experience (AMVE). Statistical analyses were used to determine the role of the three phases (PP, AP, and RP, which are constructs/independent variables) in predicting the influence of smart technologies on AMVE (dependent variable). The confirmatory procedure to examine the interrelationships in the measure and an Exploratory Factor Analysis were conducted to test whether constructs are inter-correlated and suitable for factor analysis.

Reliability and Validity Testing

Cronbach’s alpha coefficient and the composite construct reliability were used to check the measuring reliability. The results are shown in

Table 4. Tests for reliability and validity.

Construct	Items	Mean	Standard Deviation	Standard Loading	t-test	Composite Reliability	Average Variance Extracted (AVE)	Cronbach’s Alpha
1. Prospective Phase (PP)	1.1 Searching and planning	4.27	0.642	0.720	6.259	0.821	0.534	0.777
	1.2 Reducing decision risk	4.01	0.710	0.733	1.291
	1.3 Increasing interest in	4.02	0.770	0.735	1.233
	1.4 Building an understanding	4.11	0.700	0.737	2.819
2. Active Phase (AP)	2.1 Facilitating navigation and communication	4.25	0.660	0.674	0.764	0.831	0.553	0.717
	2.2 Enhancing convenience and speed	4.18	0.699	0.777	1.511
	2.3 Enhancing flexibility, engagement, and enjoyment	4.35	0.687	0.782	1.548
	2.4 Making short-term decisions	4.03	0.749	0.838	5.936
	2.5 Recording and storing/collecting memories	4.10	0.742	0.674	4.009
3. Reflective Phase (RP)	3.1 Recollecting memories	4.05	0.722	0.783	2.972	0.783	0.547	0.783
	3.2 Sharing experiences (posting photos and reviews)	4.09	0.777	0.713	3.909
	3.3 Evaluating (making recommendations and suggestions)	3.94	0.714	0.721	0.303
Attractive and Memorable Visit Experience (AMVE)	Prospective Phase	4.10	0.541	0.770	4.300	0.810	0.587	0.796
	Active Phase	4.32	0.512	0.785	14.218
	Reflective Phase	4.02	0.616	0.744	0.868

. Cronbach’s alpha of the constructs was varying between 0.717 and 0.796. At the same time, the composite reliability of constructs was between 0.783 and 0.831, which was higher than 0.700, the minimum level/value for reliability [53]. The average variance (AVE) of all constructs had a value between 0.534 and 0.587, which was higher than the minimum of 0.500. The results showed that the structure can explain a large part of variance, so the measured value had enough convergence. Therefore, it is estimated that the measurement scale had sufficient internal consistency [56,57].

Table 5. Exploratory factor analysis.

	Constructs	Prospective Phase	Active Phase	Reflective Phase
Items
1.1 Searching and planning		0.716
1.2 Reducing decision risk		0.642
1.3 Increasing interest in		0.741
1.4 Building un understanding		0.733
2.1 Navigation and communication			0.702
2.2 Enhancing convenience and speed			0.713
2.3 Flexibility, engagement, and enjoyment			0.756
2.4 Making short-term decisions			0.588
2.5 Recording and storing memories			0.801
3.1 Recollecting memories				0.766
3.2 Sharing experiences (posting reviews)				0.723
3.3 Evaluating (making recommendations)				0.792

Note: Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization. Rotation converged in 5 iterations.

depicts the results of exploratory factor analysis (EFA). The factor load of each measurement item is between 0.588 and 0.801, which is higher than the standard of 0.500. These results prove that the measurement scale had very good internal consistency and reliability [57].

These results indicate that the three research hypotheses are supported. In order to test the research model, the relationship between the constructs must be significant. The Pearson product-moment correlation was conducted to determine the inter-relationships between the constructs (see

Table 6. Pearson correlation coefficient.

Constructs	Prospective Phase	Active Phase	Reflective Phase	AMVE
Prospective Phase	1	0.601 **	0.573 **	0.458 **
Active Phase	0.601 **	1	0.550 **	0.411 **
Reflective Phase	0.573 **	0.550 **	1	0.348 **
AMVE	0.458 **	0.411 **	0.348 **	1

** Correlation is significant at the 0.01 level/Sig. (2-tailed).

). Results showed that Pearson correlation coefficient (R) was between 0.348 and 0.601, n = 503 (p ≤ 0.01), which had a strong correlation. Hence, there was a significant correlation between all factors, so we can explore the feasibility of the research model.

The model and results of regression analysis are shown in

Table 7. Regression analysis: Model and results.

Parameter Test
Model		R *	R2		Adjusted R2		Std. Error of the Estimate		Durbin-Watson		F		Sig.
1		0.794	0.624		0.620		0.684		1.835		52.391		0.000
Predictors (Independent variables) are PP, AP, RP; and dependent variable is AMVE * R, correlation coefficient; R2, coefficient of determination; ∆R2, adjusted coefficient of determination.
Regression Model
Model				Unstandardized Coefficients				Standardized Coefficients		t		Sig.
				B		Std. Error		Beta
1	AMVE *			0.447		0.280				1.594		0.111
	Prospective Phase			0.434		0.075		0.300		5.754		0.000
	Active Phase			0.307		0.077		0.201		3.983		0.000
	Reflective Phase			0.088		0.063		0.069		1.398		0.163

* Dependent variable: AMVE (Attractive and Memorable Visit Experience).

.

The Durbin-Watson coefficient had a value of 1.975, which is within the required range of 1.00-2.00. The results indicated that the three phases of customer journey (the influence of smart technologies on tourist behavior at the three phases of visit experience to a tourist attraction) were significantly correlated with the quality of visit experience, an AVME.

A simple linear regression was conducted between the influence of smart technologies on tourists at the three phases of customer journey, X1 (=0.300, t = 5.754, Sig. = 0.000), X2 (=0.201, t = 3.983, Sig. = 0.000, and X3 (=0.069, t = 1.398, Sig. = 0.16) explained of variance of 62.4% of influence (see Table 7). Based on these results, it can be claimed the model works well. Results showed that the dependent variable (AMVE) is influenced by all three phases of the customer journey/visit experience; the most determining being the Prospective and Active phases. In the two phases, corresponding Sig. value of F is 0.000, indicating that the use of smart technologies has a significant influence on improving the quality of visit experience. At the third phase (Reflective), the influence is also significant, but in lesser extent.

These results support the research model as prediction model. Smart technologies are influential at all three phases of visit experience in having an Attractive and Memorable Visit Experience (AMVE) in a tourist attraction.

The present study proposed a research framework to explore the impact of smart technologies on tourists to obtaining an attractive and memorable experience. This influence was considered and analyzed in terms of three phases (i.e., prospective, active, and reflective) of a visit to a tourist attraction, based on the theoretical background of ‘customer journey’ model. The studies in this specific field are very rare. Findings by this study can be compared and contrasted only to two similar studies. The study by Gajdosik [22]—which was about the complete tourist experience, not only to a tourist attraction—indicates that the use of smart technologies is significant before the trip to and during the stay in the destination. It does not provide more detailed findings (what is the level/degree of influence at each phase), and the reflective phase (post-trip) was not investigated. The second study by Shen et al. [41] explored the influence of a specific type of smart tourism technologies (i.e., Social Networking Sites—SNSs) within the context of responsible consumer behavior. It was found that SNSs were influential in adopting a sustainable and responsible behavior by smart tourists. It was also found that all three phases of tourist experience were equally significant.

This study’s findings confirm the results of the previous two studies and indicate that the influence of smart technologies is significant at all three phases, the stronger influence being at the first two phases (prospective and active phases).

5. Conclusions, Implications and Suggestions for Future Research

Within the digital era and smart tourism management framework, markets are technology-driven and consumer-dominated, and tourists are one of the key actors. Technological developments and advances have already transformed the way in which tourist consumers search, plan, and purchase their trips and perceive and conduct their experiences. Smart technologies are used by tourists in a wide spectrum of settings and contexts. At all phases of a tourist/visit experience, the consumer is open to external influence, and smart technologies have become an increasingly important influence, especially in the crucial first phase.

In this context, the aim of this study was to explore the influence of smart technologies on tourists to have an attractive and memorable experience in the setting of tourist attractions. The study took a consumer perspective to investigate this impact on visit experience to a Chinese cultural tourist attraction.

5.1. Main Conclusions: Theoretical Contribution and Marketing Implications

The study’s aim was to analyze the influence of smart technologies on the visit experience in the context of tourist attractions. Firstly, a research framework/model was proposed to explore the impact of smart technologies on tourists to have an attractive and memorable experience, based on the theoretical background of ‘customer journey’ model. This influence was considered and analyzed in terms of phases of ‘customer journey’; that is, prospective, active, and reflective phases of the experience. Hence, this study advanced three hypotheses related to the positive impact smart technologies can have on the quality of visit experience.

The suggested research framework was then empirically tested in the context of tourist attractions, using as a study site the Ningbo Museum, Ningbo, China. The results support the crafted research framework for the influence of smart technologies; the findings of our study support all three research hypotheses. There was a significantly positive correlation between them, showing that smart technologies have an influence in attaining an attractive and memorable visit experience in tourist attractions implementing these kinds of technologies. It was found that the stronger influence was at the first phase (prospective) and second phase (active). The third phase (reflective) was also important in terms of influence, but to a lesser extent. These findings are therefore valuable from a theoretical perspective, and have practical/marketing implications.

Our study has a theoretical contribution in the sense that it extends our knowledge in the field of smart tourism management framework in terms of conceptual approach and empirical research into the influence of smart technologies on the visit experience. It makes a theoretical contribution in that (i) it provides better understanding of tourist consumer behavior in the smart tourism ecosystem; (ii) it extends the field of application of the ‘customer journey’ model in the context and setting of experiential services provided by tourist attractions; and (iii) it suggests the integrated/ comprehensive consideration of design and use of smart technologies in specific activities of tourist attraction settings. The suggested framework encompasses the three phases of ‘customer journey’ and provides an integrated consideration to effectively investigate the impact of smart technologies on tourist behavior for visit experiences to tourist attractions. It is argued that it is imperative to consider and analyze all three phases having an influence on a visit experience in the context of ST management framework.

Furthermore, the study has marketing implications for industry practitioners. It was found that smart technologies—that are used for various tourist purposes—can positively influence the visit experience in all three phases and effectively make this visit an attractive and memorable one. Therefore, it is crucial for tourism suppliers in general, and tourist attractions in particular, to apprehend contemporary (and future) tourists and their requirements and expectations. These tourists require and have high expectations in terms of experience quality. These expectations are technology-driven and are have augmented due to technological advances. Hence, tourist attractions have to craft value propositions for their visitors/customers, based on the principles of Service-Dominant Logic (SDL). These tourist suppliers have to design, craft, and implement the appropriate smart infrastructure and services according to the key principles of SDL entails, as suggested by some authors [7,28].

Smart technologies are tools/media that can contribute to addressing tourists’ requirements and to meeting their expectations, if they are properly designed. These technologies can be used by tourism suppliers (and tourist attractions) to make more persuasive, visually appealing value proposition displays and to provide a pleasant and memorable visit experience.

Likewise, smart technologies constitute a valuable marketing tool that should not be underestimated, and tourism suppliers must respond actively to the opportunities that they offer—or suffer the consequences. The challenge for tourist attractions is how to manage effectively and efficiently the smart infrastructure, technologies, and services. It is evident that their influence starts well ahead/prior to the actual visit on site. Therefore, the study’s findings are useful for the marketing communications, which should convey consistently the right message/s, because they must build up the adequate expectations, and avoid creating high expectations. Understanding tourists’ motivations and uses is imperative. and this knowledge should be incorporated into the crafting of attractive/suitable value propositions.

Hence, the study’s findings provide a kind of guidance and directions for the adequate designing and uses of smart technologies by tourist attractions for marketing purposes, according to tourists’ expectations and requirements aiming at improving their visit experience. This study argues that the adequate use of smart technologies can contribute to attractive and memorable experiences that create benefits for all actors in the smart tourism management framework.

5.2. Limitations and Suggestions for Future Research

It should be acknowledged that our study encompasses some limitations, despite its contribution. First, the sampling techniques due to specific circumstances. This study used a convenience sample of tourists who have visited the Ningbo Museum over the last twelve months. In addition, a very large proportion of respondents are students, causing a problem of the sample’s representativeness. These sampling techniques may make the results vulnerable and do not allow generalizing the study’s findings. The tourist behavior of other age groups could be explored by future research projects with the aim to identifying similarities and differences in generational perceptions and behavior. Second, there is a limitation regarding the research design; that is, the study site was limited to one tourist attraction (Ningbo Museum). The latter was chosen based on robust criteria/factors; however, it constitutes just a cultural tourist attraction. Future research should investigate the proposed research framework in more tourist attractions, cultural and natural. Further research could explore the similarities and differences between the two types of tourist attractions.

Another limitation is the empirical testing of the model, which was performed in the Chinese context. China has an impressive number of cultural and natural tourist attractions; however, the country is not unique, and the same is valid for other countries around the world. Comparative analysis should investigate the influence of smart technologies on tourists’ visit experiences in other developed and/or emerging tourist markets. Likewise, other empirical studies could be undertaken, taking as sample international and domestic tourists; they might have differences in terms of behavior. Another interesting pathway could be incorporate and assess some additional variables, such as mediating factors (e.g., value propositions).