Tourists’ Adoption of Context-Aware Applications: A Case Study,Using Personal Locator Beacons at the Mole National Park,Ghana

Abstract

The use of technology to provide contextual information for decision-making has been a long-standing practice. The study aims to identify the antecedents influencing tourists’ use of context-aware applications. It employs the Unified Model of Electronic Government Adoption (UMEGA) and the Expectation Confirmation Model (ECM) to investigate tourists’ behaviors in the use of Personal Locator Beacons (PLBs) during the pre-adoption and post-adoption stages at the Mole National Park in Ghana. Data was analyzed using Smart PLS 3.0 software. The findings suggest that pre-adoption performance expectancy, motivational needs, and personal situation significantly affect tourists’ pre-adoption intentions to continue using context-aware applications. In addition, information recommendation, service quality, confirmation expectation, and satisfaction influenced continued use and recommendation. However, social influence and enjoyment experience did not significantly impact tourists’ behavioral intentions. The findings contribute to theory by integrating components of the two models (UMEGA & ECM) and delving into the antecedents of users’ decision-making processes.

Keywords

context-aware applications national parks PLBs UMEGA ECM technology adoption.

Introduction

The digitalization of the tourism industry and the upsurge in the use of mobile handheld technologies have offered unique possibilities for the Management of National Parks to enhance visitors’ experience and understanding of natural places (Müller-Roux, 2021; Muñoz et al., 2019; Psyché et al., 2020). The National Parks of Canada, the United States, and Australia in 2018 and 2019 received over 327 million, 24.5 million, and 34 million tourist visits, respectively (Geng et al., 2023; Hartman, 2021). The most visited national park in Africa, South Africa’s Kruger National Park, attracts over 1.4 million visitors annually (Ferreira & Harmse, 2014; Kruger et al., 2017; Plessis & Saayman, 2015). In Ghana, the Kakum National Park, known for its canopy walkway, receives around 300,000 visitors annually (Osei-Owusu et al., 2018). The Mole National Park and Bia National Park receive about 15,000 and 3,000 visitors annually. In terms of revenue, by the end of 2019, Ghana, as a destination, raked in US$ 3.3 billion with a total of 1.3 million tourist arrivals (Ghana News Agency (GNA), 2021). This number of visitors and the revenue generated may surge because the Ghana Tourism Authority launched “Beyond the Return” and “December in GH” initiatives to attract more foreign visitors to Ghana.

National Parks as tourist destinations attract tourists and these generate employment prospects, support conservation efforts, and support local businesses. It also promotes education, cultural exchange, and environmental advocacy. Via tourism activities, infrastructural improvements are made in tourism areas, enhancing and offering recreational activities (Hajiabadi et al., 2022). National Parks may act as templates of environmentally sound tourist practices, supporting eco-friendly efforts in the business. This also promotes biodiversity since wildlife and ecosystems are preserved. Tourism helps to diversify the local economy and provides a buffer during recessions (Jeong et al., 2021).

Due to inadequate technologies in managing the national parks, several human injuries and deaths are recorded as a result of wild animal attacks. The parks face various challenges, including wild animal attacks on human lives, illegal logging, poaching, mining, land encroachment, inadequate funding, and inadequate technological equipment required for easy monitoring and conservation (IUCN/PACO, 2010). In the United States, over 47,000 people sought medical attention annually after being attacked or bitten by wild animals (Conover & Resources, 2019). The Comptroller and Auditor General Department in India reported that between 2012 and 2015, 166 wild animal attacks were recorded with 133 deaths (Hindu, 2016). In Nepal, within 5 year 463 cases of injuries and deaths were recorded (Acharya et al., 2016). Incidence of animal attacks were also reported in Ghana (Duodu, 2019; GhanaWeb, 2017). As a result of these challenges, the use of context-aware applications has been introduced in the management of national parks. These applications utilize context-aware data such as location, time, and weather to enhance the user’s profile and recognize their surroundings. Context-aware applications have the potential of spontaneously obtaining functional contexts from users’ environments and providing essential information to the users, including global positioning systems, vision sensors (cameras), and locally based services (Dey, 2001; Feng, 2018). The applications use system software that offers services explicitly catered for tourists, such as satellite phones, GPS units, and Personal Locator Beacons (PLBs) (S. R. Martin & Blackwell, 2016). These include personalized recommendations, communication tools, essential utilities, and emergency assistance. Some applications utilize sensor networks (SN) and radio frequency identification (RFID) to monitor and track users, allowing emergency actions to be taken when necessary. They are designed to be universal, adaptable to different user interfaces, mobile, intelligent, heterogeneous, marketable, comprehensive, navigable, innovative, and appealing to tourists (Chang et al., 2017).

The National Parks of Ghana have embraced these mobile technologies to improve visitor experiences by offering access to basic park information, ecological facts, alerts in times of danger, and historical narratives (Agyeman et al., 2019). Through mobile apps, context-aware applications such as location-based services, personal locator beacons, intelligent museums, geotagging, and artificial intelligence, visitors can access information about any park, plan their visit, navigate trails, take photographs, and share their experiences with others while feeling safe (Ayala et al., 2014; D. Liu et al., 2016). The applications provide a means of outreach and promoting parks, ensuring that visitors are informed safe, and spend more time outdoors.

Many researchers have conducted studies in the area of tourism, national parks conservation, and context-aware applications, including information and navigation, personalized recommendations, and interactive interpretation services (Ferreira & Harmse, 2014; D. Liu et al., 2016; Lundmark et al., 2010). Through the large amounts of data collected from various sources, tourists can now interactively access helpful information and services. However, fewer studies have been conducted on the antecedents affecting tourists’ adoption of context-aware applications in third-world countries. Although the Mole National Park in Ghana receives about 15,000 visitors annually, it is still relatively minor compared to other national parks in other African countries. The Park has a sustainable growth potential that can benefit conservation and nearby communities when visitors’ experiences are improved by integrating relevant digital technologies.

The objective of this paper is to explore the antecedents affecting tourists’ adoption of context-aware applications in national parks, using Personal Locator Beacons (PLBs) at the Mole National Park as a case study, and discuss innovative measures to mitigate the technological challenges while embracing the opportunities for sustainable park tourism. The result contributes to assisting managers of national parks in understanding the expectations of tourists visiting national parks. This understanding in turn, informs strategic decision-making processes related to park management and resource allocation. It will also contribute to theoretical development in the area of technology adoption. The rest of the paper is organized as follows: Section 2 presents the literature review delving into context-aware applications, theories of user adoption behaviors, hypotheses development, and the study settings. Section 3 presents the materials and methods. Section 4 presents the results and analysis. Section 5 presents a discussion of the results. Finally, section 6 concludes.

Literature Review

The Use of Context-Aware Applications in National Parks

Context-aware applications are a valuable addition to the infrastructure of national parks. These applications can gather data and information through GPS, sensors, databases, and other simple technological devices to provide real-time information to park managers and visitors (Truong & Dustdar, 2009). The applications can send and receive feedback from within their functional setting, thus offering potential benefits to the user, including flexibility, responsiveness, versatility, and personalization (Mandato et al., 2000). Context awareness is defined as “the process of detecting context entities by various methods, such as collecting data via sensors and user input, and refining the collected information into higher-level knowledge that constitutes the context of the user, which can be useful in various applications” (Hasanov et al., 2019, p. 1). Villegas et al. (2018) explained that “context-aware system knows how to sense, negotiate, communicate, and interact with environmental systems and how to anticipate environmental system states, situations, and changes.” This information can enhance visitors’ experiences while improving safety and resource management in the park.

Several studies have been published on the application of context-aware sensors in tourism. Silva et al. (2018) presented a study on Android applications that utilizes device sensors and public APIs to gather user context-aware information. The application recommends places for tourists based on their context and provides additional options. It was found that with applications, users, through their mobile devices, can easily interact with their environment. Colomo-Palacios et al. (2017) proposed a POST-VIA 360 - a platform designed to aid in managing the entire life-cycle of tourism loyalty after the initial visit, and tested it in Salamanca, Spain. The system collects data during the initial visit through pervasive approaches. The data is analyzed, and on subsequent visits, recommendations are offered based on the positioning through a bio-inspired recommender system. The findings indicated that the system’s recommendations were highly accurate compared to previous attempts in the field. An intelligent mobile tourist guide, Mobile Guide–TAIS, has been used to test a multiple model context-aware tourism recommendation system that uses collaborative filtering, considering the user’s context, to generate attraction ratings (Kashevnik et al., 2017). Chanda et al. (2022) examined how ICT may be used to facilitate context-aware tourism in West Bengal, which could benefit the local community by providing equal access to high-quality education, professional skill development, and effective governance and administration. In Malaysia, the difficulties in implementing context-aware tourism were investigated by Ab. Rahman et al. (2020). Utilizing information acquired from two ecotourism destinations, Cameron Highland and Pulau Langkawi, discovered that the implementation process, security and privacy, and knowledge of context-aware tourism rank among the biggest obstacles encountered. The goal of Troisi et al. (2023) was to demonstrate how a data-driven strategy to digital transformation might redefine context-aware tourism and evaluate how this approach affects creativity and sustainability. The results showed that data-driven smart tourism ecosystems can foster creativity and sustainability through the activation of a data culture, various resource types and digital skills, and user participation. In their 2019 study, Hardy and Aryal (2020) described a research methodology that made use of an application (app) that was outfitted with GNSS technology and a synthesized demographic survey. Because of this, visitors to the Australian island state of Tasmania can now have their behavior automatically tracked during their whole visit.

Twumasi, Coleman, and Manu (2005) utilized ETM+ and Landsat TM data to establish a comprehensive database for Digya National Park in Ghana. The database aids in decision-making processes. The study found that the primary driver of deforestation in Digya National Park is the fluctuation of Volta Lake, which accounted for most of all deforestation variables. Twumasi (2005) published a book utilizing GIS and remote sensing technologies to provide valuable information for policymakers in Ghana to manage protected areas effectively. The book provided background information and operational research for protected area managers, researchers, foresters, students, and anyone interested in exploring remote sensing for managing natural resource conservation.

This study focused on location-based services (LBS) of context-aware applications because LBS can determine a user’s location and provide a spatially appropriate information to their current position in the field. Zhou (2011) identified some examples of LBS, including emergency evacuation, location-specific adverts, mobile navigation, and services for check-in of mobile social networking sites. A Research and Markets firm reported that the revenue of location-based services in Ghana is expected to grow at a CAGR of 27.1% from 2018 to 2023 (Markets, n.d.). Based on this, Personal Locator Beacons (PLBs) have been chosen. These devices feature an inbuilt GPS receiver and can determine the beacon’s location on the globe (longitude and latitude) and transmit this information to the Cospas-Sarsat emergency system. The purpose of these beacons is to facilitate a quick and accurate response from search and rescue teams in an emergency so that tourists can feel safe in touring the park (Manual, n.d.). The investigation have revealed that many national parks and tourist sites, including Mole National Park, Kakum National Park, Bia National Park, and many more, have started using context-aware applications.

Theories of User Adoption Behaviors

This research is based on behavioral intentions; thus, a two-stage theoretical model has been adopted to analyze the two categories of adoption behaviors and intentions in their decision-making processes. The first stage, the pre-adoption stage, explains the rationale behind users’ choice of some specific service. The user retention intentions are addressed in the post-adoption stage.

A Unified Model of Electronic Government Adoption (UMEGA), a comprehensive model grounded on an e-government-specific context, was applied in the pre-adoption phase. It was developed to elaborate on the UTAUT model (Venkatesh et al., 2003) to offer a precise theory for e-government adoption (Dwivedi et al., 2017). The primary determinants of behavioral intention to use electronic services are social influence, performance expectancy, facilitating conditions, and effort expectancy. These determinants are presumed to positively influence citizens’ behavioral intentions toward using e-government projects. Regarding these extensions, the UMEGA maintains performance and effort expectancies as the main antecedents to pre-adoption behavioral intention. The power to explain attitudes toward use improved by 3.2%, while behavioral intention also improved by 4.5% when UMEGA was extended to include constructs such as trust in government, computer self-efficacy, and trust in the internet (Verkijika & De Wet, 2018).

In the post-adoption phase, users gain a deep understanding of the electronic services and can decide to continue or discontinue using electronic services. The Expectation Confirmation Model (ECM), initially proposed by Oliver (1977), has gained wide dominance and is found to be relevant in the post-adoption research on user behavior and IT continuance usage (Joo et al., 2017). ECM proposes that customer satisfaction entirely depends on user expectation confirmation and perceived performance expectancy (Bhattacherjee, 2001). It shows evidence of an association between pre-adoption and post-adoption of technology. It includes variables such as experience with quality, post-adoption perceived usefulness, and confirmation of expectations, culminating in satisfaction and continued intentions to use (Bhattacherjee, 2001; Tzeng et al., 2021). Several academics have used ECM to study users’ intentions to use different electronic systems services continuously. To assess and predict users’ intention to continue to use e-learning platforms, TAM and ECM were incorporated into an empirical model (M. C. Lee, 2010). Susanto integrated perceived security, self-efficacy, trust, and privacy into an extended ECM framework to investigate Korean users’ intention to continue using online banking electronic services (Susanto et al., 2016). To inquire into the influential factors of consumers’ intention to keep using online mobile shopping applications, ECM was expanded by integrating perceived value into the empirical model (Shang & Wu, 2017). The study, therefore, integrates UMEGA and ECM to understand the factors influencing tourists’ pre-adoption and post-adoption of context-aware applications. The pre-adoption stage explains tourists’ pre-adoption choices in Figure 1, while the tourists’ post-adoption retention is addressed at the post-adoption stage in Figure 2.

Figure 1.

Pre-adoption stage.

Figure 2.

Post-adoption stage.

Hypotheses Development

Pre-Adoption Stage

Pre-adoption Performance Expectancy and Intention to Use

Performance expectancy is “the degree to which an individual believes that using the system will assist him or her attain gains in job performance” (Venkatesh et al., 2003, p. 450). Several research findings have suggested that performance expectancy significantly correlates with the citizens’ intention to adopt and use electronic technologies (Khan et al., 2017). In addition, performance expectancy was the strongest predictor of individuals’ technology adoption (Mohammed et al., 2018). Cviko et al. (2013) stated, “Performance expectancy has been broadly used to comprehend the behavioral intention of teachers to adopt ICT.” Based on the above, it is determined if tourists’ initial performance expectancy for context-aware applications enhances their task achievement and intentions to use such applications. Therefore, the following proposal is made;

H 1. User pre-adoption performance expectancy significantly influences users’ intention to use context-aware applications.

Motivational Needs and Intention to Use

Motivational Needs describe users’ motivation to use services that contain their instant desires and comfort and yield greater motivation that improves such feelings. Lin noted, “Motivation has been identified as a key determinant of general behavior….information technology acceptance behavior…and work-related behavior…motivation influences individual intentions regarding an activity as well as their actual behaviors” (Lin, 2007). A significant positive effect was found between motivation and the decision to take a performance action in a ubiquitous decision-making environment (K. C. Lee & Chung, 2013; Tabaeeian & Mohammad Shafiee, 2023). For personal locator beacons, if tourists are convinced that while accessing some specific animals within the forest, and there is danger, park managers would get signals from the system, they may use the PLBs. However, if there are no benefits from using the PLBs, they may not want to use them. Therefore, it is proposed:

H 2. User motivational needs significantly influence users’ intention to use context-aware applications.

Personal Situation and Intention to Use

The personal situation in this study denotes the specific environment where technology is being applied. In studying mobile information user acceptance, it was established that it is critical to fit the context of use and the information technology service provided (Van Der Heijden et al., 2005). The tendency is high for a user to use mobile technology when such services are conveniently located in the appropriate and suitable environment, leading to the users’ positive evaluation (Xu & Yuan, 2009). They further detailed empirical evidence that weather, service location, timing, mobility, and urgency influence users’ intention to use taxi services. From the above, the weather at a time, the location of the tourists from some service, and familiarity of the tourists around the park can influence the tourists’ intention to use the PLBs. We, therefore, propose;

H 3. User personal situations significantly influence users’ intention to use context-aware applications.

Social Influence and Intention to Use

From the perspective of electronic service, social influence describes the degree to which others around them can influence individuals to use IT services. It comprises the direct and indirect influence of individuals’ feelings, thoughts, and attitudes by friends, parents, opinion leaders, social groups, reference groups, media, and peers to change their intentions to use electronic services (Rakibul Hafiz Khan Rakib, 2019).

Enormous studies have inveterate the dynamic function of social influence on users’ attitudes and intentions toward technology adoption (Alalwan et al., 2018; Patil et al., 2020). However, more research needs to be conducted on the ramifications of social influence on intention to use context-aware applications such as PLBs. Hoque and Sorwar (2017) pointed out the worthwhile effect of social influence on adopting technologies. In order to fulfill this gap in the literature, the following hypothesis is thus proposed:

H 4. User social influence significantly impacts users’ intention to use context-aware applications.

Intention to Use and Actual Usage

The construct “behavioral intention to use” explains an independent perspective of users toward the acceptance and use of electronic technologies (Kalinic & Marinkovic, 2016). Behavioral intention to ply affects the utilization of new technologies (Tabaeeian & Mohammad Shafiee, 2023; Venkatesh et al., 2003). Prior studies have proven that the “behavioral intention to utilize” positively impacts usage and recommendation (Verkijika, 2020). Therefore, this study reinforced the existing knowledge that tourists’ behavioral intention to use context-aware applications affects their actual usage. Hence, it is hypothesized that:

H 5. User intentions to use context-aware applications significantly influence tourists’ actual usage.

Post-adoption Stage

User Enjoyment Experience and User Satisfaction

Enjoyment experience describes the experience associated with the pleasure and fun users gain using information technology. The study indicated that travelers using mobile electronic services achieve their expectation confirmation through their enjoyment experience with the journey undertaken, and enjoyment experience also leads to users’ satisfaction (Boulding et al., 1993; Sedera et al., 2017).

Through context-aware applications, tourists would not only gain complete information about the parks, but they could also gain the pleasure of using diverse supplementary services such as being found in times of danger. Hence, tourists who experience immediate pleasure and joy from using PLBs are more likely to confirm their satisfaction with the use of PLBs more extensively than others. The following is therefore proposed;

H 6. User enjoyment experience of context-aware applications significantly influences users’ satisfaction.

User Information Recommendation and Confirmation/Satisfaction

Information recommendation in this study context refers to previous users relaying relevant information on the use of information systems to new users. Mouakket and Sun (2019) noted that information is highly relevant to innovation technology system users. Previous users recommending information on the use of information systems to new users leads to the confirmation of their expectations and satisfaction, which, in turn, influences their decision to continue using such technologies (Tan, 2013). Informativeness is essential in technology use and fulfills tourists’ satisfaction and confirmation expectations by enabling tourists to look for relevant information for decision-making. Hence, the following is juxtaposed:

H 7. User information recommendation of context-aware applications significantly influences users’ expectation confirmation.

User Experience of Service Quality and Satisfaction

The service quality of a system describes a measure of the efficiency and effectiveness of an online support information system in responding to online queries (D. Liu et al., 2016); it is categorized into three: responsiveness, reliability, and personalization. Several studies have established the positive impact of service quality on users’ intention to use electronic services (Mensah & Piankova, 2018; Singh et al., 2020; Yavari et al., 2018). Satisfaction with service quality is much better than expected when quality expectation exceeds quality perception (Tabaeeian et al., 2023; Tzeng et al., 2021). In a study by Deng et al. (2010), the quality of electronic services and mobile value-added applications positively impacted users’ satisfaction.

Thus, in this study, the quality of the PLB can influence tourists’ satisfaction in using it. Hence, it is hypothesized that:

H 8. The user experience of service quality of context-aware applications significantly influences user satisfaction in using context-aware applications.

User Expectation Confirmation and User Satisfaction

Confirmation was described as a comparison between experience with an earlier expectation of users (Sedera et al., 2017). Several discussions and contemporary works have proven that electronic services performance is assessed based on users’ previous expectations and actual experiences (Shafiee & Bazargan, 2018; Susanto et al., 2016; Tam et al., 2020). In the post-adoption stage, satisfaction is an outcome between earlier expectation confirmation and actual consumption experience (Bhattacherjee, 2001). If tourists’ expectation of the PLBs is confirmed, it will undoubtedly lead to satisfaction with the services of PLBs. Ambalov (2018) confirmed a general connection between confirmation and satisfaction. Therefore, the following proposal is made:

H 9. User expectation confirmation of context-aware applications significantly influences users’ satisfaction.

User Satisfaction and Continued Usage

Whereas satisfaction refers to the level of pleasure derived by users from a particular information system, continuous intentions are the actions that generate deep interest in the users and motivate them to keep using such a service. It is confirmed that users who are fulfilled and satisfied with an information system have a higher chance to continue using it (Mishra et al., 2023; Mohammad Shafiee et al., 2018). Once tourists use the PLBs and have a pleasant experience, their motivation to use PLBs is enhanced, resulting in sustained usage. The effect of user satisfaction on tourists’ propensity to continue using the PLBs is a subject for investigation. The following hypothesis is proposed:

H 10. User satisfaction in context-aware applications significantly influences continued usage.

Users’ Continued Usage and Recommendation

Research has confirmed that users’ satisfaction and continued use of electronic services enhances their further recommendation of such a service to other users (B. Kim, 2012). In using information systems, users’ continued use is a critical requirement in evaluating the efficiency and effectiveness of electronic systems (Eom et al., 2006). There is a high probability for users to offer recommendations to others if they are satisfied and continue to use a particular service (De Pelsmacker et al., 2019). It presupposes that if tourists are satisfied and continue using context-aware services, they would likely recommend them to other tourists. Hence, the following hypothesis is proposed:

H 11. Users’ continued usage of context-aware applications significantly influences user recommendations.

Study Setting

In Ghana, various national parks are conserved and managed to benefit wildlife and humans. National parks are protected land areas managed and conserved to benefit wildlife and humans. Some National Parks include Mole National Park, Kakum National Park, Kyabobo National Park, Digya National Park, Bui National Park, Bia National Park, and Nini-Suhien National Park. National Parks are valuable resources that aid in conserving and protecting biodiversity and ecological heritage. They have become increasingly important, especially as the government recognizes the need for biodiversity conservation to support agriculture and tourism. The Parks in Ghana have significant potential for supporting both the economy and biodiversity conservation. Parks and wildlife conservation could aid tourism development and create employment opportunities, particularly in rural areas (Lundmark et al., 2010).

Mole National Park, located near Damongo, the capital of the Savannah region, was chosen for the study due to the region’s numerous popular tourist attractions. Beyond the park being Ghana’s premier largest national park, other tourist attractions, including the Ancient Mosque and the Mystic Stone, both at Larabanga, are very close to the Mole Park. The Ghana Wildlife Division (GWD) manages national parks in Ghana.

The Mole National Park was established in 1930 as a game reserve to protect the antelopes. The park was gazetted and upgraded as a National Park under Wildlife Reserves Regulation (LI 710) in 1971, and it encompasses an area of 4,577 km², making it the largest protected area in Ghana (Ghana News Agency (GNA), 2021; IUCN/PACO, 2010). The park is classified as a Category II Protected Area according to the IUCN system and is primarily managed to preserve the ecosystem and provide recreational activities (Acquah et al., 2016; IUCN/PACO, 2010). Its management goals are to safeguard its ecological integrity by preventing negative boundary alterations, restoring and preserving park features and ecological processes, preventing inappropriate development or exploitation, offering research opportunities to enhance knowledge of Mole and its ecosystem, and keeping Mole a top recreational and tourist destination with private sector involvement, among other objectives (Acquah et al., 2016). Although tourists visit the park for wildlife, they are open to more than just wildlife. However, they are also involved in nature-based activities inside and outside the park. These include community tours, canoe safaris, cultural entertainment, visits to an ancient mosque, and trips to a mystic stone in neighboring communities (Acquah et al., 2016).

Materials and Methods

The study employed a quantitative approach with cross-sectional data collected from tourists visiting the Mole National Park based on their experiences with PLBs. The PLBs have an inbuilt GPS receiver, which determines the beacon’s precise location and transmits this information to an emergency system. The beacons facilitate a quick and accurate response in an emergency so that the tourist can easily be located and saved (S. R. Martin & Blackwell, 2016).

The analysis was done with Smart-PLS Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0 software. The present study employs primary data because it delivers the most precise first-hand evidence on the situation at the park.

The survey was conducted in two phases for both pre-adoption and post-adoption behaviors. The Park officials reported a daily average visit of 42 tourist in 2022, translating to 294 per week which represented our study population. The questionnaires were administered for 5 days including weekends in January 2023. A total of 210 tourists were randomly selected and oriented on the functions and uses of the PLBs. A total of 147 tourists completed the first survey questionnaires on pre-adoption of PLBs, with a response rate of 70%. After using the PLBs, they were made to complete the post-adoption survey questionnaire based on their usage experiences with the PLBs. Table 1 presents the demographic characteristics of the respondents in terms of gender, age, tour type and academic qualifications.

Table 1.

Sample Characteristics (n = 210).

		Pre-adoption		Post adoption
Variable	Option	Count	Percentage (%)	Count	Percentage (%)
What is your sex?	Male	98	66.67	62	69.67
What is your sex?	Female	49	33.33	27	30.33
Age range	16–25	82	55.78	45	50.56
	26–35	38	25.85	29	32.58
	36–45	15	10.20	9	10.11
	46 and above	12	8.16	6	7.4
Tour type	Alone	30	20.40	25	28.08
Tour type	Group	117	79.59	64	71.91
Academic qualifications	High school & lower	80	54.42	33	37.08
	Undergraduate	40	27.21	31	34.83
	Postgraduate	27	18.37	25	28.09

The first part of the questionnaire covered the demographic characteristics of the respondents, and the second part consisted of 13 factors with 38 multiple questions adapted to the context and measured in a two-stage model. All the constructs for both pre-adoption and post-adoption were adapted from seasoned researchers and modified for this study. Under the pre-adoption stage, three items each from pre-adoption performance expectancy, motivational needs, user personal situation, user social influence, intentions to use, and actual system use were adapted from (Dwivedi et al., 2019; D. Liu et al., 2016; Singh et al., 2020; Taylor & Todd, 1995). Under the post-adoption stage, all three items from the constructs; user enjoyment experience, user information recommendation, user experience of service quality, user expectation confirmation, user satisfaction, user continued usage, and user recommendation were adapted from (Bougie et al., 2003; Gupta et al., 2020; Y. Liu & Li, 2011).

In this study, two items were below 0.7 out of 38 and were therefore deleted. The percentage of deletion was 5.3%. Each item was tested on a five-point Likert scale because it strengthened the scale’s validity compared to a less than four-point scale. It further provides satisfactory discriminable choices to the respondent (Asún et al., 2016). Statistical analysis was performed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0 software (Ringle et al., 2015). Smart PLS 3.0 software provides a user-friendly and comprehensive platform for performing PLS-SEM analysis and reporting the results. It is widely used in research fields such as marketing, management, and social science.

Results

Measurement Model Evaluation

An evaluation of the theoretical measurement model comprises an examination of indicator reliability-loadings, internal consistency reliability-composite reliability, and convergent validity-average variance extracted, this done by following the standard decision-making procedures (J. F. Hair et al., 2019). Estimation of the measurement model assists in comparing the theories used for the investigation as well as the actual data obtained for the study (J. F. Hair et al., 2016). Indicator reliability is defined as “a variable or set of variables is consistent regarding what it intends to measure” (Urbach Frederik, 2010, p.18). Reflective loadings of 0.708 and above are recommended (L. Liu et al., 2020; Urbach Frederik, 2010). Table 2 shows the findings of the reflective measurement model. All loadings except USI02 and USI03 (deleted) under the pre-adoption were above the required threshold of 0.708. This implies that the indicators met the minimum threshold requirement and were a good measurement of the variables. An evaluation of the measurement and structural model was done when the results were then extracted and Figure 1 also shows the indicator loadings after USI02 and USI03 were deleted and the model re-run. Furthermore, Average Variance Extracted (AVE) a criterion for measuring convergent validity requires values of 0.50 and above (Fornell & Larcker, 1981; J. F. Hair et al., 2019). Accordingly, the empirically established latent variables are considered reliable and valid, as shown in Table 2.

Table 2.

Constructs Reliability and Validity.

Pre-adaption
Constructs	Construct reliability	AVG
Actual system usage	0.907	0.767
Intention to use	0.964	0.898
User motivational needs	0.810	0.589
User personal situation	0.850	0.656
User pre-adaption performance expectancy	0.887	0.723
User social influence	1.000	1.000
Post-adoption
User continued usage	0.915	0.782
User expectation confirmation	0.946	0.853
User enjoyment and experience	0.875	0.701
User service quality	0.863	0.677
User information recommendation	0.844	0.644
User recommendation	0.946	0.854
User satisfaction	0.868	0.766

Discriminant validity, defined as the “extent to which a construct is empirically distinct from other constructs in the structural model” was also assessed using Fornell and Larcker (1981) criteria. They argued that discriminant validity is achieved if a construct shares more variance with its assigned items than with any other construct. Table 3 shows that discriminant validity has been achieved as the various constructs share more variance with their required items than other constructs, denoted by the bold numbers.

Table 3.

Discriminant Validity.

Pre-adoption
Constructs	ASU	US	UMN	UPS	UPP	USI
Actual system usage	0.876
Intention to use	0.878	0.948
User motivational needs	0.504	0.529	0.767
User personal situation	0.686	0.680	0.409	0.810
User pre-adaption performance expectancy	0.601	0.575	0.415	0.631	0.850
User social influence	−0.052	−0.055	−0.006	−0.075	−0.012	1.000
Post-adoption
Constructs	UCU	UEC	UEE	UESQ	UIR	UR	US
User continued usage	0.884
User expectation confirmation	0.445	0.924
User enjoyment and experience	0.069	0.016	0.837
User service quality	0.532	0.626	0.111	0.823
User information recommendation	0.485	0.761	0.045	0.574	0.803
User recommendation	0.696	0.593	0.030	0.677	0.595	0.924
User satisfaction	0.502	0.604	0.101	0.709	0.507	0.578	0.875

Source. Fornell and Larcker (1981) criterion.

Moreover, discriminant validity was again assessed using the cross-loadings criterion (Chin, 1998; Rönkkö & Cho, 2022). Discriminant validity is achieved where item loadings are higher for their assigned constructs than for any other construct. From Table 4, it can be inferred that discriminant validity has been achieved, as item loadings are higher for their assigned constructs than for any other construct, denoted by the bold values.

Table 4.

Discriminant Validity; Cross-Loading.

Pre-adoption
	ASU	UIU	UMN	UPS	UPP	USI
UAU01	0.933	0.861	0.496	0.686	0.537	−0.049
UAU02	0.921	0.835	0.436	0.628	0.587	−0.043
UAU03	0.762	0.569	0.387	0.457	0.443	−0.046
UIU01	0.765	0.936	0.477	0.653	0.534	−0.067
UIU02	0.819	0.969	0.536	0.625	0.527	−0.046
UIU03	0.905	0.938	0.489	0.656	0.570	−0.045
UMN01	0.498	0.508	0.832	0.399	0.430	0.036
UMN02	0.246	0.261	0.677	0.193	0.204	0.039
UMN03	0.357	0.390	0.785	0.300	0.267	−0.087
UPPE01	0.500	0.507	0.377	0.500	0.849	−0.016
UPPE02	0.463	0.448	0.309	0.497	0.856	0.032
UPPE03	0.563	0.506	0.368	0.606	0.845	−0.042
UPS01	0.572	0.557	0.415	0.813	0.578	−0.055
UPS02	0.665	0.624	0.339	0.885	0.591	−0.077
UPS03	0.398	0.456	0.226	0.723	0.332	−0.046
USI01	−0.052	−0.055	−0.006	−0.075	−0.012	1.000
Post-adoption
	UCU	UEC	UEE	UESQ	UIR	UR	US
UCU01	0.884	0.388	0.091	0.478	0.425	0.607	0.467
UCU02	0.887	0.361	0.075	0.437	0.408	0.532	0.402
UCU03	0.882	0.427	0.017	0.493	0.449	0.697	0.458
UEC01	0.449	0.927	0.002	0.602	0.761	0.609	0.583
UEC02	0.420	0.934	0.002	0.569	0.706	0.545	0.518
UEC03	0.359	0.910	0.042	0.561	0.637	0.484	0.572
UEE01	0.052	0.021	0.756	0.108	0.056	0.059	0.044
UEE02	0.027	−0.023	0.808	0.068	−0.001	−0.001	0.055
UEE03	0.076	0.026	0.938	0.104	0.050	0.025	0.119
UIR01	0.323	0.573	0.003	0.468	0.770	0.451	0.393
UIR02	0.454	0.520	0.060	0.474	0.789	0.522	0.330
UIR03	0.400	0.713	0.046	0.450	0.847	0.473	0.478
UR01	0.659	0.559	0.022	0.606	0.594	0.906	0.512
UR02	0.695	0.531	0.043	0.631	0.541	0.945	0.540
UR03	0.578	0.555	0.018	0.639	0.518	0.920	0.550
US01	0.424	0.592	0.095	0.718	0.471	0.556	0.901
US02	0.461	0.454	0.081	0.505	0.413	0.448	0.849
USQ01	0.419	0.583	0.099	0.785	0.565	0.621	0.537
USQ02	0.515	0.527	0.111	0.851	0.470	0.589	0.571
USQ03	0.379	0.432	0.062	0.831	0.379	0.459	0.642

An assessment of path coefficient significance was carried out on the structural model using bootstrapping algorithm in SmartPLS. Bootstrapping is defined as a “non-parametric resampling procedure that assesses the variability of a statistic by examining the variability of the sample data rather than using parametric assumptions to assess the precision of the estimates” (Streukens & Leroi-Werelds, 2016, p. 2). Through bootstrapping method, values for t-statistics are produced for analyzing the direct and indirect effects (J.F. Hair et al., 2016). T-values of 1.65 (10% two-tailed) and above indicate significance for relationships (J. F. Hair et al., 2011). From Table 5, nine out of the 11 hypotheses, were significantly supported as they met the minimum T-value threshold of 1.65 and consistent with Rivai et al. (2019), and two hypotheses were not supported. The hypotheses supported in the path values are also shown with their corresponding significance in Figures 3 and 4. The R square determination coefficient (R²), a widely used criterion, measures the model’s explanatory power. R² values presented in Figure 3 shows that user pre-adoption performance expectancy, user motivational needs, user personal situation, and user social influence together explain about 55.6% of the variations in user intention to use. User intention to use explains 77.2% of the variations in actual system usage. Figure 4 shows that user information recommendation explains about 57.9% of the variants in user expectation confirmation and user enjoyment experience, and the user experience of system quality explains about 63.5% of the variations in user expectation confirmation. It explains about 55.1% of the variation in user satisfaction. User satisfaction accounts for 25.3% of the variation in user continued usage, and user continuous usage accounts for 49.4% of user recommendations.

Table 5.

Direct Hypothesis Test Results.

Hypothesis	Relationship	Std beta	Std error	T-values	Decision	95.0% CI LL	95.0% CI UL
H1	UPPE→UIU	0.17	0.06	2.68	Supported	0.28	0.28
H2	UMN→UIU	0.27	0.06	4.83	Supported	0.36	0.35
H3	UPS→UIU	0.46	0.07	6.16	Supported	0.58	0.58
H4	USI→UIU	−0.02	0.03	0.61	Not supported	0.03	0.03
H5	UIU→ASU	0.88	0.02	57.39	Supported	0.90	0.90
H6	UEE→US	0.04	0.04	1.06	Not supported	0.10	0.08
H7	UIR→UEC	0.76	0.02	34.94	Supported	0.80	0.79
H8	UESQ→US	0.54	0.05	10.25	Supported	0.63	0.63
H9	UEC→US	0.27	0.06	4.55	Supported	0.37	0.36
H10	US→UCU	0.51	0.05	10.88	Supported	0.58	0.57
H11	UCU→UR	0.71	0.03	20.70	Supported	0.76	0.75

Table 6.

Model Fit (SRMR).

	Original sample (O)	Sample mean (M)	95%	99%
Saturated model	0.07	0.04	0.05	0.08
Estimated model	0.15	0.06	0.07	0.09

Figure 3.

Outer loadings pre-adoption.

Figure 4.

Outer loadings post-adoption.

Model Fit Analysis

The Standardized Root-Mean-Square Residual (SRMR) widely used for confirming the overall model fitness, was found to be 0.07 (the composite model) and 0.08 (the standard factor model), both were under the threshold of 0.08 (Bailey et al., 2017; J. F. Hair et al., 2016). This is shown in Figure 6.

Discussion

This study proposes an integrated model that explains the factors influencing tourists’ pre-adoption, post-adoption, and continued use of context-aware applications. Applying the UMEGA and ECM as the baseline theories, this study presents pertinent pre-adoption and post-adoption variables (Pre-adoption: motivational needs, pre-adoption performance expectancy, personal situation, and social influence. Post-adoption: user enjoyment experience, user information recommendation, user experience of service quality, expectation confirmation, user satisfaction, and recommendation). These factors are yet to be critically examined by other researchers in context-aware applications and national parks. In order to authenticate the relationships amongst the factors, a structural equations analysis was conducted using partial least squares (PLS-SEM) modeling and Smart PLS software 3. The result of the study indicated that out of the 11 (11) hypotheses proposed, except H4-social influence and H6-user enjoyment experience, all were supported, as indicated in the path values with their corresponding significance.

For the pre-adoption stage, the findings indicate that among the factors influencing tourists’ intention to use context-aware applications, personal situations, motivational needs, and pre-adoption performance expectancy significantly influenced tourists’ intention to use, which eventually determines their actual use of context-aware applications. Social influence was not supported. Personal situation has the most significant influence; this finding is supported by Jung et al. (2009), which means that the specific environment in the park would influence the tourists to use the PLB technology, which can positively influence their intention and then contribute to actual usage. Also, the motivational need was a crucial factor that may influence tourists’ use of context-aware applications, supported by K. C. Lee and Chung (2013). It reveals that feeling safe while visiting dangerous locations in the park for sightseeing and relaxation motivates tourists to use context-aware applications. In such circumstances, tourists would be highly inclined to use the applications in the park. It also presumes that tourists’ motivation to use the applications is associated with context. It is interesting to note that social influence was not theoretically supported in the study, which means that the views of some middle class in society do not necessarily influence the ordinary people’s use of new electronic technologies. This finding is supported by Lavuri et al. (2023).

At the post-adoption stage, the factors influencing the post-adoption experience of tourists’ use of context-aware applications, including user information recommendation, user experience with service quality, user expectation confirmation, and user satisfaction, were significantly supported. User enjoyment experience was not supported. User information recommendation has the highest impact on context-aware applications. This finding is supported by Mouakket and Sun (2019) and Tan (2013). It implies that once information on using context-aware applications is recommended to tourists and they confirm their expectations and become satisfied with it, they will continue using it. Also, users’ experience in service quality of context-aware applications was significantly influenced by satisfaction with using the applications, corroborated by prior findings by Singh et al. (2020). If PLBs can ensure responsive, personalized, and reliable service to the needs of tourists, then tourists are likely to feel satisfied with their experience (F. Martin & Bolliger, 2018; Saeed & Zyngier, 2012). The influence of user enjoyment experience on user satisfaction was not supported. This result was contrary to the findings of Boulding et al. (1993) and Sedera et al. (2017) that enjoyment experience also leads to satisfaction of users. The contrary outcome of the result was quite surprising because an enormous amount of literature supports it. However, this indicates that not all tourists who experienced enjoyment in the use of the PLBs would be satisfied.

Furthermore, the expectation confirmation of tourists using context-aware applications was shown to be significantly influenced by user satisfaction. Enormous studies have confirmed this with ECM (M. C. Lee, 2010). There is a general connection between confirmation and satisfaction (Ambalov, 2018). It implies that tourists’ tactual experience with context-aware applications would confirm their satisfaction. Satisfaction has tremendously impacted continuous usage and subsequent recommendation of electronic services. This finding adds to the works of S. S. Kim and Son (2009) and Mou et al. (2021), who discovered that word-of-mouth about online services is significantly influenced by satisfaction. Hence, tourists’ satisfaction with context-aware applications will increase their propensity to sustain their continued use. Alternatively, their dissatisfaction will cause them to discontinue using these applications. The influence of continued usage on user recommendation was highly significant. Though this was slightly similar to the findings of B. Kim (2012) and De Pelsmacker et al. (2019), there was less literature on continued usage and user recommendation. It confirmed that tourists who continued to use the applications would recommend the same to new tourists visiting national parks.

Based on the discussions above, national park managers must focus on improving the tourists’ initial adoption factors, such as personal situations, motivational needs, and pre-adoption expectations. This will ultimately lead to better decisions by post-adopters to continue using context-aware applications in the long term. Using the personal situation of tourists in the park is also an opportunity to market context-aware applications to them. The Ghana Tourism Authority has launched two initiatives—“Beyond the Return” and “December in GH ” Park operators can benefit from this initiative by promoting context-aware applications, mainly to foreigners, singles, and first-time visitors.

Policy and Theoretical Implications

The findings of this study have some implications for policy and theorical development in e-governance. First, policymakers in tourism should prioritize improving on tourists’ performance expectancy, motivational needs, personal situation, information recommendation, service quality, confirmation expectation, and satisfaction as these plays a crucial role in influencing tourists’ decisions to adopt, continue using, and recommending these applications, underscoring the importance of quality service provision.

Policies and strategies for deploying context-aware applications should consider the cultural sensitivities of tourist and contextual factors unique to African tourism destinations. Tailoring these technologies to align with local practices and preferences can enhance their acceptance and effectiveness among diverse tourist populations.

The Ghana Tourism Authority should encourage industry players to conduct research into initiatives that play a role in the use of context-aware applications at National Parks such as “Beyond the Return” and “December in GH” initiatives. Tourism policy implementers should ensure that donations from foreign agencies and funds from Government are aligned to effective development and promotion of context-aware application adoption.

The Authority should invest into additional innovative technologies, such as satellite network like Starlink for the network layer satellite phones, to continually improve and adapt these applications to evolving tourist needs and industry trends.

To improve on the overall experience of tourists, park managers should consider providing secondary recreational facilities such as table tennis, volleyball court, and a gym center for interested tourists.

This study offered theoretical contribution to the research on electronic government service adoption. It presented an integrated model of Unified Electronic Government Adoption and Expectation Confirmation Model for understanding factors influencing tourists’ pre-adoption and post-adoption of context-aware applications in tourism. It strengthened the theoretical relationships among the construct’s intentions to use, actual usage, confirmation, satisfaction, continuance intentions, and recommendation.

These policies, when implemented by tourism authorities and policymakers can contribute to the effective deployment of context-aware applications, ultimately fostering safer and more secure tourism experiences in African destinations.

Conclusion

The study aimed to investigate the factors that affect tourists’ use of context-aware applications at the pre-adoption and post-adoption stages using Mole National Park in Ghana. It was accomplished with data collected from tourists on their experiences and integrating UMEGA and UCM.

The study suggests that the success of context-aware applications lies in park managers improving tourists’ personal situations, motivational needs, and pre-adoption performance. It would influence their intentions to continue to use these applications. Tourists’ information recommendations, service quality, expectation confirmation, and satisfaction are critical for decision-making. Social influence does not necessarily impact the adoption of context-aware applications. It implies that park managers must ensure that these critical factors are fulfilled for tourists to continue recommending context-aware applications. This research highlights that post-adoption beliefs often differ from initial intentions and are greatly influenced by actual use experience.

Although the study has significantly contributed to tourism, theory, and information systems, one limitation of the study is the constrained generalizability of the findings as a result of the unique environmental conditions within the National Park such as weather patterns, access roads, and safari vehicles which may not accurately reflect the situation in other tourist destinations. Future studies may consider investigating how context-aware application use for tours varies in different environments.

In order to ensure accuracy in context-aware applications, integration of AI-powered cameras with edge computing technology is recommended, along with the utilization of a satellite network like Starlink for the network layer.

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.

Ethics Statement

Not applicable in our study

ORCID iD

Bernard John Tiika

Data Availability Statement

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

References

Ab. Rahman

S. A.

Dura

Yusof

M. A.

Nakamura

Abu Nong

(2020). Challenges of smart tourism in Malaysia eco-tourism destinations. Planning Malaysia, 18(4), 442–451. https://doi.org/10.21837/pm.v18i14.844

Acharya

K. P.

Paudel

P. K.

Neupane

P. R.

Köhl

(2016). Human-wildlife conflicts in Nepal: Patterns of human fatalities and injuries caused by large mammals. Plos One, 11(9), e0161717–e0161718. https://doi.org/10.1371/journal.pone.0161717

Acquah

Dearden

Rollins

(2016). Nature-based tourism in Mole National Park, Ghana. African Geographical Review, 35(1), 53–69. https://doi.org/10.1080/19376812.2015.1088389

Agyeman

Aboagye

Ashie

(2019). Visitor satisfaction at Kakum National Park in Ghana. Tourism Recreation Research, 44, 178–189. https://doi.org/10.1080/02508281.2019.1566048

Alalwan

Dwivedi

Rana

Algharabat

(2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. https://doi.org/10.1016/J.JRETCONSER.2017.08.026.

Ambalov

I. A.

(2018). A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561–1571. https://doi.org/10.1016/j.tele.2018.03.016

Asún

R. A.

Rdz-Navarro

Alvarado

J. M.

(2016). Developing multidimensional Likert scales using item factor analysis: The case of four-point items. Sociological Methods & Research, 45(1), 109–133. https://doi.org/10.1177/0049124114566716

Ayala

Amor

Pinto

Fuentes

Gámez

(2014). Imuseuma: An agent-based context-aware intelligent museum system. Sensors, 14, 21213–21246. https://doi.org/10.3390/s141121213

Bailey

A. A.

Pentina

Mishra

A. S.

Ben Mimoun

M. S.

(2017). Mobile payments adoption by US consumers: An extended TAM. International Journal of Retail & Distribution Management, 45(6), 626–640. https://doi.org/10.1108/ijrdm-08-2016-0144

10.

Bhattacherjee

(2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.

11.

Bougie

Pieters

Zeelenberg

(2003). Angry customers don’t come back, they get back: The experience and behavioral implications of anger and dissatisfaction in services. Journal of the Academy of Marketing Science, 31(4), 377–393. https://doi.org/10.1177/0092070303254412

12.

Boulding

Kalra

Staelin

Zeithaml

V. A.

(1993). A dynamic process model of service quality: From expectations to behavioral intentions. JMR, Journal of Marketing Research, 30(1), 7. https://doi.org/10.2307/3172510

13.

Chanda

Sajnani

Gowreesunkar

V. G.

(2022). Smart tourism technologies - A key to success and survival for sustainable eco-tourism development in Dooars Region (West Bengal) [Conference session]. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–7. IEEE. https://doi.org/10.1109/ICRITO56286.2022.9964796

14.

Chang

Yao

(2017). The design of a context-aware service system in intelligent transportation system. International Journal of Distributed Sensor Networks, 13(10), 1–18. https://doi.org/10.1177/1550147717738165

15.

Chin

W. W.

(1998). The partial least squares approach to structural equation modeling. In Marcoulides

G. A.

(Ed.), Modern Methods for Business Research (pp. 295–336). Lawrence Erlbaum Associates.

16.

Colomo-Palacios

García-Peñalvo

F. J.

Stantchev

Misra

(2017). Towards a social and context-aware mobile recommendation system for tourism. Pervasive and Mobile Computing, 38(2), 505–515. https://doi.org/10.1016/j.pmcj.2016.03.001

17.

Conover

M. R.

Resources

(2019). Numbers of human fatalities, injuries, and illnesses in the United States due to wildlife. Human–Wildlife Interactions, 13(2), 264–276.

18.

Cviko

McKenney

Voogt

(2013). The teacher as re-designer of technology integrated activities for an early literacy curriculum. Journal of Educational Computing Research, 48(4), 447–468. https://doi.org/10.2190/ec.48.4.c

19.

Deng

Wei

K. K.

Zhang

(2010). Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International Journal of Information Management, 30(4), 289–300. https://doi.org/10.1016/j.ijinfomgt.2009.10.001

20.

De Pelsmacker

Dens

Verberckmoes

(2019). How ad congruity and interactivity affect fantasy game players’ attitude toward in-game advertising. Journal of Electronic Commerce Research, 20(1), 55–74.

21.

Dey

A. K., A.

, (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7. http://dl.acm.org/citation.cfm?id=593572

22.

Duodu

(2019). Mole park elephant attacks poacher. Graphic Online. https://www.graphic.com.gh/news/general-news/ghana-news-mole-park-elephant-attacks-poacher.html

23.

Dwivedi

Y. K.

Rana

N. P.

Janssen

Lal

Williams

M. D.

Clement

(2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230. https://doi.org/10.1016/j.giq.2017.03.001

24.

Dwivedi

Y. K.

Rana

N. P.

Jeyaraj

Clement

Williams

M. D.

(2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

25.

Eom

S. B.

Wen

H. J.

Ashill

(2006). The determinants of students’ perceived learning outcomes and satisfaction in University online education: an empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215–235. https://doi.org/10.1111/j.1540-4609.2006.00114.x

26.

Feng

(2018). Context-aware computing. Tsinghua University Press and Walter de Gruyter.

27.

Ferreira

Harmse

(2014). Kruger National Park: Tourism development and issues of social carrying capacity in the management of large numbers of tourists real world problem day visitor numbers have increased and holidays has intensified in the Marula south part of KNP with concom. Journal of Ecotourism, 13(1), 16–34. https://doi.org/10.1080/14724049.2014.925907

28.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

29.

Geng

C. D.

Harshaw

H. W.

Wang

(2023). Impacts of COVID-19 on tourism and management response from Banff National Park, Canada. Journal of Forest Research, 34, 1229–1244. https://doi.org/10.1007/s11676-022-01580-4

30.

Ghana News Agency (GNA). (2021). Kakum National Park targets 10,000 visitors in December. Agency, Ghana News. https://gna.org.gh/2021/11/kakum-national-park-targets-10000-visitors-in-december/

31.

GhanaWeb. (2017). Elephant attack on Minister: We knew nothing about visit – Mole Park Management. GhanaWeb. https://www.ghanaweb.com/GhanaHomePage/NewsArchive/Elephant-attack-on-Minister-We-knew-nothing-about-visit-Mole-Park-Management-587963

32.

Gupta

Yousaf

Mishra

(2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094. https://doi.org/10.1016/j.ijinfomgt.2020.102094

33.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202

34.

Hair

J. F.

Risher

J. J.

Sarstedt

Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203

35.

Hair

J. F.

Hult

Ringle

G. T.

Sarstedt

M., C., M.

(2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.

36.

Hajiabadi

J. R.

Mohammad Shafiee

Kazemi

(2022). Developing a model of destination value co-creation by focusing on the role of the tourist personality traits as a moderator: Case study of selected destinations in Iran. Journal of Tourism and Development, 11(1), 105–127. https://doi.org/10.22034/jtd.2021.271077.2260

37.

Hardy

Aryal

(2020). Using innovations to understand tourist mobility in national parks. Journal of Sustainable Tourism, 28(2), 263–283. https://doi.org/10.1080/09669582.2019.1670186

38.

Hartman

(2021). Projected impact of visitor limitations in Glacier National Park (p. 426). Institute for Tourism and Recreation Research Publications. https://scholarworks.umt.edu/itrr_pubs/426

39.

Hasanov

Laine

T. H.

Chung

T. S.

(2019). A survey of adaptive context-aware learning environments. Journal of Ambient Intelligence and Smart Environments, 11(5), 403–428. https://doi.org/10.3233/ais-190534

40.

Hindu. (2016). 166 lives lost in 3 years due to wild animal attacks. The Hindu Group. https://www.thehindu.com/news/national/tamil-nadu/166-lives-lost-in-3-years-due-to-wild-animal-attacks/article14622058.ece

41.

Hoque

Sorwar

(2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002

42.

IUCN/PACO. (2010). Parks and reserves of Ghana: management effectiveness assessment of protected areas. IUCN.

43.

Jeong

Lee

Brown

A. D.

Choi

Son

(2021). Does a national park enhance the environment-friendliness of tourists as an ecotourism destination? International Journal of Environmental Research and Public Health, 18(16), 8321. https://doi.org/10.3390/ijerph18168321

44.

Joo

Y. J.

Park

Shin

E. K.

(2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83–90. https://doi.org/10.1016/j.chb.2016.12.025

45.

Jung

Perez-Mira

Wiley-Patton

(2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129. https://doi.org/10.1016/j.chb.2008.07.011

46.

Kalinic

Marinkovic

(2016). Determinants of users’ intention to adopt m-commerce: An empirical analysis. Information Systems and e-Business Management, 14(2), 367–387. https://doi.org/10.1007/s10257-015-0287-2

47.

Kashevnik

A. M.

Ponomarev

A. V.

Smirnov

A. V.

(2017). A multimodel context-aware tourism recommendation service: Approach and architecture. Journal of Computer and Systems Sciences International, 56(2), 245–258. https://doi.org/10.1134/s1064230717020125

48.

Khan

I. U.

Hameed

Khan

S. U.

(2017). Assessing the determinants of flow experience in the adoption of learning management systems: The moderating role of perceived institutional support. Behaviour & Information Technology, 36(11), 1162–1176. https://doi.org/10.1080/0144929x.2017.1362475

49.

Kim

(2012). Understanding key factors of users’ intentions to repurchase and recommend digital items in social virtual worlds. Cyberpsychology Behavior and Social Networking, 15(10), 543–550. https://doi.org/10.1089/cyber.2012.0128

50.

Kim

S. S.

Son

J. Y.

(2009). Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly, 33(1), 49–70. https://doi.org/10.2307/20650278

51.

Kruger

Viljoen

Saayman

(2017). Who visits the Kruger National Park, and why? Identifying target markets. Journal of Travel & Tourism Marketing, 34(3), 312–340. https://doi.org/10.1080/10548408.2016.1156618

52.

Lavuri

Jaiswal

Thaichon

(2023). Extrinsic and intrinsic motives: Panic buying and impulsive buying during a pandemic. International Journal of Retail & Distribution Management, 51, 190–204. https://doi.org/10.1108/ijrdm-01-2022-0010

53.

Lee

K. C.

Chung

(2013). The impacts of intrinsic and extrinsic motivators on ubiquitous delivery decision making. International Journal of Mobile Communications, 11(2), 140–158. https://doi.org/10.1504/ijmc.2013.052638

54.

Lee

M. C.

(2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002

55.

Lin

H. F.

(2007). Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions. Journal of Information Science, 33(2), 135–149. https://doi.org/10.1177/0165551506068174

56.

Liu

Tong

Liu

Yuan

(2016). Examining the adoption and continuous usage of context-aware services. Information Development, 32(3), 608–621. https://doi.org/10.1177/0266666914563358

57.

Liu

Ren

Wang

(2020). Calibration of the live load factor for highway bridges with different requirements of loading. Advances in Civil Engineering, 2020, 1–10. https://doi.org/10.1155/2020/7347593

58.

Liu

(2011). Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27(2), 890–898. https://doi.org/10.1016/j.chb.2010.11.014

59.

Lundmark

L. J. T.

Fredman

Sandell

(2010). National parks and protected areas and the role for employment in tourism and forest sectors: A Swedish case. Ecology and Society, 15(1), 19. https://doi.org/10.5751/es-03175-150119

60.

Manual

P. S.

(n.d). PLB-350B 406 MHz personal locator beacons (pp. 1–21).

61.

Mandato

Kovacs

Hohl

Amir-Alikhani

(2000). CAMP: a context-aware mobile portal. In IEEE Globecom ’00 Workshop. 2000 IEEE Service Portability and Virtual Customer Environments (IEEE Cat. No. 00EX498) (pp. 52-61). https://doi.org/10.1109/SPVCE.2000.934161.

62.

Martin

Bolliger

D. U.

(2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning Journal, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092

63.

Martin

S. R.

Blackwell

J. L.

(2016). Personal locator beacons: Influences on wilderness visitor behavior. International Journal of Wilderness, 22(1), 25–31.

64.

Mensah

I. K.

Piankova

(2018). Factors determining the use of e-government services: An empirical study on Russian students in China. International Journal of E-Adoption, 10(2), 1–5. https://doi.org/10.4018/IJEA.2018070101

65.

Mishra

Shukla

Rana

N. P.

Currie

W. L.

Dwivedi

Y. K.

(2023). Re-examining post-acceptance model of information systems continuance: A revised theoretical model using MASEM approach. International Journal of Information Management, 68, 102571. https://doi.org/10.1016/j.ijinfomgt.2022.102571

66.

Mohammad Shafiee

Tavakoli

Tabaeeian

R. A.

(2018). The effect of market orientation, service innovation and service quality on brand preference and willingness to pay higher prices: Study of rail transport companie’s passengers. Quarterly Journal of Brand Management, 5(1), 169–204.

67.

Mohammed

White

G. R. T.

Kai Wang

Chan

(2018). IT adoption in social care: A study of the factors that mediate technology adoption. Strategic Change, 27(3), 267–279. https://doi.org/10.1002/jsc.2200

68.

Mouakket

Sun

(2019). Examining factors that influence information disclosure on social network sites from the perspective of network externalities. Industrial Management & Data Systems, 119(4), 774–791. https://doi.org/10.1108/imds-02-2018-0060

69.

Mou

J. T.

(2021). Examining the factors influencing college students’ continuance intention to use short-form video APP. Aslib Journal of Information Management, 73(6), 992–1013. https://doi.org/10.1108/ajim-03-2021-0080

70.

Müller-Roux

(2021). Challenging connectivity during nature-based tourism: (Dis)connection at Banff National Park. In Stock

(Ed.), Progress in French tourism geographies (pp. 87–103). Springer.

71.

Muñoz

Hausner

V. H.

Monz

C. A.

(2019). Advantages and limitations of using mobile apps for protected area monitoring and management. Society & Natural Resources, 32(4), 473–488. https://doi.org/10.1080/08941920.2018.1544680

72.

Oliver

R. L.

(1977). Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480–486. https://doi.org/10.1037//0021-9010.62.4.480

73.

Osei-Owusu

Frimpong

Osei-Owusu

P. K.

Awotwe-Pratt

(2018). Making landscapes work: A case of the Kakum conservation area in Ghana. https://satoyama-initiative.org/case_studies/making-landscapes-work-a-case-of-the-kakum-conservation-area-in-ghana/

74.

Patil

Tamilmani

Rana

Raghavan

(2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144.

75.

Plessis

E. D. U.

Saayman

(2015). The relationship between value for money, motives and experience of tourists to the Kruger National Park. Journal of Contemporary Management, 12(January), 426–446.

76.

Psyché

Daniel

B. K.

Bourdeau

(2020). Learning spaces in context-aware educational networking technologies in the digital age. In Peña-Ayala

(Ed.), Educational networking (pp. 299–323). Springer.

77.

Rakibul Hafiz Khan Rakib

M. D.

(2019). Factors influencing purchase intention of cellular phones among the university students in Bangladesh. Open Journal of Business and Management, 11(2), 92–101. https://doi.org/10.7176/ejbm/11-2-10

78.

Research and Markets. (n.d). Location-based services (LBS) market in Ghana-Growth trends and forecasts (2018-2023). Research and Markets. https://www.researchandmarkets.com/reports/4581027/location-based-services-lbs-market-in-ghana

79.

Ringle

C. M.

Wende

Becker

J.-M.

(2015). SmartPLS 3. SmartPLS GmbH. http://www.smartpls.com

80.

Rivai

H. A.

Reza

D. Y.

Lukito

(2019). Distributive justice, job satisfaction and organizational commitment as antecedents of employee performance: A study in Indonesia national health insurance workers [Conference session]. 2019 International Conference on Organizational Innovation (ICOI 2019), 670–676. Atlantis Press.

81.

Rönkkö

Cho

(2022). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1), 6–14. https://doi.org/10.1177/1094428120968614

82.

Saeed

Zyngier

(2012). How motivation influences student engagement: A qualitative case study. Journal of Education and Learning, 1(2), 252–267. https://doi.org/10.5539/jel.v1n2p252

83.

Sedera

Lokuge

Atapattu

Gretzel

(2017). Likes—The key to my happiness: The moderating effect of social influence on travel experience. Information Management, 54(6), 825–836. https://doi.org/10.1016/j.im.2017.04.003

84.

Shafiee

M. M.

Bazargan

N. A.

(2018). Behavioral customer loyalty in online shopping: The role of e-service quality and e-recovery. Journal of Theoretical and Applied Electronic Commerce Research, 13(1), 26–38. https://doi.org/10.4067/s0718-18762018000100103

85.

Shang

(2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems, 117(1), 213–227. https://doi.org/10.1108/imds-02-2016-0052

86.

Silva

Toasa

Guevara

Martinez

H. D.

Vargas

(2018). Mobile application to encourage local tourism with context-aware computing (pp. 796–803) [Conference session]. In Rocha

Á.

Guarda

(Eds), Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). Springer International Publishing. https://doi.org/10.1007/978-3-319-73450-7

87.

Singh

Sahni

M. M.

Kovid

R. K.

(2020). What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675–1697. https://doi.org/10.1108/md-09-2019-1318

88.

Streukens

Leroi-Werelds

(2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618–632. https://doi.org/10.1016/j.emj.2016.06.003

89.

Susanto

Chang

(2016). Determinants of continuance intention to use the smartphone banking services. Industrial Management & Data Systems, 116(3), 508–525. https://doi.org/10.1108/imds-05-2015-0195

90.

Tabaeeian

R. A.

Mohammad Shafiee

(2023). Identifying factors affecting the motivation of games users in social networks and their impact on the user attitude and shopping intention. New Marketing Research Journal, 12(4), 51–68.

91.

Tabaeeian

R. A.

Mohammad Shafiee

Ansari

(2023). Developing a scale for gamified e-service quality in the e-retailing industry. International Journal of Retail & Distribution Management, 51(4), 444–464. https://doi.org/10.1108/ijrdm-06-2022-0181

92.

Tam

Santos

Oliveira

(2020). Exploring the influential factors of continuance intention to use mobile apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243–257. https://doi.org/10.1007/s10796-018-9864-5

93.

Tan

P. J. B.

(2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1–23 https://doi.org/10.1177/2158244013503837

94.

Taylor

Todd

P. A.

(1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.

95.

Troisi

Grimaldi

Visvizi

(2023). Digital transformation in tourism ecosystems: What impact on sustainability and innovation?. In Visvizi

Troisi

Grimaldi

(Eds.), Research and innovation forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer.

96.

Truong

Dustdar

(2009). A survey on context-aware web service systems. International Journal of Web Information Systems, 5(1), 5–31. https://doi.org/10.1108/17440080910947295

97.

Twumasi

Y. A.

(2005). Park management in Ghana using geographic information systems (GIS) and remote sensing technology. Edwin Mellin Press.

98.

Twumasi

Y. A.

Coleman

T. L.

Manu

(2005, June 20–21). Biodiversity management using remotely sensed data and GIS technologies: The case of Digya National Park, Ghana. In Proceedings of the 31st international symposium on remote sensing of environment (ISRSE) – Global monitoring for sustainability and security conference. The International Society of Photogrammetry and Remote Sensing (ISPRS). http://www.isprs.org/proceedings/2005/ISRSE/html/welcome.html

99.

Tzeng

S. Y.

Ertz

M. S.

Sarigöllü

(2021). Factors affecting customer satisfaction on online shopping holiday. Marketing Intelligence & Planning, 39(4), 516–532. https://doi.org/10.1108/mip-08-2020-0346

100.

Urbach Frederik

N., A.

, (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.

101.

Van Der Heijden

Ogertschnig

Van Der Gaast

(2005, May 26–28). Effects of context relevance and perceived risk on user acceptance of mobile information services [Conference Session]. Proceedings of the 13th European Conference on Information Systems, Information Systems in a Rapidly Changing Economy, ECIS 2005, Regensburg, Germany.

102.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

103.

Verkijika

S. F.

(2020). An affective response model for understanding the acceptance of mobile payment systems. Electronic Commerce Research and Applications, 39, 100905. https://doi.org/10.1016/j.elerap.2019.100905

104.

Verkijika

S. F.

De Wet

(2018). E-government adoption in sub-Saharan Africa. Electronic Commerce Research and Applications, 30, 83–93. https://doi.org/10.1016/j.elerap.2018.05.01230(May.

105.

Villegas

N. M.

Sánchez

Díaz-Cely

Tamura

(2018). Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140, 173–200. https://doi.org/10.1016/j.knosys.2017.11.003

106.

Yuan

(2009). The impact of context and incentives on mobile service adoption. International Journal of Mobile Communications, 7(3), 363–381. https://doi.org/10.1504/ijmc.2009.023677

107.

Yavari

Mohammad-Shafiee

Ghauor

(2018). Evaluating the service quality of selected specialized medical clinics in shiraz city, Iran, using Servqual model. Health Information Management, 14(6), 236–242.

108.

Zhou

(2011). The impact of privacy concern on user adoption of location-based services. Industrial Management & Data Systems, 111(2), 212–226. https://doi.org/10.1108/02635571111115146