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Article

Agritourism and Peer-to-Peer Accommodation: A Moderated Mediation Model

by
Ibrahim A. Elshaer
1,2,3,*,
Alaa M. S. Azazz
4,5,
Faleh A. Ameen
6,7 and
Sameh Fayyad
3
1
The Saudi Investment Bank Scholarly Chair for Investment Awareness Studies, The Deanship of Scientific Research, The Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Al-Hofuf 31982, Saudi Arabia
2
Department of Management, College of Business Administration, King Faisal University, Al-Hofuf 31982, Saudi Arabia
3
Hotel Studies Department, Faculty of Tourism and Hotels, Suez Canal University, Ismailia 41522, Egypt
4
Department of Tourism and Hospitality, Arts College, King Faisal University, Al-Hofuf 31982, Saudi Arabia
5
Tourism Studies Department, Faculty of Tourism and Hotels, Suez Canal University, Ismailia 41522, Egypt
6
Agribusiness and Consumer Sciences Department, College of Agriculture and Food Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
7
Agricultural Economic Department, Faculty of Agriculture, Assiut University, Assiut 71625, Egypt
*
Author to whom correspondence should be addressed.
Agriculture 2022, 12(10), 1586; https://doi.org/10.3390/agriculture12101586
Submission received: 7 September 2022 / Revised: 28 September 2022 / Accepted: 29 September 2022 / Published: 1 October 2022
(This article belongs to the Special Issue Agricultural Citizen Science and Sustainable Regional Development)
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Versions Notes

Abstract

:
Agritourism is not a new phenomenon; moreover, it has increased significantly over the past decade and is expected to continue growing in the future. Despite this growth, there is no shared understanding of agritourism, which is problematic because it creates confusion and diminishes its appeal among consumers, thereby impeding communication and collaboration between stakeholders. Agritourism activities can be carried out on a microscale in rural areas by families and agricultural practitioners or on a large scale by hotels, restaurants, and catering firms. Despite many developing countries’ agricultural potential, agritourism has not been inserted into development plans in these countries. Only agritourism in small accommodation enterprises is carried out on a microscale by rural families. This study seeks to test the relationship between the positive impacts of peer-to-peer (P2P) accommodation and agritourism performance, with the mediating effects of support for P2P accommodations and organizational citizenship behavior towards P2P accommodations. The data were derived from 300 Airbnb consumers and hosts of agritourism accommodations in the eastern province of the kingdom of Saudi Arabia (KSA). The results of “Structural Equation Modeling” (SEM) via the “Partial least squares” (PLS) showed a positive direct relationship between peer-to-peer accommodation and agritourism performance with a significant mediating role of Organizational Citizenship Behavior and a positive moderating role of information on Agritourism destinations. Several theoretical and practical implications are discussed.

1. Introduction

In 2011, the World Tourism Organization predicted that agritourism would be among the five significant factors in developing international tourism by 2020 [1]. The popularity of agritourism as a distinct from of tourism is continually growing [2]. The concept of “agritourism” appeared in the last 25 years of twentieth-century literature [3]. Agritourism is also known as the concept of tourist farms, holiday farms, farm-based tourism, and rural tourism [4]. Ref. [5] asserted that there is not yet a common definition for agritourism, which has limited the development of effective policies for its support. In general, any action, activity, or service that is created in farms and rural destinations to attract visitors includes a combination of activities such as tours, overnight stays, unique events and festivals, on-farm stores, hunting, bird watching, hiking, horseback riding, and so on, which are generally amusing [6].
Many policymakers, researchers, and governments have suggested agritourism as an alternative solution and a sustainable development strategy to facilitate the recovery of local economies in rural communities and a complementary source of revenue generation for growers [2,7,8,9], especially in countries where rural communities have encountered many challenges such as migration, low prices of products, and economic recession [5,10]. Rural tourism is a revitalizing tool for economic growth, sociocultural development, the provision of essential and non-essential services, and raising living standards [11,12]. By directly and indirectly bringing economic benefits to local businesses, encouraging the development of related small businesses, serving as an impetus for developing rural and isolated areas, and reviving traditional industries on the brink of extinction [13], these benefits can contribute to the development of positive attitudes among local communities concerning tourism development [14].
Agritourism activities can be carried out on a microscale in rural areas by families and agricultural practitioners [15] or on a large scale by hotels, restaurants, and catering firms [16]. Despite the agricultural potential in many countries, agritourism has not been inserted into development plans in these countries. Only agritourism in small accommodation enterprises is carried out on a microscale by rural families.
Small businesses dominate the rural economic landscape, so small accommodation enterprises represent a core service and supply a considerable share of accommodational capacity in most rural destinations and play a significant role in agritourism development, poverty alleviation, and rural revitalization [17]. With the boom of sharing economy platforms (e.g., Airbnb), peer-to-peer accommodation will continue to expand in scale and thereby reshape the hospitality industry’s future dynamics, especially in rural destinations [18,19]. Peer-to-peer accommodation (P2P) in agritourism destinations is considered a typical form of indigenous tourism, and its growth is expected to bring more financial rewards to rural homes and promote local economies [20], as well as preserve agritourism destinations’ attractiveness with respect to traditional lifestyles, quality service, and memorable experiences [18,21].
Drawing on business growth theory, increasing investment in P2P accommodations leads to increased accommodational capacity, a greater amount of additional capital invested, and more hired employees [22]. Thus, this process results in the reception of more guests, an increase in the range of services, and the improvement of the facilities in agritourism destinations, whereby more satisfactory products and services can be delivered to each guest [23,24]. According to social exchange theory (SCT), a rural community will likely support P2P accommodations if the perceived positive effects outweigh the negative consequences [25]. In fact, most P2P accommodation owners are rural locals, which causes residents to feel psychological ownership derived from a sense of emotional attachment to these places. Based on the affective events theory (AET), we argue that when residents feel psychological ownership of the P2P accommodation, they will defend it from criticism and express their loyalty to it (i.e., a commitment to an organizational citizenship behavior exhibited towards it) [26,27].
Contrary to this stream of the literature focusing on primary tourist markets, the potential effects of P2P accommodations on less developed tourism destinations (rural destinations) are rarely documented in the literature [28]. A structured literature review of 118 articles published between 2013 and 2018 on the P2P accommodation topic (i.e., Airbnb) identified five principal research themes: user motives and user kinds, reputation systems and confidence, prices and pricing, economic effects and media coverage, and legal and regulatory aspects [29]. Research on the impact of P2P accommodations and the creation and development of tourism types is still sparse and very limited. Therefore, this study strives to bridge this gap by diverging from these themes contained in most of the previous studies and clarifying how to exploit the benefits and possibilities of a P2P accommodation—whose existence has become a reality in all societies in the world as a kind of small economic business—to promote agricultural tourism to support local rural economies.
Grounded in business growth theory, the social exchange theory (SCT), and the affective events theory (AET), this study seeks to test the relationship between the positive impacts of P2P accommodations and agritourism performance, with the mediating effects of support for P2P accommodations and organizational citizenship behavior toward P2P accommodations, and the moderating effect of the information on the agritourism destination from the P2P accommodation website between the positive impacts of P2P accommodations and support for P2P accommodations.

2. Theoretical Background and Hypotheses Development

2.1. Support for P2P Accommodations and Organizational Citizenship Behavior toward P2P Accommodations as a Mediator in the Relationship between P2P Accommodations and Agritourism Performance

Today, the internet has enabled individuals to reach a broad audience more effectively to trade their goods and services [30]. The rental of vacation accommodations has attracted by far the most interest in the peer-to-peer internet trading area, with a host selling the use of a room or home to a guest for a set amount of time [31,32]. The peer-to-peer economy has developed as a serious challenger to the traditional hospitality business over the last decade, reshaping social and economic life. Tourism has been touched by four primary areas of the peer-to-peer economy: transportation, food, tour guide services, and lodging [33]. Paid online peer-to-peer accommodation is a space appropriate for overnight stays sold by a non-commercial provider (the host) to an end user (the guest) for short-term use via the direct interaction between the host and guest [32]. A host is a person or group who rents out the real property on the P2P accommodation platform, whereas a P2P accommodation guest stays overnight at the listed property [31].
The P2P accommodation industry has been heavily criticized with respect to a host of issues ranging from the security threat posed by strangers in residents’ backyards to inadequate parking, rental and housing cost increases, the impairment of job growth, the disruption caused by party houses, the unruly behavior by P2P accommodation guests towards neighbors, and the museumization of neighborhoods [34]. In short, P2P accommodations might negatively affect residents’ quality of life [35]. On the other hand, [36] it has been stated that the P2P accommodation industry was subjected to an extensive campaign that included lobbying and the funding of anti-research to display its negative socioeconomic influences. Media discourse has continually reinforced these negative results to distort the industry’s image in favor of hotels [34]. Generally, [37] argues that residents perceived P2P accommodations as having more positive than negative impacts. The P2P accommodation industry has grown to the point that countless people have been transformed into hospitality micro-entrepreneurs [38,39]. The findings of [40] study reveal that rural Airbnb listings during COVID-19 took advantage of the low density of tourism facilities and businesses and the ease of social distancing in rural areas and targeted and promoted agritourism tourists. The authors of [30] predicted that P2P accommodations could be used to strengthen rural economies. Thus, drawing on business growth theory, the following hypothesis is proposed:
Hypothesis 1 (H1).
The positive impacts of P2P accommodations are positively related to agritourism performance.
The development of tourism may differ between urban and rural areas [41]. Rural tourism operators tend to be rural families, usually showcasing regional agricultural products and cultural activities [14]. Family-owned and operated small businesses (e.g., farm stays, caravan and camps, and bed and breakfast operations) are undertaken to support the family’s goals. However, traditionally, these enterprises tend to be a supplemental form of revenue and are mainly operated by female family members [42]. To this end, rural tourism often enjoys substantial local support [14]. So, according to the social exchange theory (SCT), we argue that when P2P accommodations contribute to the support of agritourism, this in turn contributes to the development of positive attitudes among local communities concerning P2P accommodations. Therefore, we hypothesize the following:
Hypothesis 2 (H2).
The positive impacts of P2P accommodations are positively related to support for P2P accommodations.
P2P online platforms have resulted in a new organizational structure that does not necessarily concentrate on possessing main products or hiring service providers [43]. In this transition, P2P accommodations with online platforms has realized the inevitability of incorporating and satisfying individual market partners (i.e., hosts) [44]. As mentioned earlier, P2P hosts in rural destinations tend to be from rural families [14]. P2P accommodations strengthen agritourism and rural economies [30]. This reciprocal cooperation between the rural community, including the P2P hosts, and P2P firms makes both the hosts and the residents feel psychological ownership of the P2P firms. P2P hosts who feel ownership have a neighborly sense of duty, which leads to extended efforts and investments to help the P2P firm [45]. Here, based on the affective events theory (AET) and the social exchange theory (SCT), organizational citizenship behaviors OCB are formed among collaborative relations [46], and P2P hosts are more likely to engage in citizenship attitudes and behaviors toward the P2P company or their peer hosts due to the P2P accommodations’ support for the local community and vice versa. On this basis, we develop the following hypothesis:
Hypothesis 3 (H3).
Thesupport for P2P accommodations is positively related to organizational citizenship behavior toward P2P accommodations.
Social exchange theory (SCT) “proposes that social behavior is the result of an exchange process” [47]. SCT can be adapted to illustrate an interaction information system such as P2P accommodation platforms [48] as it allows researchers to explain “the formation of transaction relationships and motivations to engage in exchanges in a network of actors, given variations in the power of actors, the value of resources, costs, and unpredictability of outcomes from exchanges” [49,50]. So, according to the principle of reciprocity, the following hypothesis is proposed:
Hypothesis 4 (H4).
Organizational citizenship behavior toward P2P accommodations is positively related to agritourism performance.
Based on the justification of the previous four hypotheses, this study suggests the following hypotheses:
Hypothesis 5 (H5).
The support for P2P accommodations mediates the relationship between the positive impacts of P2P accommodations and agritourism performance.
Hypothesis 6 (H6).
The support for P2P accommodations and organizational citizenship behavior toward P2P accommodations mediates the relationship between the positive impacts of P2P accommodations and agritourism performance.

2.2. Information on Agritourism Destination in the P2P Accommodation Website as a Moderator in the Relationship between Positive Impacts of P2P Accommodations and Support for P2P Accommodations

The significance of websites has resulted in the production of various studies on technology (internet) and tourism. Several studies on the evaluation of tourism websites have concluded that information is one of the most critical success criteria [51,52]. The importance of this information increases according to its variety, accuracy, relevance, or reliability [53]. The information on the rural destination where the P2P hosts are situated concerns a critical variable influencing the adoption of websites by guests of agritourism [54]. It is also postulated that this information positively impacts the promotion of rural destinations, thereby boosting the rural community’s support for the P2P accommodations placed therein. On this basis, we develop the following hypothesis ( as shown in Figure 1):
Hypothesis 7 (H7).
The information on the agritourism destination on the P2P accommodation website moderates the influence of the positive impacts of P2P accommodations on the support for P2P accommodations, such that the relationship will be stronger when the information on the agritourism destination on the P2P accommodation website is extensive.

3. Methodology

3.1. Instrument Measurement

A questionnaire was created to test the hypotheses. An extensive review of the literature defined the study’s scales. Thus, five dimensions have been identified. The PIP2PA (Positive Impacts of Peer-to-Peer Accommodation) and SP2PA (Support for P2PA) were tested by 14 items based on the findings of [55]. The AP (Agritourism performance) was measured using the eight-item scale proposed by [56]. Seven items from [44] were employed to measure organizational Citizenship Behavior toward P2PA (OCBP2PA). Finally, the IADP2PAW (Information on Agritourism Destination on the P2PA Website) was measured using the nine-item scale proposed by [54]. A Likert scale of 1 (strongly disagree) to 5 (strongly agree) was employed. The text was transcribed and clarified. Eight Academics and eight professionals in the field tested the instrument. The content was retained and processed with no changes.

3.2. Participants and Data Collection

The research team selected Airbnb firms in KSA’s eastern province’s rural destinations to conduct the field study. As a result of its success, the number of Airbnb studies has recently grown [39], with the platform accounting for nearly 75% of peer-to-peer accommodation studies in the academic literature [33]. Data collection was accomplished through a web-based and paper questionnaire survey. The survey was divided into two stages. Residents—who were not Airbnb hosts but had Airbnb activity in their locations and were aware of this activity—were required to provide the necessary information for the PIP2PA, SP2PA, P2PA, and IADP2PAW variables in the first survey stage. A month later, Airbnb hosts in the same rural destinations completed the OCBP2PA questionnaire. In the two surveys, 300 questionnaires were distributed. After eliminating the unqualified and useless questionnaires, 246 resident–host dyads were evaluated, with an effective recovery rate of 82%. The final resident sample comprised 192 males (78%) and 54 females (22%). They were mainly aged between 28 and 45 years of age. For Airbnb hosts, the final host sample consisted of 203 males (82.5%) and 43 females (17.5%). The previous results showed that the majority of the Airbnb hosts were males They were mainly aged 24–47 years of age, with most of them having a university degree (86.5%).
The previous results showed that the majority of the respondents were educated mature (24–47 years old) males, which is consistent with the nature of residents in KSA as a dominant masculinity country. The majority of KSA women have a tendency to place their trust in their male spouses because they believe their partners are better equipped to deal with the majority of life’s challenges.
To check for non-response bias and check how representative the responding sample was, an independent t-test was used. No statistically significant difference was found between early and late responses in terms of the mean, indicating that non-response bias was not a problem in this study [57].

4. Data Analysis Results

The current study utilized “Structural Equation Modeling” (SEM) via the “Partial least squares PLS” technique to examine the hypotheses of the research with SmartPLS-3.0. The proposed theoretical model was examined using a two-step approach suggested by [57], which is illustrated as follows.

4.1. Assessment of Outer Measurement Model

To evaluate the outer model’s reliability and validity, the internal consistency reliability, indicator reliability, convergent validity, and discriminant validity were all tested. First, as displayed in Table 1, the structures’ internal consistency reliability was tested with Cronbach’s alpha (α) ranging from 0.933 to 0.966 and the composite reliability (CR) ranging from 0.946 to 0.970.
Second, the indicators’ reliability was acceptable as all the loading values of the structure indicators were higher than 0.70. Third, the convergent validity was evaluated by the average variance extracted (AVE) values exceeding the satisfactory value of 0.50 [57]. Finally, three criteria were implemented to assess the discriminant validity of the constructs: the cross-loading, Fornell–Larcker criterion, and heterotrait–monotrait ratio (HTMT) [57]. As indicated in Table 2, the outer-loading for each latent variable (underlined and bolded) was higher than the cross-loading with other measurements.
As illustrated in Table 3, the bolded values of the AVEs in the diagonals are greater than the correlation coefficients between variables. Ref. [58] states that HTMT readings should be less than 0.90. The levels for HTMT in this study were lower than this value (see Table 3). The results indicate that the model’s structure has appropriate discriminant validity. As a result, the outer measurement model’s outputs were regarded as sufficient to proceed with the structural model’s evaluation.

4.2. Assessment of the Structural Model

The hypotheses were then tested by a structural equation analysis (SQM). In particular, the model’s predictive capacity and explanatory power were analyzed [59]. With the VIF values of the manifest indicators ranging from 2.734 to 4.951 below 5, the multicollinearity of the structural model has been verified as inexistent. Next, [60] indicated that the lower limit for the R2 values is 0.10. Therefore, the R2 values for the variables of AP (R2 = 0.766), OCBP2PA (R2 = 0.397), and SP2PA (R2 = 0.646) are acceptable (Table 4). Moreover, The Stone-Geisser Q2 test indicates AP, OCBP2PA, and SP2PA values greater than zero (Table 4), indicating the adequate predictive validity of the model [61]. Accordingly, the sufficient predictive validity of the structural model was also confirmed.
Lastly, the path coefficient and t-value of the hypothesized association were analyzed using a bootstrapping technique. Table 5 and Figure 2 below display the hypothesis test results, given the path coefficient values and the relevant significance. The PIP2PA was found to have a positive and significant correlation with the AP at β = 0.291—p < 0.001 and with the SP2PA at β = 0.691—p < 0.001, so H1 and H2 were supported. The findings showed that the SP2PA significantly and positively influenced the OCBP2PA (β = 0.630, p < 0.001), supporting H3. H4 also was accepted because the correlation between the OCBP2PA and AP is positive and significant at β = 0.493 with p < 0.001. The mediation exerted by the variable SP2PA in the PIP2PA–AP relationship with β = 0.138 and p < 0.001 is significant, i.e., H5 can be accepted. In the same vein, the sequential mediation of the SP2PA and OCBP2PA in the relationship between the PIP2PA and AP yielded a result of β = 0.215 with p < 0.001, which allows us to accept H6. Finally, the results confirm the positive moderation effect of the IADP2PAW on PIP2PA towards the SP2PA at β = 0.237 with p < 0.001, which supports H7.

5. Discussion and Implications

5.1. PIP2PA, PA, SP2PA, and OCBP2PA (Direct Relationship)

This study’s results reveal a direct and positive relationship between the PIP2PA and AP (Hypothesis 1). The current findings are aligned with the notion that the PIP2PA increases agritourism’s performance and value [30,40]. It is argued that the presence of P2P accommodations in less developed tourism destinations (e.g., rural destinations) can spur ignored types of tourism (e.g., agritourism) [62] without necessarily causing the negative consequences observed in primary destinations, mainly urban tourism markets [28,63]. This study additionally confirms that the PIP2PA represent an antecedent factor in the SP2PA practice implementation (Hypothesis 2); this result matches the use of social exchange theory (SCT) to illustrate an interaction between residents and P2P accommodation firms based on the principle of reciprocity [47,48,49]. Furthermore, our findings help verify the positive influence of the SP2PA on OCBP2PA (Hypothesis 3). This study consequently extends the OCB literature by examining the OCB in the collaborative relations in a sharing economy, which is consistent with [44]. It follows from the results obtained that the OCBP2PA positively influences the AP achieved (Hypothesis 4). This supports the notion that corporate social responsibility (CSR) encourages positive attitudes among employees while suppressing negative ones. CSR can also make an OCB. In the context of the sharing economy, a P2P host is a kind of worker and service provider. From this perspective, it can be assumed that P2P hosts’ perception of a P2P firm’s social responsibility toward their community helps to increase the OCB behaviors toward it [64]. This reciprocal relationship ultimately leads to improving the performance of agritourism.

5.2. Assessing the Moderating Effect

The practical results validated the positive moderation influences of the IADP2PAW variable on the relationship between the PIP2PA and SP2PA (Hypothesis 7). In other words, the IADP2PAW can strengthen the positive relationship between the PIP2PA and SP2PA (Figure 3, Interaction plot). Returning to Figure 2 and calculating the moderator’s interaction values (0.691 + 0.237 = 0.928), we conclude that the IADP2PAW strengthened the relationship between the PIP2PA and SP2PA. This result agrees with the findings of [54]. Additionally in line with this result, Airbnb—as an example of a P2P platform—has more robust data, given the large number of users, listings, and reviews, than the metropolitan planners, placing itself as a strong knowledge broker in the future of tourism that may change travel destinations [65]. Thus, having information about the destination of agritourism on the P2P platform improves agritourism performance, and the result is an increase in community support for P2P companies.

5.3. The Mediating Role of SP2PA and OCBP2PA in the Study Model

One of the study’s main aims was to examine the mediating role of the SP2PA and OCBP2PA between the PIP2PA and AP. In relation to hypothesis 5, the results confirm that the role of the SP2PA as a mediating variable in the relationship between the PIP2PA and AP is significant and positive. Finally, the empirical evidence obtained permits us to state that the sequential mediation exerted by the variables SP2PA and OCBP2PA indirectly affects the relationship between the PIP2PA and AP (Hypothesis 6). These results are consistent with social representation theory (SR), which suggests that residents’ perceptions of tourism impacts are “informed by direct experiences, social interaction and other information sources such as the media” [66] and often dictated by instinct and practical consciousness [67,68]. According to [69], local community members are categorized into three groups: advocates, the socially and environmentally concerned, and economic skeptics. Thus, when the rural community (i.e., residents and rural P2P hosts) feels the P2P accommodation’s positive effects, it will support said accommodation.
By examining the mediating relationships in the model of this study, we find that the indirect effect of the sequential mediation of the SP2PA and OCBP2PA variables in the relationship between the PIP2PA and AP was more significant than the mediating effect of the SP2PA in the same association, and in both cases the mediation was partial. Thus, we can conclude that the relationship between the PIP2PA and AP is not dependent only on community support or organizational citizenship behaviors of the P2P hosts. Further research is required to identify other elements that may support the relationship between the PIP2PA and AP. These variables may be the responsibility of local authorities or officials of tourism affairs.
The current paper presents clear theoretical and practical implications. In terms of the theoretical implications, this study not only improves our understanding of the peer-to-peer economy but also gives it a new function by highlighting the advantages of P2P lodgings and demonstrating how they can be used to realize the economy’s potential regarding support for sustaining rural communities through the growth of agricultural tourism. In terms of practical implications, this paper delivers a valuable synthesis of P2P accommodations’ economic role and P2P firm–local community exchange relationships that should be useful to both, especially rural destinations. By supporting this type of micro-enterprise (e.g., P2P accommodations), it is possible to build local economies that protect members of society from the problems of a lack of government capabilities, in addition to P2P firms’ ability to promote the local heritage, which appeals to many segments of tourists who long for nostalgia [70,71]. In addition, it is recommended that decision-makers in Saudi Arabia plan aids or specific policies aimed at enhancing the country’s agritourism sector.

6. Conclusions and Further Studies Opportunities

The amount of research that has been undertaken on the effects of peer-to-peer accommodations on the birth and development of different tourism types is still very limited. This study directly reacts to calls by [72,73] to broaden the understanding of P2P accommodation’s effects by incorporating a holistic approach to examine the viewpoints of multiple stakeholders. We have employed an integrative view of P2P accommodations and rural destinations, which has allowed us to use the business growth theory, the social exchange theory (SCT), the affective events theory (AET), and social representation theory (SR) to explain how rural destinations can take advantage of the positives of P2P accommodations to develop agricultural tourism.
Furthermore, the study tested and confirmed four direct relationships, two mediators, and one moderator effect. Specifically, the study investigated the impact of the positive impacts of peer-to-peer accommodations (PAP2PA) on agritourism performance (AP) with the mediating role of support for P2PA (SP2PA) and an organizational citizenship behavior toward P2PA (OCBP2PA) and the moderating role of information on the agritourism destination on the P2PA website (IADP2PA). The results of the tested model showed that all the direct, mediating, and moderating impacts were supported. Additionally, the study showed the reciprocal relationship between the P2P platform and its hosts, where the hosts showed organizational citizenship behaviors towards the P2P platform as a result of mutual support between residents and P2P companies.
The current study, similar to other studies on this subject, has several limitations, and such limitations suggest investigating additional research avenues. First, apart from the employed mediating variables (SP2PA and OCBP2PA) and moderating variable (IADP2PA), other variables can be further examined as a moderator, such as gender, country, quality of life, and guest age, while other dimensions can be further investigated as mediators, such as tourists’ perceived experience and tourists’ satisfaction. Second, using cross-sectional data enables the determination of the exact causal relationships between the tested variables. In the future, researchers may try to validate the structural model of the study by using longitudinal data or multiple sources of data. Third, an evaluation of these relationships in a variety of other settings can be accomplished through the utilization of a multi-group analysis technique (country or industry).

Author Contributions

Conceptualization, I.A.E. and S.F.; methodology, I.A.E., S.F. and A.M.S.A.; software, I.A.E. and S.F.; validation, I.A.E., A.M.S.A., F.A.A. and S.F.; formal analysis, I.A.E. and A.M.S.A.; investigation, I.A.E., S.F. and A.M.S.A.; resources, I.A.E.; data curation, I.A.E.; writing—original draft preparation, S.F., I.A.E. and A.M.S.A.; writing—review and editing, I.A.E., S.F. and A.M.S.A.; visualization, I.A.E.; supervision, I.A.E.; project administration, I.A.E., S.F., F.A.A. and A.M.S.A.; funding acquisition, I.A.E. and A.M.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by The Saudi Investment Bank Scholarly Chair for Investment Awareness Studies, the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. CHAIR147).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the deanship of the scientific research ethical committee, King Faisal University (project number: CHAIR147, date of approval: 1 July 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request from researchers who meet the eligibility criteria. Kindly contact the first author privately through the e-mail.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 12 01586 g001 550
Figure 1. The proposed conceptual framework and hypotheses. PIP2PA→Positive Impacts of P2PA; AP→Agritourism performance; SP2PA→Support for P2PA; OCBP2PA→Organizational Citizenship Behavior toward P2PA; IADP2PAW→Information on Agritourism Destination on the P2PA website.
Figure 1. The proposed conceptual framework and hypotheses. PIP2PA→Positive Impacts of P2PA; AP→Agritourism performance; SP2PA→Support for P2PA; OCBP2PA→Organizational Citizenship Behavior toward P2PA; IADP2PAW→Information on Agritourism Destination on the P2PA website.
Agriculture 12 01586 g001
Agriculture 12 01586 g002 550
Figure 2. The tested structural and measurement model. PIP2PA→Positive Impacts of P2PA; AP→ Agritourism performance; SP2PA→Support for P2PA; OCBP2PA→Organizational Citizenship Behavior toward P2PA; IADP2PAW→Information on Agritourism destination on the P2PA website; * moderating effect.
Figure 2. The tested structural and measurement model. PIP2PA→Positive Impacts of P2PA; AP→ Agritourism performance; SP2PA→Support for P2PA; OCBP2PA→Organizational Citizenship Behavior toward P2PA; IADP2PAW→Information on Agritourism destination on the P2PA website; * moderating effect.
Agriculture 12 01586 g002
Agriculture 12 01586 g003 550
Figure 3. Interaction plot for the IADP2PAW moderation effect on PIP2PA towards SP2PA.
Figure 3. Interaction plot for the IADP2PAW moderation effect on PIP2PA towards SP2PA.
Agriculture 12 01586 g003
Table
Table 1. Assessment of the formative measurement model.
Table 1. Assessment of the formative measurement model.
Abbreviation Outer LoadingαC.RAVE
AbbreviationItems
Positive Impacts of P2PA 0.9530.9580.620
PIP2PA_1Creates opportunities for residents to participate in local culture.0.856
PIP2PA_2Fosters community pride. 0.784
PIP2PA_3Fosters a feeling of belonging to the community.0.824
PIP2PA_4Enables an understanding of different cultures. 0.753
PIP2PA_5Contributes to an improvement in neighborhood/housing appearance.0.798
PIP2PA_6Improves the local economy.0.773
PIP2PA_7Provides more businesses for local people and small businesses.0.790
PIP2PA_8Creates more job opportunities for local residents.0.731
PIP2PA_9Provides opportunities for cultural exchange between tourists and residents.0.841
PIP2PA_10Improves the image of the community and culture.0.819
PIP2PA_11Helps improve the quality of community services such as local police, utilities, roads, etc.0.789
PIP2PA_12Provides incentives for the preservation/restoration of local/historic buildings.0.794
PIP2PA_13Positively impacts the cultural identity of the community. 0.701
PIP2PA_14Tourism through Airbnb encourages the development of various cultural activities by local residents.0.758
AP 0.9430.9530.716
AP_1Through agritourism, we have achieved revenue targets. 0.823
AP_2Through agritourism, we have achieved profit goals. 0.810
AP_3Through agritourism, we have achieved a good stabilization of income.0.856
AP_4Through agritourism, we have generated out-of-season revenue.0.870
AP_5Through agritourism, we have made better use of the farm’s human resources.0.868
AP_6We have improved the way in which products are sold. 0.821
AP_7We have improved the loyalty of existing customers. 0.859
AP_8We have attracted a significant number of new customers.0.857
SP2PA 0.9330.9490.789
SP2PA_1Airbnb helps my neighborhood grow in the right direction.0.861
SP2PA_2I am proud that Airbnb visitors are coming to my neighborhood.0.868
SP2PA_3Airbnb will continue to play an important economic role in my neighborhood.0.900
SP2PA_4I support the development of Airbnb as it is vital to my neighborhood.0.910
SP2PA_5My neighborhood should attract more Airbnb visitors.0.899
OCBP2PA 0.9330.9460.713
OCBP2PA_1I am willing to attend functions that help Airbnb’s image. 0.832
OCBP2PA_2I am willing to keep up with developments in the Airbnb company. 0.861
OCBP2PA_3I am willing to defend the Airbnb company when others criticize it. 0.862
OCBP2PA_4I am willing to show pride when representing Airbnb in public. 0.858
OCBP2PA_5I am willing to offer ideas to improve the functioning of Airbnb. 0.856
OCBP2PA_6I am willing to express loyalty toward the Airbnb company. 0.837
OCBP2PA_7I am willing to act to protect Airbnb from potential problems.0.803
IADP2PAW 0.9660.9700.783
To what extent do you think the information is sufficient on the Airbnb website regarding the following:
IADP2PAW_1Transportation.0.837
IADP2PAW_2Tourist attractions (monuments, museums, …).0.868
IADP2PAW_3Tourism activities (excursions, touristic visits, …).0.880
IADP2PAW_4Recreational/sports activities.0.902
IADP2PAW_5Events.0.913
IADP2PAW_6Restaurants.0.903
IADP2PAW_7Local products (gastronomy, craftwork, …).0.901
IADP2PAW_8General information (history, culture, …).0.880
IADP2PAW_9Near destinations.0.879
Table
Table 2. Cross-loading results.
Table 2. Cross-loading results.
PIP2PAAPSP2PAOCBP2PAIADP2PAW
PIP2PA_10.8560.6980.7370.536−0.345
PIP2PA_20.7840.6990.5890.472−0.373
PIP2PA_30.8240.5340.6350.400−0.266
PIP2PA_40.7530.5030.5960.334−0.269
PIP2PA_50.7980.5450.6240.356−0.262
PIP2PA_60.7730.5340.5100.468−0.214
PIP2PA_70.7900.5660.5950.544−0.259
PIP2PA_80.7310.5430.4970.488−0.311
PIP2PA_90.8410.6730.6260.614−0.352
PIP2PA_100.8190.5750.7210.518−0.247
PIP2PA_110.7890.5400.6420.510−0.262
PIP2PA_120.7940.7030.7640.643−0.306
PIP2PA_130.7010.6470.4860.629−0.227
PIP2PA_140.7580.6030.5690.527−0.302
AP_10.7200.8230.5950.688−0.307
AP_20.6280.8100.5660.583−0.266
AP_30.6720.8560.7480.706−0.259
AP_40.7040.8700.7060.751−0.365
AP_50.6070.8680.6920.674−0.370
AP_60.5300.8210.5740.625−0.316
AP_70.6040.8590.5090.694−0.280
AP_80.6840.8570.5780.707−0.259
SP2PA_10.7560.6730.8610.575−0.291
SP2PA_20.6690.6690.8680.574−0.313
SP2PA_30.6810.6790.9000.589−0.241
SP2PA_40.6770.6240.9100.547−0.252
SP2PA_50.7050.6280.8990.508−0.088
OCBP2PA_10.6650.7780.6520.832−0.275
OCBP2PA_20.6330.7550.5660.861−0.422
OCBP2PA_30.6120.7010.5980.862−0.372
OCBP2PA_40.5260.6920.4890.858−0.345
OCBP2PA_50.4420.6000.4500.856−0.169
OCBP2PA_60.4710.6280.4960.837−0.188
OCBP2PA_70.3630.5420.4070.803−0.180
IADP2PAW_1−0.239−0.287−0.156−0.2080.837
IADP2PAW_2−0.250−0.237−0.188−0.2730.868
IADP2PAW_3−0.304−0.286−0.223−0.2570.880
IADP2PAW_4−0.289−0.307−0.212−0.2810.902
IADP2PAW_5−0.456−0.418−0.325−0.3860.913
IADP2PAW_6−0.317−0.327−0.221−0.3140.903
IADP2PAW_7−0.389−0.369−0.315−0.3610.901
IADP2PAW_8−0.253−0.254−0.192−0.2340.880
IADP2PAW_9−0.281−0.280−0.197−0.2990.879
Table
Table 3. Inter-construct correlations, the square root of AVE, and HTMT results.
Table 3. Inter-construct correlations, the square root of AVE, and HTMT results.
AVEs ValuesHTMT Results
1234512345
1. AP0.846
2. IADP2PAW−0.3590.885 0.363
3. OCBP2PA0.805−0.3400.844 0.8430.335
4. PIP2PA0.764−0.3650.6430.787 0.7980.3610.661
5. SP2PA0.738−0.2680.6300.7870.8880.7820.2670.6620.826
Note: Bolded values are AVE values.
Table
Table 4. Coefficient of determination R2 and Q2 of the model.
Table 4. Coefficient of determination R2 and Q2 of the model.
Endogenous Latent ConstructR2Q2
AP0.7660.509
OCBP2PA0.3970.257
SP2PA0.6460.472
Table
Table 5. The structural model’s results.
Table 5. The structural model’s results.
HypothesesBeta -βT-Valuep ValuesResults of
Hypotheses
H1PIP2PA → AP0.2913.7580.000Accepted
H2PIP2PA → SP2PA0.69116.0740.000Accepted
H3SP2PA → OCBP2PA0.63015.4160.000Accepted
H4OCBP2PA → AP0.4936.9470.000Accepted
H5PIP2PA → SP2PA → AP0.1383.4420.001Accepted
H6PIP2PA → SP2PA → OCBP2PA → AP0.2156.1910.000Accepted
H7Moderating Effect 1 (PIP2PA * IADP2PAW) → SP2PA0.2373.3490.001Accepted
Note: * moderating effect.
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Elshaer, I.A.; Azazz, A.M.S.; Ameen, F.A.; Fayyad, S. Agritourism and Peer-to-Peer Accommodation: A Moderated Mediation Model. Agriculture 2022, 12, 1586. https://doi.org/10.3390/agriculture12101586

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Elshaer IA, Azazz AMS, Ameen FA, Fayyad S. Agritourism and Peer-to-Peer Accommodation: A Moderated Mediation Model. Agriculture. 2022; 12(10):1586. https://doi.org/10.3390/agriculture12101586

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Elshaer, Ibrahim A., Alaa M. S. Azazz, Faleh A. Ameen, and Sameh Fayyad. 2022. "Agritourism and Peer-to-Peer Accommodation: A Moderated Mediation Model" Agriculture 12, no. 10: 1586. https://doi.org/10.3390/agriculture12101586

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