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The Impacts of Electronic Word of Mouth Communication on Consumers’ Intention to Choose the Peer-to-Peer Travel

Nyamweya is a Kenyan scholar who has done many years of research on a diversity of topics

the-impacts-of-electronic-word-of-mouth-communication-on-consumers-intention-to-choose-the-peer-to-peer-travel

Chapter 1: Introduction

1.1 Research background

According to Abuhashesh et al. (2019), word of mouth (WOM) is one of the oldest methods used to pass information from one person to another. Many authors have attempted to define WOM including Lkhaasuren and Nam (2018) who put forth that word of mouth is the exchange of marketing information between customers in a manner that influences their behavior and attitudes towards products and services. Bi (2010) also refers to WOM as free advertisement. Based on this, Yang et al. (2016) argue that word of mouth has a significant influence on consumer decision making process and purchasing intention. Customer attitude that influences product purchasing intention is formed when consumers share product information about products or services that they have used or they intend to use (Rodhiya & Sjabadhyni, 2018).

Today, the booming web culture has influenced peer-to-peer collaboration and sharing of information over the internet. As such, WOM has evolved to Electronic Word of Mouth (eWOM) which is expression of information through online platforms. Rise of eWOM has attracted studies from various researchers who aimed at uncovering the influence of eWOM on purchasing intentions. EWOM is considered more credible than traditional word of mouth because it is measurable (Abuhashesh et al., 2019). eWOM is found in website reviews, newsgroups, and social networking platforms among others.

Rodhiya and Sjabadhyni (2018) argue that customer reviews is one of the most influential type of eWOM because they are not just opinions but a review of the experience from the actual interaction. Industrial statistics also prove that customer reviews have a significant influence on purchasing intentions and customer purchasing decision. Bi (2010) notes that 61% of consumers base their purchasing decisions on online reviews and customer feedback. Additionally, a research by Bangsawan et al. (2017) confirms that 70 % of consumers had more trust on consumer opinions posted online. According to Yang et al. (2016) eWOM is a factor that is especially necessary in purchase of intangible products like services in the tourism or hospitality industry.

The travel industry has been significantly affected by online reviews which has influenced more than $10 billion purchases annually (Abuhashesh et al., 2019). Lkhaasuren and Nam (2018) confirm that most consumers assess customer evaluations before deciding where to stay and making a purchase. Peer to peer accommodation has grown as a result of technological advancement. As such, Abuhashesh et al. (2019) record that electronic word of mouth has played a key role in influencing customers choice of peer-to-peer (P2P) accommodation. Correspondingly, Airbnb is one of the popular P2P accommodations whose popularity is accredited to eWOM. Therefore, the research chose Airbnb as the case study to evaluate the influence of eWOM on Chinese customer’s intention of customers to choose the peer-to-peer travel accommodation model.

1.2 Aims and objectives

1.2.1 Research Aims

The main aim of this study is to establish whether electronic word of mouth has an influence on consumer’s intention to choose the model of peer-to-peer accommodation. Moreover, this study intends to investigate various eWOM variables such as eWOM quality, quantity and expertise information providers. The goal of this is to determine one or a combination of factors that will have the highest significance on the intention of consumers to choose the model of peer-to-peer accommodation (Bataineh 2015). This will be useful in providing suggestions to the management of Airbnb to improve its eWOM in order to promote the intention of consumers to select the model of peer-to-peer accommodation while travelling.

1.3.2 Research objectives

In order to achieve the above-mentioned aim, this research will assume the following objectives

  1. To analyse the impact of eWOM quality and quantity on purchase intention among Airbnb users;
  2. To determine the influence of information providers' expertise on purchase intention among Airbnb users;
  3. To analyse the influence of information credibility on purchase intention among Airbnb users;
  4. To provide suggestions to the management of Airbnb to improve its eWOM to promote consumers’ intention to choose the peer-to-peer accommodation model while traveling;

1.3 Justification of the research

There is growing researches on eWOM. For instance, a number of researchers like Zohora et al. (2017) concentrated on social media user’s motive on eWOM behaviour in various eWOM platforms. Besides, Yang et al. (2016) assessed the influence of online reviews in comparison with the impact of the opinion of friends when booking a hotel. However, the relationship between eWOM and purchase intentions in the peer to peer accommodation industry has not been extensively studied. As such, this research intends to add new knowledge on the influence of between eWOM on purchase intentions of P2P accommodation. Besides, the study of this topic could provide AirBnb, and even other similar P2P accommodation companies recommendations regarding how to effective use consumers’ eWOM to promote consumers’ purchase behaviour.


Chapter 2: Literature review

2.1 Introduction

This chapter provides a literature review about the impacts of electronic word of mouth communication on consumers’ intention to choose the peer-to-peer travel accommodation model. It begins by providing definitions of e-WOM (electronic word of mouth) and peer-to-peer accommodation model. It then proceeds to providing a critical review of the existing theories that have studied the impacts of e-WOM communication on consumers’ intention to choose the peer-to-peer travel accommodation model. Specifically, it focuses on the effects of e-WOM quantity, quality, and information providers' expertise on consumers’ intention to choose the peer-to-peer travel accommodation model. The chapter concludes by highlighting the gaps in the existing research that justify the need for conducting this research.

2.2 Definitions

2.2.1 e-WOM communication

e-WOM communication refers to the person-to-person contacts that occur over or through the help of the internet (Yan et al., 2016). If addressed appropriately in marketing, e-WOM may turn viral and catchy enough to attract the attention of customers (Yan et al., 2016). In reference to Bai et al. (2017), e-WOM is influential because it promotes sharing of information about products or services through review websites and social media, among other online forums. It also promotes sharing of advice over the internet and builds trust amongst people sharing information (Bai et al., 2017).

2.2.2 Peer-to-peer accommodation model

This model refers to or happens when individuals rent their apartments or rooms they own to other people, and this is typically enabled by digital or online platforms, for example the case of Airbnb (Tussyadiah and Pesonen, 2016). Similarly, in reference to Gutiérrez et al. (2017), the increasing connectivity of peer-to-peer accommodation is instrumented by online social network platforms to share access to their houses, rooms, or products and services for a certain fee or compensation. On the other hand, according to Martin-Fuentes et al. (2018), the major drivers of peer-to-peer accommodation model include the social appeal facilitated by customers’ social motivation to interact, get to know, or connect with specific local communities, as well as enjoy the economic appeal of low costs of high-quality accommodation.

2.3 The impacts of electronic word of mouth communication on consumers’ intention to choose the peer-to-peer travel accommodation model

2.3.1 The impact of eWOM quality and quantity on consumers’ intention to purchase the peer-to-peer travel accommodation model

e-WOM quantity refers to the total number of comments or volumes or lengths of reviews available online (Tussyadiah and Zach, 2017). Similarly, Ai, Chi, and Ouyang (2019) define e-WOM quantity as the scale or the amount of electronic comment on specific services or products. Existing theories indicate that e-WOM quantity influences consumers intention to choose peer-to-peer accommodation model. For instance, in reference to Ai, Chi, and Ouyang (2019), the volumes of reviews or discussions about products or services on online platforms make it easier for peer-to-peer travel accommodation model to become more observable. This argument is echoed by Goh (2015) who postulate that high e-WOM Quantity is a sign or a representation of a market’s performance, and as such, high number of online-posted comments about a service or product can be considered as a sign of how trendy or popular it is. Similarly, Liang, Choi, and Joppe (2018) argue that large quantities of information shared by individuals through e-WOM enhance the confidence in trusting and eventually making purchase decisions. Another study by Lim (2016) also revealed that consumers prefer popular brands as compared to alternatives in the market. This shows that the quantity of eWOM is high since this indicates how well the brand is performing in the market (Bataineh 2015). Customers also have a perception that high eWOM numbers indicate the usefulness and popularity of the product, hence their purchase intention towards such products increases (Lim 2016). As such, the quantity of information that customers receive influence their willingness to choose peer-to-peer model of travel accommodation. This argument is further echoed by Tussyadiah and Zach (2017) who claim that high number of posted opinions or messages about a product or service that customers want can easily influence them to make purchase decisions. From a different but related point of view, Ismagilova et al. (2019) escalate that the high the e-WOM quantity about a product or service, the more likely consumers are able to hear or become aware about it, hence the reason why many consumer opinions or reviews can be a signal of service or product popularity. This argument is further echoed by Ai, Chi, and Ouyang (2019) who argue that considering that the number of reviews or opinions may represent the total number of interested customers with usage experience or prior purchasing, high e-WOM quantity gives consumers more reliable impressions of the practical or real consequences of consuming a service or product.

The e-WOM dimension of quality refers to features such as path of communication, timeliness, longevity, and type, among others (Goh, 2015). Based on the existing theories or literature, this dimension has an impact on consumers’ intention to choose the peer-to-peer travel accommodation model. For instance, in reference to Tsao et al. (2015), consumers are more focused on the information quality when they are looking for information concerning peer-to-peer accommodation in the eWOM communication medium. A different study by Liang et al. (2017) also found out that when consumers are provided with quality information that is clear, helpful and easy to understand, their intention to choose peer-to-peer accommodation increases. Similarly, according to Mahadevan (2018), the quality of online argument or e-WOM influences consumers’ intention to choose the peer-to-peer travel accommodation. According to Mahadevan (2018), the convincing strengths of arguments rooted in e-WOM forms a core part in influencing trust in a product or service and positively influencing purchase intentions. Similarly, Liang (2020) asserts that e-WOM quality is by information characteristics, for example, accuracy, comprehensiveness, and timeliness. In reference to these researchers, online reviews or blogs that are comprehensive, logical, and clear with sufficient reasons supporting specific opinions about a product or service tend to have a positive effect on consumers’ purchase intentions compared to online messages or posts that are subjective or emotional. This argument is echoed further by Moon et al. (2019) who reiterate that e-WOM reviews associated with attribute value are perceived to be more informative by consumers compared to online reviews with simple suggestions. From a different perspective, when studying the impact of e-WOM quality on consumers’ intention to choose peer-to-peer accommodation model, Mahadevan (2018) found that the quality of information provided by online bloggers or in online reviews can influence the purchase intentions of customers, either negatively or positively. For example, these researchers found that the quality of e-WOM information by individuals perceived to be expert bloggers can play a significant role in forming favourable attitudes towards it. Furthermore, in reference to Liang, Choi, and Joppe (2018), the positive and large volumes of online high-quality reviews about bloggers’ recommendations about peer-to-peer accommodation model is an indication of high perceived e-WOM quality and expertise, thus, can translate into positive purchase intentions in consumers. These findings also coincide with those of Ismagilova et al. (2019) that, online reviews that are positive and informative about a specific luxury product act as complimentary data or information that to some extent reduces the perceived risks in consumers, thereby leading to positive purchase intentions. Moreover, according to Ai, Chi, and Ouyang (2019), consumers associate e-WOM quality with information characteristics for example, comprehensiveness, adequacy, persuasiveness, relevance, objectivity, and sufficiency. In reference to these researchers, these characteristics or dimensions form a core criterion when judging e-WOM quality, and as such, they define the level at which consumers are influenced to choose peer-to-peer accommodation model.

On the basis of the reasoning above, this research formulates the following hypothesis;

Hypothesis 1: The perceived quantity and quality of e-WOM has a positive impact on consumers’ intention to choose the peer-to-peer travel accommodation model.

2.3.2 The influence of information providers' expertise on consumers’ intention to choose the peer-to-peer travel accommodation model

Existing theories have discussed the impacts of e-WOM information providers’ expertise on consumers’ purchase intentions. For instance, in reference to Tsao et al. (2015), the expertise of e-WOM providers improves the message effect. In this sense, Lim (2016) hold that when customers read the reviews provided by expertise customers, they become highly receptive and this influence their decision to choose some services or products over others. On the other hand, Goh (2015) postulates that the perceptions of the information providers’ expertise in terms of skills, knowledge, and experience about a product or service influence consumers’ purchase decisions. According to these researchers, the messages posted by these experts (those perceived as knowledgeable and experienced about specific products or services) online can be persuasive in influencing purchase intentions. However, Mahadevan (2018) argue that the reverse is true of consumers perceive or associated the information providers with poor knowledge or little experience with products or services of interest. On the other hand, Liang, Choi, and Joppe (2018) contend that the high skills, experience or knowledge of a product or service associated with information source or provider increases consumers’ trust about the usefulness of the information provided, and this translates to positive purchase intentions. Similarly, Ismagilova et al. (2019) postulate that consumers tend to trust the information provided by experts, for example, bloggers who have already tasted or previously consumed particular products or services of brands that they are not affiliated to, more than those provided by those companies. Furthermore, in reference to Ai, Chi, and Ouyang (2019), information providers are perceived as experts in specific products or services when they remain neutral or do not engage in commercial activities. According to these researchers, the information provided by such experts is regarded as useful and reliable when making purchase decisions.

Based on the reasoning in the existing theories above, this research formulates the following hypothesis;

Hypothesis 2: The perceived information providers' expertise positively influences consumers’ intention to choose the peer-to-peer travel accommodation model.

2.3.3 The influence of information credibility on consumers’ intention to choose the peer-to-peer travel accommodation model

Existing theories hold that information credibility influences consumers’ intention to choose the peer-to-peer travel accommodation model. For instance, in reference to Ayeh, Au, and Law (2013), when consumers perceive or associate information about a product or service with high credibility, they often accept and trust its content or message compared to when they perceive it as not credible. This argument is echoed by Tussyadiah and Pesonen (2016) who reiterate that consumers perceive credible information as trustworthy, hence the reason why they develop positive attitudes towards a product in discussion and eventually influences their purchase decisions positively. However, according to Liang, Choi, and Joppe (2018), despite the fact that existing theories seem to agree that lack of cues for interpreting the opinions of information or content can lower trust of consumers to view the information as credible and simultinuously affect purchase intentions in a negative way, most consumers’ purchase intentions are influenced by subjective perceptions, including the judging of visual features for instance star ratings or/and online reviews, product or service prices, and the characteristics of information reviewers, among many others.

Different from the theories above, Goh (2015) escalate that the level at which consumers perceive information about a product or service to be reliable equates to the level they see it as credible, and this simultinatiously influences purchase intentions. These researchers also reiterate that when consumers judge or perceive information as accurate and reliable, they are likely to associate it with high credibility and as such, they develop trust towards it and eventually make purchase decisions. The reverse is true when they perceive information as innacurate and unreliable. On the other hand, Bangsawan, Marquette, and MS (2017) assert that when consumers perceive information as credible in terms of information accuracy or/and unbiasness, the chances of making purchase intentions is high than when they perceive them as biased or/and innacurate. Moreover, Bangsawan, Marquette, and MS (2017) reiterate that the anonymous nature of information, for example, eWOM engagement by unidentified bloggers or endorsers sometimes make it difficult for readers to evaluate information credibility, thus, rarely demonstrate positive purchase intentions. This reiteration is supported by Olmedilla, Martínez-Torres, and Toral (2019) who escalate that based on the fact that the internet is open and/or vulnerable to untrustworthy information or opinions, information source credibility also plays a significant role in influencing consumers’ purchase intentions.

Hypothesis 3: The perceived information credibility positively influences consumers’ intention to choose the peer-to-peer travel accommodation model.

2.4 Conclusion

Several studies have been conducted to investigate the impact of e-WOM on consumers’ purchase intentions and consumer behaviour. Only a few or limited studies have been carried to study the effects of e-WOM on consumers’ intention to choose the peer-to-peer travel accommodation model. Furthermore, for the few ones that have been conducted to investigate this subject have contradictory results about the e-WOM features that influence consumers’ intention to choose the peer-to-peer travel accommodation model. This forms the major reason for conducting this research. This is because it addresses the major e-WOM dimensions or features that specifically influence consumers’ intentions to choose the peer-to-peer travel accommodation model, which include; e-WOM quality and quantity, information credibility, and e-WOM information providers’ expertise. As such, this research fills the gaps that past researchers have not fully addressed, thus, is important for the future researchers because it adds new knowledge to the existing literature, which they can rely on when working on similar research.


Chapter 3: Research Methods

3.1 Chapter introduction

This chapter provides the research methods used in the study. Justification is also provided for the methods used as well as the rationale of rejecting other options. Specifically, the chapter provides the research philosophy, research strategy, research approach, research instruments and the sampling method used. Additionally, this section provides a justification of the data collection tools used, method and tools of data analysis and research ethics of the study.

3.2 Research philosophy

Research philosophy is used to explain the suggestions made by the researcher on elements used in the research (Sahay, 2016). This is illustrated in the research onion below.

Figure 3.1: Research Onion

Source: Sahay, 2016

There are three common research philosophies used in research- interpretivism, pragmatism and positivism (Melnikovas, 2018). To begin with, pragmatism infers that realities exist in multiples therefore an entire picture about a phenomenon cannot be drawn from one angle. Supporters of this approach advocate that the choice of an effective research method is influenced by the research questions (Melnikovas, 2018). On the other hand, positivism philosophy knowledge that is drawn from observation and measurement is true. In this case, Sahay (2016) opines that the interest of positivist researchers are independent from the study to minimise biased assumptions. Lastly, interpretivism emphasises on consideration of the differences among people (Melnikovas, 2018). Interpretivism is rejected since the researchers opinions might bring in bias in the study.

This research adopts positivism philosophy because the research intends to collect facts. Therefore, interpretivism and pragmatism philosophy are rejected. Positivism is adopted since the research findings are observable facts which will not be changed based on the subjective opinions of research participants. This leads to collection of credible and unbiased results (Sahay, 2016).

3.3 Research approach

The two common research approaches are deductive and inductive approaches (Ottrey et al., 2018). According to Czernek-Marszałek (2019), deductive approach is a theory testing approach that assumes breaking down problems into simple elements enhances people’s ability to understand them. On the other hand, inductive approach insists on beginning a research with a specific view and ending with a general view (Ottrey et al., 2018). Inductive approach focuses on formulation of theories from data collected while deductive theory aims at testing existing theories. The research adopted deductive approach since it is based on formulation of hypothesis from theory, assessing the hypothesis then rejecting or accepting the hypothesis. Inductive approach is rejected on grounds that it focuses on development of abstract theory formation from observation (Czernek-Marszałek, 2019). Deductive approach is adopted because the research has defined objectives that are meant to guide the relationship between variables.

3.4 Qualitative versus quantitative methods

Based on Olmedilla’s (2019) argument, quantitative research approach is used in collection of numerical data from a sample. Quantitative approach follow positivism philosophy since only factual knowledge is considered to be true. As such, the researcher in quantitative research has minimal influence in the collection and analysis of data (Hackley, 2019). On the other hand, qualitative research method focuses on collection of non-numerical data about phenomena that cannot be easily quantified (Olmedilla, 2019). Hackley (2019) opines that qualitative research is best used in answering “what” and “how” questions while quantitative method is used to answer “how much” questions. The current study adopts quantitative methods and rejects qualitative approach. First, quantitative approach coincides with positivism research philosophy adopted. Quantitative approach is best suited for this research facilitates generalisation of numerical data.

3.5 Research Instrument

Different research instruments are used to facilitate collection of data in academic research. The common tools used are interviews, surveys, questionnaires, observation and focus groups (Patten 2016). The choice of a research instrument is based on various factors like time or if the research is qualitative or quantitative among others. The current research is quantitative in nature thus questionnaires will be used to collect data. Questionnaire is selected as the appropriate tool of data collection since the researcher will be able to collect data from a huge sample population (Bryman, 2017). Additionally, Bryman (2017) records that questionnaires are easy to distribute thus minimising the time and energy used by the researcher. The researcher will also be able to save on financial resources since use of questionnaires is economical. Nonetheless, Patten (2016) reasons that the major drawback of questionnaires is provision of dishonest answers which could minimise reliability of the research.

3.6 Sampling

Hackley (2019) defines sampling as the process of identifying the most suitable population to collect data from. There are two key approaches of non-probability sampling: purposive and convenience sampling approaches (Bryman, 2017). Convenience sampling is a technique of non-probability sampling that is used to identify participants of a research study based on their availability and willingness to take part in the study. Contrariwise, purposive sampling is a non-probability sampling method that is used to identify research participants based on the characteristics of the population and research objectives (Cheng et al., 2019).

For this research, convenience method of sampling is used to select the respondents for this study. Convenience sampling was selected based on its efficiency and ability to help in saving the researcher’s time and money that would have been spent in looking for the participants (Cheng et al., 2019). In view of this, the researcher targeted customers who have used Airbnb before for their accommodation purposes. To access this category of customers, the researcher will visit Airbnb’s official fan page on WeChat. This helped in enhancing the reliability of the research study since the findings are collected directly from customers who have experienced Airbnb before.

3.7 Data collection

Before data collection, the researcher will contact Airbnb fans through social media platforms by sending messages. In the messages, the researcher will highlight the purpose and objective of the research in order to persuade them to take part in the study. Following this, the researcher will ask for the willingness of the participants to take part in the study as well as their consent to ensure that they do not participate simply because of coercions. After consent is given, the researcher will distribute questionnaires to these participants so that they fill the questions. The researcher aims to dispense about 156 questionnaires with the assumption of having a 90% recovery rate. Therefore, the researcher intends to recover 140 valid questionnaires.

3.8 Data Analysis

SPSS will be used to assess the data collected from the questionnaires. SPSS is selected as the most effective tool for analysis since it will facilitate analysis of a large pool of data within a short period (Cheng et al., 2019). Using SPSS, descriptive statistic, frequency, regression and correlation analyses will be conducted. Analysis of the frequency will enable the researcher to formulate inferences based on the average demographic of the research participants. On the other hand, descriptive statistic analysis will enable the researcher to recapitulate the attitudes of the participants from the collected data (Cheng et al., 2019). Finally, correlation and regression analysis will help in identifying and demonstrate the existence of a relationship between eWOM and the consumer’s intention to choose peer-to-peer model of accommodation. After the analysis, the findings will be compared with previous literature in order to verify and develop concrete conclusions.

3.9 Research Ethics

Adherence to research ethics is important in any research since it ensures that the respondents are not exposed to any risks as they participate in the research (Melnikovas, 2018). Based on this, the researcher ensured that the anonymity of the participants was maintained. The researcher informed the researchers not to include any personal information in the questionnaires. At the same time, special codes were assigned to every participant. Moreover, the researcher ensured that participation of the research participants was not through coercion therefore the research objectives were explained to the potential participants who would then choose whether or not they would participate in the research.

3.10 Chapter summary

The chapter highlights the research methods used by the researcher to realise the research objectives. This research adopts positivism philosophy, deductive approach and quantitative research methods. Additionally, questionnaires will be used for collection of data and the participants will be selected through convenience sampling. The collected data will be analysed using SPSS. The researcher will also adhere to research ethics during collection of data.

Chapter Four: Research Findings 4.0 Introduction

This chapter presents the findings of the study collected with the use of questionnaires. The chapter begins with the participants demographics followed by warm up questions. The subsequent part is the descriptive findings and then trend analysis. The findings are arranged in line with the study objectives. The final part is the conclusion of the chapter which essentially summarizes the findings.

4.1Participants Background Information and general question findings

Sample Characteristics

Item

Proportion (%)

Gender

Male

41.1

Female

58.9

Age

18-25

26.0

26-35

39.0

36-45

22.6

46-55 years

10.3

Over 56

2.1

Education background

Senior high school and under

7.5

Junior college

24.7

Bachelor

49.3

Master’s

11.0

Doctor/Phds

7.5

Employment Status

Full time

33.6

Part time

13.0

unemployed

14.4

student

39.0

Table 4.1 Demographic information of the participants

From table 4.1 above, more women compared to men took part in the research accounting to 58.9% against 41.1 respectively. In relation to age, participants within the age bracket of 26-35 were the leading with a percentage of 39% followed by those within 18-25. This implies that a majority of the participants were young people. The least number of participants were those that were over 56 who accounted to only 2.1%. On education, a majority of the participants had attained a bachelor’s level of education accounting to 49.3%. On the other hand, those who had at least junior college level of education were accounting to 24.7% while those with high school level tied with those in the doctoral level with 7.5%. Statistics on the table also points out that 39% of the participants were actually students while 33.6% of them had full time jobs. On the other hand, 14.4% of the participants were jobless while 13% have part time jobs.


Figure 4.1 Participant’s selection on their favourite peer to peer travel accommodation platform.

Figure 1.1 above shows that Airbnb is the most favourite peer-to-peer accommodation platform among the respondents accounting to 39.73%. Those who preferred to use Booking.com and Xhiazhu.com tied with 19%. On the other hand, those who liked TripAdvisor were 10.27% while Trivago was the least with only 7.53%.

Figure 4.2 Whether Respondents had ever booked accomodation through Airbnb marketplace

As shown in figure 1.2 above, all respondents who participated in this study had booked travel accomodation through Airbnb marketplaces. This is understandable considering that the interest of this research was specifically on Airbnb clients or fans.

Figure 1: 3: The kind of travel which the respondents had booked accomodation through the platform of Airbnb.

From figure 1.3 above, a majority of the respondents had booked for leisure travel at Airbnb accounting to 49.21%. On the other hand, 25.34% of the respondents had booked accommodation while visiting relatives and friends. 14.38% had used Airbnb while on study abroad and the least number accounting to 13.70% were those who were on business trips.

Figure 4: 4: The number of times respondents book accommodation through Airbnb

From figure 1.4 above, at least half of the respondents 50% book accommodation through Airbnb at least 1 to 2 times a year. This is followed by 34.93% who book through the platform between 3.5 times per year. However, only 15.07% of the respondents are able to book through Airbnb more than 5 times a year.

Figure 4:5: Major reason for respondents in using Airbnb to book their accomodation

According to figure 1.5 above, a majority of respondents accounting to 41.10% preffered to use Airbnb specifically due to more choices regarding the rooms. On the other hand, 29.45% used the platform due to unique room decoration while 12.33% were due to favourable price. However, only 8.22% of the respondents were positive about the platform due to privacy and only 7.53% due to convenience and easy to check in.

Table 1.2 which aspect do you think the room of Airbnb does well?

According to the findings on table 1.2 above regarding the aspects which makes Airbnb rooms to do well, “its convenient to arrive at” scored the highest mean, accounting to 3.8767. This is followed by the statement, clean and tidy room accounting to 3.6849. However, the statement “the room has various facilities (eg, air conditioner, TV, internet, kitchen etc) has the lowest mean, accounting to 3.0822. This is an indication that most of the respondents were not very much concerned about these luxuries.

Figure 4.6 : General perception about Airbnb

From figure 1.6 above, a majority of the respondents accounting to 44.52% stated their review of Airbnb as “Good” followed by 24.66% of the respondents who stated their experience as “Perfect” and the same figure stated it as Acceptable. Only 6.16% of the respondents claimed that the review was “Bad”. This is an indication that a majority of the respondents had a positive perception about their experiences on Airbnb.

4.2: Descriptive Statistics 4.2.1: eWOM Quality and Quantity





Figure 4: 7 Responses on the e-Worm Quality and Quantity

From figure 1.7, the item “the quantity of information from online reviews about Airbnb is great showing it has good sales” had the highest mean, accounting to 3.7740 . This is an indication that a majority of respodents had a concern on the number of reviews of their service providers. This was followed by the statement, “the online review and comments I get about Airbnb are helpful” which accounted to 3.6849. On the other hand, the item, “there are highly ranking reviews and recommendations about Airbnb showing it has good reputations” had the lowest mean, accounting to 3.0685 implying that these reviews and recommendations are not necessarily high ranking.

4.2.2: Information Providers' Expertise


Figure 4.8 Descriptive statistics on information provider’s expertise

On the basis of the findings indicated in figure 4: 8 above, the statement “the providers of online reviews and comments about Airbnb are experienced” scored the highest mean, accounting to 3.9589. This was followed by the item, “the persons giving online reviews and comments about Airbnb have adequate knowledge regarding the services judgement” which accounted to 3.6712. This has implication that Airbnb ensures that the person in charge of posting the comments and giving the reviews/comments are all knowledgeable about the company. However, the statement, “the persons giving online reviews about Airbnb mentioned some things I had not considered” received the least score, accounting to 2.9658.

4.2.3: Information Credibility

Figure 4: 9: Descriptive Statistics on Information Credibility

From figure 4:9 above, the statement “I find online review and comments credible” achieved the highest mean score, accounting to 3.6781 followed by “the online reviews about AirBnb confirms to the actual reality” which accounted to the mean score of 3.4658. The statement “the online reviews are from previous users who have used AirBnb” had the least score, accounting to 3.5. Purchase Intention among Airbnb Users

Figure 4: 10: Descriptive statistics on purchase intentions among Airbnb Users

As evident in figure 4; 10 above, the item “after reading online review regarding Airbnb, it makes me desire to use their services” received the highest mean score, accounting to 3.8288. This was followed by the statement, “I will consider using Airbnb services after reading online reviews” with a mean score of 3.5068. However, the item “in future, I will seek to try out services of Airbnb discussed in the online review” had the least score account to 3.2808. This response may owe to the fact that a majority of the respondents are already using Airbnb services.

4.3: Correlation Analysis and Regression Analysis 4.3.1: Correlations


Figure 4: 11 correlation analysis between ewom quality and quantity, information provider’s expertise, and information credibility and purchase intentions among Airbnb users

As indicated in figure 4: 11 the correlation coefficient between eWOm quality and quantity and purchase intention of Airbnb users is R=1 with asig. =0. This is an indication that the interaction between high eWOM quality and quantity level is positively and strongly correlated with the user’s purchase intention and a very close relationship between e-WOM quality and quantity exist. On the other hand, the correlation coefficient between information provider’s expertise and the users purchase intention is R=0.595 with a sig=0.000. This is also an indication that at the signature level of 0.001, the interaction of provider’s expertise is positively correlated to users purchase intention. The correlation coefficient between information credibility and users purchase intention is R=0.705 with a sig=0.000. This is also an indication that at 0.001 level of significance, the interaction of information credibility has a strong relationship with the user’s purchase intentions.

4.4 Urinary Regression Analysis 4.4.1. Urinary regression analysis of e-wom Quality and Quantity and User’s Purchase intention

Below is the unary regression analysis between e-WOM quality and quantity and the user’s purchase intentions.

Figure 4: 12 Model Summary, Anovab and Coefficients of the regression analysis between the relationship of e-WOM quality and quantity and users purchase intention.

The first figure above is the model summary of the regression analysis between e-WOM quality and quantity and user’s purchase intention. The second figure is the Anovab while the third figure is the Coefficients.

As observed in figure 4:12 above, R Square for model summary is =0.724 a good indication that the variations in eWom quality and quantity could explain 52.5% of the variations in the user’s purchase intentions. On the second figure, F =159.008 > Sig. = 0.000, a good indication that the regression equation for eWom quality and quantity and users purchase intention is quite positive. In the last table, the regression equation for the eWom quality and quantity and users purchase intention would be expressed as: users purchase intention =0.975 +0.741 eWOM quality and quantity where + ε whereby; ε is constant. This equation implies that the users purchase intention alongside the variation in eWom quality and quantity and each time eWom quality and quantity varies by one unity, the users purchase intention varies by 0.741 units.

4.4.2 Urinary regression analysis of information provider’s expertise and User’s Purchase intention

Figure 4: 13: The Model Summary, Anova and coefficient of the information provider’s expertise, and users purchase intention.

From figure 4: 13, R Square for Model Summary is =0.513 and the interaction of provider’s expertise could explain 26.4% of the variations in the user’s purchase intention and that the regression equation between the two variables are quite significant. On the second figure, F =51.558 > Sig. = 0.000 indicating a strong correlation between the provider’s expertise and users purchase intention. In the last table, the regression equation between the providers expertise and users purchase intention could be expressed as follows: =2.015 +0.438. This equation indicates each time the providers purchase intention varies by one unity, the users purchase intention varies by 0.438 units.

4.4.3 Urinary regression of information credibility and User’s Purchase intention

Figure 4: 14: Model Summary, Anova and Coefficients on the regression between information credibility and the user’s purchase intention.

Figure 4-24 The unary regression analysis for information quality of micro-blogging and consumer purchase intention

As figure 4:14 shows, the information credibility of online reviews can explain 30.9% of the variations in the user’s purchase intentions while the regression equation for the information credibility of reviews and users purchase intention is quite significant. In this regard, the regression equation for the information credibility and users purchase intention could be expressed as follows: users purchase intention = 2.094 + 0.406 information credibility + ε. This means that the consumer purchase intention will vary as the credibility of the information for reviews varies and each time the information credibility varies by one unit, then the users purchase intention varies by 0.406.

4.5 Summary

This chapter has basically presented the findings in line with the research objectives. The findings are in form of descriptive statistics and correlation analysis. In general perspective, it is evident from both the descriptive and correlation results that the variables, e-WOM quality and quantity, information providers expertise and information credibility harbours a significant and positive influence on the user’s purchase intentions. The subsequent part, which is chapter 5 focuses itself at analyzing these findings in line with the existing literature review.


Chapter 5: Analysis and Discussion of the Findings

5.1 Introduction

This chapter analyses the findings presented in the previous chapter (chapter four). The results will be discussed in line with the findings from the previous studies. The analysis will be carried out for both descriptive findings and correlation analysis. The last part is a conclusion which summarizes the discussions and analysis.

5.2.1 Analysis of Demographic Information

From the descriptive statistics, it is evident that more women than men participated in the study 58.9% against 41.1% respectively. Regarding age, on the issue of age, the high number of participants was between 26-35 (39%) followed by 18-25 (26%). Therefore, the majority were people in their youth who still have energy for leisure, travel, or schooling. Pertaining to education, a majority of the participants had at least a bachelor’s degree, followed by those with college level. It can be argued that these “learned people” have a higher economic status and thus the capability to seek higher social amenities such as those of Airbnb. Moreover, their reason for seeking accommodations could be attributed to their schooling endeavors.

5.2.3: Analysis of warm up questions

Regarding the favourite accommodation services, it is evident that Airbnb is the most favorite peer-to-peer accommodation platform among other peer to peer travel accommodation service providers. This is due to favourable price; more room choices, convenience, clean and tidy rooms, positive experience, and unique room decoration. There is also 100% response on the participants who had booked Airbnb room. This was actually a screening question for choosing respondents for this study. Furthermore, the 49.21% of the participants stated that they had booked Airbnb for leisure. This may owe to the fact that Airbnb are mainly centered on a short term accommodation and hence; most of its users are travelers, tourists or businessmen. This may also explain why a majority of the participants stated to book Airbnb accommodation at least 1-2 times a year.

5.2.4: Discussion of the Findings on the impact of e-Wom quality and quantity and the user’s purchase intention

As per the research question “how does e-WOM quality and quantity affect the users purchase intention? The results are summarized in table 5.1 below

Correlated variables

Analysis approach

Statistical results

The brand awareness of micro-blogging

Consumer purchase intention

Correlation analysis

Sig.=0.000

R=0.724

Regression analysis

R Square=0.521

F=159.008

User’s purchase intention=0.975+0.741 e-WOM Quality and Quantity +ε

Table 5.1: e-WOM quality and quantity and the user’s purchase intentions

The e-WOM quality and quantity and the users purchase intention are positively correlated with each other, and the e-WOM quality and quantity has a significantly positive impact on the user’s purchase intention. Furthermore, the regression equations between the e-WOM Quality and Quantity generate more plausible outcomes. This result aligns with the findings of Liang, Choi, and Joppe (2018) who noted that large quantities of information shared by individuals through e-WOM enhance the confidence in trusting and eventually making purchase decisions. Similarly, Lim (2016) observes that customers have a perception that high eWOM numbers indicate the usefulness and popularity of the product; hence their purchase intention towards such products increases. Tussyadiah and Zach (2017) claim that, the quantity of information that customer’s receive influence their willingness to choose peer-to-peer model of travel accommodation. This argument is further echoed by Tussyadiah and Zach (2017) who claim that high number of posted opinions or messages about a product or service that customers want can easily influence them to make purchase decisions. From a different but related point of view, Ismagilova et al. (2019) escalate that the high the e-WOM quantity about a product or service, the more likely consumers are able to hear or become aware about it, hence the reason why many consumer opinions or reviews can be a signal of service or product popularity.

On the basis of the findings, the dimension of quality harbors an impact on the users purchase intention to choose the peer-to-peer travel accommodation model. In reference to Tsao et al. (2015), consumers are more focused on the information quality when they are looking for information concerning peer-to-peer accommodation in the eWOM communication medium. A different study by Liang et al. (2017) also found out that when consumers are provided with quality information that is clear, helpful and easy to understand, their intention to choose peer-to-peer accommodation increases. Similarly, according to Mahadevan (2018), the quality of online argument or e-WOM influences consumers’ intention to choose the peer-to-peer travel accommodation. According to Mahadevan (2018), the convincing strengths of arguments rooted in e-WOM forms a core part in influencing trust in a product or service and positively influencing purchase intentions.

The findings thus confirms the first hypothesis of this study which stipulates that the perceived quantity and quality of e-WOM has a positive impact on consumers’ intention to choose the peer-to-peer travel accommodation model.

5.2.2: A discussion on the impact of information provider’s expertise on the user’s purchase intentions

In line with the research question, “what is the information provider’s expertise on the user’s purchase intention?” the outcome is presented in table 5.2 below

Correlated variables

Analysis approach

Statistical results

The information provider’s expertise

User’s purchase intention.

Correlation analysis

Sig.=0.000

R=0.513

Regression analysis

R Square=0.264

F=51.558

User’s purchase intention=2.015+0.438 the information provider’s expertise +ε

Table 5.2: Information provider’s expertise on the user’s purchase intention

From the above results, the information provider’s expertise has a positive and moderate correlation with the user’s purchase intention. Further, the information provider’s expertise harbours a significant and positive impact on the user’s purchase intention. This outcome confirms the findings of Goh (2015) who noted that the perceptions of the information providers’ expertise in terms of skills, knowledge, and experience about a product or service influence consumers’ purchase decisions. On his part, Lim (2016) also concurs that when customers read the reviews provided by expertise customers, they become highly receptive and this influence their decision to choose some services or products over others. What this implies is that the messages posted by these experts (those perceived as knowledgeable and experienced about specific products or services) online can be persuasive in influencing purchase intentions. On the other hand, Mahadevan (2018) argue that the reverse is true of consumers perceiving or associating the information providers with poor knowledge or little experience with products or services of interest. Similarly, findings by Liang, Choi, and Joppe (2018) indicate that the high skills, experience or knowledge of a product or service associated with information source or provider increases consumers’ trust about the usefulness of the information provided, and this translates to positive purchase intentions. Ai, Chi, and Ouyang (2019) informs that information providers are perceived as experts in specific products or services when they remain neutral or do not engage in commercial activities. Accordingly, the information provided by such experts is regarded as useful and reliable when making purchase decisions.

Thus, the findings on this objective align with the second hypothesis of this research that the perceived information providers' expertise positively influences consumers’ intention to choose the peer-to-peer travel accommodation model.

5.2.3: A discussion on the influence of information credibility on the user’s purchase intention

As for the research question, “does information credibility influence the users purchase intention?” the results are as shown in table 5.3 below

Correlated variables

Analysis approach

Statistical results

Information credibility

User’s purchase intention

Correlation analysis

Sig.=0.000

R=0.556

Regression analysis

R Square=0.309

F=64.359

User’s purchase intention=2.094+0.406 e-WOM Quality and Quantity +ε

Table 5.3: Information Credibility and the user’s purchase intention

From the results above, the information credibility of the online reviews harbors a positive and moderate correlation to the user’s purchase intention. Further, the information credibility of online reviews poses a positive and significant impact on the user’s purchase intention. This finding aligns with those of Ayeh, Au, and Law (2013), who realized that when consumers perceive or associate information about a product or service with high credibility, they often accept and trust its content or message compared to when they perceive it as not credible. Goh (2015) argues that the level at which consumers perceive information about a product or service to be reliable equates to the level they see it as credible, and this simultinatiously influences purchase intentions. These researchers also reiterate that when consumers judge or perceive information as accurate and reliable, they are likely to associate it with high credibility and as such, they develop trust towards it and eventually make purchase decisions. The reverse is true when they perceive information as innacurate and unreliable. On their part, Marquette, and MS (2017) agrees that the anonymous nature of information, for example, eWOM engagement by unidentified bloggers or endorsers sometimes make it difficult for readers to evaluate information credibility, thus, rarely demonstrate positive purchase intentions. This reiteration is supported by Olmedilla, Martínez-Torres, and Toral (2019) who escalate that based on the fact that the internet is open and/or vulnerable to untrustworthy information or opinions, information source credibility also plays a significant role in influencing consumers’ purchase intentions. According to Liang, Choi, and Joppe (2018), despite the fact that existing theories seem to agree that lack of cues for interpreting the opinions of information or content can lower trust of consumers to view the information as credible and simultinuously affect purchase intentions in a negative way, most consumers’ purchase intentions are influenced by subjective perceptions, including the judging of visual features for instance star ratings or/and online reviews, product or service prices, and the characteristics of information reviewers, among many others.

Therefore, the outcome from this objective concurs with the Hypothesis 3 of this research which articulate that the perceived information credibility positively influences consumers’ intention to choose the peer-to-peer travel accommodation model.

5.3 Chapter Summary

The focus of this chapter was to discuss and present the findings presented in the previous chapter. The discussion and analysis is made in comparison with the existing literature on the same subject. The next chapter, which is chapter 6 outlines the key conclusions as obtained from the findings and analysis in this chapter. Recommendations on the service improvements for Airbnb are also provided in the same chapter.


Chapter 6: Conclusions and Recommendations 6.1 Introduction

This chapter basically presents a conclusion of the findings generated from the study. The conclusions are presented in line with the research objectives outlined in the first chapter. These are then followed by the recommendations after which the research limitation is explained.

6.2 Study Conclusions 6.2.1 The impact of e-Wom quality and quantity and the user’s purchase intention

Correlated variables

Analysis approach

Statistical results

The brand awareness of micro-blogging

Consumer purchase intention

Correlation analysis

Sig.=0.000

R=0.724

Regression analysis

R Square=0.521

F=159.008

User’s purchase intention=0.975+0.741 e-WOM Quality and Quantity +ε

Table 5- 1 e-Wom quality and quantity and the user’s purchase intention

From this result, it is evident that there is a positive and strong correlation between e-WOM quality and quantity and user’s purchase intention. As noted, F =159.008 > Sig. = 0.000 showing that the regression equation for e-WOM quantity and quality of online reviews and users purchase intention is high. This implies that Airbnb has placed a higher volume of online reviews which are also of higher quality compared to other travel accomodation service providers. This explains the higher number of users preferring Airbnb compared to other service providers. This confirms that the quantity and quality of e-WOM (online reviews) poses a positive level of influence on user’s purchase intention. In other words, the quantity and quality of e-WOM reviews attract users to the brand’s services.

6.2.2 The impact of information providers expertise on the user’s purchase intention

Correlated variables

Analysis approach

Statistical results

The information provider’s expertise

User’s purchase intention.

Correlation analysis

Sig.=0.000

R=0.513

Regression analysis

R Square=0.264

F=51.558

User’s purchase intention=2.015+0.438 the information provider’s expertise +ε

Table 5- 2 Information providers expertise and the user’s purchase intention

From the above results, the information provider’s expertise has a positive and moderate correlation with the user’s purchase intention. As observed, F=51.558, Sig. = 0.000, indicating that the regression equation for information providers expertise and the user’s purchase intention is moderately significant. This implies that users are influenced more when the online reviews are provided by expertise or those people familiar with the products/services than if the reviewers were people who are unknown. In this regard, this is also an indication that Airbnb employs a significant level of expert information reviewers to provide online reviews for their services. This strategy appears to work going by the performance of this service provider.

6.2.3: The impact of information credibility on the user’s purchase intention

Correlated variables

Analysis approach

Statistical results

Information credibility

User’s purchase intention

Correlation analysis

Sig.=0.000

R=0.556

Regression analysis

R Square=0.309

F=64.359

User’s purchase intention=2.094+0.406 e-WOM Quality and Quantity +ε

Table 5- 3 Information credibility and the User’s Purchase Intention

As observed, F=64.359 Sig. = 0.000 indicating that the regression equation for information credibility and user’s purchase intention is significantly positive. This means that most of the information on Airbnb websites or social media platforms is considered credible by many users. Again, this may be attributed to the use of expertise in providing online reviews, as well as taking measures to ensure that most of their information are of high quality.

6.3: Recommendations on How Airbnb can improve its service offerings.

Taking into concern that the current study has confirmed the influence of the quality and quantity of inline reviews in attracting customers, Airbnb should even continue improving the quality of the information and reviews posted on its websites and social media. They must also ensure that the reviews are from a diversity of people from different countries and geographical locations. This will ostensibly add value and credibility to potential Airbnb customers. The diverse and geographical consideration in online reviews will help in stirring interest from potential clients who have been represented in these reviews.

Furthermore, Airbnb should work to ensure that most of the service reviewers given permission to make these reviews are people with real and sufficient level of experience on the actual services. This is because as already observed, the level of people making the reviews determines whether the audience will belief the message or not. This also entails Airbnb management to make use of social media influencers, popular comedians and public figures to pass their message across and in marketing their brand. These experts and influencers have the power of driving more people towards the services and the brand.

Lastly, the management of Airbnb must strive to ensure that the reviews are always true rather than being falsified to create a good impression. This is because if a customer decides to use the services out of the falsified information and his or her expectations are not met, then the customer may provide negative reviews about the services, eventually ruining the company reputation. This is why the company must verify the authenticity and quality of information so as to provide the right impression to clients.

6.4 Limitation of this Study

This study is limited in the sense that it mainly relies on the online questionnaires to get data from respondents. However, this form of data collection is froth with survey fraud whereby; some of the respondents will just choose to answer questions dishonestly since they are anonymous or away from the interviewer. In other words, some of the respondents may not have a desire to contribute to the development of the research. However, to minimize this possibility, the researcher will use a significantly reasonable number of respondents so that the answers can be validated.


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Comments

Silas Nyamweya (author) from Nairobi, Kenya on February 13, 2021:

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