Keywords

1 Introduction

Smartphones are ‘smart’ because they can be connected to the Internet and can run software applications (apps) that allow the owner to interact with other actors without geographical limitations [1]. Their penetration of the cell phone market is continuing to grow: at the end of 2014, it was estimated that 14 % of the 7 billion mobile phone subscribers owned smartphones [2, 3] while in North America, the penetration was considerably higher at 60 %. Hotel companies are recognizing that many of their guests are smartphone owners who want apps that add convenience and comfort while being reliable and secure [4]. As an example, guests are now booking online through their mobile phones and tablets [5] and, at an estimated compound growth rate of 32 %, mobile travel sales in the US will be $65 billion by 2018 [6].

The most common hotel related apps found in the app stores of iTunes and Google Play are those that support hotel and travel sales [7]. Goh et al. [8] sorted them into five groups: travel planning, transportation, reservations, portals/search engines, health & safety information and context-aware services. However, none of these groupings addressed the use of the apps when the guests were ‘on property’. Our research addresses this gap in the literature by focusing on apps that would be used by guests during their stay. Our research question is ‘what factors influence guests’ adoption of mobile apps that enhance their stay while on property’.

There are numerous apps that hotel operators and software providers could develop, such as setting the room temperature, controlling the TV, pre-ordering dinner, managing loyalty points, checking-in/checking-out and substituting as a room key [9]. The objective of our research is to guide the investment choice of those organizations that are developing and implementing apps for hotels. Our foundation is the Technology Acceptance Model (TAM) [10]. Because these hotel apps involve the exchange of personal and financial data, we add the construct of Trust [11, 12]. Furthermore, because smartphone users need to learn that an app exists and understand how it might be useful, we add the construct of Word of Mouth to the research model [13].

This paper is organized as follows. Section 2 is the literature review, where we develop our hypotheses and illustrate them with our research model. Section 3 is the research methods where we provide more background on each construct and introduce the scales by which they will be measured. Section 4 is the analysis of the results. In Sect. 5 we discus the results and include the limitations of the current research and offer suggestions for future research. We present our conclusions in Sect. 6.

2 Literature Review and Development of Hypotheses

2.1 Technology Acceptance

Our foundational theory is the Technology Acceptance Model, TAM, [10] which posits that individuals will adopt a technology if it is both useful and easy to use. [14, 15]. The model is parsimonious with two independent variables that predict intention to use: perceived usefulness (PU) and perceived ease of use (PEOU) [10]. It has been applied in many areas, including mobile commerce [16], reserving a room with the help of a mobile app [17] and using a self-serve kiosk to check-in [18]. As a well-established theory that explains an individual’s adoption of a technological innovation, we have selected TAM to be the core theoretical framework of our research.

While on property, potential uses of the smartphone would be the control of room temperature, acting as a remote for the TV, ordering room service, paying for hotel services or managing loyalty points [19]. We theorize that these apps will motivate hotel guests to use their smartphones to make their stay more comfortable and we propose the following hypothesis.

  • Hypothesis 1: Perceived usefulness positively influences intention to use specialized apps at a hotel.

Given that smartphone users are familiar with downloading apps and utilizing them, there should be no difficulty in guests learning to use new apps. Therefore, our second hypothesis is:

  • Hypothesis 2: Perceived ease of use positively influences intention to use specialized apps at a hotel.

2.2 The Role of Trust

In an e-commerce context, McKnight et al. [20] defined trust as the confidence in the other party’s competence, integrity and benevolence. Some of the transactions at a hotel involve exchanging personal information as well as payment data, such as credit card or loyalty membership number. Guests expect that the data exchanged for a hotel product or service will be performed in a dependable manner with minimal risk [11]. Consumers are concerned about the safety of their data when sharing personal data during their hotel stay [21]. In a qualitative study of attitudes towards mobile payments, Dahlberg et al. [22] found that trust was a concern and they recommended that future researchers add the concept to TAM. We therefore add trust to our model:

  • Hypothesis 3: Trust positively influences intention to use specialized apps at a hotel.

2.3 Word of Mouth

When a new innovation becomes available, consumers need to be made aware of it. For online applications, word of mouth (WOM) is a major source of information [23]. Owners of smartphones are able to find out about new apps through word of mouth [24, 25]: personal word of mouth (PWOM) refers to the personal interaction with friends, family and colleagues; virtual word of mouth (VWOM) refers to consumers learning via the virtual world, such as postings on websites; and written word of mouth (WWOM) refers to the gathering of information via articles published in magazines and newspapers. WOM has been substituted for subjective norms [25]. Similarly for services in the hospitality industry, word of mouth is an important information source for prospective guests [26]. We add word of mouth to our model, defining it as a second order construct, comprised of PWOM, VWOM and WWOM as the first order constructs. We hypothesize:

  • Hypothesis 4: Word of mouth positively influences intention to use specialized apps at a hotel.

2.4 Trust as a Mediating Variable

Consumers become aware of how they might use their smartphone at hotels through word of mouth. They may be persuaded that the apps will be useful at their next stay, but those who have less trust in the providers of the apps may be less open to adopting them. We hypothesize that trust is a mediating variable, mediating the effect of perceived usefulness on intention to use.

  • Hypothesis 5: Trust mediates the influence of perceived usefulness on intention to use specialized apps at a hotel.

2.5 Research Model

The research model is shown in Fig. 1.

Fig. 1.
figure 1

Research Model for Acceptance of Smartphone Usage at Hotels

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3 Research Methods

Design. With the assistance of subject matter experts, an online survey was designed to operationalize the research model. Scales were adopted from the literature. We used the services of a company that specializes in recruiting individuals who are willing to respond to surveys and be engaged with research. The final number of valid responses for further analysis by Partial Least Squares (PLS) was 597 (45 % of the surveys sent). The high response rate received in a short timeframe was due to the recruitment and retention expertise of the company that we used to manage the survey data collection and we recognize that this is a potential limitation of the study (See Limitations).

Data Analysis. The data collected was analyzed with PLS, which enables both the validity of the indicators and the relationships between the constructs to be evaluated. PLS was selected because it is suitable for predictive applications and theory building [27].

The first step in the analysis was the evaluation of the measurement model [28]. Internal consistency was tested by calculating Cronbach’s alpha and evaluating composite reliability. The convergence of indicators on their constructs was tested by calculating the average variance extracted. In addition, the Fornell-Larcker criterion was used to test the discriminant validity of all the constructs in the model. The second step in the analysis was the evaluation of the structural model [28]. In order to include the second order constructs in SmartPLS, we adopted the repeated indicators approach [29]. To test the role of trust as a mediating variable between perceived usefulness and intention to use, we calculated the Variance Accounted For (VAF) factor, following Preacher’s method of multiplying the indirect effects [30].

4 Results

4.1 Descriptive Statistics

In the sample, there were 296 males (49.6 %) and 301 females (50.4 %). Almost half the sample (48 %) was between 18 and 40 years of age and the remaining 52 % was 41 and above, with the oldest participant being 75. The age distribution is shown in Table 1. Age groups of sample.

Table 1. Age groups of sample
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The median length of ownership for those who possessed a smartphone was 3.5 years with 50 % having owned a smartphone for three years or more.

4.2 The Measurement Model

The cross loadings of the measurement model were calculated by the SmartPLS software and the indicators were shown to be collinear. All correlation coefficients were greater than the threshold value of 0.708 [31].

By running a Bootstrap within SmartPLS with 5,000 samples using the replacement method, the t statistic for each cross loading was calculated and in every case, the significance was p < 0.001.

The internal consistency of each construct was assessed via Cronbach’s alpha [32], where values above 0.8 indicate reliability. The Average Variance Extracted (AVE) for each construct further confirmed the reliability of the model, where the AVE was above the guideline of 0.5 with the exception of the higher order construct, word of mouth. In addition, the Composite Reliability was above the guideline of 0.6 [31].

Discriminant validity was tested using the Fornell-Larcker score, where the AVE must be greater than the square of the correlations [33]. The results satisfied these criteria, with the exception of word of mouth due to it being a higher order construct. Table 2 compares the correlations with the square root of AVE (shown in italic bold along the diagonal).

Table 2. Values for Fornell Larcker test
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4.3 The Structural Model

The SmartPLS algorithm calculated the R2 measures for each endogenous variable and the path coefficients for each path within the model. R2 for intention to use was 0.511, which is considered moderate [34]. The significance of each path coefficient was calculated by bootstrapping with 5,000 samples using the replacement method. All hypotheses were supported with p < 0.001, with the exception of hypothesis 2. Results are shown in Fig. 2.

Fig. 2.
figure 2

Results of analysis of structural model

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The effect size was calculated in a series of steps, where each exogenous variable was removed from the model in turn and the new R squared calculated. The effect size of Trust and PU were medium.

4.4 Intention to Use

In the questionnaire, participants were asked about their intention to use specific services at a hotel that would be enabled by the smartphone. See Table 3.

Table 3. Intention to use specific hotel services enabled by smartphone apps
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These results provide more details about the features that guests perceive as beneficial. Participants indicated their preference to use their smartphone to save money through using loyalty points or receiving e-coupons. Although the room key is available at some properties, this service was less important to guests.

4.5 Trust as a Mediator

We ran the bootstrap with 597 observations per sub-sample and 5,000 samples with no sign changes. First the model was run without trust, and then it was run with trust. The total effect, c, without trust, was significant as was the direct effect, c’. Further, the indirect effect was calculated as the product of a and b [30].

All paths in Fig. 3 were significant at p < 0.001, leading to the support of the hypothesis that trust is a mediator. We then calculated the Variance Accounted For (VAF) factor, as follows:

$$ \begin{aligned} {\text{VAF}} & {\text{ = a}}*{\text{b/(a*b + c')}} \\ & { = 41}\% \\ \end{aligned} $$
Fig. 3.
figure 3

Path Model for trust as a Mediator

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VAF can have values from 0 % to 100 %, where 100 % represents full mediation. A value of 41 % is a moderate VAF level [28, 35]. Our conclusion is that trust as a mediator has a moderate effect on perceived ease of use, accounting for 41 % of the variance.

4.6 Support of Hypotheses

Table 4.

Table 4. Support of hypotheses
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5 Discussion

The main influencing factor was PU, which had a medium effect on intention to use. This is consistent with other studies of TAM [36, 37]. Individuals use systems because of the promise to deliver a desired outcome. For example, in the case of smartphone apps at a hotel, guests have indicated that they would value the tracking of loyalty points and the receipt of e-coupons.

Prior to using an IT artefact, the individual must be made aware of it. With the introduction of smartphones and their promise of simplicity, applications are being developed continuously. With limited time available to learn ‘what’s new’, individuals learn through word of mouth by reading, listening and observing others. In our study, word of mouth was significant. Guests are made aware of hotel apps through their interaction with friends, reading articles in print and surfing the Internet for reviews.

In the analysis of our results, trust influenced intention to use, confirming the findings of Duane et al. [38]. Trust represents the consumers’ confidence that their data is secure, their privacy assured and that their personal information will be safe. In addition, trust mediated the effect of perceived usefulness on intention to use. A guest may determine that there are significant benefits to using a specific functionality on their smartphone, such as using loyalty points to pay for their stay, but those guests who lacked trust would be less inclined to use such an app.

5.1 Theoretical Contribution

TAM has been the theoretical foundation of a number of studies of mobile commerce [39], which is defined as the use of mobile devices to share personal information in order to conduct an array of business services, such as location based marketing, booking a hotel room, mobile ticketing and mobile banking [39]. Secure apps at the hotel are focused on optimizing the guest experience through sharing personal information, including payment-oriented data such as credit card details and loyalty membership points. Past studies have focused on guests’ use of mobile apps to access the Internet to book reservations and seek specific information about a hotel prior to their stay [40], but there have been limited studies of the acceptance of smartphone apps that add functionality and convenience for use by guests at hotels during their stay.

Our theoretical contribution is the extension of TAM to the context of guest acceptance of the use of their smartphone to enhance their experience during their stay at hotels. Smartphone users become aware of the many apps available to them through word of mouth: they see their friends using the app (an example of personal word of mouth, PWOM), they read a review from a trusted source (an example of virtual word of mouth, VWOM) or a written source (written word of mouth, WWOM). We combine the first order constructs of PWOM, VWOM and WWOM into a higher order construct, word of mouth. Although trust has been incorporated into models of TAM [11, 22], we evaluate its moderating influence on perceived usefulness where the exchange of sensitive information is involved.

A further contribution to theory is the comparison of the influence of PU to that of PEOU. Meta-analysis of the TAM literature has indicated that PU has a stronger influence than PEOU [36]. In our study we confirm these findings and agree with Gefen and Straub, who proposed that PEOU relates to the ‘intrinsic characteristics of the IT artefact…whilst PU is a response to user assessment of its extrinsic outcomes’ [41]. These results suggest that if the IT artefact is simple to use and its use is similar to current actions, PEOU has a minor influence on intention to use.

5.2 Limitations and Future Research

Our sample of respondents was provided by a professional organization experienced in conducting surveys with selected audiences. The participants have voluntarily offered their services and receive some form of compensation for taking a survey. Only participants who owned a smartphone were asked to complete the survey. Therefore the results reflect the responses of a population that is willing to respond to surveys and that owns a smartphone. Results may not be generalizable to the population as a whole. A further limitation is that the research was conducted with residents of the USA, and the findings may not be applicable to other geographical or cultural groupings.

Our theoretical contribution lays the groundwork for future researchers. The model can be tested across a broader cross section of the general population. The data can be segmented to determine whether age and income are moderating factors. Further analysis could evaluate the differences between guests who stay at different types of hotels, who are more frequent travellers and who are members of a hotel’s loyalty club. Recognizing that guests have a choice of where to stay and which apps to use, researchers could evaluate the perceptions of trust for apps from different providers.

6 Conclusion

Today, smartphones are smart enough to perform many transactions when connected to the Internet or a wi-fi network. Specialized apps can offer greater convenience for guests when they stay at hotels. The development of these apps depends upon providers investing further in the software and the infrastructure, and their decision to invest depends upon the acceptance by guests of this new technology. In order to understand the factors that influence guests, we have applied the Technology Acceptance Model, which is a seminal theory for an individual’s acceptance of a new IT artefact. As in most exchanges of personal information, the acceptance of smartphone apps is influenced by trust. Our theoretical contribution is the extension of TAM to the context of guests’ intention to use apps to enhance their experience during their stay. We have included the construct of trust and added the construct of word of mouth to explore how consumers become aware of the benefits of new technologies through word of mouth.

Many hotels have on-line websites and mobile apps that promote the brand and enable prospective guests to plan their trips and make reservations prior to their stay. Some hotels are now starting to offer their guests mobile apps to enhance their stay while on property [42, 43]. Guests are able to use their smartphones to retrieve their loyalty point balance, to pay with their points, and to receive e-receipts to keep track of their folio charges. They can check-in, open their room, control their TV and set the room temperature all from their smartphone.

The implication for hotels and their app developers is that they should focus on apps that are useful to guests during their stay. Our results show that guests are motivated to use these apps so long as they recognize their utilitarian value. In addition, as part of the design, the hotel should ensure that the app is clearly identified with their brand, in order to engender trust. Our results show that when an app requests the input of personal information, trust is an important factor.

A further contribution of this paper is that guests learn about new apps from others through word of mouth. The implication for hotels is that they should encourage this sharing of information through the hotel website, Facebook page or Twitter handle. As smartphone usage continues to grow, together with the ability of apps to offer more options, guests can look forward to improved services during their hotel stays.