Exploring Tourist Satisfaction with Mobile

International Management Review

Vol. 6 No. 1 2010

Exploring Tourist Satisfaction with Mobile Experience Technology Jung Kook Lee Division of Business, Purdue University Indiana University Columbus, Columbus IN USA

Juline E. Mills Hospitality and Tourism Management, College of Business, College of Consumer and Family Sciences University of New Haven, West Haven, CT, USA [Abstract] Wireless access with handheld devices is a promising addition to the WWW and traditional

electronic business. Handheld devices provide convenience, portable access, and large amounts of information to travelers. Tourism presents considerable potential for the use of new mobile technologies; however, limited research exists on mobile users’ perspectives with regard to satisfaction towards mobile technology. There is a need to develop an understanding of travelers’ satisfaction with mobile commerce in order to gain optimum competitive advantage. In this paper, we adapted and developed the American Customer Satisfaction Model (ACSM) to m-commerce in the tourism industry. The results of this study suggest that the degree of perception and perceived value are key factors affecting mobile travelers’ satisfaction with their mobile experiences. Satisfaction, in turn, influences the extent of intention to continue to use mobile devices during travel. The study concludes with recommendations based on our findings, as well as provides directions for future research. [Keywords] Mobile commerce; customer satisfaction; American Customer Satisfaction Model (ACSM);

mobile technology Introduction In recent years, there has been significant growth in the use of mobile devices, such as hand-phones, personal digital assistants (PDAs), and handheld computers. In U.S., mobile commerce revenue doubled to $58.4 billion in 2007 from $29 billion in 2006 (Jupiter, 2008) Mobile technology not only extends the reach of wired networks, but also serves as an alternative information channel providing new range of opportunities to travelers, as well as changing the way certain information-related activities are conducted. In the past, mobile devices were regarded as a luxury for individuals. However, mobile commerce (m-commerce) now offers great flexibility for the tourism industry both to suppliers and travelers. Users can surf the web, check e-mail, read news, pay transactions, and quote stock prices using these handheld devices. From the supplier’s perspective, the promotional message can be changed much more quickly than through the use of traditional media. M-commerce is now becoming the standard in handling travel yields effectively (Eriksson, 2002). Ninety percent of households in Japan, South Korea, and urban China now own cell phones, as do 80% of households in Western Europe, 60% in Canada, and three out of four households in the U.S. (Lombard, 2006). With this skyrocketing rate of ownership of mobile devices (Fernadez, 2000; Bughin, et al., 2001), considerable research efforts are now being devoted to understanding how mobile technology could support the information needs of travelers, ranging from touring in museums (Oppermann & Specht, 1999), transportation and parking information (Rodseth et al., 2001), location identification (Eriksson, 2002), to tracking and navigation (Corona & Winter, 2001). The findings of these research efforts imply that travelers are interested in new ways to carry out their travel plans. However, limited research exits on traveler’s satisfaction with mobile technology and mobile devices. The purpose of this study, therefore, is to develop a conceptual framework that examines and explains the factors influencing mobile users’ satisfaction and purchase intention. The study is organized as follows: first, the background of the study is described; second a tourist satisfaction model for mobile devices is proposed with corresponding hypotheses; third, the proposed model was tested using confirmatory factor analysis and structural equation modeling. The study ends by presenting conclusions and discussing study implications.

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Study Background M-commerce M-commerce, in this study, is defined as a transaction that takes place via wireless Internet-enabled technology (through handheld computers, cellular phones, personal digital assistants (PDAs), or palmtop computers) while allowing for freedom of movement for the end user (Wei & Ozok, 2005). Wireless Fidelity (Wi-Fi), the transmission of short-ranged radio signals between a fixed-based station and an end-user's mobile device is the driving technology that facilitates m-commerce (Wireless Computing, 2003). M-commerce can also be defined as any transaction with a monetary value, either direct or indirect, that is conducted over a wireless telecommunication network (Clarke, 2001). M-commerce is about the explosion of applications and services that are becoming accessible from Internet-enabled mobile devices involving new technologies, services, and business models. Mobile phones or PDAs impose very different constraints from desktop computers traditionally used in e-commerce transactions opening the door for the entry of a slew of new applications and services. These new devices and applications make it possible for the user to access the Internet while walking down the street with friends and family or while driving, looking for a nearby restaurant or gas station. There are many advantages to mobile services, including high availability, the ability to enjoy ecommerce services regardless of time or location, as well as portability and location awareness capabilities (Siau, Lim, & Shen, 2001; Varshney & Vetter, 2002). The nature of mobile technology is summarized as follows: Ubiquity - services and applications provided via wireless Internet will be available anywhere and at anytime (Tsalagidou, Veijalainen, & Pitoura, 2000; Anckar & D’Incau, 2002). Thus, travelers will have greater flexibility to access and receive information and services from different physical locations; Localization awareness - Users can obtain their physical location at any particular moment via the mobile network operator or Global Positioning System (GPS). Users are able to track personal belongings for security or request emergency services (Tsalagidou, Veijalainen, & Pitoura, 2000); Immediacy - users are given the capability to react in time critical situations, this capability also allows users to make spontaneous decision (Anckar & D’Incau, 2002); Personalization - this feature is also called customization. Since users can receive personalized service, they may gain unique experience, distinct from other existing technologies (Advani & Choudhury, 2001). Personalization is a means of meeting the customer’s needs more effectively and efficiently, making interactions faster and easier, and increasing customer satisfaction and the likelihood of repeat visits; Broadcasting - mobile technology provides an efficient means to disseminate information to a larger population, wherein users can use these features to share information of common interest with others such as picture taking while on vacation; Portability – is the functionality pertaining to the fixed and mobile networks, which allows telephone customers in the U.S., for example, to retain their local phone number if they switch to another local telephone service provider; Identification - SIMcards can be used as electronic signatures, making the individual identity always recognizable. Electronic payment with mobile devices, thus, becomes a reality. M-commerce also covers a wide range of applications. Varshney and Vetter (2002) identified several important classes of m-commerce applications, including mobile financial applications, mobile advertising, mobile inventory management, locating and shopping for products, proactive service management, wireless re-engineering, mobile auctions or reverse auctions, mobile entertainment services and games, mobile offices, mobile distance education, and wireless data centers. Mobile Technology and Tourism Mobile Internet is an enabling technology for M-Commerce in the tourism industry. Tourism has been a popular area for mobile information systems, and other PDA based systems (Fesenmaier et al., 2000). Indeed, as mobile phones and other portable devices becoming more advanced, tourism is one obvious application area. Thus, M-commerce is a new and exciting field in tourism; as such, the literature about tourist satisfaction with M-commerce is limited. Unlike other industries that will embrace

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mobile communication as an added convenience to consumers, the tourism industry view mcommerce as an integral part of their customers’ overall travel experiences (Eriksson, 2002; SchmidtBelz et al., 2003). M-commerce not only extends the benefits of the Web, but also allows for unique services enabled by the convergence of the Internet with mobile technologies. Mobile devices, such as cellular phones, PDAs, Tablet PCs, Notepads, and Smart Telephones, make possible new ways of communication as well as non-location based access to information. Because of its objective target, tourism information requires a multimedia data presentation as allows the use of most PDAs. These mobile devices differ from each other in regard to hardware capability, as well as to the operating system. Travel service providers are finding new ways to increase customer loyalty, generate supplemental revenue streams, and reduce operating costs using m-commerce. Similarly, the costs of obtaining information are reduced for customers and suppliers with a wide diversity of information being represented on one mobile device terminal, which further reduces the information search costs for potential tourists (Kumar & Zahn, 2003; Vogt et al., 2003). The development in mobile technology involves mobile phone access to the internet via WAP. WAP is a specification for a set of communication protocols to standardize the way that wireless devices, such as cellular telephones and radio transceivers, can be used for internet access, including email, the world wide web, newsgroups, and internet relay chat (IRC). The rapid implementation of mobile-commerce by business continues unabated, and the scope of applications of these technologies is pervasive. Tourism-related services have emerged as a leading product category for promotion and distribution through the Internet (Connolly et al., 1998; Millman, 1998; Sussmann & Baker, 1996; Underwood, 1996). While internet access has been possible in the past, different manufacturers have used different technologies. The technology has managed to generate a vast amount of investment in wireless phone services, such as news, weather, financial services, banking information, travel agencies, timetables, leisure, and so on. Making product information accessible to customers could be effective and helpful for traveler. For example, for decades, most reservations were conducted by phone, letter, or facsimile to travel agents or hotels. However, we would be able to do that by clicking our mobile devices. By perfectly meeting customers’ information needs about products and services prior to purchase, the tourism industry is benefiting from mobile technology in satisfying customer expectations, in improving convenience, and in decreasing costs. Mobile commerce offers great flexibility for tourism suppliers who operate in volatile markets. The promotional message can be changed more quickly on a web page than on a printed document, eliminating the lag time between the adoption and the implementation of a policy decision. Purchase Intention Previous research indicates that consumer attitudes are associated with a level of behavior. When assessing the relationship between attitudes and intentions, past researchers have been able to successfully incorporate the theoretical support exerted in the Theory of Planned Behavior (TPB; Ajzen, 1985; Ajzen 1991). The TPB extended the Theory of Reasoned Action (TRA) by adding perceived behavioral control as a factor that can influence intentions and behaviors (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). The TRA, as its name implies, indicates that individuals are rational, they make use of all available information, and they evaluate the possible implications of their action before they decide to engage or not engage in a particular decision (Ajzen, 1985). A major contribution of the TRA is the specificity of attitudes and intensions to match behavior. In other hands, the TPB suggests that an individual's intention to engage in a behavior is the immediate proximal predictor of that behavior. Intention is conceived as the summary motivation to perform a behavior (Ajzen, 1991). Each of the above-mentioned components is predictors of behavioral intentions according to the TPB. Behavioral intentions have been defined as the subjective probability that the individual will engage in the specified behavior (Fishbein & Ajzen, 1975). Intentions are comprised of all of the motivation factors that affect a behavior and are an indicator of how much effort an individual will

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exert to perform a behavior. In relation to each of the predictors and behavioral intentions, the theory posits (1) that as an individual perceives the behavior as favorable (attitude toward behavior), he or she will more likely intend to perform the behavior; (2) if the individual perceives that his or her significant others would encourage such behavior (subjective norms), the individual will be more likely to intend to engage in the behavior; (3) the greater an individual perceives that he or she has control (perceived behavioral control), the more likely the individual will intend to engage in the behavior; and (4) the stronger an individual's intent (intentions) to perform a behavior, the more likely the individual will engage in that behavior. The American Customer Satisfaction Model Propagation of mobile internet technology and m-commerce applications opens a great opportunity in tourism industry. Nevertheless, surveys on US consumers' perceptions of mobile service show that the level of satisfaction is much lower for mobile service compared to other service sectors (Consumer Report, 2005; McKinsey Quarterly, 2004). Such findings suggest that tourism industry need to be aware of drivers of customer satisfaction in order to build effective business strategies for traveler’s retention. Customer satisfaction generally means the customer’s reaction to the state of fulfillment, and customer judgment of the fulfilled state (Oliver, 1997). Normally, the main factor determining customer satisfaction is the customers’ own perception toward the quality of the product or service. Several mobile device studies attempted to explore the antecedents of customer satisfaction (Gerpott, Rams, & Schindler, 2001; Kim, Park, & Jeong, 2004). These studies build on the notion that retention measurement and analysis of factors affecting it are vital for business success. However, variables, such as loyalty and intention, were examined as one concept. The American Customer Satisfaction Model (ACSM) is a general, cross-industry model that provides market-based performance measures for firms, industries, sectors and nations (Turel & Serenko, 2006). The original ASCM measures the quality of goods and services as experienced by consumers (Fornell et al., 1996) and gauges their actual and anticipated consumption experiences. According to ACSM, there are positive associations between perceived expectation and perceived value, and perceived value and satisfaction. In turn, satisfaction has a positive association with repurchase likelihood. The original ACSM requires a defined set of constructs for the model operationalization. The three constructs, PE, PQ and PV construct, lead to customer satisfaction labeled as the American Customer Satisfaction Index (ACSI). It is determined by the difference between the actual usage experience and service expectations. Thus, satisfaction is the subscribers’ reaction to their judgment of the state of fulfillment (Oliver, 1997). The model also demonstrates high predictive capabilities. The ACSM and its adaptations have been utilized in many studies in various industries such as banking services (Mukherjee et al., 2003), conferences (Gorst et al., 1999), and retailing industries (Arnett et al., 2003). The Thomson Corporation's ISI Web of Science® Social Sciences Citation Index (SSCI)® listed 138 journal citations of the article that introduced the ACSM on March, 2006. Such studies demonstrate the viability of this model to investigate behaviors and perceptions of mobile service users.

Research Hypotheses In this study, the ACSM was used to measure the relationship among mobile travelers’ satisfaction and other constructs. The original ACSM was modified for the characteristics of both tourism industry and mobile travelers. The model is shown in Figure 1. Consistent with previous studies that employed ACSM, a number of hypotheses were developed and tested in this study. Researchers acknowledge that the satisfaction level can be influenced by such factors as perception and experiences (Turel & Serenko, 2006). Kim et al. (2005) also argue that perceived value has an effect on users’ satisfaction. It can be also assumed that satisfaction positively influences intention. Therefore, it is proposed in this study that experience and perceptions are antecedents that influence the two determinants of satisfaction and intention to use mobile devices. The following hypotheses were developed and tested.

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H1: Reliability towards m-technology is a significant predictor of satisfaction and intention to use Mtechnology; H2: Reliability toward m-technology is positively related to perception and perceived value of mcommerce usage; H3: Travelers’ m-commerce technology experiences are significant predictors of satisfaction and intention to use M-technology; H4: Travelers’ technology experiences are positively related to perceptions and perceived value of Mcommerce usage; H5: Perception and perceived value are positively related to users’ satisfaction with m-technology; H6: Users’ satisfaction with m-technology positively influences the intention to use mobile devices for travel.

Technology Experience H3

H4

Perception toward M-Technology

H4

H5 Satisfaction

H5

H2

H6

Intention to use M-technology

Perceived Value of M-technology H1

H1

H2

Reliability of M-technology

Figure 1. Hypothesized model of tourist satisfaction with mobile technology

Methodology The study employed an online data collection process. The sample for the study was purchased through E-reward. The sample was screened as the mobile users who had prior travel experience. EReward provides one of the highest quality means of conducting primary online market research and gauging consumer interest. An e-mail message was distributed to the E-reward.com members along with a cover letter and the Website questionnaire’s address. The survey comprised of 54 questions focusing largely on travelers’ responses on: (a) trip experiences; (b) general attitudes toward the use of wireless devices; (c) technology experiences; (d) the constructs that directly influence behavioral intention in the model; and (e) demographic information. The items for the five constructs uses a seven-point Likert scale (1 = strongly disagree; 7 = strongly agree). A list of the survey items and the constructs they represent is shown in Table 2. The respondents were assured that all responses were kept strictly confidential and no unsolicited marketing material would be directed them the as a result of the survey. Also, respondents were not allowed to submit more than one entry as the study used a blocking IP number process. The survey was conducted over a one-month period from April 1 to May 1, 2007. A follow-up reminder e-mail was sent out to maximize the return rate. In total, 332 members of the E-reward.com Panel were contacted, and this effort resulted in 241 qualified completed responses for a 72.6% response rate.

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Results Profile of Respondents First, the demographic profile of respondents in the survey was identified through frequency analysis. Table 1 provides a profile of the respondents. Among the 241 survey respondents, there are slightly more females than males, while over 50% of the respondents were in the 30-49 age range. Another 25% of the mobile users were over 50 years of age. One notable characteristic of the respondents was that a large majority (91%) of the respondents were highly educated (some college education or higher), and about 60% of the respondents’ household incomes were over $60,000. This is consistent with the previous findings that online respondents are more likely to be professionals and report higher incomes and education (McDonald & Adam, 2003). In terms of mobile experience, approximately 84% of the respondents had used mobile devices for more than three years. With regard to Internet experience, over 90% of the respondents had used the Internet for more than four years, and over 50% of the respondents surfed the Web more than 10 hours per week. Table 1. Profile of Respondents Characteristic Gender Male Female Age Under 20 21 to 30 31 to 40 41 to 50 51 to 60 Over 69 Education High School Some College College graduate Post graduate Household income Less than 24,999 25,000 to 39,999 40,000 to 59,999 60,000 to 79,999 80,000 to 99,999 Over 100,000

f

%

137 107

55.6 44.4

1 49 71 58 44 18

0.4 20.3 29.5 24.1 18.3 7.4

22 68 95 56

9.1 28.2 39.4 23.2

18 34 47 46 28 68

7.5 14.1 19.5 19.1 11.6 28.2

Characteristic How long using mobile devices Less than 1 year 1 to 2 years 3 to 4 years 4 to 6 years More than 6 years How long using the Internet Less than 1 year 1 to 2 years 3 to 4 years 4 to 6 years More than 6 years Web surfing hours in a week Less than 1 hour 2 to 5 hours 6 to 10 hours 11 to 20 hours Over 20 hours

f

%

16 25 88 54 58

6.6 10.4 36.5 22.4 24.1

2 4 14 47 174

0.8 1.7 5.8 19.5 72.2

9 33 65 60 74

3.7 13.7 27.0 24.9 30.7

Measurement Model Confirmatory factor analysis (CFA) was performed to analyze the underlying factor structure of the proposed constructs (Schumacker & Lomax, 1996; Byrne, 2001). Analysis of Moment Structures (AMOS) 4.0 structural equation modeling software was used to perform the CFA analysis. As the data are continuous, the maximum likelihood estimation procedure with covariance matrix method was chosen within the software to perform the analysis. For the CFA process, individual parameters in the model are assessed for feasibility and the statistical significance of the parameter estimates, as well as the appropriateness of the standard errors. Key statistics (item means, confirmation factor loadings, and reliability tests) for all variables considered in this study are shown in Table 2. Each scale was assessed for construct validity by examining the standardized CFA factor loading of items. Scale reliabilities were estimated and all six constructs exceeded the standard acceptance norm of 0.70 (Landis & Koch, 1977). Three items did not meet the standard of construct validity and were eliminated.

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Table 2. The Result of Confirmatory Factor Analysis Latent variables

Observed variables of items

Mobile Experience Mobile devices enhance the quality of my travels Mobile devices enable me to have more convenient travels Using mobile devices enhance the effectiveness on my travels I have the knowledge necessary to use mobile device while I am traveling Perceived Value of M-technology Using mobile devices increase my travel productivity Using mobile devices during my travel fits into my travel style Learning to operate mobile devices would be easy for me Perception towards M-technology Mobile technology can't be trusted because there are many technical problems In general, I can't rely on mobile technology for communication Mobile technology is useful when I consider the cost Mobile technology is valuable when I consider the cost Satisfaction towards M-technology Overall, I am satisfied with mobile devices Overall, I am satisfied with my travels with mobile devices Intention to use M-technology I predict I would use mobile devices for my “future travels” I intend to use mobile devices for my “next travel” I intend to use mobile devices at hotels for my “future travels”

Mean

CFA Loadings

5.84 5.47 5.53 5.58

0.77** 0.76** 0.94** 0.87**

5.28 5.48 5.59

0.83** 0.77** 0.60**

5.09

0.94**

4.74 4.77 4.72

0.87** 0.70** 0.73**

5.11 5.16

0.91** 0.89**

5.54 5.52 5.30

0.92** 0.94** 0.85**

Construct Reliability 0.79

0.74

0.82

0.84

0.75

Structural Equation Model A separate set of structural equation model (SEMs) was run to assess the associations between the dimensions experience factors, to perception and perceived value, satisfaction and purchase intention. SEM was used here because of its capacity to measure the causal relationship between sets of unobserved (latent) variables. SEM specifies the causal relationships among the latent variables while describing the amount of unexplained variance. Further, variables described in this study contain potentially sizeable measurement errors, and SEM takes these errors into account. The SEM method estimates the unknown coefficients in a set of linear structural equations. Variables in the equation system may be either directly observed variables (results of the survey questions) or unmeasured latent variables (principal components) that are not directly observed but relate to the observed variables. The model assumes a causal relationship among a set of latent variables and that the observed variables are indicators of the latent variables (Byrne, 2001). In assessing the model, the following goodness-of-fit statistics: chi-square, the degrees of freedom, and the p value are reported. The analyses are presented in the Figure 1, which shows the path diagrams and relevant results. The chi-square statistics for the analysis was statistically significant. The goodness-of-fit index (GFI), a measure of the relative amount of variance and covariance in the sample data, and adjusted for degrees of freedom goodness-of-fit index (AGFI), which adjusts for the number of degrees of freedom in the model, is presented in the figure. These two indices should fall between 0 and 1, and a value closer to 1 indicates an acceptable fit. The root mean square error of approximation (RMSEA) is also presented in each figure as a measure of the average- across all elements in the analyzed sample covariance matrix--of unexplained variance and covariance. The error measures should not exceed .1 and ideally lie between .05 and .08, given that at least some error can be expected (Kline, 1998). In assessing the model as a whole, the goodness-of-fit statistics were examined (Byrne, 2001). Figure 1 presents the estimates of the path and goodness-of-fit statistics from the analysis of the proposed research model. The model had a somewhat satisfactory goodness-of-fit statistics. The overall fit indices for the structural model indicate a chi-

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square (χ²) of 158.6 with 128 degrees of freedom (p = 0.00). The χ² test becomes more sensitive as the number of indicators increases (Hair et al., 1998). As shown in Table 3, a number of additional factors were taken into consideration in assessing the model fit (Hair et. al., 1998). The results indicate that all hypothesized paths are supported. As hypothesized, dimensions of personalization are positively and directly related to attitude, the latter in turn positively influence purchase intention. The same pattern applies to privacy dimensions, as well. That is, dimensions of privacy are positively related to attitude towards privacy, which, in turn, exerts positive influence on purchase intention. The model also shows that attitudes influence purchase intention. Consequently, in summary, we can say that higher privacy and personalization levels can potentially lead to higher product purchase intention online. The standardized estimates for the model were substantively reasonable and statistically significant at the p < .05 level. The model revealed no negative error variances or unreasonable correlations (greater than 1.00). [ χ² = 158.6; df = 128; p = 0.001; Normed chi-square; GFI=0.96; NFI=0.92; CFI=0.96; RMSEA=0.06.] Table 3. Goodness of fit Indices Values obtained Absolute fit measures Chi-square p-level Non-centrality parameter (NCP) Goodness of fit index (GFI) Root mean square residual (RMSR) Root mean square error of approximation (RMSEA) Expected cross validation index (ECVI) Incremental fit measures Adjusted goodness of fit index (AGFI) Tucker Lewis index (TLI) Normed fit index (NFI) Comparative fit index (CFI) Parsimonious fit measures Normed chi-square Akaike information criterion (AIC)

158.600 0.000 979.93 0.96 0.09 0.06 1.64 0.91 0.95 0.92 0.96 2.23 226.62

The path diagram for the SEM model (Figure 1) represents the direction and magnitude of the direct impact through the positive and negative signs of the path coefficients and the absolute value of the standardized coefficients. The results of the path coefficients supported all of the hypotheses except for H1 and H2. The results indicated that the direct relationship between technology experience to perception (0.74) and perceived value (0.82), which were positive at p < 0.01 level. This supports the third research hypothesis. Both perception (0.76) and perceived value (0.87) were positively related to users’ satisfaction (H5). This also indicates that perceived value has more influence on users’ satisfaction. Finally, users’ satisfaction was highly related to intention to use mobile devices (H6). However, the first two hypotheses were rejected at p < 0.01 level. The final SEM Model result is shown in Figure 2.

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e4

p4 .65

e1 p1 .70 res7

e23

ex5 .68

e22 e21 e20

Perception toward M-Technology

ex4

.74

.91 .84

ex3 .82

.76 sa2

Technology Experience

.91

.23

e24 sa1 .87

res5 res6

Satisfaction

.82

ex2

e25

.05

res4

Intention to use M-technology

.81 Perceived Value of M-technology

res2 .80

.89

.60

pv1

pv2

pv3

e10

e11

e12

.65

pv4

.10

.83

in3 e28

.93

in2 e27

.93

in1 e26

e13

[Model fit stat; chi-square =158.6, p=0.00, GFI=0.96, NFI=0.92, CFI=0.96, RMSEA=0.06] Figure 2. Final model of tourist satisfaction of mobile technology

Conclusions and Implications The purpose of the study was to apply an adaptation of the ACSM to mobile travelers. This measure of satisfaction is suggested to be an important performance indicator for the tourism industry. The study demonstrates that the adaptation of the ACSM adequately describes the relationship between perceptions and experience to satisfaction of mobile travelers. Particularly, the study suggests that the degree of perception and perceived value are key factors affecting mobile travelers’ satisfaction with their mobile experiences. The findings of this research also indicate that developing positive travelers’ perception and perceived value is crucial to their satisfaction. Satisfaction, in turn, influences the extent of intention to continue to use mobile technology. As such, highly satisfied customers tend to demonstrate a high likelihood of repurchasing mobile technology devices. There are some limitations in this study. The variables were limited in perception and perceived value and some variables in both perception and perceived value were overlapped. Future studies should examine those variables and should make sure about that. Another limitation is the generalizability of the sample in the study. This research drew from a sample from E-reward.com Panel. Therefore, it cannot be assumes that the respondents were a representative sample of the population of all travelers in the U.S. Mobile commerce is set to become one of the most promising and lucrative growth markets. Understanding consumers’ perception and satisfaction is very important in tourism industry. Directions for future research on satisfaction, perception, perceived values, and experience of mobile travelers in travel industry are as follows: first, our analysis relies on cross-sectional data; thus, to provide an even more convincing case for causal interpretations of variable correlations, additional longitudinal research is needed in which exogenous factors are captured before data on endogenous criteria are collected. Second, the constructs in this study were measured by questionnaire items asking for respondents’ attitudinal opinions. New research can help by explaining this measurement approach to include indicators of actual customer usage behaviors. Future research should also focus on the use

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of mobile devices in its contribution to the tourism industry and the way in which the tourism industry is being conducted. Our work did not take into consideration views from the industry perspectives. Empirical research exploring determinants of the constructs from the industry perspectives would be beneficial and aid with greater generalization. Third, there are other factors influencing users’ satisfaction. These must be structurally analyzed for their effects on travelers’ satisfaction. In particular, an importance research task is to examine whether these factors function as an adjusting variables in the existing interaction. Our study on mobile users’ satisfaction is an initial step toward understanding customer behavior in M-commerce. A major contribution, we believe, is that this study addresses the gap in the literature by developing a model that explores specific characteristics of tourist satisfaction with mobile device usage. We hope our findings will encourage further research and more in-depth and extensive analyses to demystify the driving forces of mobile commerce.

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