Document Type : Research Paper
Authors
1 Master of Industrial Engineering, , University of Tehran, Fouman Faculty of Engineering Campus, Fuman, Guilan, Iran
2 Assistant Professor, Industrial and Systems Engineering, University of Tehran, Fouman Faculty of Engineering Campus, Fuman, Guilan, Iran
Abstract
It is an important issue that what is the most effective service or efficient designing part as a distinctive factor between a tourism app and its competitors in the highly competitive environment in this field? In this survey, as the first step, the Tourism Mobile Apps (TMAs) functional criteria (success factors) are extracted by an appropriate literature review and the opinion of experts who have competitive intelligence knowledge. Then a case study is done on five famous tourism apps in the accommodation sector using the Fuzzy ANOVA. According to the achieved results, users believe that there isn’t any difference between considered applications in criteria such as “Reliability of M-technology”, “Perceived ease of use”, “Perceived Trust”, “Perceived Cost Transparency” and “security issues”. Consequently, the first group is identified as critical success criteria and has more influence on user satisfaction and, subsequently, the success of TMA.
Introduction
The tourism industry is the most profitable part of the economy in advanced countries and has undeniable effects on economic, social, cultural, and ecological areas (Sadeghi et al., 2019). Today, information technology and using different mobile applications attract many tourists to this market. Numerous competitors, further making the choosing process for users difficult, create a set of qualified services for consumers.
This research is a case study on top five accommodation (as one of the most important sections of the tourism industry) applications. Therefore, it extracted the performance criteria initially and then investigated the similarity rate in fulfilment levels of those mobile apps. In other words, the final purpose of the current study is to identify the critical success factors through performance adaptation of success TMAs to each other between success criteria.
In this regard, the previous research related to TMA is investigated to achieve an initial success framework.
Materials and Methods
The best method for investigating differences between several groups of data is ANOVA. Likewise, Fuzzy ANOVA is a powerful and efficient variance analysis method for fuzzy data.
This study evaluates the fulfilment levels of 10 performance criteria in top 5 TMA in Iran. Initially, 20 performance criteria were extracted, then mitigated to 10 factors, using the Friedman test. A Likert scale questionnaire in five linguistic variables was provided and distributed between experts for this aim. The total average of criteria is achieved at 2.82, and it means all the criteria by an average less than 2.82 will be omitted from the initial list.
Five TMAs by the most active installation are identified at the next step. Respectively, these mobile apps are Alibaba, Snaptrip, AP, Booking.com, and Trip Advisor.
Before considering the analysis of achieved data from experts, it’s needed to calculate the reliability and validity of questionnaires. For this purpose, a Likert scale questionnaire is designed in the present study, and the validity and reliability of criteria are calculated using discriminant/convergent validity and Cronbach alpha. Furthermore, a secondary approach (composite reliability) is also used to evaluate the reliability. This process is implemented using SmartPLS software.
Finally, the comparison between TMAs is made by using the fuzzy ANOVA approach.
Discussion and Result
The distributed questionnaire was answered by 10 experts who were selected by using the purposive sampling method. All the respondents at least have one year experience of working with a TMA. The collected data by respondents requires validity and reliability evaluation. The achieved results prove the questionnaire reliability and validity of the criteria. The result is demonstrated in table 1.
Table (1). Questionnaire Reliability
Row
criteria
original sample
Sample average
Standard deviation
T-Statistics
P-Values
Cronbach alpha
Composite Reliability
1
PVMT
0.469
0.492
0.103
4.538
0.000
0.709
0.801
2
ROMT
0.537
0.702
0.095
7.325
0.000
0.888
0.919
3
PE
0.540
0.589
0.083
6.699
0.000
0.765
0.842
4
PEU
0.372
0.550
0.148
2.516
0.013
0.836
0.713
5
UIIT
0.449
0.553
0.149
3.632
0.000
0.799
0.854
6
PT
0.554
0.569
0.104
5.189
0.000
0.788
0.845
7
PSQ
0.695
0.617
0.084
7.048
0.000
0.810
0.874
8
PCT
0.797
0.488
0.176
2.554
0.011
0.854
0.791
9
ISQ
0.593
0.635
0.095
6.703
0.000
0.851
0.895
10
SEC
0.634
0.799
0.092
8.624
0.000
0.935
0.951
At the next step, the fuzzy ANOVA approach proves that in five criteria, the relationship p of is true and verifies the zero hypnosis, and for the others, the correct equation is that proves the null one.
Conclusion
The final output of this study demonstrates that a TMA for success achievement must focus on “Perceived value of M-technology”,” Perceived ease of use”,” Perceived Trust”,” Perceived Cost Transparency” and “Security issues” because all these criteria were implemented equally in all of five TMA.
Keywords
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