Impact of Marketing Mix on Tourism: Non-Parametric Stats Analysis

Comparative Analysis of Non-Parametric Statistics in Extant Literature

2 Topic: Marketing Mix Various elements impact the effectiveness of a marketing campaign, such as the marketing mix and artificial intelligence (AI) or machine learning (ML). However, the 4Ps of the Marketing theoretical framework greatly affect marketing techniques and how to approach audiences while keeping their expectations in mind. Therefore, the research topic will be how the marketing mix affects tourism. This paper will examine non-parametric statistics, such as Friedman, Wilcoxon, chi-square, and CAAR. Nonparametric statistics refers to a method in statistics where the data are not assumed to come from the given models that were determined by a small number of parameters; this type of statistical method usually uses ordinal data and includes nonparametric descriptive statistics, statistical models, inference, and statistical tests (Grant, 2021). According to the Friedman Test (n.d.) , the Friedman test extends from the Wilcoxon signed-ranked test and the nonparametric analog of one-way represented measures; it tests the null hypothesis that k-related variables derive from the same population. According to Hayes (2023), the Wilcoxon test, referred to as the rank sum or signed rank test, is nonparametrically and compares two paired groups. It calculates the difference between the variable sets and analyses them to determine if they are statistically significantly different from each other. The chi-square test answers questions regarding the association or difference between categorical variables and is one of the more misinterpreted tests ( Franke et al., 2012) . The CAAR is used in event studies, and the null hypothesis for this test is E(CAAR)=0, which assumes that the expected value of CAAR is zero ( Aar and CAAR, n.d.).

3 Case Study #1 Chi-Square and Friedman Tests: The Impact Of Marketing Mix On Attracting Audiences To The Cinema According to Mohammadian and Habibi (2012), the focus of the research is to identify marketing aspects of filmmaking (under the scope of the 4 Ps Marketing Mix) and determine how the marketing techniques attract Iranian viewers. The research hypotheses are the following: 1. Products factor into attracting Iranian movie audiences to cinema. 2. Pricing factors into attracting Iranian movie audiences to cinema. 3. Placement factors into attracting Iranian movie audiences to cinema. 4. Finally, Promotion factors into attracting Iranian movie audiences to cinema. The statistical tests used were the Friedman and Chi-Square tests, in which Friedman was used for ranking the indices and main variables in the research model, and the chi-square and significance level for each variable were examined (Mohammadian and Habibi, 2012). Table 1 The Results of the Friedman Test for Ranking the Indices of Product, Place, and Promotion Variables Note. This table represents the results of each Indices of Chi-square, Significance level, and numbers. Table 2 The Result of the Friedman Test for Ranking the Variables

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4 Note. This image represents the Chi-square, Significance level, and numbers for ranking the variables. Table 3 Ranking Product Indices Note. This image represents the rank (ordinal) from the highest index mean rank to the lowest regarding product indices. Table 4 Ranking Place Indices Note. This table represents the ordinal rank from the highest index mean rank to the lowest regarding trailer placement indices. Table 5 Ranking Promotion Indices Note. This table represents the ordinal promotion indices mean rank from high to low.

5 Table 6 Ranking Research Variables Note. This table represents the ordinal ranking of the research variables from high to low. The general findings were that all hypotheses except the 2nd one (Pricing factors into attracting Iranian movie audiences to cinema) were confirmed. The placement was the most influential factor in attracting audiences to theaters, and the indices' priority was theater location and convenience for viewers. Product was of the second highest importance on the variables ranking that would attract audiences, and all indices were important except the films’ budgets. Promotions and the seven indices were influential in attracting audiences, while price was not influential in attracting audiences. Case Study #2 Wilcoxon Test: The Effectiveness Of Movie Trailer Advertising According to Karray and Debernitz (2017), the study is regarding the marketing mix and how advertising is a major aspect of this conceptual framework. Additionally, it focuses on how movie trailer advertising affects viewership, ultimately impacting film revenue. The research hypotheses focus on the effect of trailer advertisement and a film’s revenue. In contrast, the others focus on the effects of trailer content and execution and the effects of these aspects on film revenue (Karray and Debernitz, 2017). The statistical tests used for research are the Wilcoxon and linear regression with the Cumulative Average Abnormal Return (CAAR). Table 7 The CAAR Regression Equation

6 Note. This image represents the CAAR regression equation in which the CAAR is the dependent variable while the trailer and control variables are predictors. B and C denote the regression coefficient vectors and control variables, which  is the regression error term. The trailer variables are storyline, know gap, violence, sex, humor, special effects, total scenes lead time between the first trailer and movie, length, star power, director, sequel, genre, and the season. Table 8 The CAAR Over Time Note. This chart represents the CAAR over time to determine if a new trailer release results in abnormal stock returns. The Shapiro-Wil test was used to verify that CAAR follows normal distribution: W=.985, V=1.67, z=1.158, p=.123and a sample t -test results were that the CAARs are significant, which is t =14.47, p = 0.00. Not due to outliners, the Wilcoxon results is z = 9.40 and p = 0.00. The general findings were that viewers and investors preferred trailers with appealing plots, violence, and sexual or humorous scenes that capture viewer attention. However, too many scenes make trailers complex and hard to remember aspects of the film, which negatively affects

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7 revenue. Additionally, special effects do not contribute to box office performance, and the first trailer is more valuable to viewers than subsequent trailers.

8 Synthesis Both articles focus on marketing aspects of increasing viewership, which increases a film’s success at the box office. The first case study shows that concerning the 4ps Marketing Mix, placement may be the most influential factor in attracting audiences to theaters, especially concerning how close theaters are to viewers. Product was of the second highest importance on the variables ranking that would attract audiences, while the budget was unimportant to viewers. Promotions and the seven indices were influential in attracting audiences. Pricing is the least viewers care about. Additionally, viewers and investors prefer trailers with appealing plots, violence, and sexual or humorous scenes that capture viewer attention. Still, marketers and filmmakers should avoid excessive scenes; special effects are un important. Compare and Contrast Both articles focus on aspects of marketing to attract audiences to films, raise their revenue, and boost box office success, and both utilize nonparametric statistics. However, the first case focuses on the 4Ps Marketing Mix. Friedman testing and chi-square were used to test the differences between groups because the dependent variable was ordinal and to determine if the differences between observed and expected data were due to random occurrence or a relationship between the variables. The second case focuses on how advertisement trailers for a film affect a film’s success and the box office revenue. The Wilcoxon tests are sometimes used instead of t-tests when one is not interested in the means of a population, and the test is to compare two independent variables. The test can compare two different, same, or related samples. The CAAR can analyze even effects over periods instead of individual instances within the variables tested.

9 Conclusion This paper examines two case studies that utilized nonparametric statistical tests. These tests were Friedman, Wilcoxon, chi-square, and CAAR. The approaches that could have been taken within the studies were to examine additional factors that relate to the box office success of a film, such as the theatrical run of a film before syndication to streaming, actor, and viewer races, and expanding the nationalities of whom the tests were administered to receive a more accurate assumption of the best marketing tools to use to market to viewers. Concerning the circumstances requiring non-parametrical statistical methods, if the original data do not meet the assumptions about the sample population, small sample size, or the data is nominal or ordinal, then non-parametrical tests must be used to gain information regarding a larger population ( Taylor, 2023). Therefore, these are the reasons to utilize this type of statistical testing.

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10 References Aar And CAAR Test Statistics . (N.D.). Event Study. https://eventstudy.de/statistics/aar_caar_statistics.html#cross-sectional-test-csect-t-caar Franke, T. M., Ho, T., & Christie, C. A. (2012). The Chi-Square Test: Often Used And More Often Misinterpreted. American Journal Of Evaluation , 33 (3), 448-458 Friedman Test . (n.d.). SPSS Statistics. IBM. https://www.ibm.com/docs/en/spss-statistics/beta? topic=tests-friedman-test Grant, M. (2021). Nonparametric Statistics: Overview, Types, And Examples . Investopedia. Https://Www.Investopedia.Com/Terms/N/Nonparametric-Statistics.Asp Hayes, A. (2023). Wilcoxon Test: Definition In Statistics, Types, And Calculation . Investopedia. Https://Www.Investopedia.Com/Terms/W/Wilcoxon-Test.Asp Karray, S., & Debernitz, L. (2017). The Effectiveness Of Movie Trailer Advertising. International Journal Of Advertising: The Review of Marketing Communications , 36(2), 368–392. https://doi.org/10.1080/02650487.2015.1090521 Mohammadian, M., & Habibi, E. S. (2012). The Impact Of Marketing Mix On Attracting Audiences To The Cinema. International Business And Management , 5 (1), 99-106. Taylor, S. (2023). Nonparametric Tests . Corporate Finance Institute. Https://Corporatefinanceinstitute.Com/Resources/Data-Science/Nonparametric-Tests/

11 Appendix A Table 1 The Results of the Friedman Test for Ranking the Indices of Product, Place, and Promotion Variables

12 Appendix B Table 2 The Result of the Friedman Test for Ranking the Variables

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13 Appendix C Table 3 Ranking Product Indices

14 Appendix D Table 4 Ranking Place Indices

15 Appendix E Table 5 Ranking Promotion Indices

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16 Appendix F Table 6 Ranking Research Variables

17 Appendix G Table 7 The CAAR Regression Equation

18 Appendix H Table 8 The CAAR Over Time

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